Incorporating Error-Rate-Controlled Prior in Modelling Brain Functional Connectivity
Bibliographic record
Abstract
Inferring effective connectivity using fMRI is of increasing importance for understanding brain function. Dynamic Bayesian network (DBN) modeling has been suggested as a promising and suitable method for this purpose. However, in practice, the success of DBN modeling is largely limited for reasons of intensive computational complexity in large networks and of accurately controlling the error rate of the model structural features. Very recently, we have developed a framework that is able to control the false discovery rate (FDR) of the discovered network edges. In this paper, we propose incorporating an FDR- controlled network-structure prior into DBN modeling for brain functional connectivity. Simulation results show that the proposed method can significantly accelerate the DBN learning process while simultaneously controlling the FDR. Its application to a real fMRI study revealed that certain functional connectivities in Parkinson's disease patients' brain are "recovered" by L-dopa medication, and also that extra connections between brain regions may represent compensatory mechanisms.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".