% Encoding: UTF-8 @Article{Adler2012, Title = {RECONSTRUCTION OF THE HUMAN HIPPOCAMPUS IN 3D FROM HISTOLOGY AND HIGH-RESOLUTION EX-VIVO MRI.}, Author = {Adler, Daniel H. and Liu, Alex Yang and Pluta, John and Kadivar, Salmon and Orozco, Sylvia and Wang, Hongzhi and Gee, James C. and Avants, Brian B. and Yushkevich, Paul A.}, Journal = {Proc IEEE Int Symp Biomed Imaging}, Year = {2012}, Month = {Dec}, Pages = {294--297}, Volume = {2012}, Abstract = {In this paper, we present methods for the reconstruction of 3D histological volumes of the human hippocampal formation from histology slices. Inter-slice alignment is guided by a graph-theoretic approach that minimizes the impact of badly distorted slices. The reconstruction is refined by iterative affine and deformable co-registration with a high-resolution MRI of the postmortem tissue sample. We present an evaluation of reconstruction accuracy that is based on measures of similarity between boundaries drawn on both histology and MRI. Our methodology is currently being applied to an MRI atlas of the human hippocampal formation, in which atlas anatomical labels are derived from segmentation of reconstructed histology.}, Doi = {10.1109/ISBI.2012.6235542}, Institution = {Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {24443672}, Timestamp = {2015.10.04}, Url = {http://dx.doi.org/10.1109/ISBI.2012.6235542} } @Article{Adler2014, Title = {Histology-derived volumetric annotation of the human hippocampal subfields in postmortem {MRI}.}, Author = {Adler, Daniel H. and Pluta, John and Kadivar, Salmon and Craige, Caryne and Gee, James C. and Avants, Brian B. and Yushkevich, Paul A.}, Journal = {Neuroimage}, Year = {2014}, Month = jan, Pages = {505--523}, Volume = {84}, Abstract = {Recently, there has been a growing effort to analyze the morphometry of hippocampal subfields using both in vivo and postmortem magnetic resonance imaging (MRI). However, given that boundaries between subregions of the hippocampal formation (HF) are conventionally defined on the basis of microscopic features that often lack discernible signature in MRI, subfield delineation in MRI literature has largely relied on heuristic geometric rules, the validity of which with respect to the underlying anatomy is largely unknown. The development and evaluation of such rules are challenged by the limited availability of data linking MRI appearance to microscopic hippocampal anatomy, particularly in three dimensions (3D). The present paper, for the first time, demonstrates the feasibility of labeling hippocampal subfields in a high resolution volumetric MRI dataset based directly on microscopic features extracted from histology. It uses a combination of computational techniques and manual post-processing to map subfield boundaries from a stack of histology images (obtained with 200$\mu$m spacing and 5$\mu$m slice thickness; stained using the Kluver-Barrera method) onto a postmortem 9.4Tesla MRI scan of the intact, whole hippocampal formation acquired with 160$\mu$m isotropic resolution. The histology reconstruction procedure consists of sequential application of a graph-theoretic slice stacking algorithm that mitigates the effects of distorted slices, followed by iterative affine and diffeomorphic co-registration to postmortem MRI scans of approximately 1cm-thick tissue sub-blocks acquired with 200$\mu$m isotropic resolution. These 1cm blocks are subsequently co-registered to the MRI of the whole HF. Reconstruction accuracy is evaluated as the average displacement error between boundaries manually delineated in both the histology and MRI following the sequential stages of reconstruction. The methods presented and evaluated in this single-subject study can potentially be applied to multiple hippocampal tissue samples in order to construct a histologically informed MRI atlas of the hippocampal formation.}, Doi = {10.1016/j.neuroimage.2013.08.067}, Institution = {Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3600 Market Street, Suite 370, Philadelphia, PA 19104, USA; Department of Bioengineering, University of Pennsylvania, USA. Electronic address: danadler@seas.upenn.edu.}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pii = {S1053-8119(13)00932-4}, Pmid = {24036353}, Timestamp = {2014.08.26} } @Article{Aguirre2007, Title = {Canine and human visual cortex intact and responsive despite early retinal blindness from RPE65~{m}utation}, Author = {Aguirre, Geoffrey K. and Komaromy, Andras M. and Cideciyan, Artur V. and Brainard, David H. and Aleman, Tomas S. and Roman, Alejandro J. and Avants, Brian B. and Gee, James C. and Korczykowski, Marc and Hauswirth, William W. and Acland, Gregory M. and Aguirre, Gustavo D. and Jacobson, Samuel G.}, Journal = {PLoS Med.}, Year = {2007}, Month = jun, Number = {6}, Pages = {1117--1128}, Volume = {4}, Article-number = {e230}, Doi = {10.1371/journal.pmed.0040230}, ISSN = {1549-1277}, Orcid-numbers = {Cideciyan, Artur/0000-0002-2018-0905}, Researcherid-numbers = {Cideciyan, Artur/A-1075-2007}, Unique-id = {ISI:000247476300023} } @Article{Ash2010, Title = {Speech errors in progressive non-fluent aphasia}, Author = {Ash, Sharon and McMillan, Corey and Gunawardena, Delani and Avants, Brian and Morgan, Brianna and Khan, Alea and Moore, Peachie and Gee, James and Grossman, Murray}, Journal = {Brain Lang.}, Year = {2010}, Month = apr, Number = {1}, Pages = {13--20}, Volume = {113}, Doi = {10.1016/j.bandl.2009.12.001}, ISSN = {0093-934X}, Unique-id = {ISI:000276287100002} } @Article{Ash2009, Title = {Non-fluent speech in frontotemporal lobar degeneration}, Author = {Ash, Sharon and Moore, Peachie and Vesely, Luisa and Gunawardena, Delani and McMillan, Corey and Anderson, Chivon and Avants, Brian and Grossman, Murray}, Journal = {Journal of Neurolinguistics}, Year = {2009}, Month = jul, Number = {4}, Pages = {370--383}, Volume = {22}, Doi = {10.1016/j.jneuroling.2008.12.001}, ISSN = {0911-6044}, Unique-id = {ISI:000266893500004} } @Article{Ashtari2011, Title = {Medial temporal structures and memory functions in adolescents with heavy cannabis use}, Author = {Ashtari, Manzar and Avants, Brian and Cyckowski, Laura and Cervellione, Kelly L. and Roofeh, David and Cook, Philip and Gee, James and Sevy, Serge and Kumra, Sanjiv}, Journal = {J. Psychiatr. Res.}, Year = {2011}, Month = aug, Number = {8}, Pages = {1055--1066}, Volume = {45}, Doi = {10.1016/j.jpsychires.2011.01.004}, ISSN = {0022-3956}, Unique-id = {ISI:000293938900009} } @Article{Avants2007, Title = {Spatiotemporal normalization for longitudinal analysis of gray matter atrophy in frontotemporal dementia.}, Author = {Avants, Brian and Anderson, Chivon and Grossman, Murray and Gee, James C.}, Journal = {Med Image Comput Comput Assist Interv}, Year = {2007}, Number = {Pt 2}, Pages = {303--310}, Volume = {10}, __markedentry = {[stnava:1]}, Abstract = {We present a unified method, based on symmetric diffeomorphisms, for studying longitudinal neurodegeneration. Our method first uses symmetric diffeomorphic normalization to find a spatiotemporal parameterization of an individual's image time series. The second step involves mapping a representative image or set of images from the time series into an optimal template space. The template mapping is then combined with the intrasubject spatiotemporal map to enable pairwise statistical tests to be performed on a population of normalized time series images. Here, we apply this longitudinal analysis protocol to study the gray matter atrophy patterns induced by frontotemporal dementia (FTD). We sample our normalized spatiotemporal maps at baseline (time zero) and time one year to generate an annualized atrophy map (AAM) that estimates the annual effect of FTD. This spatiotemporal normalization enables us to locate neuroanatomical regions that consistently undergo significant annual gray matter atrophy across the population. We found the majority of annual atrophy to occur in the frontal and temporal lobes in our population of 20 subjects. We also found significant effects in the hippocampus, insula and cingulate gyrus. Our novel results, significant at p < 0.05 after false discovery rate correction, are represented in local template space but also assigned Talairach coordinates and Brodmann and Anatomical Automatic Labeling (AAL) labels. This paper shows the statistical power of symmetric diffeomorphic normalization for performing deformation-based studies of longitudinal atrophy.}, Institution = {Dept. of Radiology, University of Pennsylvania, Philadelphia, PA 19104-6389, USA. avants@grasp.cis.upenn.edu}, Keywords = {Algorithms; Atrophy, pathology; Cerebral Cortex, pathology; Dementia, pathology; Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Imaging, Three-Dimensional, methods; Longitudinal Studies; Magnetic Resonance Imaging, methods; Neurons, pathology; Reproducibility of Results; Sensitivity and Specificity}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {18044582}, Timestamp = {2013.05.29} } @Article{Avants2010, Title = {Sparse unbiased analysis of anatomical variance in longitudinal imaging.}, Author = {Avants, Brian and Cook, Philip A. and McMillan, Corey and Grossman, Murray and Tustison, Nicholas J. and Zheng, Yuanjie and Gee, James C.}, Journal = {Med Image Comput Comput Assist Interv}, Year = {2010}, Number = {Pt 1}, Pages = {324--331}, Volume = {13}, __markedentry = {[stnava:1]}, Abstract = {We present a new algorithm for reliable, unbiased, multivariate longitudinal analysis of cortical and white matter atrophy rates with penalized statistical methods. The pipeline uses a step-wise approach to transform and personalize template information first to a single-subject template (SST) and then to the individual's time series data. The first stream of information flows from group template to the SST; the second flows from the SST to the individual time-points and provides unbiased, prior-based segmentation and measurement of cortical thickness. MRI-bias correction, consistent longitudinal segmentation, cortical parcellation and cortical thickness estimation are all based on strong use of the subject-specific priors built from initial diffeomorphic mapping between the SST and optimal group template. We evaluate our approach with both test-retest data and with application to a driving biological problem. We use test-retest data to show that this approach produces (a) zero change when the retest data contains the same image content as the test data and (b) produces normally distributed, low variance estimates of thickness change centered at zero when test-retest data is collected near in time to test data. We also show that our approach--when combined with sparse canonical correlation analysis--reveals plausible, significant, annualized decline in cortical thickness and white matter volume when contrasting frontotemporal dementia and normal aging.}, Institution = {Dept. of Radiology, University of Pennsylvania, Philadelphia, PA 19104-6389 USA. avants@grasp.cis.upenn.edu}, Keywords = {Algorithms; Analysis of Variance; Brain Diseases, pathology; Brain, pathology; Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Longitudinal Studies; Magnetic Resonance Imaging, methods; Pattern Recognition, Automated, methods; Prognosis; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {20879247}, Timestamp = {2013.05.29} } @Article{Avants2012, Title = {Eigenanatomy improves detection power for longitudinal cortical change.}, Author = {Avants, Brian and Dhillon, Paramveer and Kandel, Benjamin M. and Cook, Philip A. and McMillan, Corey T. and Grossman, Murray and Gee, James C.}, Journal = {Med Image Comput Comput Assist Interv}, Year = {2012}, Number = {Pt 3}, Pages = {206--213}, Volume = {15}, __markedentry = {[stnava:1]}, Abstract = {We contribute a novel and interpretable dimensionality reduction strategy, eigenanatomy, that is tuned for neuroimaging data. The method approximates the eigendecomposition of an image set with basis functions (the eigenanatomy vectors) that are sparse, unsigned and are anatomically clustered. We employ the eigenanatomy vectors as anatomical predictors to improve detection power in morphometry. Standard voxel-based morphometry (VBM) analyzes imaging data voxel-by-voxel--and follows this with cluster-based or voxel-wise multiple comparisons correction methods to determine significance. Eigenanatomy reverses the standard order of operations by first clustering the voxel data and then using standard linear regression in this reduced dimensionality space. As with traditional region-of-interest (ROI) analysis, this strategy can greatly improve detection power. Our results show that eigenanatomy provides a principled objective function that leads to localized, data-driven regions of interest. These regions improve our ability to quantify biologically plausible rates of cortical change in two distinct forms of neurodegeneration. We detail the algorithm and show experimental evidence of its efficacy.}, Institution = {Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.}, Keywords = {Aging, physiology; Algorithms; Brain, anatomy /\&/ histology/physiology; Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Information Storage and Retrieval, methods; Longitudinal Studies; Magnetic Resonance Imaging, methods; Pattern Recognition, Automated, methods; Reproducibility of Results; Sensitivity and Specificity}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {23286132}, Timestamp = {2013.05.29} } @Article{Avants2008, Title = {Multivariate Analysis of Structural and Diffusion Imaging in Traumatic Brain Injury}, Author = {Avants, Brian and Duda, Jeffrey T. and Kim, Junghoon and Zhang, Hui and Pluta, John and Gee, James C. and Whyte, John}, Journal = {Acad. Radiol.}, Year = {2008}, Month = nov, Number = {11}, Pages = {1360--1375}, Volume = {15}, Doi = {10.1016/j.acra.2008.07.007}, ISSN = {1076-6332}, Unique-id = {ISI:000260707600004} } @InProceedings{Avants2005a, Title = {Geodesic image interpolation: Parameterizing and interpolating spatiotemporal images}, Author = {Avants, BB and Epstein, CL and Gee, JC}, Booktitle = {VARIATIONAL, GEOMETRIC, AND LEVEL SET METHODS IN COMPUTER VISION, PROCEEDINGS}, Year = {2005}, Editor = {Paragios, N and Faugeras, O and Chan, T and Schnorr, C}, Note = {3rd International Workshop on Variational, Geometric, and Level Set Methods in Computer Vision, Beijing, PEOPLES R CHINA, OCT 16, 2005}, Pages = {247--258}, Series = {Lecture Notes in Computer Science}, Volume = {3752}, __markedentry = {[stnava:1]}, ISBN = {3-540-29348-5}, ISSN = {0302-9743}, Unique-id = {ISI:000233133600021} } @InProceedings{Avants2003a, Title = {Formulation and evaluation of variational curve matching with prior constraints}, Author = {Avants, B and Gee, J}, Booktitle = {BIOMEDICAL IMAGE REGISTRATION}, Year = {2003}, Editor = {Gee, JC and Maintz, JBA and Vannier, MW}, Note = {2nd International Workshop on Biomedical Image Registration, PHILADELPHIA, PENNSYLVANIA, JUN 23-24, 2003}, Organization = {Siemens Med Solut; Siemens Corp Res; Natl Lib Med; Univ Penn, Vice Provost Res}, Pages = {21--30}, Series = {LECTURE NOTES IN COMPUTER SCIENCE}, Volume = {2717}, __markedentry = {[stnava:1]}, ISBN = {3-540-20343-5}, ISSN = {0302-9743}, Unique-id = {ISI:000187954800003} } @InProceedings{Avants2003b, Title = {Continuous curve matching with scale-space curvature and extrema-based scale selection}, Author = {Avants, B and Gee, J}, Booktitle = {SCALE SPACE METHODS IN COMPUTER VISION, PROCEEDINGS}, Year = {2003}, Editor = {Griffin, LD and Lillholm, M}, Note = {4th International Conference on Scale Space Methods in Computer Vision, ISLE SKYE, SCOTLAND, JUN 10-12, 2003}, Organization = {British Machine Vis Assoc; Kings Coll London; IT Univ Copenhagen}, Pages = {798--813}, Series = {LECTURE NOTES IN COMPUTER SCIENCE}, Volume = {2695}, __markedentry = {[stnava:1]}, ISBN = {3-540-40368-X}, ISSN = {0302-9743}, Unique-id = {ISI:000185043200056} } @InProceedings{Avants2004, Title = {Symmetric geodesic shape averaging and shape interpolation}, Author = {Avants, B and Gee, J}, Booktitle = {COMPUTER VISION AND MATHEMATICAL METHODS IN MEDICAL AND BIOMEDICAL IMAGE ANALYSIS}, Year = {2004}, Editor = {Sonka, M and Kakadiaris, IA and Kybic, J}, Note = {Workshop on Computer Vision Approaches to Medical Image Analysis (CVAMIA)/Mathematical Methods in Biomedical Image Analysis (MMBIA) held in conjunction with the 8th ECCV, Prague, CZECH REPUBLIC, MAY 15, 2004}, Pages = {99--110}, Series = {Lecture Notes in Computer Science}, Volume = {3117}, __markedentry = {[stnava:1]}, ISBN = {3-540-22675-3}, ISSN = {0302-9743}, Unique-id = {ISI:000224372600009} } @Article{Avants2004a, Title = {Geodesic estimation for large deformation anatomical shape averaging and interpolation}, Author = {Avants, B and Gee, JC}, Journal = {Neuroimage}, Year = {2004}, Note = {Conference on Mathermatics in Brain Imaging, Univ Calif Los Angeles, Inst Pure \& Appl Math, Los Angeles, CA, JUL 12-23, 2004}, Number = {1}, Pages = {S139-S150}, Volume = {23}, Doi = {10.1016/j.neuroimage.2004.07.010}, ISSN = {1053-8119}, Unique-id = {ISI:000225374100013} } @Article{Avants2003, Title = {The shape operator for differential analysis of images.}, Author = {Avants, Brian and Gee, James}, Journal = {Inf Process Med Imaging}, Year = {2003}, Month = jul, Pages = {101--113}, Volume = {18}, Abstract = {This work provides a new technique for surface oriented volumetric image analysis. The method makes no assumptions about topology, instead constructing a local neighborhood from image information, such as a segmentation or edge map, to define a surface patch. Neighborhood constructions using extrinsic and intrinsic distances are given. This representation allows one to estimate differential properties directly from the image's Gauss map. We develop a novel technique for this purpose which estimates the shape operator and yields both principal directions and curvatures. Only first derivatives need be estimated, making the method numerically stable. We show the use of these measures for multi-scale classification of image structure by the mean and Gaussian curvatures. Finally, we propose to register image volumes by surface curvature. This is particularly useful when geometry is the only variable. To illustrate this, we register binary segmented data by surface curvature, both rigidly and non-rigidly. A novel variant of Demons registration, extensible for use with differentiable similarity metrics, is also applied for deformable curvature-driven registration of medical images.}, Institution = {University of Pennsylvania, Philadelphia, PA 19104-6389, USA. avants@grasp.cis.upenn.edu}, Keywords = {Algorithms; Animals; Brain, anatomy /&/ histology; Computer Simulation; Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Imaging, Three-Dimensional, methods; Magnetic Resonance Imaging, methods; Models, Biological; Models, Statistical; Pan troglodytes; Pattern Recognition, Automated; Subtraction Technique}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {15344450}, Timestamp = {2014.08.26} } @Article{Avants2005b, Title = {A new method for assessing endbcast morphology: calculating local curvature from {3D CT} images.}, Author = {Avants, B and Gee, J and Schoenemann, PT and Monge, J and Lewis, JE and Holloway, RL}, Journal = {Am. J. Phys. Anthropol.}, Year = {2005}, Number = {40}, Pages = {67}, ISSN = {0002-9483}, Unique-id = {ISI:000227214900023} } @Article{Avants2004b, Title = {Validation of plaster endocast morphology through {3D CT} image analysis}, Author = {Avants, B. and Gee, J. and Schoenemann, P. T. and Monge, J. and Lewis, J. E. and Holloway, R. L.}, Journal = {Am. J. Phys. Anthropol.}, Year = {2004}, Number = {38}, Pages = {56}, ISSN = {0002-9483}, Unique-id = {ISI:000207846400027} } @Article{Avants2005, Title = {The correlation of cognitive decline with frontotemporal dementia induced annualized gray matter loss using diffeomorphic morphometry.}, Author = {Avants, Brian and Grossman, Murray and Gee, James C.}, Journal = {Alzheimer Dis. Assoc. Disord.}, Year = {2005}, Pages = {S25--S28}, Volume = {19 Suppl 1}, Abstract = {This study uses large deformation medical image registration to analyze, in a disease-specific normalized space, the annual rate of gray matter atrophy caused by frontotemporal dementia (FTD) and its correlation with cognitive decline. The analysis consists of three parts. First, a labeled structural MRI atlas is deformed into the shape of an average FTD brain. Second, annualized FTD-related atrophy of gray matter structures is estimated for each patient in the database. Third, the group-wise annualized atrophy rate caused by FTD is correlated, for each gray matter voxel, with declining performance on cognitive tests. This study gives insight into the relationship between FTD-related progressive cortical atrophy and loss in cognitive function.}, Institution = {University of Pennsylvania School of Medicine, Philadelphia, 19104-6389, USA. avants@grasp.cis.upenn.edu}, Keywords = {Aged; Atrophy, pathology/psychology; Cerebral Cortex, pathology; Cognition Disorders, etiology/pathology; Disease Progression; Humans; Image Processing, Computer-Assisted; Longitudinal Studies; Magnetic Resonance Imaging; Middle Aged; Pick Disease of the Brain, pathology/psychology}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pii = {00002093-200510001-00006}, Pmid = {16317254}, Timestamp = {2014.08.26} } @Article{Avants2009, Title = {Longitudinal Cortical Atrophy in Amyotrophic Lateral Sclerosis With Frontotemporal Dementia}, Author = {Avants, Brian and Khan, Alea and McCluskey, Leo and Elman, Lauren and Grossman, Murray}, Journal = {Arch. Neurol.}, Year = {2009}, Month = jan, Number = {1}, Pages = {138--139}, Volume = {66}, ISSN = {0003-9942}, Unique-id = {ISI:000262398900022} } @Article{Avants1999, Title = {Measuring the electrical conductivity of the earth}, Author = {Avants, B and Soodak, D and Ruppeiner, G}, Journal = {American Journal of Physics}, Year = {1999}, Month = jul, Number = {7}, Pages = {593--598}, Volume = {67}, Doi = {10.1119/1.19329}, ISSN = {0002-9505}, Unique-id = {ISI:000081137000005} } @InProceedings{Avants2000, Title = {An adaptive minimal path generation technique for vessel tracking in {CT}A/CE-MRA volume images}, Author = {Avants, BB and Williams, JP}, Booktitle = {MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2000}, Year = {2000}, Editor = {Delp, S and DiGioia, AM and Jaramaz, B}, Note = {3rd International Conference on Medical Image Computing and Computer-Assisted Intervention, PITTSBURGH, PA, OCT 11-14, 2000}, Pages = {707--716}, Series = {Lecture Notes in Computer Science}, Volume = {1935}, __markedentry = {[stnava:1]}, ISBN = {3-540-41189-5}, ISSN = {0302-9743}, Unique-id = {ISI:000171938700073} } @Article{Avants2010a, Title = {Dementia induces correlated reductions in white matter integrity and cortical thickness: A multivariate neuroimaging study with sparse canonical correlation analysis}, Author = {Avants, Brian B. and Cook, Philip A. and Ungar, Lyle and Gee, James C. and Grossman, Murray}, Journal = {Neuroimage}, Year = {2010}, Month = apr, Number = {3}, Pages = {1004--1016}, Volume = {50}, Doi = {10.1016/j.neuroimage.2010.01.041}, ISSN = {1053-8119}, Unique-id = {ISI:000275408200015} } @Article{Avants2015c, Title = {The pediatric template of brain perfusion.}, Author = {Avants, Brian B. and Duda, Jeffrey T. and Kilroy, Emily and Krasileva, Kate and Jann, Kay and Kandel, Benjamin T. and Tustison, Nicholas J. and Yan, Lirong and Jog, Mayank and Smith, Robert and Wang, Yi and Dapretto, Mirella and Wang, Danny J J.}, Journal = {Sci Data}, Year = {2015}, Pages = {150003}, Volume = {2}, Abstract = {Magnetic resonance imaging (MRI) captures the dynamics of brain development with multiple modalities that quantify both structure and function. These measurements may yield valuable insights into the neural patterns that mark healthy maturation or that identify early risk for psychiatric disorder. The Pediatric Template of Brain Perfusion (PTBP) is a free and public neuroimaging resource that will help accelerate the understanding of childhood brain development as seen through the lens of multiple modality neuroimaging and in relation to cognitive and environmental factors. The PTBP uses cross-sectional and longitudinal MRI to quantify cortex, white matter, resting state functional connectivity and brain perfusion, as measured by Arterial Spin Labeling (ASL), in 120 children 7-18 years of age. We describe the PTBP and show, as a demonstration of validity, that global summary measurements capture the trajectories that demarcate critical turning points in brain maturation. This novel resource will allow a more detailed understanding of the network-level, structural and functional landmarks that are obtained during normal adolescent brain development.}, Doi = {10.1038/sdata.2015.3}, Institution = {Department of Neurology, University of California , Los Angeles, California 90095, USA.