Dual-Task Performance and Brain Morphologic Characteristics in Parkinson’s Disease
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».