Compensatory Role of the Amygdala During Motor Timing and Selection in Parkinson's Disease
Bibliographic record
Abstract
Alterations of amygdala function in Parkinson's Disease (PD) are associated with emotion-related clinical features such as impaired facial recognition, but the effects on motor performance in an emotionally-neutral task are unclear. We studied fMRI from healthy and PD subjects while they squeezed a rubber bulb to keep a bar within two parallel “tracks” that were scrolling downward. At discrete intervals, there were bifurcations of each track, and the subject had to follow either the inside or outside track requiring squeezing at 5% or 15% of maximum voluntary contraction. During the control condition (Control), subjects had to follow the inside and outside tracks alternately. In the timing (Timing) and selection (Selection) tasks, the time between bifurcations jittered randomly and the color of the bar determined which path to choose, respectively. We determined which Regions of Interest (ROIs) were activated at the time of bifurcations, by assessing both the connectivity between ROIs and the timing of activation. The caudate and putamen were activated in both (Selection-Control) and (Timing-Control) contrasts in all subjects, however only in PD subjects was the amygdala significantly activated. In addition, the amygdala was activated faster in both Selection and Timing tasks compared to the Control task in PD subjects. In PD subjects, the greatest connectivity was to/from the amygdala, while in healthy subjects the strongest connectivity was seen between the caudate and putamen. Our results suggest that PD subjects recruit the amygdala to maintain performance in motor timing and program selection even during emotionally-neutral tasks.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".