Prediction of Obsessive-Compulsive Disorder: Importance of Neurobiology-Aided Feature Design and Cross-Diagnosis Transfer Learning
Bibliographic record
Abstract
BACKGROUND: Machine learning applications using neuroimaging provide a multidimensional, data-driven approach that captures the level of complexity necessary for objectively aiding diagnosis and prognosis in psychiatry. However, models learned from small training samples often have limited generalizability, which continues to be a problem with automated diagnosis of mental illnesses such as obsessive-compulsive disorder (OCD). Earlier studies have shown that features incorporating prior neurobiological knowledge of brain function and combining brain parcellations from various sources can potentially improve the overall prediction. However, it is unknown whether such knowledge-driven methods can provide a performance that is comparable to state-of-the-art approaches based on neural networks. METHODS: In this study, we apply a transparent and explainable multiparcellation ensemble learning framework EMPaSchiz (Ensemble algorithm with Multiple Parcellations for Schizophrenia prediction) to the task of predicting OCD, based on a resting-state functional magnetic resonance imaging dataset of 350 subjects. Furthermore, we apply transfer learning using the features found effective for schizophrenia to OCD to leverage the commonality in brain alterations across these psychiatric diagnoses. RESULTS: We show that our knowledge-based approach leads to a prediction performance of 80.3% accuracy for OCD diagnosis that is better than domain-agnostic and automated feature design using neural networks. Furthermore, we show that a selection of reduced feature sets can be transferred from schizophrenia to the OCD prediction model without significant loss in prediction performance. CONCLUSIONS: This study presents a machine learning framework for OCD prediction with neurobiology-aided feature design using resting-state functional magnetic resonance imaging that is generalizable and reasonably interpretable.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".