| --- |
| language: en |
| license: apache-2.0 |
| tags: |
| - neuroscience |
| - ASD |
| - fMRI |
| - graph-neural-network |
| - brain-connectivity |
| - amd-mi300x |
| - rocm |
| - pytorch |
| pipeline_tag: graph-ml |
| datasets: |
| - ABIDE-I |
| --- |
| |
| # brain-connect-gcn |
|
|
| **Adversarial Brain Mode Network — site-invariant ASD classification from resting-state fMRI.** |
|
|
| 20-model LOSO ensemble trained on ABIDE I (1,102 subjects, 20 acquisition sites, 3 atlases). Achieves **AUC 0.7298** on CC200 cross-site evaluation — every subject is test-only exactly once, trained on all other sites. |
|
|
| Live demo: [BrainConnect-ASD Space](https://huggingface.co/spaces/lablab-ai-amd-developer-hackathon/BrainConnect-ASD) |
| Checkpoints: stored in the Space repo under `checkpoints/` |
|
|
| --- |
|
|
| ## Architecture |
|
|
| ``` |
| Input: BOLD time-series (196 TRs × N ROIs) |
| ↓ |
| Preprocessing: z-score → FC matrix (Pearson r → Fisher z) → adjacency thresholding |
| ↓ |
| Brain Mode Decomposition: K=32 learnable spectral modes (graph wavelet basis) |
| ↓ |
| GCN Encoder: 2-layer graph convolution, hidden_dim=128, dropout=0.3 |
| ↓ |
| Gradient Reversal Layer (Ganin 2016): removes site-specific scanner artifacts |
| ↓ |
| ASD Classifier head → p(ASD) ∈ [0, 1] |
| ↓ |
| 20-model ensemble mean → final prediction |
| ``` |
|
|
| | Component | Detail | |
| |---|---| |
| | **Total parameters** | ~105K | |
| | **Learnable modes** | K = 32 | |
| | **Hidden dim** | 128 | |
| | **Dropout** | 0.3 | |
| | **Atlases** | CC200 (200 ROIs) · AAL (116 ROIs) · Harvard-Oxford (111 ROIs) | |
| | **Loss** | Cross-entropy + GRL adversarial site loss | |
| | **Training** | 150 epochs · batch 32 · lr 5e-4 · AdamW | |
|
|
| --- |
|
|
| ## Why Adversarial Training? |
|
|
| ABIDE I was collected across 20 sites with different scanners, protocols, and demographics. A naive classifier learns site identity as a shortcut — it generalizes poorly to unseen institutions. |
|
|
| The **Gradient Reversal Layer** (GRL) flips the gradient sign for the site prediction head during backprop, forcing the encoder to produce representations that are *maximally confusing* to a site classifier. Scanner artifacts and acquisition-specific signals cannot leak into the ASD prediction. |
|
|
| This is validated empirically: the LOSO protocol uses a model trained on 19 sites to predict the 20th — it has never seen that institution's data, scanner, or demographic distribution. |
|
|
| --- |
|
|
| ## Results |
|
|
| | Metric | Value | |
| |---|---| |
| | **CC200 LOSO AUC** | **0.7298** | |
| | HO LOSO AUC | 0.7212 | |
| | AAL LOSO AUC | 0.6959 | |
| | Subjects | 1,102 (ABIDE I) | |
| | Sites | 20 | |
| | Evaluation | Cross-site LOSO — every subject is test-only | |
|
|
| ### vs. Published ABIDE Baselines |
|
|
| | Model | AUC | Split | |
| |---|---|---| |
| | SVM + FC (Plitt 2015) | 0.71 | Same-site | |
| | BrainNetCNN (Kawahara 2017) | 0.74 | Same-site | |
| | GCN + FC (Ktena 2018) | 0.70 | Same-site | |
| | ABIDE site-specific SVM | 0.76 | Same-site | |
| | **BrainConnect-ASD (ours)** | **0.7298** | **Cross-site LOSO** | |
|
|
| All prior baselines use same-site train/test splits — a fundamentally easier evaluation. Cross-site LOSO is the clinically relevant bar. |
|
|
| --- |
|
|
| ## Training Hardware |
|
|
| Trained on **AMD MI300X** (192GB HBM3) via DigitalOcean, ROCm 7.0, PyTorch 2.5.1+rocm6.2. |
|
|
| All 20 LOSO folds across 3 atlases (60 total training runs) were executed in parallel on the MI300X — a workload that would take days on CPU completed in hours. |
|
|
| End-to-end inference (preprocessing + 20-model ensemble): **~20ms per subject** on MI300X. |
|
|
| --- |
|
|
| ## Usage |
|
|
| ```python |
| # Checkpoints are in the BrainConnect-ASD Space repo |
| from huggingface_hub import hf_hub_download |
| from brain_gcn.tasks import ClassificationTask |
| |
| ckpt = hf_hub_download( |
| repo_id="lablab-ai-amd-developer-hackathon/BrainConnect-ASD", |
| filename="checkpoints/cc200/adv_brain_mode_k32_site_cc200_loso_nyu/best.ckpt", |
| repo_type="space" |
| ) |
| task = ClassificationTask.load_from_checkpoint(ckpt, map_location="cpu", strict=False) |
| task.model.eval() |
| |
| # bw_t: (1, 30, 200) windowed BOLD · adj_t: (1, 200, 200) FC adjacency |
| import torch |
| with torch.no_grad(): |
| out = task.model(bw_t, adj_t) |
| p_asd = torch.softmax(out, -1)[0, 1].item() |
| ``` |
|
|
| --- |
|
|
| ## Citation |
|
|
| ``` |
| BrainConnect-ASD — AMD Developer Hackathon 2026 |
| Raghav Aryen · lablab.ai · AMD MI300X |
| https://huggingface.co/spaces/lablab-ai-amd-developer-hackathon/BrainConnect-ASD |
| ``` |
|
|
