Select High-Quality Imaging Data Based on Available QC Metrics — select_high_quality

This function filters a data frame to include only subjects/scans that meet a set of predefined quality control (QC) criteria. It dynamically applies filters only for the QC columns that are present in the input data frame, and prints a message for any skipped QC checks.

select_high_quality_data(
  data,
  t1_grade_thresh = 0.8,
  rsfmri_fd_thresh = 0.5,
  dti_fd_thresh = 10,
  rsfmri_minutes_thresh = 2,
  study_name = NULL
)

Arguments

data: A data frame containing some or all of the QC variables.
t1_grade_thresh: A numeric value for the minimum acceptable T1 image quality grade. Filtering is skipped if T1Hier_resnetGrade column is absent. Default is 0.8.
rsfmri_fd_thresh: A numeric value for the maximum acceptable mean framewise displacement (FD) in rsfMRI data. Filtering is skipped if rsfMRI_fcnxpro122_FD_mean column is absent. Default is 0.5.
dti_fd_thresh: A numeric value for the maximum acceptable mean framewise displacement (FD) in DTI data. Filtering is skipped if DTI_dti_FD_mean column is absent. Default is 10.
rsfmri_minutes_thresh: A numeric value for the minimum acceptable duration of uncensored rsfMRI data in minutes. Filtering is skipped if rsfMRI_fcnxpro122_minutes_censored_data column is absent. Default is 2.
study_name: An optional character string. If provided, the data will also be filtered to include only rows where the studyName column matches this value. Filtering is skipped if studyName column is absent. Default is NULL.

Value

A logical vector of the same length as nrow(data). TRUE indicates a row that passes all available QC criteria, and FALSE indicates a row that fails at least one.

Examples

# Create a dummy data frame that is missing the DTI column
mock_data <- data.frame(
  T1Hier_resnetGrade = c(0.9, 0.7, 0.95, 0.85),
  rsfMRI_fcnxpro122_FD_mean = c(0.2, 0.6, 0.3, 0.4),
  rsfMRI_fcnxpro122_minutes_censored_data = c(5, 4, 3, 6),
  studyName = c("ADNI", "ADNI", "PPMI", "ADNI")
)

# The function will run, apply all possible filters, and print a message
# about the missing DTI column.
qc_passed <- select_high_quality_data(mock_data, study_name = "ADNI")
#> QC check for 'DTI_dti_FD_mean' skipped: column not found.
#> Message: QC check for 'DTI_dti_FD_mean' skipped: column not found.
print(qc_passed)
#> [1]  TRUE FALSE FALSE  TRUE
#> [1]  TRUE FALSE FALSE  TRUE