Dual-Task Performance and Brain Morphologic Characteristics in Parkinson’s Disease
Bibliographic record
Abstract
INTRODUCTION: Parkinson's disease (PD) reduces an individual's capacity for automaticity which limits their ability to perform two tasks simultaneously, negatively impacting daily function. Understanding the neural correlates of dual tasks (DTs) may pave the way for targeted therapies. To better understand automaticity in PD, we aimed to explore whether individuals with differing DT performances possessed differences in brain morphologic characteristics. METHODS: Data were obtained from 34 individuals with PD and 47 healthy older adults including (1) demographics (age, sex), (2) disease severity (Movement Disorder Society - Unified Parkinson's Disease Rating Scale [MDS-UPDRS], Hoehn and Yahr, levodopa equivalent daily dose [LEDD]), (3) cognition (Montreal Cognitive Assessment), (4) LEDD, (5) single-task and DT performance during a DT-timed-up-and-go test utilizing a serial subtraction task, and (6) cortical thicknesses and subcortical volumes obtained from volumetric MRI. Participants were categorized as low or high DT performers if their combined DT effect was greater than the previously determined mean value for healthy older adults (μ = -74.2). Nonparametric testing using Quade's ANCOVA was conducted to compare cortical thicknesses and brain volumes between the highDT and lowDT groups while controlling for covariates: age, sex, MDS-UPDRS part III, LEDD, and intracranial volume. Secondarily, similar comparisons were made between the healthy older adult group and the highDT and lowDT groups. Lastly, a hierarchical linear regression model was conducted regressing combined DT effect on covariates (block one) and cortical thicknesses (block 2) in stepwise fashion. RESULTS: The highDT group had thicker cortices than the lowDT group in the right primary somatosensory cortex (p = 0.001), bilateral primary motor cortices (p ≤ 0.001, left; p = 0.002, right), bilateral supplementary motor areas (p = 0.001, left; p < 0.001, right), and mean of the bilateral hemispheres (p = 0.001, left; p < 0.001, right). Of note, left primary cortex thickness (p = 0.002), left prefrontal cortex thickness (p < 0.001), and right supplementary motor area thickness (p = 0.003) differed when adding a healthy comparison group. Additionally, the regression analysis found that the left paracentral lobule thickness explained 20.8% of the variability in combined DT effect (p = 0.011) beyond the influence of covariates. CONCLUSIONS: These results suggest regions underlying DT performance, specifically, a convergence of neural control relying on sensorimotor integration, motor planning, and motor activation to achieve higher levels of DT performance for individuals with PD. INTRODUCTION: Parkinson's disease (PD) reduces an individual's capacity for automaticity which limits their ability to perform two tasks simultaneously, negatively impacting daily function. Understanding the neural correlates of dual tasks (DTs) may pave the way for targeted therapies. To better understand automaticity in PD, we aimed to explore whether individuals with differing DT performances possessed differences in brain morphologic characteristics. METHODS: Data were obtained from 34 individuals with PD and 47 healthy older adults including (1) demographics (age, sex), (2) disease severity (Movement Disorder Society - Unified Parkinson's Disease Rating Scale [MDS-UPDRS], Hoehn and Yahr, levodopa equivalent daily dose [LEDD]), (3) cognition (Montreal Cognitive Assessment), (4) LEDD, (5) single-task and DT performance during a DT-timed-up-and-go test utilizing a serial subtraction task, and (6) cortical thicknesses and subcortical volumes obtained from volumetric MRI. Participants were categorized as low or high DT performers if their combined DT effect was greater than the previously determined mean value for healthy older adults (μ = -74.2). Nonparametric testing using Quade's ANCOVA was conducted to compare cortical thicknesses and brain volumes between the highDT and lowDT groups while controlling for covariates: age, sex, MDS-UPDRS part III, LEDD, and intracranial volume. Secondarily, similar comparisons were made between the healthy older adult group and the highDT and lowDT groups. Lastly, a hierarchical linear regression model was conducted regressing combined DT effect on covariates (block one) and cortical thicknesses (block 2) in stepwise fashion. RESULTS: The highDT group had thicker cortices than the lowDT group in the right primary somatosensory cortex (p = 0.001), bilateral primary motor cortices (p ≤ 0.001, left; p = 0.002, right), bilateral supplementary motor areas (p = 0.001, left; p < 0.001, right), and mean of the bilateral hemispheres (p = 0.001, left; p < 0.001, right). Of note, left primary cortex thickness (p = 0.002), left prefrontal cortex thickness (p < 0.001), and right supplementary motor area thickness (p = 0.003) differed when adding a healthy comparison group. Additionally, the regression analysis found that the left paracentral lobule thickness explained 20.8% of the variability in combined DT effect (p = 0.011) beyond the influence of covariates. CONCLUSIONS: These results suggest regions underlying DT performance, specifically, a convergence of neural control relying on sensorimotor integration, motor planning, and motor activation to achieve higher levels of DT performance for individuals with 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".