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Reconstruction framework

The published framework estimates a target hemisphere from its contralateral counterpart. Instead of treating lateralization only as a direct left–right subtraction, the model learns a nonlinear cross-hemispheric mapping and quantifies what the reconstruction does not explain.

Published conceptual sequence

  1. Convert T1-weighted MRI into MNI152 1.5 mm *_GM_masked.nii.gz gray-matter maps; see Input and preprocessing.
  2. Split/crop the maps into left and right hemisphere inputs.
  3. Train direction-specific context-encoder-style DGN models:
  4. left-to-right reconstruction;
  5. right-to-left reconstruction.
  6. Apply the trained model to held-out participants.
  7. Compare each reconstructed target hemisphere with the actual target hemisphere.
  8. Compute ANS and RNS from the actual–reconstructed difference.

This framework originates from Wang et al. (2024); see Citation.

HemiSpec runtime sequence

HemiSpec distributes trained generator checkpoints and runs both reconstruction directions. For each preprocessed whole-brain GM input, the software crops the configured source hemisphere, predicts the target hemisphere, pastes the prediction into the whole-volume grid, and computes directional ANS/RNS maps. It then merges the target-side outputs into bilateral left/right maps.

Method boundary

  • Published contribution: cross-hemispheric DGN formulation and ANS/RNS framework.
  • HemiSpec contribution: operational preprocessing documentation, model discovery/download, reusable inference code, bilateral export, ROI summaries, optional validation, and user interfaces.
  • Study-specific responsibility: acquisition harmonization, preprocessing quality control, exclusion criteria, confound handling, statistical analysis, and external validation.