How to handle heterogeneous single-band resting-state fMRI labels (“Resting State fMRI”, “rsfMRI”, “fcMRI”, “MoCoSeries”) in an otherwise uniform ADNI cohort?

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Hi all,

I’ve just built a multimodal ADNI dataset for an Alzheimer’s-disease classification project and could use some advice on the fMRI side.

  • Subjects: 172 total — 60 AD / 60 CN / 52 MCI
  • T1-weighted: identical plain “MPRAGE” series for every subject (no GRAPPA, SENSE, etc.).
  • rs-fMRI: single-band BOLD only (TR ≈ 3 s), but four different series labels appear:
    • Resting State fMRI
    • Axial rsfMRI (Eyes Open)
    • Axial fcMRI (EYES OPEN)
    • MoCoSeries (ADNI’s on-the-fly motion-corrected export)

No multiband, no perfusion/ASL. Age difference between each subject’s MPRAGE and fMRI ≤ 1 year (usually 0).

My questions

  1. Are these four labels truly interchangeable once I run fMRIPrep?
  • I know slice-timing and motion correction will be applied anyway, but does starting from MoCoSeries (already motion-corrected) introduce downstream bias or over-correction compared to the raw runs?
  1. Would you exclude any of these labels to minimise variability?
  • For example, keep only Resting State fMRI and rsfMRI and drop fcMRI / MoCoSeries, at the cost of losing ~15 % of subjects?
  1. If I keep them all, what is the best practice for harmonising before feeding a CNN?
  • ComBat on extracted FC matrices?
  • Add one-hot “label type” covariates?
  • Simply rely on fMRIPrep’s outputs + data augmentation?
  1. Any gotchas with FreeSurfer -3T + fMRIPrep when the T1s are uniform but fMRI labels differ?

The end goal is a multimodal deep-learning model (T1 + rs-fMRI) with XAI (Grad-CAM / SHAP) explanations. I’d love to hear what has worked (or failed) for others tackling similar label heterogeneity in ADNI.

Thanks in advance!