}, Language = {eng}, Medline-pst = {epublish}, Owner = {stnava}, Pmid = {25977810}, Timestamp = {2015.05.23}, Url = {http://dx.doi.org/10.1038/sdata.2015.3} } @InProceedings{Avants2006, Title = {Geodesic image normalization and temporal parameterization in the space of diffeomorphisms}, Author = {Avants, Brian B. and Epstein, C. L. and Gee, J. C.}, Booktitle = {MEDICAL IMAGING AND AUGMENTED REALITY}, Year = {2006}, Editor = {Yang, GZ and Jiang, T and Shen, DG and Gu, L and Yang, J}, Note = {3rd International Workshop on Medical Imaging and Augmented Reality (MIAR 2006), Shanghai, PEOPLES R CHINA, AUG 17-18, 2006}, Pages = {9--16}, Series = {LECTURE NOTES IN COMPUTER SCIENCE}, Volume = {4091}, __markedentry = {[stnava:1]}, ISBN = {3-540-37220-2}, ISSN = {0302-9743}, Unique-id = {ISI:000240079500002} } @Article{Avants2008a, Title = {Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain}, Author = {Avants, B. B. and Epstein, C. L. and Grossman, M. and Gee, J. C.}, Journal = {Med. Image Anal.}, Year = {2008}, Month = feb, Note = {3rd International Workshop on Biomedical Image Registration, Utrecht Univ, Utrecht, NETHERLANDS, JUL 09-11, 2006}, Number = {1}, Pages = {26--41}, Volume = {12}, Doi = {10.1016/j.media.2007.06.004}, ISSN = {1361-8415}, Organization = {Philips Med Syst}, Unique-id = {ISI:000254032800004} } @InProceedings{Avants2006a, Title = {Symmetric diffeomorphic image registration: Evaluating automated labeling of elderly and neurodegenerative cortex and frontal lobe}, Author = {Avants, Brian B. and Grossman, Murray and Gee, James C.}, Booktitle = {BIOMEDICAL IMAGE REGISTRATION, PROCEEDINGS}, Year = {2006}, Editor = {Pluim, JPW and Likar, B and Gerritsen, FA}, Note = {3rd International Workshop on Biomedical Image Registration, Utrecht Univ, Utrecht, NETHERLANDS, JUL 09-11, 2006}, Organization = {Philips Med Syst}, Pages = {50--57}, Series = {LECTURE NOTES IN COMPUTER SCIENCE}, Volume = {4057}, __markedentry = {[stnava:1]}, ISBN = {3-540-35648-7}, ISSN = {0302-9743}, Unique-id = {ISI:000239485200007} } @Article{Avants2015a, Title = {Relation of Childhood Home Environment to Cortical Thickness in Late Adolescence: Specificity of Experience and Timing}, Author = {Avants, B. B. and Hackman, D. and Betancourt, L. and Lawson, G. M. and Hurt, H. and Farah}, Journal = {PLOS ONE}, Year = {2015}, Owner = {stnava}, Timestamp = {2015.03.16} } @Article{Avants2007a, Title = {Effects of heavy in utero cocaine exposure on adolescent caudate morphology}, Author = {Avants, Brian B. and Hurt, Hallam and Giannetta, Joan M. and Epstein, Charles L. and Shera, David M. and Rao, Hengyi and Wang, Jiongjiong and Gee, James C.}, Journal = {Pediatr. Neurol.}, Year = {2007}, Month = oct, Number = {4}, Pages = {275--279}, Volume = {37}, Doi = {10.1016/j.pediatrneurol.2007.06.012}, ISSN = {0887-8994}, Researcherid-numbers = {Rao, Hengyi/A-7064-2009}, Unique-id = {ISI:000250295000007} } @Article{Avants2015b, Title = {Neuroinformatics and the The Insight ToolKit.}, Author = {Avants, Brian B. and Johnson, Hans J. and Tustison, Nicholas J.}, Journal = {Front Neuroinform}, Year = {2015}, Pages = {5}, Volume = {9}, Doi = {10.3389/fninf.2015.00005}, Institution = {Department of Radiology and Medical Imaging, University of Virginia Charlottesville, VA, USA.}, Language = {eng}, Medline-pst = {epublish}, Owner = {stnava}, Pmid = {25859213}, Timestamp = {2015.05.23}, Url = {http://dx.doi.org/10.3389/fninf.2015.00005} } @Article{Avants2014a, Title = {Sparse canonical correlation analysis relates network-level atrophy to multivariate cognitive measures in a neurodegenerative population.}, Author = {Avants, Brian B. and Libon, David J. and Rascovsky, Katya and Boller, Ashley and McMillan, Corey T. and Massimo, Lauren and Coslett, H Branch and Chatterjee, Anjan and Gross, Rachel G. and Grossman, Murray}, Journal = {Neuroimage}, Year = {2014}, Month = jan, Pages = {698--711}, Volume = {84}, Abstract = {This study establishes that sparse canonical correlation analysis (SCCAN) identifies generalizable, structural MRI-derived cortical networks that relate to five distinct categories of cognition. We obtain multivariate psychometrics from the domain-specific sub-scales of the Philadelphia Brief Assessment of Cognition (PBAC). By using a training and separate testing stage, we find that PBAC-defined cognitive domains of language, visuospatial functioning, episodic memory, executive control, and social functioning correlate with unique and distributed areas of gray matter (GM). In contrast, a parallel univariate framework fails to identify, from the training data, regions that are also significant in the left-out test dataset. The cohort includes164 patients with Alzheimer's disease, behavioral-variant frontotemporal dementia, semantic variant primary progressive aphasia, non-fluent/agrammatic primary progressive aphasia, or corticobasal syndrome. The analysis is implemented with open-source software for which we provide examples in the text. In conclusion, we show that multivariate techniques identify biologically-plausible brain regions supporting specific cognitive domains. The findings are identified in training data and confirmed in test data.}, Doi = {10.1016/j.neuroimage.2013.09.048}, Institution = {Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA.}, Keywords = {Aged; Atrophy; Brain, pathology/physiopathology; Cognition, physiology; Female; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Male; Middle Aged; Multivariate Analysis; Neurodegenerative Diseases, pathology/physiopathology; Neuropsychological Tests}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pii = {S1053-8119(13)00984-1}, Pmid = {24096125}, Timestamp = {2014.08.26} } @Article{Avants2006b, Title = {Lagrangian frame diffeomorphic image registration: Morphometric comparison of human and chimpanzee cortex}, Author = {Avants, Brian B. and Schoenemann, P. Thomas and Gee, James C.}, Journal = {Med. Image Anal.}, Year = {2006}, Month = jun, Note = {2nd International Workshop on Biomedical Image Registration, Univ Penn, PHILADELPHIA, PA, JUN 23-24, 2003}, Number = {3}, Pages = {397--412}, Volume = {10}, Doi = {10.1016/j.media.2005.03.005}, ISSN = {1361-8415}, Organization = {Siemens Med Solut; Siemens Corp Res; Natl Lib Med; Univ Penn, Vice Provost Res}, Unique-id = {ISI:000237818100009} } @Article{Avants2011, Title = {A reproducible evaluation of ANTs similarity metric performance in brain image registration}, Author = {Avants, Brian B. and Tustison, Nicholas J. and Song, Gang and Cook, Philip A. and Klein, Arno and Gee, James C.}, Journal = {Neuroimage}, Year = {2011}, Month = feb, Number = {3}, Pages = {2033--2044}, Volume = {54}, Doi = {10.1016/j.neuroimage.2010.09.025}, ISSN = {1053-8119}, Unique-id = {ISI:000286302000027} } @Article{Avants2014, Title = {The Insight ToolKit image registration framework.}, Author = {Avants, Brian B. and Tustison, Nicholas J. and Stauffer, Michael and Song, Gang and Wu, Baohua and Gee, James C.}, Journal = {Front Neuroinform}, Year = {2014}, Pages = {44}, Volume = {8}, Abstract = {Publicly available scientific resources help establish evaluation standards, provide a platform for teaching and improve reproducibility. Version 4 of the Insight ToolKit (ITK(4)) seeks to establish new standards in publicly available image registration methodology. ITK(4) makes several advances in comparison to previous versions of ITK. ITK(4) supports both multivariate images and objective functions; it also unifies high-dimensional (deformation field) and low-dimensional (affine) transformations with metrics that are reusable across transform types and with composite transforms that allow arbitrary series of geometric mappings to be chained together seamlessly. Metrics and optimizers take advantage of multi-core resources, when available. Furthermore, ITK(4) reduces the parameter optimization burden via principled heuristics that automatically set scaling across disparate parameter types (rotations vs. translations). A related approach also constrains steps sizes for gradient-based optimizers. The result is that tuning for different metrics and/or image pairs is rarely necessary allowing the researcher to more easily focus on design/comparison of registration strategies. In total, the ITK(4) contribution is intended as a structure to support reproducible research practices, will provide a more extensive foundation against which to evaluate new work in image registration and also enable application level programmers a broad suite of tools on which to build. Finally, we contextualize this work with a reference registration evaluation study with application to pediatric brain labeling.}, Doi = {10.3389/fninf.2014.00044}, Institution = {Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania Philadelphia, PA, USA.}, Language = {eng}, Medline-pst = {epublish}, Owner = {stnava}, Pmid = {24817849}, Timestamp = {2014.08.26} } @Article{Avants2011a, Title = {An Open Source Multivariate Framework for n-Tissue Segmentation with Evaluation on Public Data}, Author = {Avants, Brian B. and Tustison, Nicholas J. and Wu, Jue and Cook, Philip A. and Gee, James C.}, Journal = {Neuroinformatics}, Year = {2011}, Month = dec, Number = {4}, Pages = {381--400}, Volume = {9}, Doi = {10.1007/s12021-011-9109-y}, ISSN = {1539-2791}, Unique-id = {ISI:000297150000006} } @Article{Avants2010b, Title = {The optimal template effect in hippocampus studies of diseased populations}, Author = {Avants, Brian B. and Yushkevich, Paul and Pluta, John and Minkoff, David and Korczykowski, Marc and Detre, John and Gee, James C.}, Journal = {Neuroimage}, Year = {2010}, Month = feb, Number = {3}, Pages = {2457--2466}, Volume = {49}, Doi = {10.1016/j.neuroimage.2009.09.062}, ISSN = {1053-8119}, Unique-id = {ISI:000273626400048} } @Article{Badea2012, Title = {Quantitative mouse brain phenotyping based on single and multispectral MR protocols}, Author = {Badea, Alexandra and Gewalt, Sally and Avants, Brian B. and Cook, James J. and Johnson, G. Allan}, Journal = {Neuroimage}, Year = {2012}, Month = nov, Number = {3}, Pages = {1633--1645}, Volume = {63}, Doi = {10.1016/j.neuroimage.2012.07.021}, ISSN = {1053-8119}, Unique-id = {ISI:000310379100063} } @Article{Boller2011, Title = {Philadelphia Brief Assessment of Cognition (PBAC): A Validated Screening Measure for Dementia}, Author = {Boller, Ashley and Libon, David and Rascovsky, Katya and Gross, Rachel Goldmann and Dreyfuss, Michael and Avants, Brian and Massimo, Lauren and Moore, Peachie and Kitain, Jessica and Coslett, H. and Chatterjee, Anjan and Grossman, Murray}, Journal = {Neurology}, Year = {2011}, Month = mar, Note = {63rd AAN Annual Meeting, Honolulu, HI, APR 09-16, 2011}, Number = {9, 4}, Pages = {A511}, Volume = {76}, ISSN = {0028-3878}, Unique-id = {ISI:000288149302599} } @Article{Bonner2009, Title = {Reversal of the concreteness effect in semantic dementia}, Author = {Bonner, Michael F. and Vesely, Luisa and Price, Catherine and Anderson, Chivon and Richmond, Lauren and Farag, Christine and Avants, Brian and Grossman, Murray}, Journal = {Cognitive Neuropsychology}, Year = {2009}, Number = {6}, Pages = {568--579}, Volume = {26}, Doi = {10.1080/02643290903512305}, ISSN = {0264-3294}, Unique-id = {ISI:000275195300004} } @Article{Cook2012, Title = {Multimodal neuroimaging reveals gray and white matter associations with verbal fluency in frontotemporal degeneration}, Author = {Cook, P. A. and Avants, B. B. and McMillan, C. T. and Powers, J. and Gee, J. C. and Grossman, M.}, Journal = {Dement. Geriatr. Cogn. Disord.}, Year = {2012}, Note = {8th International Conference on Frontotemporal Dementias, Manchester, ENGLAND, SEP 05-07, 2012}, Number = {1}, Pages = {154--155}, Volume = {33}, ISSN = {1420-8008}, Unique-id = {ISI:000308612400222} } @Article{Cook2014, Title = {Relating brain anatomy and cognitive ability using a multivariate multimodal framework.}, Author = {Cook, Philip A. and McMillan, Corey T. and Avants, Brian B. and Peelle, Jonathan E. and Gee, James C. and Grossman, Murray}, Journal = {Neuroimage}, Year = {2014}, Month = oct, Pages = {477--486}, Volume = {99}, Abstract = {Linking structural neuroimaging data from multiple modalities to cognitive performance is an important challenge for cognitive neuroscience. In this study we examined the relationship between verbal fluency performance and neuroanatomy in 54 patients with frontotemporal degeneration (FTD) and 15 age-matched controls, all of whom had T1- and diffusion-weighted imaging. Our goal was to incorporate measures of both gray matter (voxel-based cortical thickness) and white matter (fractional anisotropy) into a single statistical model that relates to behavioral performance. We first used eigenanatomy to define data-driven regions of interest (DD-ROIs) for both gray matter and white matter. Eigenanatomy is a multivariate dimensionality reduction approach that identifies spatially smooth, unsigned principal components that explain the maximal amount of variance across subjects. We then used a statistical model selection procedure to see which of these DD-ROIs best modeled performance on verbal fluency tasks hypothesized to rely on distinct components of a large-scale neural network that support language: category fluency requires a semantic-guided search and is hypothesized to rely primarily on temporal cortices that support lexical-semantic representations; letter-guided fluency requires a strategic mental search and is hypothesized to require executive resources to support a more demanding search process, which depends on prefrontal cortex in addition to temporal network components that support lexical representations. We observed that both types of verbal fluency performance are best described by a network that includes a combination of gray matter and white matter. For category fluency, the identified regions included bilateral temporal cortex and a white matter region including left inferior longitudinal fasciculus and frontal-occipital fasciculus. For letter fluency, a left temporal lobe region was also selected, and also regions of frontal cortex. These results are consistent with our hypothesized neuroanatomical models of language processing and its breakdown in FTD. We conclude that clustering the data with eigenanatomy before performing linear regression is a promising tool for multimodal data analysis.}, Doi = {10.1016/j.neuroimage.2014.05.008}, Institution = {Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pii = {S1053-8119(14)00373-5}, Pmid = {24830834}, Timestamp = {2014.08.26} } @InProceedings{Cook2005, Title = {An automated approach to connectivity-based partitioning of brain structures}, Author = {Cook, PA and Zhang, H and Avants, BB and Yushkevich, P and Alexander, DC and Gee, JC and Ciccarelli, O and Thompson, AJ}, Booktitle = {MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2005, PT 1}, Year = {2005}, Editor = {Duncan, JS and Gerig, G}, Note = {8th International Conference on Medical Image Computing and Computer-Assisted Intervention, Palm Springs, CA, OCT 26-29, 2005}, Organization = {No Digital Inc Waterloo; Springer Lecture Notes Comp Sci; GE Healthcare; Medtron Navigat; Siemens Corp Res; NIBIB}, Pages = {164--171}, Series = {Lecture Notes in Computer Science}, Volume = {3749}, __markedentry = {[stnava:1]}, ISBN = {3-540-29327-2}, ISSN = {0302-9743}, Researcherid-numbers = {Thompson, Alan/C-2654-2008}, Unique-id = {ISI:000233337000021} } @Article{Das2009, Title = {Registration based cortical thickness measurement.}, Author = {Das, Sandhitsu R. and Avants, Brian B. and Grossman, Murray and Gee, James C.}, Journal = {Neuroimage}, Year = {2009}, Month = apr, Number = {3}, Pages = {867--879}, Volume = {45}, Abstract = {Cortical thickness is an important biomarker for image-based studies of the brain. A diffeomorphic registration based cortical thickness (DiReCT) measure is introduced where a continuous one-to-one correspondence between the gray matter-white matter interface and the estimated gray matter-cerebrospinal fluid interface is given by a diffeomorphic mapping in the image space. Thickness is then defined in terms of a distance measure between the interfaces of this sheet like structure. This technique also provides a natural way to compute continuous estimates of thickness within buried sulci by preventing opposing gray matter banks from intersecting. In addition, the proposed method incorporates neuroanatomical constraints on thickness values as part of the mapping process. Evaluation of this method is presented on synthetic images. As an application to brain images, a longitudinal study of thickness change in frontotemporal dementia (FTD) spectrum disorder is reported.}, Doi = {10.1016/j.neuroimage.2008.12.016}, Institution = {Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA. sudas@seas.upenn.edu}, Keywords = {Aged; Algorithms; Brain Mapping, methods; Cerebral Cortex, anatomy /&/ histology; Dementia, pathology; Humans; Image Interpretation, Computer-Assisted; Magnetic Resonance Imaging; Middle Aged}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pii = {S1053-8119(08)01278-0}, Pmid = {19150502}, Timestamp = {2014.08.26} } @Article{Das2012, Title = {Measuring longitudinal change in the hippocampal formation from in vivo high-resolution T2-weighted {MRI}}, Author = {Das, Sandhitsu R. and Avants, Brian B. and Pluta, John and Wang, Hongzhi and Suh, Jung W. and Weiner, Michael W. and Mueller, Susanne G. and Yushkevich, Paul A.}, Journal = {Neuroimage}, Year = {2012}, Month = apr, Number = {2}, Pages = {1266--1279}, Volume = {60}, Doi = {10.1016/j.neuroimage.2012.01.098}, ISSN = {1053-8119}, Unique-id = {ISI:000303272300042} } @Article{Das2009b, Title = {Structure Specific Analysis of the Hippocampus in Temporal Lobe Epilepsy}, Author = {Das, Sandhitsu R. and Mechanic-Hamilton, Dawn and Korczykowski, Marc and Pluta, John and Glynn, Simon and Avants, Brian B. and Detre, John A. and Yushkevich, Paul A.}, Journal = {Hippocampus}, Year = {2009}, Note = {1st Computational Hippocampal Anatomy and Physiology Workshop, New York Univ, New York, NY, SEP 06, 2008}, Number = {6}, Pages = {517--525}, Volume = {19}, Doi = {10.1002/hipo.20620}, ISSN = {1050-9631}, Unique-id = {ISI:000266824000003} } @Article{Das2011, Title = {Heterogeneity of functional activation during memory encoding across hippocampal subfields in temporal lobe epilepsy.}, Author = {Das, Sandhitsu R. and Mechanic-Hamilton, Dawn and Pluta, John and Korczykowski, Marc and Detre, John A. and Yushkevich, Paul A.}, Journal = {Neuroimage}, Year = {2011}, Month = oct, Number = {4}, Pages = {1121--1130}, Volume = {58}, Abstract = {Pathology studies have shown that the anatomical subregions of the hippocampal formation are differentially affected in various neurological disorders, including temporal lobe epilepsy (TLE). Analysis of structure and function within these subregions using magnetic resonance imaging (MRI) has the potential to generate insights on disease associations as well as normative brain function. In this study, an atlas-based normalization method (Yushkevich, P.A., Avants, B.B., Pluta, J., Das, S., Minkoff, D., Mechanic-Hamilton, D., Glynn, S., Pickup, S., Liu, W., Gee, J.C., Grossman, M., Detre, J.A., 2009. A high-resolution computational atlas of the human hippocampus from postmortem magnetic resonance imaging at 9.4 T. NeuroImage 44 (2), 385-398) was used to label hippocampal subregions, making it possible to examine subfield-level functional activation during an episodic memory task in two different cohorts of healthy controls and subjects diagnosed with intractable unilateral TLE. We report, for the first time, functional activation patterns within hippocampal subfields in TLE. We detected group differences in subfield activation between patients and controls as well as inter-hemispheric activation asymmetry within subfields in patients, with dentate gyrus (DG) and the anterior hippocampus region showing the greatest effects. DG was also found to be more active than CA1 in controls, but not in patients' epileptogenic side. These preliminary results will encourage further research on the utility of subfield-based biomarkers in TLE.}, Doi = {10.1016/j.neuroimage.2011.06.085}, Institution = {Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, PA, USA. sudas@seas.upenn.edu}, Keywords = {Algorithms; Atlases as Topic; CA1 Region, Hippocampal, physiology; Cadaver; Cohort Studies; Epilepsy, Temporal Lobe, physiopathology/psychology/surgery; Hippocampus, physiology; Humans; Image Processing, Computer-Assisted; Linear Models; Magnetic Resonance Imaging; Memory, physiology}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pii = {S1053-8119(11)00750-6}, Pmid = {21763431}, Timestamp = {2013.05.29} } @Article{Datta2012, Title = {A Digital Atlas of the Dog Brain}, Author = {Datta, Ritobrato and Lee, Jongho and Duda, Jeffrey and Avants, Brian B. and Vite, Charles H. and Tseng, Ben and Gee, James C. and Aguirre, Gustavo D. and Aguirre, Geoffrey K.}, Journal = {PLOS ONE}, Year = {2012}, Month = dec, Number = {12}, Volume = {7}, Article-number = {e52140}, Doi = {10.1371/journal.pone.0052140}, ISSN = {1932-6203}, Unique-id = {ISI:000312794500099} } @InProceedings{Dhillon2013, Title = {Prior-based Eigenanatomy}, Author = {Paramveer Dhillon and Ben Kandel and Lyle Ungar and David A. Wolk and James C. Gee and Brian Avants}, Booktitle = {Pattern Recognition in Neuroimaging}, Year = {2013}, __markedentry = {[stnava:1]}, Owner = {stnava}, Timestamp = {2013.05.30} } @Article{Dhillon2014, Title = {Subject-specific functional parcellation via Prior Based Eigenanatomy.}, Author = {Dhillon, Paramveer S. and Wolk, David A. and Das, Sandhitsu R. and Ungar, Lyle H. and Gee, James C. and Avants, Brian B.}, Journal = {Neuroimage}, Year = {2014}, Month = oct, Pages = {14--27}, Volume = {99}, Abstract = {We present a new framework for prior-constrained sparse decomposition of matrices derived from the neuroimaging data and apply this method to functional network analysis of a clinically relevant population. Matrix decomposition methods are powerful dimensionality reduction tools that have found widespread use in neuroimaging. However, the unconstrained nature of these totally data-driven techniques makes it difficult to interpret the results in a domain where network-specific hypotheses may exist. We propose a novel approach, Prior Based Eigenanatomy (p-Eigen), which seeks to identify a data-driven matrix decomposition but at the same time constrains the individual components by spatial anatomical priors (probabilistic ROIs). We formulate our novel solution in terms of prior-constrained {\ell}1 penalized (sparse) principal component analysis. p-Eigen starts with a common functional parcellation for all the subjects and refines it with subject-specific information. This enables modeling of the inter-subject variability in the functional parcel boundaries and allows us to construct subject-specific networks with reduced sensitivity to ROI placement. We show that while still maintaining correspondence across subjects, p-Eigen extracts biologically-relevant and patient-specific functional parcels that facilitate hypothesis-driven network analysis. We construct default mode network (DMN) connectivity graphs using p-Eigen refined ROIs and use them in a classification paradigm. Our results show that the functional connectivity graphs derived from p-Eigen significantly aid classification of mild cognitive impairment (MCI) as well as the prediction of scores in a Delayed Recall memory task when compared to graph metrics derived from 1) standard registration-based seed ROI definitions, 2) totally data-driven ROIs, 3) a model based on standard demographics plus hippocampal volume as covariates, and 4) Ward Clustering based data-driven ROIs. In summary, p-Eigen incarnates a new class of prior-constrained dimensionality reduction tools that may improve our understanding of the relationship between MCI and functional connectivity.}, Doi = {026}, Institution = {Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pii = {S1053-8119(14)00391-7}, Pmid = {24852460}, Timestamp = {2014.08.26}, Url = {http://dx.doi.org/026} } @Article{Dubb2003, Title = {Characterization of sexual dimorphism in the human corpus callosum}, Author = {Dubb, A and Gur, R and Avants, B and Gee, J}, Journal = {Neuroimage}, Year = {2003}, Month = sep, Number = {1}, Pages = {512--519}, Volume = {20}, Doi = {10.1016/S1053-8119(03)00313-6}, ISSN = {1053-8119}, Unique-id = {ISI:000185746400047} } @Article{Duda2013, Title = {Fusing functional signals by sparse canonical correlation analysis improves network reproducibility.}, Author = {Duda, Jeffrey T. and Detre, John A. and Kim, Junghoon and Gee, James C. and Avants, Brian B.