Probabilistic Boolean Network Analysis of Brain Connectivity in Parkinson's Disease
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Bibliographic record
Abstract
Recent research has suggested that disrupted interactions between certain brain regions may contribute to the symptoms of neurological diseases such as Parkinson's disease (PD). It is therefore important to develop models for inferring brain functional connectivity from non-invasive imaging data, such as functional magnetic resonance imaging (fMRI). In this paper, we propose applying probabilistic Boolean networks (PBNs) for modeling brain connectivity due to its solid stochastic properties, computational simplicity, robustness to uncertainty, and ability to deal with small-size data, typical for fMRI data sets. Applying the proposed PBN framework to real fMRI data recorded from PD and control subjects, the PBN method detected statistically significant differing interactions between task-related regions of interest (ROIs) across groups. Comparing the PBN results in PD subjects before and after they had taken L-dopa medication, the principal treatment for PD, suggests that a key mechanism of action of this medication is relative normalization of disrupted brain connectivity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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