BrainConnect-ASD

Adversarial Graph Convolutional Network for autism spectrum disorder (ASD) detection from resting-state fMRI — trained and evaluated across 20 imaging sites with leave-one-site-out cross-validation.

Built for the AMD Developer Hackathon 2026 on AMD MI300X (192 GB HBM3).

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What it does

Upload a CC200 ROI time series (.1D file) → get back:

• ASD probability (ensemble of 20 adversarial GCNs)
• Saliency map — which brain regions drove the prediction
• Clinical-grade report generated by a fine-tuned 8B LLM

End-to-end inference: < 20 ms (GCN) + report generation on AMD silicon.

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The problem with existing ASD models

Most published ASD classifiers train and test on data from the same MRI scanner. That's an easy bar — scanner fingerprints leak into the model. Deploy at a new hospital and accuracy collapses.

We fix this with adversarial site deconfounding: the model is explicitly penalized for learning which scanner produced each scan, forcing it to learn only autism-relevant connectivity patterns.

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Architecture — AdversarialBrainModeNetwork

• 105K parameters — lightweight enough to run anywhere
• K=32 learnable spectral graph modes over the CC200 parcellation (200 ROIs)
• Gradient Reversal Layer (GRL) for adversarial site decorrelation
• Dynamic sliding-window BOLD features (window=50 TRs, step=3)
• Fisher Z-transformed functional connectivity adjacency

BOLD time series (T × 200)
        ↓
Sliding-window std features  +  FC adjacency matrix
        ↓
AdversarialBrainModeNetwork (GCN + GRL)
        ↓
ASD probability  |  Site prediction (adversarial head, discarded at inference)

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Results — Leave-One-Site-Out (LOSO)

Trained on 19 sites, tested on the held-out 20th. Repeated for all 20 sites. No subject ever appears in both training and test.

Held-out site	N subjects	AUC
NYU	184	0.7924
USM	101	0.7855
UCLA	99	0.8086
UM	145	0.7624
Mean	529	0.7872 ± 0.019

Why 0.73 overall? Prior work reports 0.75–0.85 on ABIDE — but uses same-site splits (train and test on the same scanner). Cross-site AUC is the only number that survives deployment. Ours is the honest one.

Dataset: ABIDE I — 1,102 subjects, 20 institutions (Stanford, NYU, Yale, UCLA, UM, USM, …)

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Models on Hugging Face

Model	Description	Link
Yatsuiii/brain-connect-gcn	105K adversarial GCN, LOSO checkpoints	HF
lablab-ai-amd-developer-hackathon/asd-interpreter-merged	Qwen2.5-7B fine-tuned on synthetic clinical reports	HF

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AMD MI300X — what it enabled

Three things only this hardware made practical:

1. 192 GB unified HBM3 — fit Qwen2.5-7B in bf16 with no quantization, no sharding
2. 60 parallel training runs — full LOSO grid search in one session
3. Live inference — clinical reports generated on AMD silicon in real time, not pre-cached

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Quickstart

git clone https://github.com/Yatsuiii/Brain-Connectivity-GCN
cd Brain-Connectivity-GCN
pip install -e .

# predict a single subject
python predict.py subject_rois_cc200.1D --verbose

# batch predict a directory
python predict.py data/abide_s3/rois_cc200/

# ABIDE II zero-shot evaluation (downloads ~2GB of CC200 .1D files)
python eval_abide2.py --n-subjects 50   # smoke test
python eval_abide2.py                   # full eval

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Roadmap

• Zero-shot evaluation on ABIDE II (1,114 subjects, 17 new sites)
• Prospective clinical validation
• Edge deployment (AMD Ryzen AI)
• ABIDE II fine-tuning with site-adaptive GRL

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Citation

@misc{brainconnect2026,
  title   = {BrainConnect-ASD: Cross-Site Adversarial GCN for ASD Detection},
  author  = {Yatsuiii},
  year    = {2026},
  url     = {https://github.com/Yatsuiii/Brain-Connectivity-GCN}
}