}, Journal = {Med Image Comput Comput Assist Interv}, Year = {2013}, Number = {Pt 3}, Pages = {635--642}, Volume = {16}, __markedentry = {[stnava:1]}, Abstract = {We contribute a novel multivariate strategy for computing the structure of functional networks in the brain from arterial spin labeling (ASL) MRI. Our method fuses and correlates multiple functional signals by employing an interpretable dimensionality reduction method, sparse canonical correlation analysis (SCCA). There are two key aspects of this contribution. First, we show how SCCA may be used to compute a multivariate correlation between different regions of interest (ROI). In contrast to averaging the signal over the ROI, this approach exploits the full information within the ROI. Second, we show how SCCA may simultaneously exploit both the ASL-BOLD and ASL-based cerebral blood flow (CBF) time series to produce network measurements. Our approach to fusing multiple time signals in network studies improves reproducibility over standard approaches while retaining the interpretability afforded by the classic ROI region-averaging methods. We show experimentally in test-retest data that our sparse CCA method extracts biologically plausible and stable functional network structures from ASL. We compare the ROI approach to the CCA approach while using CBF measurements alone. We then compare these results to the joint BOLD-CBF networks in a reproducibility study and in a study of functional network structure in traumatic brain injury (TBI). Our results show that the SCCA approach provides significantly more reproducible results compared to region-averaging, and in TBI the SCCA approach reveals connectivity differences not seen with the region averaging approach.}, Institution = {University of Pennsylvania, USA.}, Keywords = {Brain Injuries, diagnosis/physiopathology; Brain Mapping, methods; Brain, physiopathology; Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Magnetic Resonance Imaging, methods; Nerve Net, physiopathology; Reproducibility of Results; Sensitivity and Specificity; Statistics as Topic}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {24505815}, Timestamp = {2014.08.26} } @Article{Fan2007, Title = {Multivariate examination of brain abnormality using both structural and functional {MRI}}, Author = {Fan, Yong and Rao, Hengyi and Hurt, Hallam and Giannetta, Joan and Korczykowski, Marc and Shera, David and Avants, Brian B. and Gee, James C. and Wang, Jiongjiong and Shen, Dinggang}, Journal = {Neuroimage}, Year = {2007}, Month = jul, Number = {4}, Pages = {1189--1199}, Volume = {36}, Doi = {10.1016/j.neuroimage.2007.04.009}, ISSN = {1053-8119}, Researcherid-numbers = {Rao, Hengyi/A-7064-2009 Fan, Yong/B-8306-2012}, Unique-id = {ISI:000248152400013} } @Article{Farag2010, Title = {Hierarchical Organization of Scripts: Converging Evidence from fMRI and Frontotemporal Degeneration}, Author = {Farag, Christine and Troiani, Vanessa and Bonner, Michael and Powers, Chivon and Avants, Brian and Gee, James and Grossman, Murray}, Journal = {Cereb. Cortex}, Year = {2010}, Month = oct, Number = {10}, Pages = {2453--2463}, Volume = {20}, Doi = {10.1093/cercor/bhp313}, ISSN = {1047-3211}, Unique-id = {ISI:000281715500017} } @Article{Ghosh2012, Title = {Learning from open source software projects to improve scientific review.}, Author = {Ghosh, Satrajit S. and Klein, Arno and Avants, Brian and Millman, K Jarrod}, Journal = {Front Comput Neurosci}, Year = {2012}, Pages = {18}, Volume = {6}, Abstract = {Peer-reviewed publications are the primary mechanism for sharing scientific results. The current peer-review process is, however, fraught with many problems that undermine the pace, validity, and credibility of science. We highlight five salient problems: (1) reviewers are expected to have comprehensive expertise; (2) reviewers do not have sufficient access to methods and materials to evaluate a study; (3) reviewers are neither identified nor acknowledged; (4) there is no measure of the quality of a review; and (5) reviews take a lot of time, and once submitted cannot evolve. We propose that these problems can be resolved by making the following changes to the review process. Distributing reviews to many reviewers would allow each reviewer to focus on portions of the article that reflect the reviewer's specialty or area of interest and place less of a burden on any one reviewer. Providing reviewers materials and methods to perform comprehensive evaluation would facilitate transparency, greater scrutiny, and replication of results. Acknowledging reviewers makes it possible to quantitatively assess reviewer contributions, which could be used to establish the impact of the reviewer in the scientific community. Quantifying review quality could help establish the importance of individual reviews and reviewers as well as the submitted article. Finally, we recommend expediting post-publication reviews and allowing for the dialog to continue and flourish in a dynamic and interactive manner. We argue that these solutions can be implemented by adapting existing features from open-source software management and social networking technologies. We propose a model of an open, interactive review system that quantifies the significance of articles, the quality of reviews, and the reputation of reviewers.}, Doi = {10.3389/fncom.2012.00018}, Institution = {McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge MA, USA.}, Language = {eng}, Medline-pst = {epublish}, Owner = {stnava}, Pmid = {22529798}, Timestamp = {2014.08.26} } @Article{Gross2012, Title = {Sentence processing in Lewy body spectrum disorder: The role of working memory}, Author = {Gross, Rachel G. and McMillan, Corey T. and Chandrasekaran, Keerthi and Dreyfuss, Michael and Ash, Sharon and Avants, Brian and Cook, Philip and Moore, Peachie and Libon, David J. and Siderowf, Andrew and Grossman, Murray}, Journal = {Brain Cogn.}, Year = {2012}, Month = mar, Number = {2}, Pages = {85--93}, Volume = {78}, Doi = {10.1016/j.bandc.2011.12.004}, ISSN = {0278-2626}, Unique-id = {ISI:000299853900001} } @Article{Grossman2008, Title = {Impaired action knowledge in amyotrophic lateral sclerosis}, Author = {Grossman, M. and Anderson, C. and Khan, A. and Avants, B. and Elman, L. and McCluskey, L.}, Journal = {Neurology}, Year = {2008}, Month = oct, Number = {18}, Pages = {1396--1401}, Volume = {71}, Doi = {10.1212/01.wnl.0000319701.50168.8c}, ISSN = {0028-3878}, Unique-id = {ISI:000260426100004} } @Article{Grossman2008a, Title = {Neural basis for impaired action knowledge in amyotrophic lateral sclerosis}, Author = {Grossman, Murray and Anderson, Chivon and Khan, Alea and Avants, Brian and Elman, Lauren and McCluskey, Leo}, Journal = {Neurology}, Year = {2008}, Month = mar, Note = {6th Annual Meeting of the American-Academy-of-Neurology, Chicago, IL, APR 12-19, 2008}, Number = {11, 1}, Pages = {A248}, Volume = {70}, ISSN = {0028-3878}, Organization = {Amer Acad Neurol}, Unique-id = {ISI:000257197201336} } @Article{Grossman2010, Title = {The role of ventral medial prefrontal cortex in social decisions Converging evidence from fMRI and frontotemporal lobar degeneration}, Author = {Grossman, Murray and Eslinger, Paul J. and Troiani, Vanessa and Anderson, Chivon and Avants, Brian and Gee, James C. and McMillan, Corey and Massimo, Lauren and Khan, Alea and Antani, Shweta}, Journal = {Neuropsychologia}, Year = {2010}, Month = oct, Number = {12}, Pages = {3505--3512}, Volume = {48}, Doi = {10.1016/j.neuropsychologia.2010.07.036}, ISSN = {0028-3932}, Unique-id = {ISI:000284017300015} } @Article{Gunawardena2010, Title = {Why are patients with progressive nonfluent aphasia nonfluent?}, Author = {Gunawardena, D. and Ash, S. and McMillan, C. and Avants, B. and Gee, J. and Grossman, M.}, Journal = {Neurology}, Year = {2010}, Month = aug, Number = {7}, Pages = {588--594}, Volume = {75}, ISSN = {0028-3878}, Unique-id = {ISI:000281066700004} } @Article{Hanson2012, Title = {Structural Variations in Prefrontal Cortex Mediate the Relationship between Early Childhood Stress and Spatial Working Memory}, Author = {Hanson, Jamie L. and Chung, Moo K. and Avants, Brian B. and Rudolph, Karen D. and Shirtcliff, Elizabeth A. and Gee, James C. and Davidson, Richard J. and Pollak, Seth D.}, Journal = {J. Neurosci.}, Year = {2012}, Month = jun, Number = {23}, Pages = {7917--7925}, Volume = {32}, Doi = {10.1523/JNEUROSCI.0307-12.2012}, ISSN = {0270-6474}, Unique-id = {ISI:000305091800016} } @Article{Hanson2010, Title = {Early Stress Is Associated with {Altera}tions in the Orbitofrontal Cortex: A Tensor-Based Morphometry Investigation of Brain Structure and Behavioral Risk}, Author = {Hanson, Jamie L. and Chung, Moo K. and Avants, Brian B. and Shirtcliff, Elizabeth A. and Gee, James C. and Davidson, Richard J. and Pollak, Seth D.}, Journal = {J. Neurosci.}, Year = {2010}, Month = jun, Number = {22}, Pages = {7466--7472}, Volume = {30}, Doi = {10.1523/JNEUROSCI.0859-10.2010}, ISSN = {0270-6474}, Researcherid-numbers = {Pollak, Seth/G-2345-2011}, Unique-id = {ISI:000278288200004} } @Article{Hanson2012a, Title = {Robust Automated Amygdala Segmentation via Multi-Atlas Diffeomorphic Registration.}, Author = {Hanson, Jamie L. and Suh, Jung W. and Nacewicz, Brendon M. and Sutterer, Matthew J. and Cayo, Amelia A. and Stodola, Diane E. and Burghy, Cory A. and Wang, Hongzhi and Avants, Brian B. and Yushkevich, Paul A. and Essex, Marilyn J. and Pollak, Seth D. and Davidson, Richard J.}, Journal = {Front Neurosci}, Year = {2012}, Pages = {166}, Volume = {6}, Abstract = {Here, we describe a novel method for volumetric segmentation of the amygdala from MRI images collected from 35 human subjects. This approach is adapted from open-source techniques employed previously with the hippocampus (Suh et al., 2011; Wang et al., 2011a,b). Using multi-atlas segmentation and machine learning-based correction, we were able to produce automated amygdala segments with high Dice (Mean?=?0.918 for the left amygdala; 0.916 for the right amygdala) and Jaccard coefficients (Mean?=?0.850 for the left; 0.846 for the right) compared to rigorously hand-traced volumes. This automated routine also produced amygdala segments with high intra-class correlations (consistency?=?0.830, absolute agreement?=?0.819 for the left; consistency?=?0.786, absolute agreement?=?0.783 for the right) and bivariate (r?=?0.831 for the left; r?=?0.797 for the right) compared to hand-drawn amygdala. Our results are discussed in relation to other cutting-edge segmentation techniques, as well as commonly available approaches to amygdala segmentation (e.g., Freesurfer). We believe this new technique has broad application to research with large sample sizes for which amygdala quantification might be needed.}, Doi = {10.3389/fnins.2012.00166}, Institution = {Department of Psychology, University of Wisconsin-Madison Madison, WI, USA ; Waisman Center, University of Wisconsin-Madison Madison, WI, USA.}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {23226114}, Timestamp = {2013.05.29} } @Article{Hopkins2013, Title = {Regional and Hemispheric Variation in Cortical Thickness in Chimpanzees (Pan troglodytes)}, Author = {Hopkins, William D. and Avants, Brian B.}, Journal = {J. Neurosci.}, Year = {2013}, Month = mar, Number = {12}, Pages = {5241--5248}, Volume = {33}, Doi = {10.1523/JNEUROSCI.2996-12.2013}, ISSN = {0270-6474}, Unique-id = {ISI:000316553800017} } @Article{Hurst2012, Title = {How well does endocranial morphology predict behavioral differences in primates?}, Author = {Hurst, Delanie R. and Schoenemann, P. Thomas and Loyet, Mackenzie M. and Avants, Brian B. and Gee, James C.}, Journal = {Am. J. Phys. Anthropol.}, Year = {2012}, Note = {81st Annual Meeting of the American-Association-of-Physical-Anthropologists, Portland, OR, 2012}, Number = {54}, Pages = {171}, Volume = {147}, ISSN = {0002-9483}, Organization = {Amer Assoc Phys Anthropol}, Unique-id = {ISI:000300498700404} } @Article{Isgum2015, Title = {Evaluation of automatic neonatal brain segmentation algorithms: The NeoBrainS12 challenge.}, Author = {I{\v{s}}gum, Ivana and Benders, Manon J N L. and Avants, Brian and Cardoso, M Jorge and Counsell, Serena J. and Gomez, Elda Fischi and Gui, Laura and Hűppi, Petra S. and Kersbergen, Karina J. and Makropoulos, Antonios and Melbourne, Andrew and Moeskops, Pim and Mol, Christian P. and Kuklisova-Murgasova, Maria and Rueckert, Daniel and Schnabel, Julia A. and Srhoj-Egekher, Vedran and Wu, Jue and Wang, Siying and {de Vries}, Linda S. and Viergever, Max A.}, Journal = {Med Image Anal}, Year = {2015}, Month = {Feb}, Number = {1}, Pages = {135--151}, Volume = {20}, Abstract = {A number of algorithms for brain segmentation in preterm born infants have been published, but a reliable comparison of their performance is lacking. The NeoBrainS12 study (http://neobrains12.isi.uu.nl), providing three different image sets of preterm born infants, was set up to provide such a comparison. These sets are (i) axial scans acquired at 40weeks corrected age, (ii) coronal scans acquired at 30weeks corrected age and (iii) coronal scans acquired at 40weeks corrected age. Each of these three sets consists of three T1- and T2-weighted MR images of the brain acquired with a 3T MRI scanner. The task was to segment cortical grey matter, non-myelinated and myelinated white matter, brainstem, basal ganglia and thalami, cerebellum, and cerebrospinal fluid in the ventricles and in the extracerebral space separately. Any team could upload the results and all segmentations were evaluated in the same way. This paper presents the results of eight participating teams. The results demonstrate that the participating methods were able to segment all tissue classes well, except myelinated white matter.}, Doi = {10.1016/j.media.2014.11.001}, Institution = {Image Sciences Institute, University Medical Center Utrecht, Netherlands.}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pii = {S1361-8415(14)00158-3}, Pmid = {25487610}, Timestamp = {2015.02.08}, Url = {http://dx.doi.org/10.1016/j.media.2014.11.001} } @Article{Jain2012, Title = {Longitudinal Reproducibility and Accuracy of Pseudo-Continuous Arterial Spin-labeled Perfusion MR Imaging in Typically Developing Children}, Author = {Jain, Varsha and Duda, Jeffrey and Avants, Brian and Giannetta, Mariel and Xie, Sharon X. and Roberts, Timothy and Detre, John A. and Hurt, Hallam and Wehrli, Felix W. and Wang, Danny J. J.}, Journal = {Radiology}, Year = {2012}, Month = may, Number = {2}, Pages = {527--536}, Volume = {263}, Doi = {10.1148/radiol.12111509}, ISSN = {0033-8419}, Unique-id = {ISI:000303104300025} } @Article{Kandel2015a, author = {Kandel, Benjamin M. and Avants, Brian B. and Gee, James C. and Arnold, Steven E. and Wolk, David A.}, title = {Neuropsychological Testing Predicts Cerebrospinal Fluid Amyloid-$\beta$ in Mild Cognitive Impairment.}, journal = {J Alzheimers Dis}, year = {2015}, volume = {46}, number = {4}, pages = {901--912}, month = {Jun}, abstract = {Psychometric tests predict conversion of mild cognitive impairment (MCI) to probable Alzheimer's disease (AD). Because the definition of clinical AD relies on those same psychometric tests, the ability of these tests to identify underlying AD pathology remains unclear.To determine the degree to which psychometric testing predicts molecular evidence of AD amyloid pathology, as indicated by cerebrospinal fluid (CSF) amyloid-β (Aβ)1 - 42, in patients with MCI, as compared to neuroimaging biomarkers.We identified 408 MCI subjects with CSF Aβ levels, psychometric test data, FDG-PET scans, and acceptable volumetric MR scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We used psychometric tests and imaging biomarkers in univariate and multivariate models to predict Aβ status.The 30-min delayed recall score of the Rey Auditory Verbal Learning Test was the best predictor of Aβ status among the psychometric tests, achieving an AUC of 0.67 ± 0.02 and odds ratio of 2.5 ± 0.4. FDG-PET was the best imaging-based biomarker (AUC 0.67 ± 0.03, OR 3.2 ± 1.2), followed by hippocampal volume (AUC 0.64 ± 0.02, OR 2.4 ± 0.3). A multivariate analysis based on the psychometric tests improved on the univariate predictors, achieving an AUC of 0.68 ± 0.03 (OR 3.38 ± 1.2). Adding imaging biomarkers to the multivariate analysis did not improve the AUC.Psychometric tests perform as well as imaging biomarkers to predict presence of molecular markers of AD pathology in MCI patients and should be considered in the determination of the likelihood that MCI is due to AD.}, doi = {10.3233/JAD-142943}, institution = {Department of Neurology, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, PA, USA.}, language = {eng}, medline-pst = {ppublish}, owner = {stnava}, pii = {Y67547X252743107}, pmid = {25881908}, timestamp = {2015.10.04}, url = {http://dx.doi.org/10.3233/JAD-142943}, } @Article{Kandel2015, Title = {Decomposing cerebral blood flow MRI into functional and structural components: a non-local approach based on prediction.}, Author = {Kandel, Benjamin M. and Wang, Danny J J. and Detre, John A. and Gee, James C. and Avants, Brian B.}, Journal = {Neuroimage}, Year = {2015}, Month = {Jan}, Pages = {156--170}, Volume = {105}, Abstract = {We present RIPMMARC (Rotation Invariant Patch-based Multi-Modality Analysis aRChitecture), a flexible and widely applicable method for extracting information unique to a given modality from a multi-modal data set. We use RIPMMARC to improve the interpretation of arterial spin labeling (ASL) perfusion images by removing the component of perfusion that is predicted by the underlying anatomy. Using patch-based, rotation invariant descriptors derived from the anatomical image, we learn a predictive relationship between local neuroanatomical structure and the corresponding perfusion image. This relation allows us to produce an image of perfusion that would be predicted given only the underlying anatomy and a residual image that represents perfusion information that cannot be predicted by anatomical features. Our learned structural features are significantly better at predicting brain perfusion than tissue probability maps, which are the input to standard partial volume correction techniques. Studies in test-retest data show that both the anatomically predicted and residual perfusion signals are highly replicable for a given subject. In a pediatric population, both the raw perfusion and structurally predicted images are tightly linked to age throughout adolescence throughout the brain. Interestingly, the residual perfusion also shows a strong correlation with age in selected regions including the hippocampi (corr = 0.38, p-value <10(-6)), precuneus (corr = -0.44, p < 10(-5)), and combined default mode network regions (corr = -0.45, p < 10(-8)) that is independent of global anatomy-perfusion trends. This finding suggests that there is a regionally heterogeneous pattern of functional specialization that is distinct from that of cortical structural development.}, Doi = {10.1016/j.neuroimage.2014.10.052}, Institution = {Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pii = {S1053-8119(14)00891-X}, Pmid = {25449745}, Timestamp = {2015.02.08}, Url = {http://dx.doi.org/10.1016/j.neuroimage.2014.10.052} } @Article{Kandel2015b, Title = {Eigenanatomy: sparse dimensionality reduction for multi-modal medical image analysis.}, Author = {Kandel, Benjamin M. and Wang, Danny J J. and Gee, James C. and Avants, Brian B.}, Journal = {Methods}, Year = {2015}, Month = {Feb}, Pages = {43--53}, Volume = {73}, Abstract = {Rigorous statistical analysis of multimodal imaging datasets is challenging. Mass-univariate methods for extracting correlations between image voxels and outcome measurements are not ideal for multimodal datasets, as they do not account for interactions between the different modalities. The extremely high dimensionality of medical images necessitates dimensionality reduction, such as principal component analysis (PCA) or independent component analysis (ICA). These dimensionality reduction techniques, however, consist of contributions from every region in the brain and are therefore difficult to interpret. Recent advances in sparse dimensionality reduction have enabled construction of a set of image regions that explain the variance of the images while still maintaining anatomical interpretability. The projections of the original data on the sparse eigenvectors, however, are highly collinear and therefore difficult to incorporate into multi-modal image analysis pipelines. We propose here a method for clustering sparse eigenvectors and selecting a subset of the eigenvectors to make interpretable predictions from a multi-modal dataset. Evaluation on a publicly available dataset shows that the proposed method outperforms PCA and ICA-based regressions while still maintaining anatomical meaning. To facilitate reproducibility, the complete dataset used and all source code is publicly available.}, Doi = {10.1016/j.ymeth.2014.10.016}, Institution = {Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, United States; Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, United States.}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pii = {S1046-2023(14)00333-8}, Pmid = {25448483}, Timestamp = {2015.05.23}, Url = {http://dx.doi.org/10.1016/j.ymeth.2014.10.016} } @Article{Kandel2014, Title = {Single-subject structural networks with closed-form rotation invariant matching mprove power in developmental studies of the cortex.}, Author = {Kandel, Benjamin M. and Wang, Danny J J. and Gee, James C. and Avants, Brian B.}, Journal = {Med Image Comput Comput Assist Interv}, Year = {2014}, Number = {Pt 3}, Pages = {137--144}, Volume = {17}, __markedentry = {[stnava:1]}, Abstract = {Although much attention has recently been focused on single-subject functional networks, using methods such as resting-state functional MRI, methods for constructing single-subject structural networks are in their infancy. Single-subject cortical networks aim to describe the self-similarity across the cortical structure, possibly signifying convergent developmental pathways. Previous methods for constructing single-subject cortical networks have used patch-based correlations and distance metrics based on curvature and thickness. We present here a method for constructing similarity-based cortical structural networks that utilizes a rotation-invariant representation of structure. The resulting graph metrics are closely linked to age and indicate an increasing degree of closeness throughout development in nearly all brain regions, perhaps corresponding to a more regular structure as the brain matures. The derived graph metrics demonstrate a four-fold increase in power for detecting age as compared to cortical thickness. This proof of concept study indicates that the proposed metric may be useful in identifying biologically relevant cortical patterns.}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {25320792}, Timestamp = {2014.11.01} } @Article{Kandel2013a, Title = {Predicting cognitive data from medical images using sparse linear regression.}, Author = {Kandel, Benjamin M. and Wolk, David A. and Gee, James C. and Avants, Brian}, Journal = {Inf Process Med Imaging}, Year = {2013}, Pages = {86--97}, Volume = {23}, __markedentry = {[stnava:1]}, Abstract = {We present a new framework for predicting cognitive or other continuous-variable data from medical images. Current methods of probing the connection between medical images and other clinical data typically use voxel-based mass univariate approaches. These approaches do not take into account the multivariate, network-based interactions between the various areas of the brain and do not give readily interpretable metrics that describe how strongly cognitive function is related to neuroanatomical structure. On the other hand, high-dimensional machine learning techniques do not typically provide a direct method for discovering which parts of the brain are used for making predictions. We present a framework, based on recent work in sparse linear regression, that addresses both drawbacks of mass univariate approaches, while preserving the direct spatial interpretability that they provide. In addition, we present a novel optimization algorithm that adapts the conjugate gradient method for sparse regression on medical imaging data. This algorithm produces coefficients that are more interpretable than existing sparse regression techniques.}, Keywords = {Algorithms; Brain, pathology; Computer Simulation; Data Interpretation, Statistical; Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Linear Models; Magnetic Resonance Imaging, methods; Mild Cognitive Impairment, diagnosis; Models, Statistical; Pattern Recognition, Automated, methods; Prognosis; Regression Analysis; Reproducibility of Results; Sensitivity and Specificity}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {24683960}, Timestamp = {2014.11.01} } @Article{Kim2008, Title = {Structural consequences of diffuse traumatic brain injury: A large deformation tensor-based morphometry study}, Author = {Kim, Junghoon and Avants, Brian and Patel, Sunil and Whyte, John and Coslett, Branch H. and Pluta, John and Detre, John A. and Gee, James C.