The Importance of Anti-Correlations in Graph Theory Based Classification of Autism Spectrum Disorder
Bibliographic record
Abstract
Abstract In recent years, there has been a significant growth in the number of applications of machine learning (ML) techniques to the study and identification of neurological disorders. These methods rely heavily on what features are made available to the ML algorithm. Features such as graph theoretical metrics of resting-state fMRI-based brain networks have proven useful. However, the computation of functional brain networks relies on making an arbitrary choice about whether the obtained anti-correlations, representing the strengths of functional connections in the brain, should be discarded or not. In this study, we examine how this choice affects the performance of a support vector machine (SVM) model for classifying autism spectrum disorder. We extracted graph theoretical features using three different pipelines for constructing the functional network graph. These pipelines primarily used positive weights, negative weights (anti-correlations) and only the absolute value of weights of the correlation matrix derived from fMRI time-series. Our results suggest that in the presence of Global Signal Regression (GSR) the features extracted from anti-correlations play a major role in improving model performance. However, this does not undermine the importance of features from other pipelines.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".