Diagnosis of Autism Spectrum Disorder Based on Eigenvalues of Brain Networks
Bibliographic record
Abstract
Autism spectrum disorder (ASD) is a neuro dysfunction which causes the repetitive behavior and social instability of patients. Diagnosing ASD has been of great interest. However, due to the lack of discriminate differences between neuroimages of healthy persons and ASD patients, there has been no powerful diagnosis approach. In this study, we have designed brain network-based features for the diagnosis of ASD. Specifically, we have used the 264 regions based parcellation scheme to construct a brain network from a brain functional magnetic resonance imaging (fMRI). Then we have defined 264 raw brain features by the 264 eigenvalues of the Laplacian matrix of the brain network and another three features by network centralities. By applying a feature selection algorithm, we have obtained 64 discriminate features. Furthermore, we have trained several machine learning models for diagnosing ASD with our obtained features on ABIDE (Autism Brain Imaging Data Exchange) dataset. With our derived features, the linear discriminant analysis has achieved the classification accuracy of 77.7%, which is better than the state-of-the-art results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".