A Framework for Group Analysis of fMRI Data using Dynamic Bayesian Networks
Why this work is in the frame
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Bibliographic record
Abstract
FMRI experiments are usually performed to make inferences about groups of subjects, but current group analysis methods for dynamic Bayesian networks (DBNs) do not easily allow incorporation of covariates of interest. In this paper, we propose a group-analysis method which uses multivariate analysis of variance (MANOVA) to address this issue. The method is performed in two stages: first, deriving a DBN connectivity network among brain regions for each subject separately; second, regressing the connectivity coefficients of DBNs to the factors of interest and performing MANOVA. A case study involving fMRI data from Parkinson's disease (PD) subjects yields promising results. Ten out of the thirteen potential connections between Regions of Interest (ROIs) which are associated with disease state are functionally improved after medication (Table I), consistent with clinical observations. The results confirm that improvement in PD symptoms after medications is in part mediated by enhanced functional brain connectivity between brain regions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it