Schizophrenia Detection using non-Orthogonal Adaptive Constrained Independent Vector Analysis with Multivariate Distribution
Bibliographic record
Abstract
Advancements in blind source separation (BSS) techniques have significantly improved the ability to disentangle complex data structures. Independent vector analysis (IVA), a data-driven approach, simultaneously extracts global spatial and temporal patterns from multi-subject functional magnetic resonance imaging data while preserving individual variability. However, the performance of IVA deteriorates when the number of datasets and components increases—especially in scenarios where correlations among components across datasets are weak. In this paper, we propose a novel model: the non-orthogonal adaptive constrained independent vector analysis integrated with a multivariate generalized Gaussian mixture model (non-orthogonal acIVAMGGMM). This model introduces adaptive control over the relationship between estimated components and reference signals, thereby facilitating the integration of multiple reference signals into the IVA framework. Experimental results highlight the effectiveness of the proposed method, showing sub-stantial improvements in component separation. The extracted components are further utilized to identify brain networks impacted by Schizophrenia.
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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 | 0.002 |
| 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 | 0.001 |
| 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".