Adaptive Constrained IVAMGGMM: Application to Mental Disorders Detection
Bibliographic record
Abstract
The demand for adaptable approaches to analyze extensive fMRI data is growing, focusing on capturing population patterns while preserving individual uniqueness. Independent component analysis (ICA) is increasingly used to uncover spatio-temporal patterns in brain imaging but struggles with separating correlated sources in multivariate data like fMRI. For that, we propose an ICA-based multivariate generalized Gaussian mixture model combined with the constrained ICA to form the cICA-MGGMM. This model relaxes the independence assumption of ICA. Also, we propose the adaptive constrained ICA-MGGMM (acICA-MGGMM) to adaptively control the association between reference signals and estimated sources. Independent vector analysis (IVA) calculates global spatial and temporal patterns from multi-subject fMRI data while preserving individual variability but performs poorly with large datasets and weak component correlations. This paper proposes integrating reference signals into the formulation to address the problem and provide guidance in high-dimensional situations. For that, we propose cIVA-MGGMM to address ICA limitations for multivariate data, offering a framework for references but relying on user-defined constraint parameters to enforce reference-estimated sources associations. To tackle these limitations, we introduce the adaptive cIVA-MGGMM (acIVA-MGGMM) to adapt and separate the activated brain sources. This model employs a full covariance matrix, which consider the feature correlation. Our four constrained methods incorporate prior information about the sources into the ICA and IVA models to address the limitations of ICA and IVA in high-dimensional data. We validate our models on simulation, Alzheimer's, Schizophrenia, EEG, and ADHD datasets, demonstrating superior performance over base models.
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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 | 0.005 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".