}, Journal = {Neuroimage}, Year = {2008}, Month = feb, Number = {3}, Pages = {1014--1026}, Volume = {39}, Doi = {10.1016/j.neuroimage.2007.10.005}, ISSN = {1053-8119}, Unique-id = {ISI:000252691800011} } @Article{Kim2013, Title = {Methodological considerations in longitudinal morphometry of traumatic brain injury.}, Author = {Kim, Junghoon and Avants, Brian and Whyte, John and Gee, James C.}, Journal = {Front Hum Neurosci}, Year = {2013}, Pages = {52}, Volume = {7}, Abstract = {Traumatic brain injury (TBI) has recently been reconceptualized as a chronic, evolving disease process. This new view necessitates quantitative assessment of post-injury changes in brain structure that may allow more accurate monitoring and prediction of recovery. In particular, TBI is known to trigger neurodegenerative processes and therefore quantifying progression of diffuse atrophy over time is currently of utmost interest. However, there are various methodological issues inherent to longitudinal morphometry in TBI. In this paper, we first overview several of these methodological challenges: lesion evolution, neurosurgical procedures, power, bias, and non-linearity. We then introduce a sensitive, reliable, and unbiased longitudinal multivariate analysis protocol that combines dimensionality reduction and region of interest approaches. This analysis pipeline is demonstrated using a small dataset consisting of four chronic TBI survivors.}, Doi = {10.3389/fnhum.2013.00052}, Institution = {Moss Rehabilitation Research Institute Elkins Park, PA, USA.}, Language = {eng}, Medline-pst = {epublish}, Owner = {stnava}, Pmid = {23549059}, Timestamp = {2014.08.26} } @Article{Kim2010, Title = {Resting Cerebral Blood Flow {Altera}tions in Chronic Traumatic Brain Injury: An Arterial Spin Labeling Perfusion fMRI Study}, Author = {Kim, Junghoon and Whyte, John and Patel, Sunil and Avants, Brian and Europa, Eduardo and Wang, Jiongjiong and Slattery, John and Gee, James C. and Coslett, H. Branch and Detre, John A.}, Journal = {J. Neurotrauma}, Year = {2010}, Month = aug, Number = {8}, Pages = {1399--1411}, Volume = {27}, Doi = {10.1089/neu.2009.1215}, ISSN = {0897-7151}, Unique-id = {ISI:000280984900005} } @Article{Klein2009, Title = {Evaluation of 14 nonlinear deformation algorithms applied to human brain {MRI} registration}, Author = {Klein, Arno and Andersson, Jesper and Ardekani, Babak A. and Ashburner, John and Avants, Brian and Chiang, Ming-Chang and Christensen, Gary E. and Collins, D. Louis and Gee, James and Hellier, Pierre and Song, Joo Hyun and Jenkinson, Mark and Lepage, Claude and Rueckert, Daniel and Thompson, Paul and Vercauteren, Tom and Woods, Roger P. and Mann, J. John and Parsey, Ramin V.}, Journal = {Neuroimage}, Year = {2009}, Month = jul, Number = {3}, Pages = {786--802}, Volume = {46}, Doi = {10.1016/j.neuroimage.2008.12.037}, ISSN = {1053-8119}, Researcherid-numbers = {Rueckert, Daniel/C-4393-2008}, Unique-id = {ISI:000265938700025} } @Article{Klein2010, Title = {Evaluation of volume-based and surface-based brain image registration methods}, Author = {Klein, Arno and Ghosh, Satrajit S. and Avants, Brian and Yeo, B. T. T. and Fischl, Bruce and Ardekani, Babak and Gee, James C. and Mann, J. J. and Parsey, Ramin V.}, Journal = {Neuroimage}, Year = {2010}, Month = may, Number = {1}, Pages = {214--220}, Volume = {51}, Doi = {10.1016/j.neuroimage.2010.01.091}, ISSN = {1053-8119}, Unique-id = {ISI:000276480200021} } @Article{Lawson2013, Title = {Associations between children's socioeconomic status and prefrontal cortical thickness.}, Author = {Lawson, Gwendolyn M. and Duda, Jeffrey T. and Avants, Brian B. and Wu, Jue and Farah, Martha J.}, Journal = {Dev Sci}, Year = {2013}, Month = sep, Number = {5}, Pages = {641--652}, Volume = {16}, Abstract = {Childhood socioeconomic status (SES) predicts executive function performance and measures of prefrontal cortical function, but little is known about its anatomical correlates. Structural MRI and demographic data from a sample of 283 healthy children from the NIH MRI Study of Normal Brain Development were used to investigate the relationship between SES and prefrontal cortical thickness. Specifically, we assessed the association between two principal measures of childhood SES, family income and parental education, and gray matter thickness in specific subregions of prefrontal cortex and on the asymmetry of these areas. After correcting for multiple comparisons and controlling for potentially confounding variables, parental education significantly predicted cortical thickness in the right anterior cingulate gyrus and left superior frontal gyrus. These results suggest that brain structure in frontal regions may provide a meaningful link between SES and cognitive function among healthy, typically developing children.}, Doi = {10.1111/desc.12096}, Institution = {Department of Psychology, University of Pennsylvania, USA.}, Keywords = {Adolescent; Child; Educational Status; Executive Function, physiology; Female; Humans; Image Processing, Computer-Assisted; Income; Linear Models; Magnetic Resonance Imaging; Male; Organ Size; Parents; Prefrontal Cortex, anatomy /&/ histology/physiology; Social Class; United States}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {24033570}, Timestamp = {2014.08.26} } @Article{Libon2012, Title = {Deficits in Concept Formation in Amyotrophic Lateral Sclerosis}, Author = {Libon, David J. and McMillan, Corey and Avants, Brian and Boller, Ashley and Morgan, Brianna and Burkholder, Lisa and Chandrasekaran, Keerthi and Elman, Lauren and McCluskey, Leo and Grossman, Murray}, Journal = {Neuropsychology}, Year = {2012}, Month = jul, Number = {4}, Pages = {422--429}, Volume = {26}, Doi = {10.1037/a0028668}, ISSN = {0894-4105}, Unique-id = {ISI:000306036300003} } @Article{Loyet2012, Title = {Associations between localized variation in brain anatomy and social behavior in healthy human subjects.}, Author = {Loyet, Mackenzie M. and Schoenemann, P. Thomas and Avants, Brian B. and Gee, James C.}, Journal = {Am. J. Phys. Anthropol.}, Year = {2012}, Note = {81st Annual Meeting of the American-Association-of-Physical-Anthropologists, Portland, OR, 2012}, Number = {54}, Pages = {196}, Volume = {147}, ISSN = {0002-9483}, Organization = {Amer Assoc Phys Anthropol}, Unique-id = {ISI:000300498700518} } @Article{Massimo2009, Title = {Neuroanatomy of Apathy and Disinhibition in Frontotemporal Lobar Degeneration}, Author = {Massimo, Lauren and Powers, Chivon and Moore, Peachie and Vesely, Luisa and Avants, Brian and Gee, James and Libon, David J. and Grossman, Murray}, Journal = {Dement. Geriatr. Cogn. Disord.}, Year = {2009}, Number = {1}, Pages = {96--104}, Volume = {27}, Doi = {10.1159/000194658}, ISSN = {1420-8008}, Unique-id = {ISI:000262899200013} } @Article{Massimo2008, Title = {Neuroanatomical correlates of apathy and disinhibition in frontotemporal dementia}, Author = {Massimo, Lauren M. and Anderson, Chivon and Moore, Peachie and Avants, Brian and Libon, David and Cynwyd, Bala and Grossman, Murray}, Journal = {Neurology}, Year = {2008}, Month = mar, Note = {6th Annual Meeting of the American-Academy-of-Neurology, Chicago, IL, APR 12-19, 2008}, Number = {11, 1}, Pages = {A443}, Volume = {70}, ISSN = {0028-3878}, Organization = {Amer Acad Neurol}, Unique-id = {ISI:000257197202464} } @Article{McMillan2013, Title = {Can {MRI} screen for CSF biomarkers in neurodegenerative disease?}, Author = {McMillan, Corey T. and Avants, Brian and Irwin, David J. and Toledo, Jon B. and Wolk, David A. and Van Deerlin, Vivianna M. and Shaw, Leslie M. and Trojanoswki, John Q. and Grossman, Murray}, Journal = {Neurology}, Year = {2013}, Month = jan, Number = {2}, Pages = {132--138}, Volume = {80}, Doi = {10.1212/WNL.0b013e31827b9147}, ISSN = {0028-3878}, Unique-id = {ISI:000313344700008} } @Article{McMillan2014, Title = {The power of neuroimaging biomarkers for screening frontotemporal dementia.}, Author = {McMillan, Corey T. and Avants, Brian B. and Cook, Philip and Ungar, Lyle and Trojanowski, John Q. and Grossman, Murray}, Journal = {Hum. Brain Mapp.}, Year = {2014}, Month = sep, Number = {9}, Pages = {4827--4840}, Volume = {35}, Abstract = {Frontotemporal dementia (FTD) is a clinically and pathologically heterogeneous neurodegenerative disease that can result from either frontotemporal lobar degeneration (FTLD) or Alzheimer's disease (AD) pathology. It is critical to establish statistically powerful biomarkers that can achieve substantial cost-savings and increase the feasibility of clinical trials. We assessed three broad categories of neuroimaging methods to screen underlying FTLD and AD pathology in a clinical FTD series: global measures (e.g., ventricular volume), anatomical volumes of interest (VOIs) (e.g., hippocampus) using a standard atlas, and data-driven VOIs using Eigenanatomy. We evaluated clinical FTD patients (N\hspace{0.167em}=\hspace{0.167em}93) with cerebrospinal fluid, gray matter (GM) magnetic resonance imaging (MRI), and diffusion tensor imaging (DTI) to assess whether they had underlying FTLD or AD pathology. Linear regression was performed to identify the optimal VOIs for each method in a training dataset and then we evaluated classification sensitivity and specificity in an independent test cohort. Power was evaluated by calculating minimum sample sizes required in the test classification analyses for each model. The data-driven VOI analysis using a multimodal combination of GM MRI and DTI achieved the greatest classification accuracy (89\% sensitive and 89\% specific) and required a lower minimum sample size (N\hspace{0.167em}=\hspace{0.167em}26) relative to anatomical VOI and global measures. We conclude that a data-driven VOI approach using Eigenanatomy provides more accurate classification, benefits from increased statistical power in unseen datasets, and therefore provides a robust method for screening underlying pathology in FTD patients for entry into clinical trials. Hum Brain Mapp 35:4827-4840, 2014. {\copyright} 2014 Wiley Periodicals, Inc.}, Doi = {10.1002/hbm.22515}, Institution = {Department of Neurology, Penn Frontotemporal Degeneration Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {24687814}, Timestamp = {2014.08.26} } @Article{McMillan2013a, Title = {White matter imaging helps dissociate tau from TDP-43 in frontotemporal lobar degeneration.}, Author = {McMillan, Corey T. and Irwin, David J. and Avants, Brian B. and Powers, John and Cook, Philip A. and Toledo, Jon B. and {McCarty Wood}, Elisabeth and {Van Deerlin}, Vivianna M. and Lee, Virginia M-Y. and Trojanowski, John Q. and Grossman, Murray}, Journal = {J. Neurol. Neurosurg. Psychiatry}, Year = {2013}, Month = mar, Abstract = {BACKGROUND: Frontotemporal lobar degeneration (FTLD) is most commonly associated with TAR-DNA binding protein (TDP-43) or tau pathology at autopsy, but there are no in vivo biomarkers reliably discriminating between sporadic cases. As disease-modifying treatments emerge, it is critical to accurately identify underlying pathology in living patients so that they can be entered into appropriate etiology-directed clinical trials. Patients with tau inclusions (FTLD-TAU) appear to have relatively greater white matter (WM) disease at autopsy than those patients with TDP-43 (FTLD-TDP). In this paper, we investigate the ability of white matter (WM) imaging to help discriminate between FTLD-TAU and FTLD-TDP during life using diffusion tensor imaging (DTI). METHODS: Patients with autopsy-confirmed disease or a genetic mutation consistent with FTLD-TDP or FTLD-TAU underwent multimodal T1 volumetric MRI and diffusion weighted imaging scans. We quantified cortical thickness in GM and fractional anisotropy (FA) in WM. We performed Eigenanatomy, a statistically robust dimensionality reduction algorithm, and used leave-one-out cross-validation to predict underlying pathology. Neuropathological assessment of GM and WM disease burden was performed in the autopsy-cases to confirm our findings of an ante-mortem GM and WM dissociation in the neuroimaging cohort. RESULTS: ROC curve analyses evaluated classification accuracy in individual patients and revealed 96\% sensitivity and 100\% specificity for WM analyses. FTLD-TAU had significantly more WM degeneration and inclusion severity at autopsy relative to FTLD-TDP. CONCLUSIONS: These neuroimaging and neuropathological investigations provide converging evidence for greater WM burden associated with FTLD-TAU, and emphasise the role of WM neuroimaging for in vivo discrimination between FTLD-TAU and FTLD-TDP.}, Doi = {10.1136/jnnp-2012-304418}, Institution = {Department of Neurology, Perelman School of Medicine, Frontotemporal Degeneration Center, University of Pennsylvania, , Philadelphia, Pennsylvania, USA.}, Language = {eng}, Medline-pst = {aheadofprint}, Owner = {stnava}, Pii = {jnnp-2012-304418}, Pmid = {23475817}, Timestamp = {2013.05.29} } @Article{McMillan2014a, Title = {Genetic and neuroanatomic associations in sporadic frontotemporal lobar degeneration.}, Author = {McMillan, Corey T. and Toledo, Jon B. and Avants, Brian B. and Cook, Philip A. and Wood, Elisabeth M. and Suh, Eunran and Irwin, David J. and Powers, John and Olm, Christopher and Elman, Lauren and McCluskey, Leo and Schellenberg, Gerard D. and Lee, Virginia M-Y. and Trojanowski, John Q. and {Van Deerlin}, Vivianna M. and Grossman, Murray}, Journal = {Neurobiol. Aging}, Year = {2014}, Month = jun, Number = {6}, Pages = {1473--1482}, Volume = {35}, Abstract = {Genome-wide association studies have identified single nucleotide polymorphisms (SNPs) that are sensitive for tau or TDP-43 pathology in frontotemporal lobar degeneration (FTLD). Neuroimaging analyses have revealed distinct distributions of disease in FTLD patients with genetic mutations. However, genetic influences on neuroanatomic structure in sporadic FTLD have not been assessed. In this report, we use novel multivariate tools, Eigenanatomy, and sparse canonical correlation analysis to identify associations between SNPs and neuroanatomic structure in sporadic FTLD. Magnetic resonance imaging analyses revealed that rs8070723 (MAPT) was associated with gray matter variance in the temporal cortex. Diffusion tensor imaging analyses revealed that rs1768208 (MOBP), rs646776 (near SORT1), and rs5848 (PGRN) were associated with white matter variance in the midbrain and superior longitudinal fasciculus. In an independent autopsy series, we observed that rs8070723 and rs1768208 conferred significant risk of tau pathology relative to TDP-43, and rs646776 conferred increased risk of TDP-43 pathology relative to tau. Identified brain regions and SNPs may help provide an in vivo screen for underlying pathology in FTLD and contribute to our understanding of sporadic FTLD.}, Doi = {10.1016/j.neurobiolaging.2013.11.029}, Institution = {Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Penn Frontotemporal Degeneration Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pii = {S0197-4580(13)00613-1}, Pmid = {24373676}, Timestamp = {2014.08.26} } @Article{Morgan2011, Title = {Some is not enough: Quantifier comprehension in corticobasal syndrome and behavioral variant frontotemporal dementia}, Author = {Morgan, Brianna and Gross, Rachel G. and Clark, Robin and Dreyfuss, Michael and Boller, Ashley and Camp, Emily and Liang, Tsao-Wei and Avants, Brian and McMillan, Corey T. and Grossman, Murray}, Journal = {Neuropsychologia}, Year = {2011}, Month = nov, Number = {13}, Pages = {3532--3541}, Volume = {49}, Doi = {10.1016/j.neuropsychologia.2011.09.005}, ISSN = {0028-3932}, Unique-id = {ISI:000297455900003} } @Article{Murphy2011, Title = {Evaluation of Registration Methods on Thoracic {CT}: The EMPIRE10~{C}hallenge}, Author = {Murphy, Keelin and van Ginneken, Bram and Reinhardt, Joseph M. and Kabus, Sven and Ding, Kai and Deng, Xiang and Cao, Kunlin and Du, Kaifang and Christensen, Gary E. and Garcia, Vincent and Vercauteren, Tom and Ayache, Nicholas and Commowick, Olivier and Malandain, Gregoire and Glocker, Ben and Paragios, Nikos and Navab, Nassir and Gorbunova, Vladlena and Sporring, Jon and de Bruijne, Marleen and Han, Xiao and Heinrich, Mattias P. and Schnabel, Julia A. and Jenkinson, Mark and Lorenz, Cristian and Modat, Marc and McClelland, Jamie R. and Ourselin, Sebastien and Muenzing, Sascha E. A. and Viergever, Max A. and De Nigris, Dante and Collins, D. Louis and Arbel, Tal and Peroni, Marta and Li, Rui and Sharp, Gregory C. and Schmidt-Richberg, Alexander and Ehrhardt, Jan and Werner, Rene and Smeets, Dirk and Loeckx, Dirk and Song, Gang and Tustison, Nicholas and Avants, Brian and Gee, James C. and Staring, Marius and Klein, Stefan and Stoel, Berend C. and Urschler, Martin and Werlberger, Manuel and Vandemeulebroucke, Jef and Rit, Simon and Sarrut, David and Pluim, Josien P. W.}, Journal = {IEEE Trans Med Imaging}, Year = {2011}, Month = nov, Number = {11}, Pages = {1901--1920}, Volume = {30}, Doi = {10.1109/TMI.2011.2158349}, ISSN = {0278-0062}, Orcid-numbers = {van Ginneken, Bram/0000-0003-2028-8972 }, Researcherid-numbers = {van Ginneken, Bram/A-3728-2012 Staring, Marius/A-9517-2009 Ding, Kai/E-1943-2013}, Unique-id = {ISI:000296455500003} } @Article{Murphy2011a, Title = {Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge.}, Author = {Murphy, Keelin and {van Ginneken}, Bram and Reinhardt, Joseph M. and Kabus, Sven and Ding, Kai and Deng, Xiang and Cao, Kunlin and Du, Kaifang and Christensen, Gary E. and Garcia, Vincent and Vercauteren, Tom and Ayache, Nicholas and Commowick, Olivier and Malandain, Gr{\'{e}}goire and Glocker, Ben and Paragios, Nikos and Navab, Nassir and Gorbunova, Vladlena and Sporring, Jon and {de Bruijne}, Marleen and Han, Xiao and Heinrich, Mattias P. and Schnabel, Julia A. and Jenkinson, Mark and Lorenz, Cristian and Modat, Marc and McClelland, Jamie R. and Ourselin, S{\'{e}}bastien and Muenzing, Sascha E A. and Viergever, Max A. and {De Nigris}, Dante and Collins, D Louis and Arbel, Tal and Peroni, Marta and Li, Rui and Sharp, Gregory C. and Schmidt-Richberg, Alexander and Ehrhardt, Jan and Werner, Ren{\'{e}} and Smeets, Dirk and Loeckx, Dirk and Song, Gang and Tustison, Nicholas and Avants, Brian and Gee, James C. and Staring, Marius and Klein, Stefan and Stoel, Berend C. and Urschler, Martin and Werlberger, Manuel and Vandemeulebroucke, Jef and Rit, Simon and Sarrut, David and Pluim, Josien P W.}, Journal = {IEEE Trans Med Imaging}, Year = {2011}, Month = {Nov}, Number = {11}, Pages = {1901--1920}, Volume = {30}, Abstract = {EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intrapatient thoracic CT image pairs. Evaluation of nonrigid registration techniques is a nontrivial task. This is compounded by the fact that researchers typically test only on their own data, which varies widely. For this reason, reliable assessment and comparison of different registration algorithms has been virtually impossible in the past. In this work we present the results of the launch phase of EMPIRE10, which comprised the comprehensive evaluation and comparison of 20 individual algorithms from leading academic and industrial research groups. All algorithms are applied to the same set of 30 thoracic CT pairs. Algorithm settings and parameters are chosen by researchers expert in the configuration of their own method and the evaluation is independent, using the same criteria for all participants. All results are published on the EMPIRE10 website (http://empire10.isi.uu.nl). The challenge remains ongoing and open to new participants. Full results from 24 algorithms have been published at the time of writing. This paper details the organization of the challenge, the data and evaluation methods and the outcome of the initial launch with 20 algorithms. The gain in knowledge and future work are discussed.}, Doi = {10.1109/TMI.2011.2158349}, Institution = {Image Sciences Institute, University Medical Center, Utrecht, The Netherlands.}, Keywords = {Algorithms; Animals; Databases, Factual; Lung, radiography; Observer Variation; Radiographic Image Enhancement; Radiographic Image Interpretation, Computer-Assisted, methods; Radiography, Thoracic, methods; Reference Standards; Reproducibility of Results; Sensitivity and Specificity; Sheep; Software Validation; Thorax; Tomography, X-Ray Computed, methods}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {21632295}, Timestamp = {2015.05.22}, Url = {http://dx.doi.org/10.1109/TMI.2011.2158349} } @Article{Ng2007, Title = {Neuroinformatics for genome-wide {3D} gene expression mapping in the mouse brain}, Author = {Ng, Lydia and Pathak, Sayan D. and Kuan, Chihchau and Lau, Christopher and Dong, Hongwei and Sodt, Andrew and Dang, Chinh and Avants, Brian and Yushkevich, Paul and Gee, James C. and Haynor, David and Lein, Ed and Jones, Allan and Hawrylycz, Mike}, Journal = {IEEE-ACM T. Comput. Bi.}, Year = {2007}, Month = jul, Number = {3}, Pages = {382--393}, Volume = {4}, Doi = {10.1109/TCBB.2007.1035}, ISSN = {1545-5963}, Unique-id = {ISI:000248414700005} } @Article{Pluta2009, Title = {Appearance and Incomplete Label Matching for Diffeomorphic Template Based Hippocampus Segmentation}, Author = {Pluta, John and Avants, Brian B. and Glynn, Simon and Awate, Sttyash and Gee, James C. and Detre, John A.}, Journal = {Hippocampus}, Year = {2009}, Note = {1st Computational Hippocampal Anatomy and Physiology Workshop, New York Univ, New York, NY, SEP 06, 2008}, Number = {6}, Pages = {565--571}, Volume = {19}, Doi = {10.1002/hipo.20619}, ISSN = {1050-9631}, Unique-id = {ISI:000266824000009} } @Article{Pustina2015, Title = {Predicting the laterality of temporal lobe epilepsy from PET, MRI, and DTI: A multimodal study.}, Author = {Pustina, Dorian and Avants, Brian and Sperling, Michael and Gorniak, Richard and He, Xiaosong and Doucet, Gaelle and Barnett, Paul and Mintzer, Scott and Sharan, Ashwini and Tracy, Joseph}, Journal = {Neuroimage Clin}, Year = {2015}, Pages = {20--31}, Volume = {9}, Abstract = {Pre-surgical evaluation of patients with temporal lobe epilepsy (TLE) relies on information obtained from multiple neuroimaging modalities. The relationship between modalities and their combined power in predicting the seizure focus is currently unknown. We investigated asymmetries from three different modalities, PET (glucose metabolism), MRI (cortical thickness), and diffusion tensor imaging (DTI; white matter anisotropy) in 28 left and 30 right TLE patients (LTLE and RTLE). Stepwise logistic regression models were built from each modality separately and from all three combined, while bootstrapped methods and split-sample validation verified the robustness of predictions. Among all multimodal asymmetries, three PET asymmetries formed the best predictive model (100\% success in full sample, >95\% success in split-sample validation). The combinations of PET with other modalities did not perform better than PET alone. Probabilistic classifications were obtained for new clinical cases, which showed correct lateralization for 7/7 new TLE patients (100\%) and for 4/5 operated patients with discordant or non-informative PET reports (80\%). Metabolism showed closer relationship with white matter in LTLE and closer relationship with gray matter in RTLE. Our data suggest that metabolism is a powerful modality that can predict seizure laterality with high accuracy, and offers high value for automated predictive models. The side of epileptogenic focus can affect the relationship of metabolism with brain structure. The data and tools necessary to obtain classifications for new TLE patients are made publicly available.}, Doi = {10.1016/j.nicl.2015.07.010}, Institution = {Department of Neurology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA ; Department of Radiology, Thomas Jefferson University/Sidney Kimmel Medical College, Philadelphia, PA 19107, USA.}, Language = {eng}, Medline-pst = {epublish}, Owner = {stnava}, Pii = {S2213-1582(15)00129-1}, Pmid = {26288753}, Timestamp = {2015.10.04}, Url = {http://dx.doi.org/10.1016/j.nicl.2015.07.010} } @Article{Rao2010, Title = {Early parental care is important for hippocampal maturation: Evidence from brain morphology in humans}, Author = {Rao, Hengyi and Betancourt, Laura and Giannetta, Joan M. and Brodsky, Nancy L. and Korczykowski, Marc and Avants, Brian B. and Gee, James C. and Wang, Jiongjiong and Hurt, Hallam and Detre, John A. and Farah, Martha J.}, Journal = {Neuroimage}, Year = {2010}, Month = jan, Number = {1}, Pages = {1144--1150}, Volume = {49}, Doi = {10.1016/j.neuroimage.2009.07.003}, ISSN = {1053-8119}, Researcherid-numbers = {Rao, Hengyi/A-7064-2009}, Unique-id = {ISI:000272031700116} } @Article{Rao2007, Title = {Altered resting cerebral blood flow in adolescents with in utero cocaine exposure revealed by perfusion functional {MRI}}, Author = {Rao, Hengyi and Wang, Jiongjiong and Giannetta, Joan and Korczykowski, Marc and Shera, David and Avants, Brian B. and Gee, James and Detre, John A. and Hurt, Hallam}, Journal = {Pediatrics}, Year = {2007}, Month = nov, Number = {5}, Pages = {E1245-E1254}, Volume = {120}, Doi = {10.1542/peds.2006-2596}, ISSN = {0031-4005}, Researcherid-numbers = {Rao, Hengyi/A-7064-2009}, Unique-id = {ISI:000250618900059} } @Article{Rohlfing2012, Title = {"Nonparametric Local Smoothing" is not image registration.