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HemiSpec

HemiSpec is a research-software toolkit for converting preprocessed gray-matter maps into bilateral reconstruction-derived hemispheric measures: ANS (absolute neuroanatomical specificity) and RNS (relative neuroanatomical specificity).

Input boundary

HemiSpec does not accept raw T1-weighted MRI directly as DGN input. First convert each T1 image into an MNI152 1.5 mm masked gray-matter map (*_GM_masked.nii.gz) using the documented FSL preprocessing workflow.

Input and preprocessing Quick start Installation

End-to-end workflow

HemiSpec workflow overview
T1-weighted MRI → FSL gray-matter preprocessing → MNI152 1.5 mm GM input → bilateral DGN reconstruction → ANS/RNS maps → optional ROI summaries and validation → downstream analyses.
Stage Input Main operation Output
Preprocessing T1-weighted NIfTI FSL brain extraction, tissue segmentation, affine MNI registration, GM threshold/mask *_GM_masked.nii.gz
Reconstruction Preprocessed GM map Left-to-right and right-to-left DGN inference Reconstructed target hemispheres
Metric computation Actual and reconstructed GM ANS/RNS residual metrics ANS.L, ANS.R, RNS.L, RNS.R maps
Optional summaries Voxel maps + compatible atlas ROI aggregation Long and wide ROI tables
Optional validation Maps/ROI features Hemisphere classification and/or TRT analysis Validation tables and plots

What belongs to the original method, and what HemiSpec adds

The cross-hemispheric DGN framework and ANS/RNS definitions originate from Wang et al. (2024). HemiSpec packages that method into an installable API/CLI/GUI workflow, model-asset discovery, bilateral map export, ROI summaries, validation utilities, documentation, and release tooling.

  • Original method and metrics: cite Wang et al. (2024).
  • HemiSpec software or downstream studies: cite the relevant public software/manuscript record when available.

See Citation for the complete reference and citation boundaries.

Choose your path

  • Prepare real MRI input

    Start from T1-weighted NIfTI, run the packaged FSL script, and verify the DGN input grid and quality-control checks.

    Input and preprocessing

  • Run HemiSpec

    Install from the GitHub Release wheel or a source checkout, inspect model readiness, and run the GUI or bilateral CLI workflow.

    Quick start

  • Understand the method

    Review the reconstruction framework, original ANS/RNS definitions, and HemiSpec implementation details.

    Methods

  • Models and atlas assets

    Learn how DGN checkpoints, classifier bundles, and optional ROI atlases are located and distributed.

    Data and models

Current software scope

HemiSpec v0.1.0 is a public beta distributed through GitHub Releases and the source repository; the PyPI project is not public yet. The GUI and CLI generate voxel-wise ANS/RNS maps; ROI tables, hemisphere-classifier validation, and TRT validation are currently optional downstream steps.

Source: github.com/mqqq333/HemiSpec. Documentation built with Material for MkDocs.