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Modality Features: Functional MRI (rs-fMRI)

Source Inputs

  • Preprocessed rs-fMRI timeseries (e.g., fMRIPrep outputs, custom pipelines).
  • Atlas definitions (Schaefer-400, Gordon, volumetric grid).
  • Motion / QC metrics (FD, DVARS, censoring masks).

Option A: Functional Connectivity (Fast Baseline)

  1. Parcellate time series per subject.
  2. Compute Pearson correlations per ROI pair; apply Fisher z-transform.
  3. Flatten upper triangle; optionally reduce via PCA (100–256 dims) before projecting to 512-D.
  4. Residualize covariates (age, sex, site/scanner, motion FD, TR, SES) within folds.
  5. Store embeddings + QC flags in artifacts/generated/embeddings/fmri_fc/.

Option B: Foundation Model Embeddings

  • Supported encoders: BrainLM, Brain-JEPA, Brain Harmony, SwiFT, BrainMT.
  • Preprocessing needs: TR normalization (Harmony TAPE), mask collators, gradient positional encodings.
  • Pooling: CLS token, mean over spatial tokens, hub tokens; document choice.
  • 512-D projector (PCA/MLP) fit per fold; log checkpoint versions.

Motion & Site Handling

  • Include FD (mean, max) and number of censored volumes as covariates.
  • Consider site-aware splits or mixed-effects modeling if imbalance severe.
  • For heterogeneous TRs: align via resampling or Harmony’s TAPE (PI-resize) before embedding.

Covariate Residualization

  • Age, sex, site/scanner, motion FD, TR group, SES, acquisition batch.
  • Document design matrices and residualization scripts.

Integration Notes

  • Align subject IDs with genomics/sMRI intersections; record dropouts.
  • Provide config references (configs/projectors/fmri_pca512.yaml, kb/datasets/<cohort>.yaml).
  • Track version of preprocessing (e.g., fMRIPrep 23.2) and smoothing parameters.

References

  • BrainLM, Brain-JEPA, Brain Harmony, SwiFT, BrainMT papers.
  • Internal notebooks for FC pipeline and FM embedding extraction.