}, Author = {Rohlfing, Torsten and Avants, Brian}, Journal = {BMC Res Notes}, Year = {2012}, Pages = {610}, Volume = {5}, Abstract = {Image registration is one of the most important and universally useful computational tasks in biomedical image analysis. A recent article by Xing \& Qiu (IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(10):2081-2092, 2011) is based on an inappropriately narrow conceptualization of the image registration problem as the task of making two images look alike, which disregards whether the established spatial correspondence is plausible. The authors propose a new algorithm, Nonparametric Local Smoothing (NLS) for image registration, but use image similarities alone as a measure of registration performance, although these measures do not relate reliably to the realism of the correspondence map.Using data obtained from its authors, we show experimentally that the method proposed by Xing \& Qiu is not an effective registration algorithm. While it optimizes image similarity, it does not compute accurate, interpretable transformations. Even judged by image similarity alone, the proposed method is consistently outperformed by a simple pixel permutation algorithm, which is known by design not to compute valid registrations.This study has demonstrated that the NLS algorithm proposed recently for image registration, and published in one of the most respected journals in computer science, is not, in fact, an effective registration method at all. Our results also emphasize the general need to apply registration evaluation criteria that are sensitive to whether correspondences are accurate and mappings between images are physically interpretable. These goals cannot be achieved by simply reporting image similarities.}, Doi = {10.1186/1756-0500-5-610}, Institution = {Neuroscience Program, SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025, USA. rohlfing@ieee.org}, Language = {eng}, Medline-pst = {epublish}, Owner = {stnava}, Pii = {1756-0500-5-610}, Pmid = {23116330}, Timestamp = {2013.05.29} } @Article{Schoenemann2004, Title = {Analysis of chimp-human brain differences via non-rigid deformation of {3D} MR images}, Author = {Schoenemann, P. T. and Avants, B. B. and Gee, J. C. and Glotzer, L. D. and Sheehan, M. J.}, Journal = {Am. J. Phys. Anthropol.}, Year = {2004}, Number = {38}, Pages = {174--175}, ISSN = {0002-9483}, Unique-id = {ISI:000207846400484} } @Article{Schoenemann2007, Title = {Validation of plaster endocast morphology through {3D CT} image analysis}, Author = {Schoenemann, P. Thomas and Gee, James and Avants, Brian and Holloway, Ralph L. and Monge, Janet and Lewis, Jason}, Journal = {Am. J. Phys. Anthropol.}, Year = {2007}, Month = feb, Number = {2}, Pages = {183--192}, Volume = {132}, Doi = {10.1002/ajpa.20499}, ISSN = {0002-9483}, Unique-id = {ISI:000243782700003} } @Article{Schoenemann2007b, Title = {Validation of plaster endocast morphology through 3D CT image analysis.}, Author = {Schoenemann, P Thomas and Gee, James and Avants, Brian and Holloway, Ralph L. and Monge, Janet and Lewis, Jason}, Journal = {Am J Phys Anthropol}, Year = {2007}, Month = {Feb}, Number = {2}, Pages = {183--192}, Volume = {132}, Abstract = {A crucial component of research on brain evolution has been the comparison of fossil endocranial surfaces with modern human and primate endocrania. The latter have generally been obtained by creating endocasts out of rubber latex shells filled with plaster. The extent to which the method of production introduces errors in endocast replicas is unknown. We demonstrate a powerful method of comparing complex shapes in 3-dimensions (3D) that is broadly applicable to a wide range of paleoanthropological questions. Pairs of virtual endocasts (VEs) created from high-resolution CT scans of corresponding latex/plaster endocasts and their associated crania were rigidly registered (aligned) in 3D space for two Homo sapiens and two Pan troglodytes specimens. Distances between each cranial VE and its corresponding latex/plaster VE were then mapped on a voxel-by-voxel basis. The results show that between 79.7\% and 91.0\% of the voxels in the four latex/plaster VEs are within 2 mm of their corresponding cranial VEs surfaces. The average error is relatively small, and variation in the pattern of error across the surfaces appears to be generally random overall. However, inferior areas around the cranial base and the temporal poles were somewhat overestimated in both human and chimpanzee specimens, and the area overlaying Broca's area in humans was somewhat underestimated. This study gives an idea of the size of possible error inherent in latex/plaster endocasts, indicating the level of confidence we can have with studies relying on comparisons between them and, e.g., hominid fossil endocasts.}, Doi = {10.1002/ajpa.20499}, Institution = {Department of Behavioral Sciences, University of Michigan-Dearborn, Dearborn, MI 48128, USA. ptoms@umd.umich.edu}, Keywords = {Animals; Fossils; Humans; Imaging, Three-Dimensional; Paleontology; Pan troglodytes, anatomy /&/ histology; Skull, anatomy /&/ histology; Tomography, X-Ray Computed, methods}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {17103425}, Timestamp = {2015.02.23}, Url = {http://dx.doi.org/10.1002/ajpa.20499} } @Article{Schoenemann2011, Title = {Differences in endocranial shape between Homo and Pongids assessed through non-rigid deformation analysis of high-resolution {CT} images.}, Author = {Schoenemann, P. Thomas and Holloway, Ralph and Monge, Janet and Avants, Brian and Gee, James}, Journal = {Am. J. Phys. Anthropol.}, Year = {2011}, Note = {80th Annual Meeting of the American-Association-of-Physical-Anthropologists, Minneapolis, MN, APR 11-16, 2011}, Number = {52}, Pages = {265--266}, Volume = {144}, ISSN = {0002-9483}, Organization = {Amer Assoc Phys Anthropol}, Unique-id = {ISI:000288034000754} } @Article{Schoenemann2008, Title = {Endocast asymmetry in pongids assessed via non-rigid deformation analysis of high-resolution {CT} images.}, Author = {Schoenemann, P. T. and Holloway, R. L. and Avants, B. B. and Gee, J. C.}, Journal = {Am. J. Phys. Anthropol.}, Year = {2008}, Note = {77th Annual Meeting of the American-Association-of-Physical-Anthropologists, Columbus, OH, APR 09-12, 2008}, Number = {46}, Pages = {188}, ISSN = {0002-9483}, Organization = {Amer Assoc Phys Anthropol}, Unique-id = {ISI:000253342000576} } @Article{Schoenemann2008a, Title = {The role of micro-morphological stress markers in the differential diagnosis of infectious bone diseases.}, Author = {Schoenemann, P. T. and Holloway, R. L. and AvantS, B. B. and Gee, J. C.}, Journal = {Am. J. Phys. Anthropol.}, Year = {2008}, Note = {77th Annual Meeting of the American-Association-of-Physical-Anthropologists, Columbus, OH, APR 09-12, 2008}, Number = {46}, Pages = {188--189}, ISSN = {0002-9483}, Organization = {Amer Assoc Phys Anthropol}, Unique-id = {ISI:000253342000580} } @Article{Schoenemann2009, Title = {An atlas of modern human cranial morpbology constructed via non-rigid deformation analysis of high-resolution {CT} images.}, Author = {Schoenemann, P. T. and Monge, J. and Avants, B. B. and Gee, J. C.}, Journal = {Am. J. Phys. Anthropol.}, Year = {2009}, Note = {78th Annual Meeting of the American-Association-of-Physical-Anthropologists, Chicago, IL, MAR 31-APR 03, 2009}, Pages = {231}, ISSN = {0002-9483}, Organization = {Amer Assoc Phys Anthropol}, Unique-id = {ISI:000263442701254} } @Article{Schoenemann2007a, Title = {Sex differences in cranial form assessed via non-rigid deformation analysis of high-resolution {CT} images.}, Author = {Schoenemann, P. T. and Monge, J. and Avants, B. B. and Glotzer, D. and Gee, J. C.}, Journal = {Am. J. Phys. Anthropol.}, Year = {2007}, Number = {44}, Pages = {209}, ISSN = {0002-9483}, Unique-id = {ISI:000244656500644} } @Article{Schoenemann2010, Title = {Creating statistical atlases of modern primate endocranial morphology rising non-rigid deformation analysis of high-resolution {CT} images.}, Author = {Schoenemann, P. Thomas and Monge, Janet and Holloway, Ralph L. and Avants, Brian B. and Gee, James C.}, Journal = {Am. J. Phys. Anthropol.}, Year = {2010}, Note = {79th Annual Meeting of the American-Association-of-Physical-Anthropologists, Albuquerque, NM, APR 14-17, 2010}, Number = {50}, Pages = {208--209}, ISSN = {0002-9483}, Organization = {Amer Assoc Phys Anthropol}, Unique-id = {ISI:000275295200695} } @Article{Simon2008a, Title = {Atypical cortical connectivity and visuospatial cognitive impairments are related in children with chromosome 22q11.2 deletion syndrome.}, Author = {Simon, Tony J. and Wu, Zhongle and Avants, Brian and Zhang, Hui and Gee, James C. and Stebbins, Glenn T.}, Journal = {Behav Brain Funct}, Year = {2008}, Pages = {25}, Volume = {4}, Abstract = {Chromosome 22q11.2 deletion syndrome is one of the most common genetic causes of cognitive impairment and developmental disability yet little is known about the neural bases of those challenges. Here we expand upon our previous neurocognitive studies by specifically investigating the hypothesis that changes in neural connectivity relate to cognitive impairment in children with the disorder.Whole brain analyses of multiple measures computed from diffusion tensor image data acquired from the brains of children with the disorder and typically developing controls. We also correlated diffusion tensor data with performance on a visuospatial cognitive task that taps spatial attention.Analyses revealed four common clusters, in the parietal and frontal lobes, that showed complementary patterns of connectivity in children with the deletion and typical controls. We interpreted these results as indicating differences in connective complexity to adjoining cortical regions that are critical to the cognitive functions in which affected children show impairments. Strong, and similarly opposing patterns of correlations between diffusion values in those clusters and spatial attention performance measures considerably strengthened that interpretation.Our results suggest that atypical development of connective patterns in the brains of children with chromosome 22q11.2 deletion syndrome indicate a neuropathology that is related to the visuospatial cognitive impairments that are commonly found in affected individuals.}, Doi = {10.1186/1744-9081-4-25}, Institution = {M,I,N,D, Institute, University of California, Davis, 2825 50th Street, Sacramento, CA 95817, USA. tjsimon@ucdavis.edu.}, Language = {eng}, Medline-pst = {epublish}, Owner = {stnava}, Pii = {1744-9081-4-25}, Pmid = {18559106}, Timestamp = {2013.05.29} } @Article{Song2013, Title = {Using region trajectories to construct an accurate and efficient polyaffine transform model.}, Author = {Song, Gang and Liu, Yang and Wu, Baohua and Avants, Brian and Gee, James C.}, Journal = {Inf Process Med Imaging}, Year = {2013}, Pages = {668--679}, Volume = {23}, __markedentry = {[stnava:1]}, Abstract = {In this paper we propose a novel way to construct a diffeomorphic polyaffine model. Each affine transform is defined on a local region and the resulting diffeomorphism encapsulates all the local transforms by a smooth and invertible displacement field. Compared with traditional weighting schemes used in combining local transforms, our new scheme guarantees that the resulting transform precisely preserves the value of each local affine transform. By introducing the trajectory of local regions instead of using regions themselves, the new approach encodes precisely each local affine transform using a diffeomorphism with one or more stationary velocity fields. Experiments show that our new polyaffine model is both accurate and efficient.}, Keywords = {Algorithms; Computer Simulation; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Models, Biological; Models, Statistical; Reproducibility of Results; Sensitivity and Specificity}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {24684008}, Timestamp = {2014.08.26} } @Article{Song2006a, Title = {Integrated graph cuts for brain {MRI} segmentation.}, Author = {Song, Zhuang and Tustison, Nicholas and Avants, Brian and Gee, James C.}, Journal = {Med Image Comput Comput Assist Interv}, Year = {2006}, Number = {Pt 2}, Pages = {831--838}, Volume = {9}, __markedentry = {[stnava:1]}, Abstract = {Brain MRI segmentation remains a challenging problem in spite of numerous existing techniques. To overcome the inherent difficulties associated with this segmentation problem, we present a new method of information integration in a graph based framework. In addition to image intensity, tissue priors and local boundary information are integrated into the edge weight metrics in the graph. Furthermore, inhomogeneity correction is incorporated by adaptively adjusting the edge weights according to the intermediate inhomogeneity estimation. In the validation experiments of simulated brain MRIs, the proposed method outperformed a segmentation method based on iterated conditional modes (ICM), which is a commonly used optimization method in medical image segmentation. In the experiments of real neonatal brain MRIs, the results of the proposed method have good overlap with the manual segmentations by human experts.}, Institution = {Penn Image Computing and Science Lab, University of Pennsylvania, USA. songz@seas.upenn.edu}, Keywords = {Algorithms; Artificial Intelligence; Brain, anatomy /\&/ histology; Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Imaging, Three-Dimensional, methods; Magnetic Resonance Imaging, methods; Pattern Recognition, Automated, methods; Reproducibility of Results; Sensitivity and Specificity}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {17354850}, Timestamp = {2013.05.29} } @Article{Sun2008, Title = {Cardiac medial modeling and time-course heart wall thickness analysis.}, Author = {Sun, Hui and Avants, Brian B. and Frangi, Alejandro F. and Sukno, Federico and Geel, James C. and Yushkevich, Paul A.}, Journal = {Med Image Comput Comput Assist Interv}, Year = {2008}, Number = {Pt 2}, Pages = {766--773}, Volume = {11}, __markedentry = {[stnava:1]}, Abstract = {The medial model is a powerful shape representation method that models a 3D object by explicitly defining its skeleton (medial axis) and deriving the boundary geometry according to medial geometry. It has been recently extended to model complex shapes with multi-figures, i.e., shapes whose skeletons can not be described by a single sheet in 3D. This paper applied the medial model to a 2-chamber heart data set consisting of 428 cardiac shapes from 90 subjects. The results show that the medial model can capture the heart shape accurately. To demonstrate the usage of the medial model, the changes of the heart wall thickness over time are analyzed. We calculated the mean heart wall thickness map of 90 subjects for different phases of the cardiac cycle, as well as the mean thickness change between phases.}, Institution = {Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.}, Keywords = {Algorithms; Computer Simulation; Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Imaging, Three-Dimensional, methods; Magnetic Resonance Imaging, methods; Models, Cardiovascular; Myocardium, pathology; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {18982674}, Timestamp = {2013.05.29} } @Article{Sundaram2005, Title = {Towards a dynamic model of pulmonary parenchymal deformation: evaluation of methods for temporal reparameterization of lung data.}, Author = {Sundaram, Tessa A. and Avants, Brian B. and Gee, James C.}, Journal = {Med Image Comput Comput Assist Interv}, Year = {2005}, Number = {Pt 2}, Pages = {328--335}, Volume = {8}, Abstract = {We approach the problem of temporal reparameterization of dynamic sequences of lung MR images. In earlier work, we employed capacity-based reparameterization to co-register temporal sequences of 2-D coronal images of the human lungs. Here, we extend that work to the evaluation of a ventilator-acquired 3-D dataset from a normal mouse. Reparameterization according to both deformation and lung volume is evaluated. Both measures provide results that closely approximate normal physiological behavior, as judged from the original data. Our ultimate goal is to be able to characterize normal parenchymal biomechanics over a population of healthy individuals, and to use this statistical model to evaluate lung deformation under various pathological states.}, Institution = {University of Pennsylvania, Philadelphia PA 19104, USA.}, Keywords = {Algorithms; Animals; Computer Simulation; Databases, Factual; Elasticity; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Imaging, Three-Dimensional, methods; Lung, anatomy /\&/ histology/physiology; Magnetic Resonance Imaging, methods; Mice; Models, Biological; Reproducibility of Results; Respiratory Mechanics; Sensitivity and Specificity; Subtraction Technique}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {16685976}, Timestamp = {2013.05.29} } @InProceedings{Sundaram2004, Title = {A dynamic model of average lung deformation using capacity-based reparameterization and shape averaging of lung MR images}, Author = {Sundaram, TA and Avants, BB and Gee, JC}, Booktitle = {MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2004, PT 2, PROCEEDINGS}, Year = {2004}, Editor = {Barillot, C and Haynor, DR and Hellier, P}, Note = {7th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2004), St Malo, FRANCE, SEP 26-29, 2004}, Number = {2}, Organization = {IRISA; CNRS; Univ Rennes}, Pages = {1000--1007}, Series = {LECTURE NOTES IN COMPUTER SCIENCE}, Volume = {3217}, __markedentry = {[stnava:1]}, ISBN = {3-540-22977-9}, ISSN = {0302-9743}, Unique-id = {ISI:000224322400121} } @Article{Tustison2011, Title = {MULTIVARIATE ANALYSIS OF DIFFUSION TENSOR IMAGING {AND} CORTICAL THICKNESS {MAP}S IN A TRAUMATIC BRAIN INJURY (TBI) COHORT USING ADVANCED {NOR}MALIZATION TOOLS (ANTS)}, Author = {Tustison, Nicholas and Avants, Brian and Cook, Philip and Kim, Junghoon and Whyte, John and Gee, James and Ahlers, Stephen and Stone, James}, Journal = {J. Neurotrauma}, Year = {2011}, Month = jun, Note = {29th Annual National Neurotrauma Symposium, Hollywood Beach, FL, JUL 10-13, 2011}, Number = {6}, Pages = {A111}, Volume = {28}, ISSN = {0897-7151}, Unique-id = {ISI:000292457600346} } @Article{Tustison2013, Title = {Explicit B-spline regularization in diffeomorphic image registration.}, Author = {Tustison, Nicholas J. and Avants, Brian B.}, Journal = {Front Neuroinform}, Year = {2013}, Pages = {39}, Volume = {7}, Abstract = {Diffeomorphic mappings are central to image registration due largely to their topological properties and success in providing biologically plausible solutions to deformation and morphological estimation problems. Popular diffeomorphic image registration algorithms include those characterized by time-varying and constant velocity fields, and symmetrical considerations. Prior information in the form of regularization is used to enforce transform plausibility taking the form of physics-based constraints or through some approximation thereof, e.g., Gaussian smoothing of the vector fields [a la Thirion's Demons (Thirion, 1998)]. In the context of the original Demons' framework, the so-called directly manipulated free-form deformation (DMFFD) (Tustison et al., 2009) can be viewed as a smoothing alternative in which explicit regularization is achieved through fast B-spline approximation. This characterization can be used to provide B-spline "flavored" diffeomorphic image registration solutions with several advantages. Implementation is open source and available through the Insight Toolkit and our Advanced Normalization Tools (ANTs) repository. A thorough comparative evaluation with the well-known SyN algorithm (Avants et al., 2008), implemented within the same framework, and its B-spline analog is performed using open labeled brain data and open source evaluation tools.}, Doi = {10.3389/fninf.2013.00039}, Institution = {Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania Philadelphia, PA, USA.}, Language = {eng}, Medline-pst = {epublish}, Owner = {stnava}, Pmid = {24409140}, Timestamp = {2014.08.26} } @Article{Tustison2012, Title = {Logical circularity in voxel-based analysis: Normalization strategy may induce statistical bias.}, Author = {Tustison, Nicholas J. and Avants, Brian B. and Cook, Philip A. and Kim, Junghoon and Whyte, John and Gee, James C. and Stone, James R.}, Journal = {Hum. Brain Mapp.}, Year = {2012}, Month = nov, Abstract = {Recent discussions within the neuroimaging community have highlighted the problematic presence of selection bias in experimental design. Although initially centering on the selection of voxels during the course of fMRI studies, we demonstrate how this bias can potentially corrupt voxel-based analyses. For such studies, template-based registration plays a critical role in which a representative template serves as the normalized space for group alignment. A standard approach maps each subject's image to a representative template before performing statistical comparisons between different groups. We analytically demonstrate that in these scenarios the popular sum of squared difference (SSD) intensity metric, implicitly surrogating as a quantification of anatomical alignment, instead explicitly maximizes effect size-an experimental design flaw referred to as "circularity bias." We illustrate how this selection bias varies in strength with the similarity metric used during registration under the hypothesis that while SSD-related metrics, such as Demons, will manifest similar effects, other metrics which are not formulated based on absolute intensity differences will produce less of an effect. Consequently, given the variability in voxel-based analysis outcomes with similarity metric choice, we caution researchers specifically in the use of SSD and SSD-related measures where normalization and statistical analysis involve the same image set. Instead, we advocate a more cautious approach where normalization of the individual subject images to the reference space occurs through corresponding image sets which are independent of statistical testing. Alternatively, one can use similarity terms that are less sensitive to this bias. Hum Brain Mapp, 2012. {\copyright} 2012 Wiley Periodicals, Inc.}, Doi = {10.1002/hbm.22211}, Institution = {Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia. ntustison@virginia.edu.}, Language = {eng}, Medline-pst = {aheadofprint}, Owner = {stnava}, Pmid = {23151955}, Timestamp = {2013.05.29} } @Article{Tustison2010, Title = {N4ITK: improved N3~{b}ias correction.}, Author = {Tustison, Nicholas J. and Avants, Brian B. and Cook, Philip A. and Zheng, Yuanjie and Egan, Alexander and Yushkevich, Paul A. and Gee, James C.}, Journal = {IEEE Trans Med Imaging}, Year = {2010}, Month = jun, Number = {6}, Pages = {1310--1320}, Volume = {29}, Abstract = {A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction. Given the superb performance of N3 and its public availability, it has been the subject of several evaluation studies. These studies have demonstrated the importance of certain parameters associated with the B-spline least-squares fitting. We propose the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field correction over the original N3 algorithm. Similar to the N3 algorithm, we also make the source code, testing, and technical documentation of our contribution, which we denote as "N4ITK," available to the public through the Insight Toolkit of the National Institutes of Health. Performance assessment is demonstrated using simulated data from the publicly available Brainweb database, hyperpolarized (3)He lung image data, and 9.4T postmortem hippocampus data.}, Doi = {10.1109/TMI.2010.2046908}, Institution = {Department of Radiology, University of Pennsylvania, Philadelphia, PA 19140, USA. ntustison@wustl.edu}, Keywords = {Algorithms; Artifacts; Brain, anatomy /&/ histology; Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Magnetic Resonance Imaging, methods; Reproducibility of Results; Sensitivity and Specificity}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {20378467}, Timestamp = {2014.08.26} } @Article{Tustison2010a, Title = {N4ITK: improved N3 bias correction.}, Author = {Tustison, Nicholas J. and Avants, Brian B. and Cook, Philip A. and Zheng, Yuanjie and Egan, Alexander and Yushkevich, Paul A. and Gee, James C.