Dynamic analysis of Probabilistic Boolean Network for fMRI study in Parkinson's Disease
Bibliographic record
Abstract
Probabilistic Boolean Networks (PBNs) have recently been applied to infer functional connectivity between brain regions of interested (ROIs), and to identify the existence of connectivity abnormality in Parkinson's Disease (PD). In addition to PBNs' promising application in inferring significant brain connections, PBN modeling for brain ROIs also enables researchers to study dynamic activities of the system under stochastic condition, gaining essential information regarding asymptotic behaviors of ROIs for potential therapeutic intervention in PD. In this paper, we will present a PBN model for fMRI analysis and study its asymptotic behavior. The PBN results indicate significant differences in asymptotic behaviors between PD patients and normal subjects. Hypothesizing the observed feature states for normal subject as the desired functional states, we further explore possible methods to manipulate the dynamical network behavior of PD patients in the favor of the desired states from the view of random perturbation as well as intervention.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".