Pairwise Functional Connectivity Estimation in Spinocerebellar Ataxia Type 3 Using Sparse Gaussian Markov Network: Integrating Group and Individual Analyses of rs-fMRI
Bibliographic record
Abstract
Functional connectivity (FC) patterns from resting-state fMRI data provide relevant information for understanding brain function. In this research, we propose a representation of FC patterns in the form of a sparse graphical model that characterizes brain dynamics. Given that neurological processes typically involve specific brain regions interacting with only a few other regions for given tasks, it is reasonable to introduce sparsity into a graphical model to describe brain dynamics better. However, a challenge in constructing individual Gaussian Markov networks for each subject's rs-fMRI data set is the small number of time points in clinical data. This limitation can lead to unreliable and inaccurate estimation of precision metrics. Therefore, we propose a novel three-step approach that reduces the spatial information of the individual-level based on propagating information from group-level to individual-level analysis. In addition, this approach simultaneously considers different sparsity patterns outside each group and similar sparsity patterns inside each group, considering individual variability. We evaluate the effectiveness of our approach by training the model to differentiate between spinocerebellar ataxia patients (SCA3) and healthy controls. Our results show that our proposed method outperforms other state-of-the-art methods in performance and interoperability. Moreover, our approach gives an explainable subset of FC patterns, which includes information on regions and connections that are conditionally independent and can be used for future studies of SCA type 3 patients.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".