A Graph Attention Neural Network for Diagnosing ASD with fMRI Data
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) is a common psychiatric disorder disease that typically causes impaired communication and compromised social interactions. Functional magnetic resonance imaging (fMRI) data is one of the common neuroimaging modalities for understanding human brain functionalities as well as the diagnosis and treatment of brain disorders. There are many successful applications of deep learning to fMRI analytics where fMRI data is mostly considered structured Euclidean grids or time series, and features were usually extracted from the computer vision or time series perspective. Graph neural networks (GNNs) are neural networks that learn the interactions of graphs via message passing between the nodes of graphs. Recently variants of GNNs such as graph attention network (GAT) have demonstrated outstanding performances on many machine learning tasks. In this paper, we proposed a connectivity based graph attention network for autism diagnosis using functional connectivity (FC) patterns obtained from resting-state fMRI (rs-fMRI). We define graphs based on F Cs and statistics of fMRI time series and present a connectivity-based GATs model for fMRI data analysis. The graph-theoretic based approach enables us to pass messages among connectomic (Non-Euclidean) neighborhoods, which is consistent with the brain functional networks. To evaluate the performance of our proposed GAT, we apply the GAT model to the classification of A SD patients from normal controls. Our results show that GAT can effectively capture salient features for ASD classifications in fMRI analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".