Brain connectivity during simulated balance in older adults with and without Parkinson's disease
Bibliographic record
Abstract
Individuals with Parkinson's disease often experience postural instability, a debilitating and largely treatment-resistant symptom. A better understanding of the neural substrates contributing to postural instability could lead to more effective treatments. Constraints of current functional neuroimaging techniques, such as the horizontal orientation of most MRI scanners (forcing participants to lie supine), complicates investigating cortical and subcortical activation patterns and connectivity networks involved in healthy and parkinsonian balance control. In this cross-sectional study, we utilized a newly-validated MRI-compatible balance simulator (based on an inverted pendulum) that enabled participants to perform balance-relevant tasks while supine in the scanner. We utilized functional MRI to explore effective connectivity underlying static and dynamic balance control in healthy older adults (n = 17) and individuals with Parkinson's disease while on medication (n = 17). Participants performed four tasks within the scanner with eyes closed: resting, proprioceptive tracking of passive ankle movement, static balancing of the simulator, and dynamic responses to random perturbations of the simulator. All analyses were done in the participant's native space without spatial transformation to a common template. Effective connectivity between 57 regions of interest was computed using a Bayesian Network learning approach with false discovery rate set to 5%. The first 12 principal components of the connection weights, binomial logistic regression, and cross-validation were used to create 4 separate models: contrasting static balancing vs {rest, proprioception} and dynamic balancing vs {rest, proprioception} for both controls and individuals with Parkinson's disease. In order to directly compare relevant connections between controls and individuals with Parkinson's disease, we used connections relevant for predicting a task in either controls or individuals with Parkinson's disease in logistic regression with Least Absolute Shrinkage and Selection Operator regularization. During dynamic balancing, we observed decreased connectivity between different motor areas and increased connectivity from the brainstem to several cortical and subcortical areas in controls, while individuals with Parkinson's disease showed increased connectivity associated with motor and parietal areas, and decreased connectivity from brainstem to other subcortical areas. No significant models were found for static balancing in either group. Our results support the notion that dynamic balance control in individuals with Parkinson's disease relies more on cortical motor areas compared to healthy older adults, who show a preference of subcortical control during dynamic balancing.
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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 | 0.002 |
| 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".