Classification of first-episode schizophrenia spectrum disorders and controls from whole brain white matter fractional anisotropy using machine learning
Bibliographic record
Abstract
Background Schizophrenia is a chronic disorder with an early onset and high disease burden in terms of life disability. Its early recognition may delay the resulting brain structural/functional alterations and improve treatment outcomes. Unlike conventional group-statistics, machine-learning techniques made it possible to classify patients and controls based on the disease patterns on an individual level. Diagnostic classification in first-episode schizophrenia to date was mostly performed on sMRI or fMRI data. DTI modalities have not gained comparable attention. Methods We performed the classification of 77 FES patients and 77 healthy controls matched by age and sex from fractional anisotropy data from using linear support-vector machine (SVM). We further analyzed the effect of medication and symptoms on the classification performance using standard statistical measures ( t -test, linear regression) and machine learning (Kernel–Ridge regression). Results The SVM distinguished between patients and controls with significant accuracy of 62.34% ( P = 0.005). There was no association between the classification performance and medication nor symptoms. Group level statistical analysis yielded brain-wide significant differences in FA. Conclusion The SVM in combination with brain white-matter fractional anisotropy might help differentiate FES from HC. The performance of our classification model was not associated with symptoms or medications and therefore reflects trait markers in the early course of the disease. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.000 |
| 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".