}, Journal = {IEEE Trans Med Imaging}, Year = {2010}, Month = {Jun}, Number = {6}, Pages = {1310--1320}, Volume = {29}, Abstract = {A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction. Given the superb performance of N3 and its public availability, it has been the subject of several evaluation studies. These studies have demonstrated the importance of certain parameters associated with the B-spline least-squares fitting. We propose the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field correction over the original N3 algorithm. Similar to the N3 algorithm, we also make the source code, testing, and technical documentation of our contribution, which we denote as "N4ITK," available to the public through the Insight Toolkit of the National Institutes of Health. Performance assessment is demonstrated using simulated data from the publicly available Brainweb database, hyperpolarized (3)He lung image data, and 9.4T postmortem hippocampus data.}, Doi = {10.1109/TMI.2010.2046908}, Institution = {Department of Radiology, University of Pennsylvania, Philadelphia, PA 19140, USA. ntustison@wustl.edu}, Keywords = {Algorithms; Artifacts; Brain, anatomy /&/ histology; Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Magnetic Resonance Imaging, methods; Reproducibility of Results; Sensitivity and Specificity}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {20378467}, Timestamp = {2015.02.23}, Url = {http://dx.doi.org/10.1109/TMI.2010.2046908} } @Article{Tustison2011a, Title = {Ventilation-Based Segmentation of the Lungs Using Hyperpolarized He-3 {MRI}}, Author = {Tustison, Nicholas J. and Avants, Brian B. and Flors, Lucia and Altes, Talissa A. and de lange, Eduard E. and Mugler, III, John P. and Gee, James C.}, Journal = {J. Magn. Reson. Imaging}, Year = {2011}, Month = oct, Number = {4}, Pages = {831--841}, Volume = {34}, Doi = {10.1002/jmri.22738}, ISSN = {1053-1807}, Orcid-numbers = {Mugler, John/0000-0002-4140-308X}, Researcherid-numbers = {Mugler, John/B-9432-2013}, Unique-id = {ISI:000295394400012} } @Article{Tustison2011c, Title = {Ventilation-based segmentation of the lungs using hyperpolarized (3)He MRI.}, Author = {Tustison, Nicholas J. and Avants, Brian B. and Flors, Lucia and Altes, Talissa A. and {de Lange}, Eduard E. and Mugler, 3rd, John P and Gee, James C.}, Journal = {J Magn Reson Imaging}, Year = {2011}, Month = {Oct}, Number = {4}, Pages = {831--841}, Volume = {34}, Abstract = {To develop an automated segmentation method to differentiate the ventilated lung volume on (3) He magnetic resonance imaging (MRI).Computational processing (CP) for each subject consisted of the following three essential steps: 1) inhomogeneity bias correction, 2) whole lung segmentation, and 3) subdivision of the lung segmentation into regions of similar ventilation. Evaluation consisted of two comparative analyses: i) comparison of the number of defects scored by two human readers in 43 subjects, and ii) simultaneous truth and performance level estimation (STAPLE) in 18 subjects in which the ventilation defects were manually segmented by four human readers.There was excellent correlation between the number of ventilation defects tabulated by CP and reader #1 (intraclass correlation coefficient [ICC] = 0.86), CP and reader #2 (ICC = 0.85), and between the two readers (ICC = 0.97). The STAPLE results from the second analysis yielded the following sensitivity/specificity numbers: CP (0.898/0.905), radiologist #1 (0.743/0.897), radiologist #2 (0.501/0.985), radiologist #3 (0.898/0.848), and the first author (0.600/0.984).We developed and evaluated an automated method for quantifying the ventilated lung volume on (3) He MRI. The findings strongly indicate that our proposed algorithmic processing may be a reliable, automatic method for quantitating ventilation defects.}, Doi = {10.1002/jmri.22738}, Institution = {Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA. ntustison@virginia.edu}, Keywords = {Administration, Inhalation; Asthma, diagnosis; Automation; Case-Control Studies; Cystic Fibrosis, diagnosis; Female; Helium, diagnostic use; Humans; Image Processing, Computer-Assisted; Lung, pathology; Magnetic Resonance Imaging, methods; Male; Pulmonary Gas Exchange, physiology; Pulmonary Ventilation, physiology; Sensitivity and Specificity}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {21837781}, Timestamp = {2015.02.23}, Url = {http://dx.doi.org/10.1002/jmri.22738} } @Article{Tustison2009, Title = {Directly Manipulated Free-Form Deformation Image Registration}, Author = {Tustison, Nicholas J. and Avants, Brian B. and Gee, James C.}, Journal = {IEEE Trans Image Process}, Year = {2009}, Month = mar, Number = {3}, Pages = {624--635}, Volume = {18}, Doi = {10.1109/TIP.2008.2010072}, ISSN = {1057-7149}, Unique-id = {ISI:000263604700014} } @Article{Tustison2011b, Title = {Topological Well-Composedness and Glamorous Glue: A Digital Gluing Algorithm for Topologically Constrained Front Propagation}, Author = {Tustison, Nicholas J. and Avants, Brian B. and Siqueira, Marcelo and Gee, James C.}, Journal = {IEEE Trans Image Process}, Year = {2011}, Month = jun, Number = {6}, Pages = {1756--1761}, Volume = {20}, Doi = {10.1109/TIP.2010.2095021}, ISSN = {1057-7149}, Unique-id = {ISI:000290732600023} } @InProceedings{Tustison2006, Title = {A generalization of Free-Form Deformation image registration within the ITK finite element framework}, Author = {Tustison, Nicholas J. and Avants, Brian B. and Sundaram, Tessa A. and Duda, Jeffrey T. and Gee, James C.}, Booktitle = {BIOMEDICAL IMAGE REGISTRATION, PROCEEDINGS}, Year = {2006}, Editor = {Pluim, JPW and Likar, B and Gerritsen, FA}, Note = {3rd International Workshop on Biomedical Image Registration, Utrecht Univ, Utrecht, NETHERLANDS, JUL 09-11, 2006}, Organization = {Philips Med Syst}, Pages = {238--246}, Series = {LECTURE NOTES IN COMPUTER SCIENCE}, Volume = {4057}, __markedentry = {[stnava:1]}, ISBN = {3-540-35648-7}, ISSN = {0302-9743}, Unique-id = {ISI:000239485200029} } @Article{Tustison2014, Title = {Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements.}, Author = {Tustison, Nicholas J. and Cook, Philip A. and Klein, Arno and Song, Gang and Das, Sandhitsu R. and Duda, Jeffrey T. and Kandel, Benjamin M. and {van Strien}, Niels and Stone, James R. and Gee, James C. and Avants, Brian B.}, Journal = {Neuroimage}, Year = {2014}, Month = oct, Pages = {166--179}, Volume = {99}, Abstract = {Many studies of the human brain have explored the relationship between cortical thickness and cognition, phenotype, or disease. Due to the subjectivity and time requirements in manual measurement of cortical thickness, scientists have relied on robust software tools for automation which facilitate the testing and refinement of neuroscientific hypotheses. The most widely used tool for cortical thickness studies is the publicly available, surface-based FreeSurfer package. Critical to the adoption of such tools is a demonstration of their reproducibility, validity, and the documentation of specific implementations that are robust across large, diverse imaging datasets. To this end, we have developed the automated, volume-based Advanced Normalization Tools (ANTs) cortical thickness pipeline comprising well-vetted components such as SyGN (multivariate template construction), SyN (image registration), N4 (bias correction), Atropos (n-tissue segmentation), and DiReCT (cortical thickness estimation). In this work, we have conducted the largest evaluation of automated cortical thickness measures in publicly available data, comparing FreeSurfer and ANTs measures computed on 1205 images from four open data sets (IXI, MMRR, NKI, and OASIS), with parcellation based on the recently proposed Desikan-Killiany-Tourville (DKT) cortical labeling protocol. We found good scan-rescan repeatability with both FreeSurfer and ANTs measures. Given that such assessments of precision do not necessarily reflect accuracy or an ability to make statistical inferences, we further tested the neurobiological validity of these approaches by evaluating thickness-based prediction of age and gender. ANTs is shown to have a higher predictive performance than FreeSurfer for both of these measures. In promotion of open science, we make all of our scripts, data, and results publicly available which complements the use of open image data sets and the open source availability of the proposed ANTs cortical thickness pipeline.}, Doi = {10.1016/j.neuroimage.2014.05.044}, Institution = {Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, USA.}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pii = {S1053-8119(14)00409-1}, Pmid = {24879923}, Timestamp = {2014.10.27} } @Article{Tustison2013a, Title = {Instrumentation bias in the use and evaluation of scientific software: recommendations for reproducible practices in the computational sciences.}, Author = {Tustison, Nicholas J. and Johnson, Hans J. and Rohlfing, Torsten and Klein, Arno and Ghosh, Satrajit S. and Ibanez, Luis and Avants, Brian B.}, Journal = {Front Neurosci}, Year = {2013}, Pages = {162}, Volume = {7}, Doi = {10.3389/fnins.2013.00162}, Institution = {Department of Radiology and Medical Imaging, University of Virginia Charlottesville, VA, USA.}, Language = {eng}, Medline-pst = {epublish}, Owner = {stnava}, Pmid = {24058331}, Timestamp = {2014.08.26} } @Article{Tustison2014b, Title = {Optimal Symmetric Multimodal Templates and Concatenated Random Forests for Supervised Brain Tumor Segmentation (Simplified) with ANTsR.}, Author = {Tustison, Nicholas J. and Shrinidhi, K. L. and Wintermark, Max and Durst, Christopher R. and Kandel, Benjamin M. and Gee, James C. and Grossman, Murray C. and Avants, Brian B.}, Journal = {Neuroinformatics}, Year = {2014}, Month = {Nov}, Abstract = {Segmenting and quantifying gliomas from MRI is an important task for diagnosis, planning intervention, and for tracking tumor changes over time. However, this task is complicated by the lack of prior knowledge concerning tumor location, spatial extent, shape, possible displacement of normal tissue, and intensity signature. To accommodate such complications, we introduce a framework for supervised segmentation based on multiple modality intensity, geometry, and asymmetry feature sets. These features drive a supervised whole-brain and tumor segmentation approach based on random forest-derived probabilities. The asymmetry-related features (based on optimal symmetric multimodal templates) demonstrate excellent discriminative properties within this framework. We also gain performance by generating probability maps from random forest models and using these maps for a refining Markov random field regularized probabilistic segmentation. This strategy allows us to interface the supervised learning capabilities of the random forest model with regularized probabilistic segmentation using the recently developed ANTsR package-a comprehensive statistical and visualization interface between the popular Advanced Normalization Tools (ANTs) and the R statistical project. The reported algorithmic framework was the top-performing entry in the MICCAI 2013 Multimodal Brain Tumor Segmentation challenge. The challenge data were widely varying consisting of both high-grade and low-grade glioma tumor four-modality MRI from five different institutions. Average Dice overlap measures for the final algorithmic assessment were 0.87, 0.78, and 0.74 for "complete", "core", and "enhanced" tumor components, respectively.}, Doi = {10.1007/s12021-014-9245-2}, Institution = {Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, USA, ntustison@virginia.edu.}, Language = {eng}, Medline-pst = {aheadofprint}, Owner = {stnava}, Pmid = {25433513}, Timestamp = {2015.05.23}, Url = {http://dx.doi.org/10.1007/s12021-014-9245-2} } @Article{Wang2010a, Title = {Estimation of Perfusion and Arterial Transit Time in Myocardium Using Free-Breathing Myocardial Arterial Spin Labeling With Navigator-Echo}, Author = {Wang, Danny J. J. and Bi, Xiaoming and Avants, Brian B. and Meng, Tongbai and Zuehlsdorff, Sven and Detre, John A.}, Journal = {Magn. Reson. Med.}, Year = {2010}, Month = nov, Number = {5}, Pages = {1289--1295}, Volume = {64}, Doi = {10.1002/mrm.22630}, ISSN = {0740-3194}, Unique-id = {ISI:000283616900008} } @Article{Wang2010, Title = {Standing on the shoulders of giants: improving medical image segmentation via bias correction.}, Author = {Wang, Hongzhi and Das, Sandhitsu and Pluta, John and Craige, Caryne and Altinay, Murat and Avants, Brian and Weiner, Michael and Mueller, Susanne and Yushkevich, Paul}, Journal = {Med Image Comput Comput Assist Interv}, Year = {2010}, Number = {Pt 3}, Pages = {105--112}, Volume = {13}, __markedentry = {[stnava:1]}, Abstract = {We propose a simple strategy to improve automatic medical image segmentation. The key idea is that without deep understanding of a segmentation method, we can still improve its performance by directly calibrating its results with respect to manual segmentation. We formulate the calibration process as a bias correction problem, which is addressed by machine learning using training data. We apply this methodology on three segmentation problems/methods and show significant improvements for all of them.}, Institution = {Department of Radiology, University of Pennsylvania, USA.}, Keywords = {Algorithms; Artifacts; Brain, anatomy /\&/ histology; Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Magnetic Resonance Imaging, methods; Pattern Recognition, Automated, methods; Reproducibility of Results; Sensitivity and Specificity}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {20879389}, Timestamp = {2013.05.29} } @Article{Wang2011, Title = {A learning-based wrapper method to correct systematic errors in automatic image segmentation: Consistently improved performance in hippocampus, cortex and brain segmentation}, Author = {Wang, Hongzhi and Das, Sandhitsu R. and Suh, Jung Wook and Altinay, Murat and Pluta, John and Craige, Caryne and Avants, Brian and Yushkevich, Paul A. and Alzheimers Dis Neuroimaging Initia}, Journal = {Neuroimage}, Year = {2011}, Month = apr, Number = {3}, Pages = {968--985}, Volume = {55}, Doi = {10.1016/j.neuroimage.2011.01.006}, ISSN = {1053-8119}, Unique-id = {ISI:000288313800012} } @Article{Weber2013, Title = {Reproducibility of functional network metrics and network structure: a comparison of task-related BOLD, resting ASL with BOLD contrast, and resting cerebral blood flow.}, Author = {Weber, Matthew J. and Detre, John A. and Thompson-Schill, Sharon L. and Avants, Brian B.}, Journal = {Cogn. Affect. Behav. Neurosci.}, Year = {2013}, Month = sep, Number = {3}, Pages = {627--640}, Volume = {13}, Abstract = {Network analysis is an emerging approach to functional connectivity in which the brain is construed as a graph and its connectivity and information processing estimated by mathematical characterizations of graphs. There has been little to no work examining the reproducibility of network metrics derived from different types of functional magnetic resonance imaging (fMRI) data (e.g., resting vs. task related, or pulse sequences other than standard blood oxygen level dependent [BOLD] data) or of measures of network structure at levels other than summary statistics. Here, we take up these questions, comparing the reproducibility of graphs derived from resting arterial spin-labeling perfusion fMRI with those derived from BOLD scans collected while the participant was performing a task. We also examine the reproducibility of the anatomical connectivity implied by the graph by investigating test-retest consistency of the graphs' edges. We compare two measures of graph-edge consistency both within versus between subjects and across data types. We find a dissociation in the reproducibility of network metrics, with metrics from resting data most reproducible at lower frequencies and metrics from task-related data most reproducible at higher frequencies; that same dissociation is not recapitulated, however, in network structure, for which the task-related data are most consistent at all frequencies. Implications for the practice of network analysis are discussed.}, Doi = {10.3758/s13415-013-0181-7}, Institution = {Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA, mweb@psych.upenn.edu.}, Keywords = {Adult; Brain Mapping, methods; Brain, blood supply/physiology; Cerebrovascular Circulation, physiology; Female; Humans; Magnetic Resonance Imaging, methods; Male; Reproducibility of Results; Rest, physiology; Time Factors; Young Adult}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {23813017}, Timestamp = {2014.08.26} } @Article{Wu2015, author = {Wu, J. and Awate, S. P. and Licht, D. J. and Clouchoux, C. and {du Plessis}, A. J. and Avants, B. B. and Vossough, A. and Gee, J. C. and Limperopoulos, C.}, title = {Assessment of MRI-Based Automated Fetal Cerebral Cortical Folding Measures in Prediction of Gestational Age in the Third Trimester.}, journal = {AJNR Am J Neuroradiol}, year = {2015}, volume = {36}, number = {7}, pages = {1369--1374}, month = {Jul}, __markedentry = {[stnava:]}, abstract = {Traditional methods of dating a pregnancy based on history or sonographic assessment have a large variation in the third trimester. We aimed to assess the ability of various quantitative measures of brain cortical folding on MR imaging in determining fetal gestational age in the third trimester.We evaluated 8 different quantitative cortical folding measures to predict gestational age in 33 healthy fetuses by using T2-weighted fetal MR imaging. We compared the accuracy of the prediction of gestational age by these cortical folding measures with the accuracy of prediction by brain volume measurement and by a previously reported semiquantitative visual scale of brain maturity. Regression models were constructed, and measurement biases and variances were determined via a cross-validation procedure.The cortical folding measures are accurate in the estimation and prediction of gestational age (mean of the absolute error, 0.43 ± 0.45 weeks) and perform better than (P = .024) brain volume (mean of the absolute error, 0.72 ± 0.61 weeks) or sonography measures (SDs approximately 1.5 weeks, as reported in literature). Prediction accuracy is comparable with that of the semiquantitative visual assessment score (mean, 0.57 ± 0.41 weeks).Quantitative cortical folding measures such as global average curvedness can be an accurate and reliable estimator of gestational age and brain maturity for healthy fetuses in the third trimester and have the potential to be an indicator of brain-growth delays for at-risk fetuses and preterm neonates.}, doi = {10.3174/ajnr.A4357}, institution = {Advanced Pediatric Brain Imaging Research Laboratory (C.C., C.L.), Children's National Medical Center, Washington, DC Departments of Neurology, Radiology, and Pediatrics (C.C., A.J.d.P., C.L.), George Washington University School of Medicine and Health Sciences, Washington, DC.}, language = {eng}, medline-pst = {ppublish}, owner = {stnava}, pii = {ajnr.A4357}, pmid = {26045578}, timestamp = {2015.10.04}, url = {http://dx.doi.org/10.3174/ajnr.A4357}, } @Article{Xie2014, Title = {Automatic clustering and thickness measurement of anatomical variants of the human perirhinal cortex.}, Author = {Xie, Long and Pluta, John and Wang, Hongzhi and Das, Sandhitsu R. and Mancuso, Lauren and Kliot, Dasha and Avants, Brian B. and Ding, Song-Lin and Wolk, David A. and Yushkevich, Paul A.}, Journal = {Med Image Comput Comput Assist Interv}, Year = {2014}, Number = {Pt 3}, Pages = {81--88}, Volume = {17}, __markedentry = {[stnava:1]}, Abstract = {The entorhinal cortex (ERC) and the perirhinal cortex (PRC) are subregions of the medial temporal lobe (MTL) that play important roles in episodic memory representations, as well as serving as a conduit between other neocortical areas and the hippocampus. They are also the sites where neuronal damage first occurs in Alzheimer's disease (AD). The ability to automatically quantify the volume and thickness of the ERC and PRC is desirable because these localized measures can potentially serve as better imaging biomarkers for AD and other neurodegenerative diseases. However, large anatomical variation in the PRC makes it a challenging area for analysis. In order to address this problem, we propose an automatic segmentation, clustering, and thickness measurement approach that explicitly accounts for anatomical variation. The approach is targeted to highly anisotropic (0.4x0.4x2.0mm3 ) T2-weighted MRI scans that are preferred by many authors for detailed imaging of the MTL, but which pose challenges for segmentation and shape analysis. After automatically labeling MTL substructures using multi-atlas segmentation, our method clusters subjects into groups based on the shape of the PRC, constructs unbiased population templates for each group, and uses the smooth surface representations obtained during template construction to extract regional thickness measurements in the space of each subject. The proposed thickness measures are evaluated in the context of discrimination between patients with Mild Cognitive Impairment (MCI) and normal controls (NC).}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {25320785}, Timestamp = {2014.11.01} } @Article{Yushkevich2010, Title = {Bias in estimation of hippocampal atrophy using deformation-based morphometry arises from asymmetric global normalization: An illustration in ADNI 3~{T MRI} data}, Author = {Yushkevich, Paul A. and Avants, Brian B. and Das, Sandhitsu R. and Pluta, John and Altinay, Murat and Craige, Caryne and Alzheimer's Dis Neuroimaging Initi}, Journal = {Neuroimage}, Year = {2010}, Month = apr, Number = {2}, Pages = {434--445}, Volume = {50}, Doi = {10.1016/j.neuroimage.2009.12.007}, ISSN = {1053-8119}, Unique-id = {ISI:000274948400009} } @InProceedings{Yushkevich2006, Title = {{3D} mouse brain reconstruction from histology using a coarse-to-fine approach}, Author = {Yushkevich, Paul A. and Avants, Brian B. and Ng, Lydia and Hawrylycz, Michael and Burstein, Pablo D. and Zhang, Hui and Gee, James C.}, Booktitle = {BIOMEDICAL IMAGE REGISTRATION, PROCEEDINGS}, Year = {2006}, Editor = {Pluim, JPW and Likar, B and Gerritsen, FA}, Note = {3rd International Workshop on Biomedical Image Registration, Utrecht Univ, Utrecht, NETHERLANDS, JUL 09-11, 2006}, Organization = {Philips Med Syst}, Pages = {230--237}, Series = {LECTURE NOTES IN COMPUTER SCIENCE}, Volume = {4057}, __markedentry = {[stnava:1]}, ISBN = {3-540-35648-7}, ISSN = {0302-9743}, Unique-id = {ISI:000239485200028} } @Article{Yushkevich2009, Title = {A high-resolution computational atlas of the human hippocampus from postmortem magnetic resonance imaging at 9.4~{T}}, Author = {Yushkevich, Paul A. and Avants, Brian B. and Pluta, John and Das, Sandhitsu and Minkoff, David and Mechanic-Hamilton, Dawn and Glynn, Simon and Pickup, Stephen and Liu, Weixia and Gee, James C. and Grossman, Murray and Detre, John A.}, Journal = {Neuroimage}, Year = {2009}, Month = jan, Number = {2}, Pages = {385--398}, Volume = {44}, Doi = {10.1016/j.neuroimage.2008.08.042}, ISSN = {1053-8119}, Unique-id = {ISI:000262301100010} } @Article{Yushkevich2008, Title = {Shape-based alignment of hippocampal subfields: evaluation in postmortem {MRI}.}, Author = {Yushkevich, Paul A. and Avants, Brian B. and Pluta, John and Minkoff, David and Detre, John A. and Grossman, Murray and Gee, James C.}, Journal = {Med Image Comput Comput Assist Interv}, Year = {2008}, Number = {Pt 1}, Pages = {510--517}, Volume = {11}, __markedentry = {[stnava:1]}, Abstract = {This paper estimates the accuracy of hippocampal subfield alignment via shape-based normalization. Evaluation takes place in postmortem MRI dataset acquired at 9.4 Tesla with many averages and approximately 0.01 mm3 voxel resolution. Continuous medial representations (cm-reps) are used to establish geometrical correspondences between hippocampal formations in different images; the extent to which these correspondences match up subfields is evaluated and compared to normalization driven by image forces. Shape-based normalization is shown to perform only slightly worse than image-based normalization; this is encouraging because the former is more applicable to in vivo MRI, which typically lacks features that distinguish hippocampal subfields.}, Institution = {Department of Radiology, University of Pennsylvania, USA.}, Keywords = {Algorithms; Artificial Intelligence; Cadaver; Computer Simulation; Hippocampus, anatomy /\&/ histology; Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Information Storage and Retrieval, methods; Magnetic Resonance Imaging, methods; Models, Biological; Models, Statistical; Pattern Recognition, Automated, methods; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {18979785}, Timestamp = {2013.05.29} } @Article{Yushkevich2012, Title = {From label fusion to correspondence fusion: a new approach to unbiased groupwise registration.}, Author = {Yushkevich, Paul A. and Wang, Hongzhi and Pluta, John and Avants, Brian B.