Connectome Discovery Workflow

Overview

connectome-discovery is a scientific interpretation workflow, not a second CPM implementation. It consumes fitted-model outputs or aligned network maps and produces map similarities, empirical significance, and candidate target rankings.

Stage	Canonical owner
Connectome prediction	cpm, BrainGNN, BNT, or another model skill
Atlas/space validation	fmri-skill, nibabel-skill
Map similarity and permutation	models/connectome_discovery/mapping.py
Surface/network rendering	brain-visualization

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Installation

pip install numpy scipy pandas

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Workflows

1. Generate model evidence

Run cpm or another connectome model and freeze its held-out predictions, selected edges, atlas, and node ordering.

2. Align maps

Reference and candidate maps must use the same atlas, node order, hemisphere convention, and value orientation. Resampling or atlas mapping must be recorded.

3. Score and rank targets

Use models/connectome_discovery/mapping.py for:

• cosine_similarity_map
• permutation_pvalue
• rank_targets

Save the observed score, null distribution settings, permutation count, random seed, atlas, and coordinate space.

4. Visualize

Route final ROI/network values to brain-visualization. Do not infer an anatomical target from an unlabeled edge vector.

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Input / Output Summary

Item	Format
Input	aligned ROI/network maps or model-derived connectome signatures
Statistics	cosine similarity and empirical permutation P value
Ranking	target identifier, similarity, rank, atlas/space metadata
Visualization	publication-ready network or surface map

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Testing

pytest models/tests/test_extended_models.py -q

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Directory Reference

models/connectome_discovery/
├── __init__.py
└── mapping.py          map similarity, permutation, and ranking

skills/connectome-discovery/
└── SKILL.md

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Reference

• Use skills/cpm/SKILL.md for CPM training.
• Use skills/brain-visualization/SKILL.md for spatial rendering.

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Created At: 2026-07-26 HKT Last Updated At: 2026-07-29 HKT Author: chengwang96