Assessing functional connectivity of brainstem nuclei in fMRI data
Bibliographic record
Abstract
The functional connectivity of brainstem nuclei with other cortical and subcortical regions (brainstem-brain connectivity) has not yet been completely delineated. The objective of this study was to model the brainstem-brain connectivity using functional magnetic resonance imaging (fMRI) signals. We proposed a novel two-step framework for brainstem-brain connectivity estimation based on partial least squares (PLS) and Bayesian networks. We applied the proposed framework to investigating the functional connectivity between the pedun-culopontine nucleus (PPN), a brainstem nucleus critical for the control of locomotion, and other cortical and subcortical regions (PPN-brain connectivity) in Parkinson's disease (PD). We further examined the impact that the galvanic vestibular stimulation (GVS), a process of sending specific electric messages to a nerve in the ear that maintains balance, could have on the PPN-brain connectivity in PD. The results suggest that the PPN-brain connectivity can be reliably assessed by our proposed framework, and the GVS can affect the PPN-brain connectivity in a stimulus-dependent manner. Our work is potentially useful for the future clinical studies, facilitating the understanding of how the PPN interacts with other brain regions to involve in the control of locomotion, and providing insights into the mechanism through which the GVS assists balance in PD.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".