Subgroup Identification in Resting-State fMRI Using Common Subspace Independent Vectors Analysis
Bibliographic record
Abstract
Joint blind source separation (JBSS) is commonly used to uncover latent structures across multiple datasets. However, it often struggles with high-dimensional data and inaccurate latent dimensionality estimation, limiting its scalability and separation accuracy. To address these challenges, we propose a novel common subspace independent vector analysis model based on a bounded multivariate generalized Gaussian mixture distribution, referred to as BMIVA-CS. The model extracts low-rank common sources across datasets while modeling subject-specific variations. It incorporates a bounded indicator function and leverages spatial correlations to improve robustness, particularly under noisy, high-dimensional conditions. We validate the model through simulations and experiments on resting-state fMRI datasets from individuals with schizophrenia and autism. BMIVA-CS reliably identifies clinically relevant sources and consistently localizes affected brain regions across subjects. These results demonstrate the effectiveness of BMIVA-CS and its potential as a diagnostic tool for neurological and psychiatric disorders using rs-fMRI data.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".