}, Journal = {Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit}, Year = {2012}, Pages = {956--963}, __markedentry = {[stnava:1]}, Abstract = {Label fusion strategies are used in multi-atlas image segmentation approaches to compute a consensus segmentation of an image, given a set of candidate segmentations produced by registering the image to a set of atlases [19, 11, 8]. Effective label fusion strategies, such as local similarity-weighted voting [1, 13] substantially reduce segmentation errors compared to single-atlas segmentation. This paper extends the label fusion idea to the problem of finding correspondences across a set of images. Instead of computing a consensus segmentation, weighted voting is used to estimate a consensus coordinate map between a target image and a reference space. Two variants of the problem are considered: (1) where correspondences between a set of atlases are known and are propagated to the target image; (2) where correspondences are estimated across a set of images without prior knowledge. Evaluation in synthetic data shows that correspondences recovered by fusion methods are more accurate than those based on registration to a population template. In a 2D example in real MRI data, fusion methods result in more consistent mappings between manual segmentations of the hippocampus.}, Doi = {10.1109/CVPR.2012.6247771}, Institution = {Department of Radiology, University of Pennsylvania, Philadelphia, USA.}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {24457950}, Timestamp = {2014.08.26} } @Article{Yushkevich2010a, Title = {Nearly automatic segmentation of hippocampal subfields in in vivo focal T2-weighted {MRI}}, Author = {Yushkevich, Paul A. and Wang, Hongzhi and Pluta, John and Das, Sandhitsu R. and Craige, Caryne and Avants, Brian B. and Weiner, Michael W. and Mueller, Susanne}, Journal = {Neuroimage}, Year = {2010}, Month = dec, Number = {4}, Pages = {1208--1224}, Volume = {53}, Doi = {10.1016/j.neuroimage.2010.06.040}, ISSN = {1053-8119}, Unique-id = {ISI:000282165800004} } @Article{Zhang2007a, Title = {High-dimensional spatial normalization of diffusion tensor images improves the detection of white matter differences: an example study using amyotrophic lateral sclerosis.}, Author = {Zhang, Hui and Avants, Brian B. and Yushkevich, Paul A. and Woo, John H. and Wang, Sumei and McCluskey, Leo F. and Elman, Lauren B. and Melhem, Elias R. and Gee, James C.}, Journal = {IEEE Trans Med Imaging}, Year = {2007}, Month = nov, Number = {11}, Pages = {1585--1597}, Volume = {26}, Abstract = {Spatial normalization of diffusion tensor images plays a key role in voxel-based analysis of white matter (WM) group differences. Currently, it has been achieved using low-dimensional registration methods in the large majority of clinical studies. This paper aims to motivate the use of high-dimensional normalization approaches by generating evidence of their impact on the findings of such studies. Using an ongoing amyotrophic lateral sclerosis (ALS) study, we evaluated three normalization methods representing the current range of available approaches: low-dimensional normalization using the fractional anisotropy (FA), high-dimensional normalization using the FA, and high-dimensional normalization using full tensor information. Each method was assessed in terms of its ability to detect significant differences between ALS patients and controls. Our findings suggest that inadequate normalization with low-dimensional approaches can result in insufficient removal of shape differences which in turn can confound FA differences in a complex manner, and that utilizing high-dimensional normalization can both significantly minimize the confounding effect of shape differences to FA differences and provide a more complete description of WM differences in terms of both size and tissue architecture differences. We also found that high-dimensional approaches, by leveraging full tensor features instead of tensor-derived indices, can further improve the alignment of WM tracts.}, Doi = {10.1109/TMI.2007.906784}, Institution = {Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA 19104, USA.}, Keywords = {Adult; Aged; Algorithms; Amyotrophic Lateral Sclerosis, pathology; Artificial Intelligence; Brain, pathology; Diffusion Magnetic Resonance Imaging, methods; Female; Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted, methods; Imaging, Three-Dimensional, methods; Male; Middle Aged; Nerve Fibers, Myelinated, pathology; Pattern Recognition, Automated, methods; Reproducibility of Results; Sensitivity and Specificity}, Language = {eng}, Medline-pst = {ppublish}, Owner = {stnava}, Pmid = {18041273}, Timestamp = {2013.05.29} } @Article{Pustina2018, author = {Pustina, Dorian and Avants, Brian and Faseyitan, Olufunsho K and Medaglia, John D and Coslett, H Branch}, title = {Improved accuracy of lesion to symptom mapping with multivariate sparse canonical correlations.}, journal = {Neuropsychologia}, year = {2018}, volume = {115}, pages = {154--166}, month = jul, issn = {1873-3514}, __markedentry = {[stnava:6]}, abstract = {Lesion to symptom mapping (LSM) is a crucial tool for understanding the causality of brain-behavior relationships. The analyses are typically performed by applying statistical methods on individual brain voxels (VLSM), a method called the mass-univariate approach. Several authors have shown that VLSM suffers from limitations that may decrease the accuracy and reliability of the findings, and have proposed the use of multivariate methods to overcome these limitations. In this study, we propose a multivariate optimization technique known as sparse canonical correlation analysis for neuroimaging (SCCAN) for lesion to symptom mapping. To validate the method and compare it with mass-univariate results, we used data from 131 patients with chronic stroke lesions in the territory of the middle cerebral artery, and created synthetic behavioral scores based on the lesion load of 93 brain regions (putative functional units). LSM analyses were performed with univariate VLSM or SCCAN, and the accuracy of the two methods was compared in terms of both overlap and displacement from the simulated functional areas. Overall, SCCAN produced more accurate results - higher dice overlap and smaller average displacement - compared to VLSM. This advantage persisted at different sample sizes (N = 20-131) and different multiple comparison corrections (false discovery rate, FDR; Bonferroni; permutation-based family wise error rate, FWER). These findings were replicated with a fully automated SCCAN routine that relied on cross-validated predictive accuracy to find the optimal sparseness value. Simulations of one, two, and three brain regions showed a systematic advantage of SCCAN over VLSM; under no circumstance could VLSM exceed the accuracy obtained with SCCAN. When considering functional units composed of multiple brain areas VLSM identified fewer areas than SCCAN. The investigation of real scores of aphasia severity (aphasia quotient and picture naming) showed that SCCAN could accurately identify known language-critical areas, while VLSM either produced diffuse maps (FDR correction) or few scattered voxels (FWER correction). Overall, this study shows that a multivariate method, such as, SCCAN, outperforms VLSM in a number of scenarios, including functional dependency on single or multiple areas, different sample sizes, different multi-area combinations, and different thresholding mechanisms (FWER, Bonferroni, FDR). These results support previous claims that multivariate methods are in general more accurate than mass-univariate approaches, and should be preferred over traditional VLSM approaches. All the methods described in this study are available in the newly developed LESYMAP package for R.}, country = {England}, doi = {10.1016/j.neuropsychologia.2017.08.027}, issn-linking = {0028-3932}, keywords = {Aphasia; Brunner; Motor; Nonparametric; SCCAN; Sparse; Stroke}, nlm-id = {0020713}, owner = {NLM}, pii = {S0028-3932(17)30321-4}, pmid = {28882479}, pubmodel = {Print-Electronic}, pubstatus = {ppublish}, revised = {2018-06-16}, timestamp = {2018.07.07}, } @Article{Chiao2018, author = {Chiao, Ping and Bedell, Barry J and Avants, Brian and Zijdenbos, Alex P and Grand'Maison, Marilyn and O'Neill, Paul and O'Gorman, John and Chen, Tianle and Koeppe, Robert}, title = {Impact of Reference/Target Region Selection on Amyloid PET Standard Uptake Value Ratios in the Phase 1b PRIME Study of Aducanumab.}, journal = {Journal of nuclear medicine : official publication, Society of Nuclear Medicine}, year = {2018}, month = may, issn = {1535-5667}, __markedentry = {[stnava:6]}, abstract = {Standardized uptake value ratios (SUVRs) are commonly used to quantify tracer uptake in amyloid beta positron emission tomography (Aβ-PET). Here, we explore the impact of target and reference region of interest (ROI) selection on SUVR effect size using interventional data from the ongoing Phase Ib PRIME study (NCT01677572) of aducanumab (BIIB037) in patients with prodromal or mild Alzheimer's disease (AD). Florbetapir PET SUVR was calculated at baseline (screening) and Weeks 26 and 54 for patients randomized to placebo and each of four aducanumab doses (1, 3, 6, and 10 mg/kg), using whole cerebellum, cerebellar grey matter, cerebellar white matter, pons, and subcortical white matter as reference regions. In addition to the pre-specified composite cortex target ROI, individual cerebral cortical ROIs were assessed as targets. Of the reference regions used, subcortical white matter, cerebellum white matter, or pons, alone or in combination, generated the largest effect sizes. The use of anterior cingulate cortex as target ROI resulted in greater effect sizes than composite cortex. SUVR calculations were not affected by correction for brain volume change over time. Dose- and time-dependent reductions in amyloid PET SUVR were consistently observed with aducanumab only in cortical regions prone to amyloid plaque deposition, regardless of amyloid-plaque-devoid reference region used. These data support the hypothesis that florbetapir SUVR responses associated with aducanumab treatment are a result of specific dose- and time-dependent reductions in amyloid burden in patients with AD.}, country = {United States}, doi = {10.2967/jnumed.118.209130}, issn-linking = {0161-5505}, keywords = {Alzheimer’s disease; Neurology; PET/MRI; Research Methods; SUVR; aducanumab; amyloid PET; amyloid beta}, nlm-id = {0217410}, owner = {NLM}, pii = {jnumed.118.209130}, pmid = {29777003}, pubmodel = {Print-Electronic}, pubstatus = {aheadofprint}, revised = {2018-05-19}, timestamp = {2018.07.07}, } @Article{Muschelli2018, author = {Muschelli, John and Gherman, Adrian and Fortin, Jean-Philippe and Avants, Brian and Whitcher, Brandon and Clayden, Jonathan D and Caffo, Brian S and Crainiceanu, Ciprian M}, title = {Neuroconductor: an R platform for medical imaging analysis.}, journal = {Biostatistics (Oxford, England)}, year = {2018}, month = jan, issn = {1468-4357}, __markedentry = {[stnava:6]}, abstract = {Neuroconductor (https://neuroconductor.org) is an open-source platform for rapid testing and dissemination of reproducible computational imaging software. The goals of the project are to: (i) provide a centralized repository of R software dedicated to image analysis, (ii) disseminate software updates quickly, (iii) train a large, diverse community of scientists using detailed tutorials and short courses, (iv) increase software quality via automatic and manual quality controls, and (v) promote reproducibility of image data analysis. Based on the programming language R (https://www.r-project.org/), Neuroconductor starts with 51 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing. We provide a description of the purpose of Neuroconductor and the user and developer experience.}, country = {England}, doi = {10.1093/biostatistics/kxx068}, issn-linking = {1465-4644}, keywords = {Bioinformatics; Image analysis; Statistical modelling}, nlm-id = {100897327}, owner = {NLM}, pii = {4791943}, pmid = {29325029}, pubmodel = {Print-Electronic}, pubstatus = {aheadofprint}, revised = {2018-01-11}, timestamp = {2018.07.07}, } @Article{Pustina2017, author = {Pustina, Dorian and Coslett, Harry Branch and Ungar, Lyle and Faseyitan, Olufunsho K and Medaglia, John D and Avants, Brian and Schwartz, Myrna F}, title = {Enhanced estimations of post-stroke aphasia severity using stacked multimodal predictions.}, journal = {Human brain mapping}, year = {2017}, volume = {38}, pages = {5603--5615}, month = nov, issn = {1097-0193}, __markedentry = {[stnava:6]}, abstract = {The severity of post-stroke aphasia and the potential for recovery are highly variable and difficult to predict. Evidence suggests that optimal estimation of aphasia severity requires the integration of multiple neuroimaging modalities and the adoption of new methods that can detect multivariate brain-behavior relationships. We created and tested a multimodal framework that relies on three information sources (lesion maps, structural connectivity, and functional connectivity) to create an array of unimodal predictions which are then fed into a final model that creates "stacked multimodal predictions" (STAMP). Crossvalidated predictions of four aphasia scores (picture naming, sentence repetition, sentence comprehension, and overall aphasia severity) were obtained from 53 left hemispheric chronic stroke patients (age: 57.1 ± 12.3 yrs, post-stroke interval: 20 months, 25 female). Results showed accurate predictions for all four aphasia scores (correlation true vs. predicted: r = 0.79-0.88). The accuracy was slightly smaller but yet significant (r = 0.66) in a full split crossvalidation with each patient considered as new. Critically, multimodal predictions produced more accurate results that any single modality alone. Topological maps of the brain regions involved in the prediction were recovered and compared with traditional voxel-based lesion-to-symptom maps, revealing high spatial congruency. These results suggest that neuroimaging modalities carry complementary information potentially useful for the prediction of aphasia scores. More broadly, this study shows that the translation of neuroimaging findings into clinically useful tools calls for a shift in perspective from unimodal to multimodal neuroimaging, from univariate to multivariate methods, from linear to nonlinear models, and, conceptually, from inferential to predictive brain mapping. Hum Brain Mapp 38:5603-5615, 2017. © 2017 Wiley Periodicals, Inc.}, chemicals = {Oxygen}, citation-subset = {IM}, completed = {2018-05-29}, country = {United States}, doi = {10.1002/hbm.23752}, issn-linking = {1065-9471}, issue = {11}, keywords = {Aphasia, diagnostic imaging, etiology, physiopathology; Brain, diagnostic imaging, physiopathology; Cerebrovascular Circulation, physiology; Chronic Disease; Connectome, methods; Female; Humans; Language Tests; Linear Models; Magnetic Resonance Imaging, methods; Male; Middle Aged; Multimodal Imaging, methods; Multivariate Analysis; Nonlinear Dynamics; Oxygen, blood; Rest; Severity of Illness Index; Stroke, complications, diagnostic imaging, physiopathology; DTI; bold; cognitive; disease; language; machine learning; neurology; resting state}, mid = {NIHMS929470}, nlm-id = {9419065}, owner = {NLM}, pmc = {PMC5765865}, pmid = {28782862}, pubmodel = {Print-Electronic}, pubstatus = {ppublish}, revised = {2018-06-16}, timestamp = {2018.07.07}, } @Article{Maga2017, author = {Maga, A Murat and Tustison, Nicholas J and Avants, Brian B}, title = {A population level atlas of Mus musculus craniofacial skeleton and automated image-based shape analysis.}, journal = {Journal of anatomy}, year = {2017}, volume = {231}, pages = {433--443}, month = sep, issn = {1469-7580}, __markedentry = {[stnava:6]}, abstract = {Laboratory mice are staples for evo/devo and genetics studies. Inbred strains provide a uniform genetic background to manipulate and understand gene-environment interactions, while their crosses have been instrumental in studies of genetic architecture, integration and modularity, and mapping of complex biological traits. Recently, there have been multiple large-scale studies of laboratory mice to further our understanding of the developmental basis, evolution, and genetic control of shape variation in the craniofacial skeleton (i.e. skull and mandible). These experiments typically use micro-computed tomography (micro-CT) to capture the craniofacial phenotype in 3D and rely on manually annotated anatomical landmarks to conduct statistical shape analysis. Although the common choice for imaging modality and phenotyping provides the potential for collaborative research for even larger studies with more statistical power, the investigator (or lab-specific) nature of the data collection hampers these efforts. Investigators are rightly concerned that subtle differences in how anatomical landmarks were recorded will create systematic bias between studies that will eventually influence scientific findings. Even if researchers are willing to repeat landmark annotation on a combined dataset, different lab practices and software choices may create obstacles for standardization beyond the underlying imaging data. Here, we propose a freely available analysis system that could assist in the standardization of micro-CT studies in the mouse. Our proposal uses best practices developed in biomedical imaging and takes advantage of existing open-source software and imaging formats. Our first contribution is the creation of a synthetic template for the adult mouse craniofacial skeleton from 25 inbred strains and five F1 crosses that are widely used in biological research. The template contains a fully segmented cranium, left and right hemi-mandibles, endocranial space, and the first few cervical vertebrae. We have been using this template in our lab to segment and isolate cranial structures in an automated fashion from a mixed population of mice, including craniofacial mutants, aged 4-12.5 weeks. As a secondary contribution, we demonstrate an application of nearly automated shape analysis, using symmetric diffeomorphic image registration. This approach, which we call diGPA, closely approximates the popular generalized Procrustes analysis (GPA) but negates the collection of anatomical landmarks. We achieve our goals by using the open-source advanced normalization tools (ANT) image quantification library, as well as its associated R library (ANTsR) for statistical image analysis. Finally, we make a plea to investigators to commit to using open imaging standards and software in their labs to the extent possible to increase the potential for data exchange and improve the reproducibility of findings. Future work will incorporate more anatomical detail (such as individual cranial bones, turbinals, dentition, middle ear ossicles) and more diversity into the template.}, citation-subset = {IM}, completed = {2018-05-09}, country = {England}, doi = {10.1111/joa.12645}, issn-linking = {0021-8782}, issue = {3}, keywords = {Animals; Female; Image Interpretation, Computer-Assisted; Mice, anatomy & histology; Skull, anatomy & histology, diagnostic imaging; X-Ray Microtomography; geometrics morphometrics; image processing; image-based shape analysis; landmarking; microCT; segmentation}, mid = {NIHMS889469}, nlm-id = {0137162}, owner = {NLM}, pmc = {PMC5554826}, pmid = {28656622}, pubmodel = {Print-Electronic}, pubstatus = {ppublish}, revised = {2018-05-09}, timestamp = {2018.07.07}, } @Article{Betancourt2016, author = {Betancourt, Laura M and Avants, Brian and Farah, Martha J and Brodsky, Nancy L and Wu, Jue and Ashtari, Manzar and Hurt, Hallam}, title = {Effect of socioeconomic status (SES) disparity on neural development in female African-American infants at age 1 month.}, journal = {Developmental science}, year = {2016}, volume = {19}, pages = {947--956}, month = nov, issn = {1467-7687}, __markedentry = {[stnava:6]}, abstract = {There is increasing interest in both the cumulative and long-term impact of early life adversity on brain structure and function, especially as the brain is both highly vulnerable and highly adaptive during childhood. Relationships between SES and neural development have been shown in children older than age 2 years. Less is known regarding the impact of SES on neural development in children before age 2. This paper examines the effect of SES, indexed by income-to-needs (ITN) and maternal education, on cortical gray, deep gray, and white matter volumes in term, healthy, appropriate for gestational age, African-American, female infants. At 5 weeks postnatal age, unsedated infants underwent MRI (3.0T Siemens Verio scanner, 32-channel head coil). Images were segmented based on a locally constructed template. Utilizing hierarchical linear regression, SES effects on MRI volumes were examined. In this cohort of healthy African-American female infants of varying SES, lower SES was associated with smaller cortical gray and deep gray matter volumes. These SES effects on neural outcome at such a young age build on similar studies of older children, suggesting that the biological embedding of adversity may occur very early in development.}, citation-subset = {IM}, completed = {2018-02-12}, country = {England}, doi = {10.1111/desc.12344}, issn-linking = {1363-755X}, issue = {6}, keywords = {African Americans; Child Development, physiology; Female; Gray Matter, growth & development; Humans; Infant; Infant, Newborn; Magnetic Resonance Imaging; Maternal Inheritance; Nervous System, growth & development; Social Class; White Matter, growth & development}, nlm-id = {9814574}, owner = {NLM}, pmid = {26489876}, pubmodel = {Print-Electronic}, pubstatus = {ppublish}, revised = {2018-03-16}, timestamp = {2018.07.07}, } @Article{Olm2016, author = {Olm, Christopher A and Kandel, Benjamin M and Avants, Brian B and Detre, John A and Gee, James C and Grossman, Murray and McMillan, Corey T}, title = {Arterial spin labeling perfusion predicts longitudinal decline in semantic variant primary progressive aphasia.}, journal = {Journal of neurology}, year = {2016}, volume = {263}, pages = {1927--1938}, month = oct, issn = {1432-1459}, __markedentry = {[stnava:6]}, abstract = {The objective of the study was to evaluate the prognostic value of regional cerebral blood flow (CBF) measured by arterial spin labeled (ASL) perfusion MRI in patients with semantic variant primary progressive aphasia (svPPA). We acquired pseudo-continuous ASL (pCASL) MRI and whole-brain T1-weighted structural MRI in svPPA patients (N = 13) with cerebrospinal fluid biomarkers consistent with frontotemporal lobar degeneration pathology. Follow-up T1-weighted MRI was available in a subset of patients (N = 8). We performed whole-brain comparisons of partial volume-corrected CBF and cortical thickness between svPPA and controls, and compared baseline and follow-up cortical thickness in regions of significant hypoperfusion and hyperperfusion. Patients with svPPA showed partial volume-corrected hypoperfusion relative to controls in left temporal lobe and insula. svPPA patients also had typical cortical thinning in anterior temporal, insula, and inferior frontal regions at baseline. Volume-corrected hypoperfusion was seen in areas of significant cortical thinning such as the left temporal lobe and insula. Additional regions of hypoperfusion corresponded to areas without cortical thinning. We also observed regions of hyperperfusion, some associated with cortical thinning and others without cortical thinning, including right superior temporal, inferior parietal, and orbitofrontal cortices. Regions of hypoperfusion and hyperperfusion near cortical thinning at baseline had significant longitudinal thinning between baseline and follow-up scans, but perfusion changes in distant areas did not show progressive thinning. Our findings suggest ASL MRI may be sensitive to functional changes not readily apparent in structural MRI, and specific changes in perfusion may be prognostic markers of disease progression in a manner consistent with cell-to-cell spreading pathology. }, chemicals = {Spin Labels}, citation-subset = {IM}, completed = {2017-04-07}, country = {Germany}, doi = {10.1007/s00415-016-8221-1}, issn-linking = {0340-5354}, issue = {10}, keywords = {Aged; Analysis of Variance; Aphasia, Primary Progressive, diagnostic imaging, physiopathology; Brain, diagnostic imaging, physiopathology; Cerebrovascular Circulation, physiology; Female; Humans; Image Processing, Computer-Assisted; Longitudinal Studies; Magnetic Resonance Imaging; Male; Middle Aged; Neuropsychological Tests; Semantics; Spin Labels; Arterial spin labeling; Frontotemporal degeneration; Functional neuroimaging; MRI; Semantic variant primary progressive aphasia}, mid = {NIHMS800287}, nlm-id = {0423161}, owner = {NLM}, pii = {10.1007/s00415-016-8221-1}, pmc = {PMC5097861}, pmid = {27379517}, pubmodel = {Print-Electronic}, pubstatus = {ppublish}, revised = {2017-10-01}, timestamp = {2018.07.07}, } @Article{Gennatas2017, author = {Gennatas, Efstathios D and Avants, Brian B and Wolf, Daniel H and Satterthwaite, Theodore D and Ruparel, Kosha and Ciric, Rastko and Hakonarson, Hakon and Gur, Raquel E and Gur, Ruben C}, title = {Age-Related Effects and Sex Differences in Gray Matter Density, Volume, Mass, and Cortical Thickness from Childhood to Young Adulthood.}, journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience}, year = {2017}, volume = {37}, pages = {5065--5073}, month = may, issn = {1529-2401}, __markedentry = {[stnava:6]}, abstract = {Developmental structural neuroimaging studies in humans have long described decreases in gray matter volume (GMV) and cortical thickness (CT) during adolescence. Gray matter density (GMD), a measure often assumed to be highly related to volume, has not been systematically investigated in development. We used T1 imaging data collected on the Philadelphia Neurodevelopmental Cohort to study age-related effects and sex differences in four regional gray matter measures in 1189 youths ranging in age from 8 to 23 years. Custom T1 segmentation and a novel high-resolution gray matter parcellation were used to extract GMD, GMV, gray matter mass (GMM; defined as GMD × GMV), and CT from 1625 brain regions. Nonlinear models revealed that each modality exhibits unique age-related effects and sex differences. While GMV and CT generally decrease with age, GMD increases and shows the strongest age-related effects, while GMM shows a slight decline overall. Females have lower GMV but higher GMD than males throughout the brain. Our findings suggest that GMD is a prime phenotype for the assessment of brain development and likely cognition and that periadolescent gray matter loss may be less pronounced than previously thought. This work highlights the need for combined quantitative histological MRI studies. This study demonstrates that different MRI-derived gray matter measures show distinct age and sex effects and should not be considered equivalent but complementary. It is shown for the first time that gray matter density increases from childhood to young adulthood, in contrast with gray matter volume and cortical thickness, and that females, who are known to have lower gray matter volume than males, have higher density throughout the brain. A custom preprocessing pipeline and a novel high-resolution parcellation were created to analyze brain scans of 1189 youths collected as part of the Philadelphia Neurodevelopmental Cohort. A clear understanding of normal structural brain development is essential for the examination of brain-behavior relationships, the study of brain disease, and, ultimately, clinical applications of neuroimaging.}, citation-subset = {IM}, completed = {2017-08-18}, country = {United States}, doi = {10.1523/JNEUROSCI.3550-16.2017}, issn-linking = {0270-6474}, issue = {20}, keywords = {Adolescent; Aging, pathology; Brain, anatomy & histology; Child; Connectome, methods; Female; Gray Matter, anatomy & histology; Humans; Magnetic Resonance Imaging, methods; Male; Organ Size; Reproducibility of Results; Sensitivity and Specificity; Sex Characteristics; Young Adult; MRI; T1-weighted imaging; brain structure; cortical thickness; gray matter density; gray matter volume}, nlm-id = {8102140}, owner = {NLM}, pii = {JNEUROSCI.3550-16.2017}, pmc = {PMC5444192}, pmid = {28432144}, pubmodel = {Print-Electronic}, pubstatus = {ppublish}, revised = {2017-11-18}, timestamp = {2018.07.07}, } @Article{Kandel2016, author = {Kandel, Benjamin M and Avants, Brian B and Gee, James C and McMillan, Corey T and Erus, Guray and Doshi, Jimit and Davatzikos, Christos and Wolk, David A}, title = {White matter hyperintensities are more highly associated with preclinical Alzheimer's disease than imaging and cognitive markers of neurodegeneration.}, journal = {Alzheimer's \& dementia (Amsterdam, Netherlands)}, year = {2016}, volume = {4}, pages = {18--27}, issn = {2352-8729}, __markedentry = {[stnava:6]}, abstract = {Cognitive tests and nonamyloid imaging biomarkers do not consistently identify preclinical AD. The objective of this study was to evaluate whether white matter hyperintensity (WMH) volume, a cerebrovascular disease marker, is more associated with preclinical AD than conventional AD biomarkers and cognitive tests. Elderly controls enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 158) underwent florbetapir-PET scans, psychometric testing, neuroimaging with MRI and PET, and APOE genetic testing. Elderly controls the Parkinson's progression markers initiative (PPMI, n = 58) had WMH volume, cerebrospinal fluid (CSF) Aβ1-42, and APOE status measured. In the ADNI cohort, only WMH volume and APOE ε4 status were associated with cerebral Aβ (standardized β = 0.44 and 1.25, P = .03 and .002). The association between WMH volume and APOE ε4 status with cerebral Aβ (standardized β = 1.12 and 0.26, P = .048 and .045) was confirmed in the PPMI cohort. WMH volume is more highly associated with preclinical AD than other AD biomarkers.}, completed = {2016-08-04}, country = {Netherlands}, doi = {10.1016/j.dadm.2016.03.001}, keywords = {Aging; Alzheimer's disease; Amyloid; Leukoaraiosis; MRI; PET; Preclinical Alzheimer's disease; White matter}, nlm-id = {101654604}, owner = {NLM}, pii = {S2352-8729(16)30019-7}, pmc = {PMC4950175}, pmid = {27489875}, pubmodel = {Electronic-eCollection}, pubstatus = {epublish}, revised = {2017-02-20}, timestamp = {2018.07.07}, } @Article{Pustina2016, author = {Pustina, Dorian and Coslett, H Branch and Turkeltaub, Peter E and Tustison, Nicholas and Schwartz, Myrna F and Avants, Brian}, title = {Automated segmentation of chronic stroke lesions using LINDA: Lesion identification with neighborhood data analysis.}, journal = {Human brain mapping}, year = {2016}, volume = {37}, pages = {1405--1421}, month = apr, issn = {1097-0193}, __markedentry = {[stnava:6]}, abstract = {The gold standard for identifying stroke lesions is manual tracing, a method that is known to be observer dependent and time consuming, thus impractical for big data studies. We propose LINDA (Lesion Identification with Neighborhood Data Analysis), an automated segmentation algorithm capable of learning the relationship between existing manual segmentations and a single T1-weighted MRI. A dataset of 60 left hemispheric chronic stroke patients is used to build the method and test it with k-fold and leave-one-out procedures. With respect to manual tracings, predicted lesion maps showed a mean dice overlap of 0.696 ± 0.16, Hausdorff distance of 17.9 ± 9.8 mm, and average displacement of 2.54 ± 1.38 mm. The manual and predicted lesion volumes correlated at r = 0.961. An additional dataset of 45 patients was utilized to test LINDA with independent data, achieving high accuracy rates and confirming its cross-institutional applicability. To investigate the cost of moving from manual tracings to automated segmentation, we performed comparative lesion-to-symptom mapping (LSM) on five behavioral scores. Predicted and manual lesions produced similar neuro-cognitive maps, albeit with some discussed discrepancies. Of note, region-wise LSM was more robust to the prediction error than voxel-wise LSM. Our results show that, while several limitations exist, our current results compete with or exceed the state-of-the-art, producing consistent predictions, very low failure rates, and transferable knowledge between labs. This work also establishes a new viewpoint on evaluating automated methods not only with segmentation accuracy but also with brain-behavior relationships. LINDA is made available online with trained models from over 100 patients.}, citation-subset = {IM}, completed = {2016-12-13}, country = {United States}, doi = {10.1002/hbm.23110}, issn-linking = {1065-9471}, issue = {4}, keywords = {Aged; Diffusion Magnetic Resonance Imaging, methods; Female; Humans; Image Interpretation, Computer-Assisted, methods; Male; Middle Aged; Statistics as Topic, methods; Stroke, diagnostic imaging, metabolism; VLSM; automatic; hierarchical; machine learning; random forests; subacute}, mid = {NIHMS747597}, nlm-id = {9419065}, owner = {NLM}, pmc = {PMC4783237}, pmid = {26756101}, pubmodel = {Print-Electronic}, pubstatus = {ppublish}, revised = {2017-04-03}, timestamp = {2018.07.07}, } @Article{Xie2017, author = {Xie, Long and Pluta, John B and Das, Sandhitsu R and Wisse, Laura E M and Wang, Hongzhi and Mancuso, Lauren and Kliot, Dasha and Avants, Brian B and Ding, Song-Lin and Manjón, José V and Wolk, David A and Yushkevich, Paul A}, title = {Multi-template analysis of human perirhinal cortex in brain MRI: Explicitly accounting for anatomical variability.}, journal = {NeuroImage}, year = {2017}, volume = {144}, pages = {183--202}, month = jan, issn = {1095-9572}, __markedentry = {[stnava:6]}, abstract = {The human perirhinal cortex (PRC) plays critical roles in episodic and semantic memory and visual perception. The PRC consists of Brodmann areas 35 and 36 (BA35, BA36). In Alzheimer's disease (AD), BA35 is the first cortical site affected by neurofibrillary tangle pathology, which is closely linked to neural injury in AD. Large anatomical variability, manifested in the form of different cortical folding and branching patterns, makes it difficult to segment the PRC in MRI scans. Pathology studies have found that in ~97% of specimens, the PRC falls into one of three discrete anatomical variants. However, current methods for PRC segmentation and morphometry in MRI are based on single-template approaches, which may not be able to accurately model these discrete variants METHODS: A multi-template analysis pipeline that explicitly accounts for anatomical variability is used to automatically label the PRC and measure its thickness in T2-weighted MRI scans. The pipeline uses multi-atlas segmentation to automatically label medial temporal lobe cortices including entorhinal cortex, PRC and the parahippocampal cortex. Pairwise registration between label maps and clustering based on residual dissimilarity after registration are used to construct separate templates for the anatomical variants of the PRC. An optimal path of deformations linking these templates is used to establish correspondences between all the subjects. Experimental evaluation focuses on the ability of single-template and multi-template analyses to detect differences in the thickness of medial temporal lobe cortices between patients with amnestic mild cognitive impairment (aMCI, n=41) and age-matched controls (n=44). The proposed technique is able to generate templates that recover the three dominant discrete variants of PRC and establish more meaningful correspondences between subjects than a single-template approach. The largest reduction in thickness associated with aMCI, in absolute terms, was found in left BA35 using both regional and summary thickness measures. Further, statistical maps of regional thickness difference between aMCI and controls revealed different patterns for the three anatomical variants.}, citation-subset = {IM}, completed = {2018-02-09}, country = {United States}, doi = {10.1016/j.neuroimage.2016.09.070}, issn-linking = {1053-8119}, issue = {Pt A}, keywords = {Aged; Aged, 80 and over; Cognitive Dysfunction, diagnostic imaging, pathology; Female; Humans; Image Processing, Computer-Assisted, methods; Magnetic Resonance Imaging, methods; Male; Middle Aged; Perirhinal Cortex, anatomy & histology, diagnostic imaging, pathology; Alzheimer's disease; Anatomical variability; Cortical thickness analysis; Magnetic resonance imaging; Manifold learning; Perirhinal cortex}, mid = {NIHMS827961}, nlm-id = {9215515}, owner = {NLM}, pii = {S1053-8119(16)30547-X}, pmc = {PMC5183532}, pmid = {27702610}, pubmodel = {Print-Electronic}, pubstatus = {ppublish}, revised = {2018-02-09}, timestamp = {2018.07.07}, } @Article{Menze2015, author = {Menze, Bjoern H and Jakab, Andras and Bauer, Stefan and Kalpathy-Cramer, Jayashree and Farahani, Keyvan and Kirby, Justin and Burren, Yuliya and Porz, Nicole and Slotboom, Johannes and Wiest, Roland and Lanczi, Levente and Gerstner, Elizabeth and Weber, Marc-André and Arbel, Tal and Avants, Brian B and Ayache, Nicholas and Buendia, Patricia and Collins, D Louis and Cordier, Nicolas and Corso, Jason J and Criminisi, Antonio and Das, Tilak and Delingette, Hervé and Demiralp, Çağatay and Durst, Christopher R and Dojat, Michel and Doyle, Senan and Festa, Joana and Forbes, Florence and Geremia, Ezequiel and Glocker, Ben and Golland, Polina and Guo, Xiaotao and Hamamci, Andac and Iftekharuddin, Khan M and Jena, Raj and John, Nigel M and Konukoglu, Ender and Lashkari, Danial and Mariz, José Antonió and Meier, Raphael and Pereira, Sérgio and Precup, Doina and Price, Stephen J and Raviv, Tammy Riklin and Reza, Syed M S and Ryan, Michael and Sarikaya, Duygu and Schwartz, Lawrence and Shin, Hoo-Chang and Shotton, Jamie and Silva, Carlos A and Sousa, Nuno and Subbanna, Nagesh K and Szekely, Gabor and Taylor, Thomas J and Thomas, Owen M and Tustison, Nicholas J and Unal, Gozde and Vasseur, Flor and Wintermark, Max and Ye, Dong Hye and Zhao, Liang and Zhao, Binsheng and Zikic, Darko and Prastawa, Marcel and Reyes, Mauricio and Van Leemput, Koen}, title = {The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).}, journal = {IEEE transactions on medical imaging}, year = {2015}, volume = {34}, pages = {1993--2024}, month = oct, issn = {1558-254X}, __markedentry = {[stnava:6]}, abstract = {In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource. }, citation-subset = {IM}, completed = {2016-07-06}, country = {United States}, doi = {10.1109/TMI.2014.2377694}, issn-linking = {0278-0062}, issue = {10}, keywords = {Algorithms; Benchmarking; Glioma, pathology; Humans; Magnetic Resonance Imaging, methods, standards; Neuroimaging, methods, standards}, mid = {NIHMS775317}, nlm-id = {8310780}, owner = {NLM}, pmc = {PMC4833122}, pmid = {25494501}, pubmodel = {Print-Electronic}, pubstatus = {ppublish}, revised = {2017-09-22}, timestamp = {2018.07.07}, } @Article{Legant2016, author = {Legant, Wesley R and Shao, Lin and Grimm, Jonathan B and Brown, Timothy A and Milkie, Daniel E and Avants, Brian B and Lavis, Luke D and Betzig, Eric}, title = {High-density three-dimensional localization microscopy across large volumes.}, journal = {Nature methods}, year = {2016}, volume = {13}, pages = {359--365}, month = apr, issn = {1548-7105}, __markedentry = {[stnava:6]}, abstract = {Extending three-dimensional (3D) single-molecule localization microscopy away from the coverslip and into thicker specimens will greatly broaden its biological utility. However, because of the limitations of both conventional imaging modalities and conventional labeling techniques, it is a challenge to localize molecules in three dimensions with high precision in such samples while simultaneously achieving the labeling densities required for high resolution of densely crowded structures. Here we combined lattice light-sheet microscopy with newly developed, freely diffusing, cell-permeable chemical probes with targeted affinity for DNA, intracellular membranes or the plasma membrane. We used this combination to perform high-localization precision, ultrahigh-labeling density, multicolor localization microscopy in samples up to 20 μm thick, including dividing cells and the neuromast organ of a zebrafish embryo. We also demonstrate super-resolution correlative imaging with protein-specific photoactivable fluorophores, providing a mutually compatible, single-platform alternative to correlative light-electron microscopy over large volumes. }, chemicals = {Fluorescent Dyes}, citation-subset = {IM}, completed = {2016-08-16}, country = {United States}, doi = {10.1038/nmeth.3797}, issn-linking = {1548-7091}, issue = {4}, keywords = {Animals; COS Cells; Cell Membrane, ultrastructure; Cercopithecus aethiops; Embryo, Nonmammalian, ultrastructure; Fluorescent Dyes; Image Processing, Computer-Assisted, methods; Imaging, Three-Dimensional; LLC-PK1 Cells; Microscopy, Electron, methods; Microscopy, Fluorescence, methods; Mitochondria, ultrastructure; Swine; Zebrafish, embryology}, mid = {HHMIMS758713}, nlm = {HHMIMS758713}, nlm-id = {101215604}, owner = {NLM}, pii = {nmeth.3797}, pmc = {PMC4889433}, pmid = {26950745}, pubmodel = {Print-Electronic}, pubstatus = {ppublish}, revised = {2017-09-22}, timestamp = {2018.07.07}, } @Article{Stone2020Blast, author = {Stone, James R and Avants, Brian B and Tustison, Nicholas J and Wassermann, Eric M and Gill, Jessica and Polejaeva, Elena and Dell, Kristine C and Carr, Walter and Yarnell, Angela M and LoPresti, Matthew L}, title = {Functional and structural neuroimaging correlates of repetitive low-level blast exposure in career breachers}, journal = {Journal of Neurotrauma}, year = {2020}, volume = {37}, number = {23}, pages = {2468--2481}, } @Article{Tustison2021ANTsX, author = {Tustison, Nicholas J and Cook, Philip A and Holbrook, Andrew J and Johnson, Hans J and Muschelli, John and Devenyi, Gabriel A and Duda, Jeffrey T and Das, Sandhitsu R and Cullen, Nicholas C and Gillen, Daniel L and Yassa, Michael A and Stone, James R and Gee, James C and Avants, Brian B}, title = {The {ANTsX} ecosystem for quantitative biological and medical imaging}, journal = {Scientific Reports}, year = {2021}, volume = {11}, number = {1}, pages = {9068}, doi = {10.1038/s41598-021-87564-6}, } @Article{Avants2021SiMLR, author = {Avants, Brian B and Tustison, Nicholas J and Stone, James R}, title = {Similarity-driven multi-view embeddings from high-dimensional biomedical data}, journal = {Nature Computational Science}, year = {2021}, volume = {1}, number = {2}, pages = {143--152}, doi = {10.1038/s43588-021-00029-8}, } @Article{Tustison2021Pulmonary, author = {Tustison, Nicholas J and Altes, Talissa A and Qing, Kun and He, Mu and Miller, G Wilson and Avants, Brian B and Shim, Yun M and Gee, James C and Mugler III, John P and Mata, Jaime F}, title = {Image-versus histogram-based considerations in semantic segmentation of pulmonary hyperpolarized gas images}, journal = {Magnetic Resonance in Medicine}, year = {2021}, volume = {86}, number = {5}, pages = {2822--2836}, } @Article{Diamond2021Fish, author = {Diamond, Kelly M and Avants, Brian B and Maga, A Murat}, title = {Machine learning-based segmentation and landmarking of 2D fish images}, journal = {Integrative and Comparative Biology}, year = {2021}, volume = {61}, pages = {E1100--E1101}, } @Article{Elhamod2022Species, author = {Elhamod, Mohannad and Diamond, Kelly M and Maga, A Murat and Bakis, Yasin and Bart Jr, Henry L and Mabee, Paula and Dahdul, Wasila and Leipzig, Jeremy and Greenberg, Jane and Avants, Brian}, title = {Hierarchy-guided neural network for species classification}, journal = {Methods in Ecology and Evolution}, year = {2022}, volume = {13}, number = {3}, pages = {642--652}, } @InCollection{Avants2022ATLAS, author = {Avants, Brian B and Tustison, Nicholas J}, title = {Mapping the Spatial Distribution of Lesions in Stroke: Effect of Diffeomorphic Registration Strategy in the {ATLAS} Dataset}, booktitle = {Lesion-to-Symptom Mapping: Principles and Tools}, publisher = {Springer}, year = {2022}, pages = {79--94}, } @Article{Stone2022Neuroinflammation, author = {Stone, James and Avants, Brian and Tustison, Nicholas and Gill, Jessica and Wilde, Elisabeth and Neumann, Kiel and Gladney, Leslie and Kilgore, Madison and Modica, LT and Bowling, F}, title = {Increased Neuroinflammation in special operators with a history of blast overpressure exposure}, journal = {Journal of Neurotrauma}, year = {2022}, volume = {39}, number = {11-12}, pages = {A27--A28}, } @Article{Avants2023SuperRes, author = {Avants, Brian and Tustison, Nicholas J and McMillan, Corey T and Gosselin, Taylor and Gunn, Roger and Hesterman, Jacob}, title = {Concurrent 3D super resolution on intensity and segmentation maps improves detection of structural effects in neurodegenerative disease}, journal = {medRxiv}, year = {2023}, note = {2023.02.02.23285376}, } @Article{Dadu2023AlignedUMAP, author = {Dadu, Anant and Satone, Vipul K and Kaur, Rachneet and Koretsky, Mathew J and Iwaki, Hirotaka and Qi, Yue A and Ramos, Daniel M and Avants, Brian and Hesterman, Jacob and Gunn, Roger}, title = {Application of {Aligned-UMAP} to longitudinal biomedical studies}, journal = {Patterns}, year = {2023}, volume = {4}, number = {6}, pages = {100741}, } @InProceedings{Wu2023ICLR, author = {Wu, Zhenbang and Dadu, Anant and Tustison, Nicholas and Avants, Brian and Nalls, Mike and Sun, Jimeng and Faghri, Faraz}, title = {Multimodal Patient Representation Learning with Missing Modalities and Labels}, booktitle = {The Twelfth International Conference on Learning Representations (ICLR)}, year = {2023}, } @Article{Tustison2024UKBB, author = {Tustison, Nicholas J and Yassa, Michael A and Rizvi, Batool and Cook, Philip A and Holbrook, Andrew J and Sathishkumar, Mithra T and Tustison, Mia G and Gee, James C and Stone, James R and Avants, Brian B}, title = {{ANTsX} neuroimaging-derived structural phenotypes of {UK Biobank}}, journal = {Scientific Reports}, year = {2024}, volume = {14}, number = {1}, pages = {8848}, } @Article{Stone2024SpecialOps, author = {Stone, James R and Avants, Brian B and Tustison, Nicholas J and Gill, Jessica and Wilde, Elisabeth A and Neumann, Kiel D and Gladney, Leslie A and Kilgore, Madison O and Bowling, F and Wilson, Christopher M}, title = {Neurological effects of repeated blast exposure in Special Operations personnel}, journal = {Journal of Neurotrauma}, year = {2024}, volume = {41}, number = {7-8}, pages = {942--956}, } @Article{Tustison2024NNL, author = {Tustison, Nicholas and Avants, Brian and Whitlow, Christopher T and Lancashire, Lee and Bhatt, Shashank D and Haas, Magali}, title = {Normative Neuroimaging Library: Designing a Comprehensive and Demographically Diverse Dataset of Healthy Controls to Support Traumatic Brain Injury Diagnostic and Therapeutic Development}, journal = {Journal of Neurotrauma}, year = {2024}, volume = {41}, number = {23-24}, pages = {2497--2512}, } @Article{Yang2024BrainAging, author = {Yang, Zhijian and Wen, Junhao and Erus, Guray and Avants, Brian B and others}, title = {Brain aging patterns in a large and diverse cohort of 49,482 individuals}, journal = {Nature Medicine}, year = {2024}, volume = {30}, pages = {3015--3026}, } @Article{Dennis2024LIMBIC, author = {Dennis, Emily L and Rowland, Jared and Esopenko, Carrie and Tustison, Nicholas and Newsome, Mary and Avants, Brian and Gill, Jessica and Hinds, Sidney and Kenney, Kimbra and Lindsey, Hannah}, title = {Brain volume changes following blast-related mild {TBI} in service members and veterans: a {LIMBIC-CENC} study}, journal = {medRxiv}, year = {2024}, note = {2024.02.27.24303460}, } @Article{Polfus2024PD, author = {Polfus, Linda and Shirazi, Hamed Masnadi and Reardon, Alexandra and Varga, Matthew and Tchourbanov, Alexander and Gosselin, Taylor and Dadu, Anant and Avants, Brian and Faghri, Faraz and Nalls, Mike and Hesterman, Jacob}, title = {Genetic and brain imaging phenotype joint prediction of longitudinal {Parkinson's} Disease subtypes}, journal = {European Journal of Human Genetics}, year = {2024}, volume = {32}, pages = {653}, } @Article{Avants2024PPMI, author = {Avants, Brian B and Fonville, L and Hampton, O and Reardon, A and Stenger, A and Wang, X and Tustison, Nicholas J and Stone, James R and Cook, Philip A and Marebwa, B and Chahine, Lana M}, title = {Magnetic Resonance Imaging Data Phenotypes for the {Parkinson's} Progression Markers Initiative}, journal = {medRxiv}, year = {2024}, } @Article{Avants2025NSAFlow, author = {Avants, Brian B and Tustison, Nicholas J and Stone, James R}, title = {{Non-Negative Stiefel Approximating Flow}: Orthogonalish Matrix Optimization for Interpretable Embeddings}, journal = {arXiv preprint arXiv:2511.06425}, year = {2025}, } @Article{Avants2025Multiview, author = {Avants, Brian B and Tustison, Nicholas J and Stone, James R}, title = {Joint representations from multi-view {MRI}-based learning support cognitive and functional performance domains}, journal = {medRxiv}, year = {2025}, } @Article{Pustina2026HD, author = {Pustina, Dorian and Bogale, Haikel and Das, Sandhitsu and Avants, Brian and Sathe, Swati and Sampaio, Cristina and Wood, Andrew}, title = {Geometric properties of caudate and putamen mark the progression of {Huntington's} disease}, journal = {Imaging Neuroscience}, year = {2026}, volume = {4}, pages = {IMAG.a.1351}, doi = {10.1162/IMAG.a.1351}, } @Article{Theodorou2026MRI2PET, author = {Theodorou, Brandon and Dadu, Anant and Avants, Brian and Nalls, Mike and Sun, Jimeng and Faghri, Faraz}, title = {Realistic {PET} image synthesis from {MRI} for automated inference of brain atrophy and {Alzheimer's}}, journal = {iScience}, year = {2026}, volume = {29}, number = {5}, pages = {115747}, } @Article{Tustison2026NormalizingFlows, author = {Tustison, Nicholas J and Avants, Brian B and Cook, Philip A and Gee, James C and Stone, James R}, title = {Deep Computational Anatomy via Latent-Aligned Multiview Normalizing Flows}, journal = {bioRxiv / PubMed}, year = {2026}, pmid = {42146611}, } @Comment{jabref-meta: databaseType:bibtex;}