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Cortical thickness as predictor of response to exercise in people with Parkinson's disease.

ABSTRACT: We previously showed that dual-task cost (DTC) on gait speed in people with Parkinson's disease (PD) improved after 6?weeks of the Agility Boot Camp with Cognitive Challenge (ABC-C) exercise program. Since deficits in dual-task gait speed are associated with freezing of gait and gray matter atrophy, here we performed preplanned secondary analyses to answer two questions: (a) Do people with PD who are freezers present similar improvements compared to nonfreezers in DTC on gait speed with ABC-C? (b) Can cortical thickness at baseline predict responsiveness to the ABC-C? The DTC from 39 freezers and 43 nonfreezers who completed 6?weeks of ABC-C were analyzed. A subset of 51 participants (21 freezers and 30 nonfreezers) with high quality imaging data were used to characterize relationships between baseline cortical thickness and delta (?) DTC on gait speed following ABC-C. Freezers showed larger ?DTC on gait speed than nonfreezers with ABC-C program (p?

SUBMITTER: Silva-Batista C

PROVIDER: S-EPMC7721225 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Cortical thickness as predictor of response to exercise in people with Parkinson's disease.

Silva-Batista Carla C Ragothaman Anjanibhargavi A Mancini Martina M Carlson-Kuhta Patricia P Harker Graham G Jung Se Hee SH Nutt John G JG Fair Damien A DA Horak Fay B FB Miranda-Domínguez Oscar O

Human brain mapping 20201009 1

We previously showed that dual-task cost (DTC) on gait speed in people with Parkinson's disease (PD) improved after 6 weeks of the Agility Boot Camp with Cognitive Challenge (ABC-C) exercise program. Since deficits in dual-task gait speed are associated with freezing of gait and gray matter atrophy, here we performed preplanned secondary analyses to answer two questions: (a) Do people with PD who are freezers present similar improvements compared to nonfreezers in DTC on gait speed with ABC-C? ( ...[more]

PMID: 33035370

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Project description:Parkinson's disease (PD) is a progressive neurological disorder characterized by motor and non-motor symptoms for which only symptomatic treatments exist. Exercise is a widely studied complementary treatment option. Aerobic exercise, defined as continuous movement of the body's large muscles in a rhythmic manner for a sustained period that increases caloric requirements and aims at maintaining or improving physical fitness, appears promising. We performed both a scoping review and a systematic review on the generic and disease-specific health benefits of aerobic exercise for people with PD. We support this by a meta-analysis on the effects on physical fitness (VO2max), motor symptoms (Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) motor section), and health-related quality of life (39-item Parkinson's disease Questionnaire (PDQ-39)). Aerobic exercise has generic health benefits for people with PD, including a reduced incidence of cardiovascular disease, a lower mortality, and an improved bone health. Additionally, there is level 1 evidence that aerobic exercise improves physical fitness (VO2max) and attenuates motor symptoms (MDS-UPDRS motor section) in the off-medication state, although the long-term effects (beyond 6 months) remain unclear. Dosing the exercise matters: improvements appear to be greater after training at higher intensities compared with moderate intensities. We found insufficient evidence for a beneficial effect of aerobic exercise on health-related quality of life (PDQ-39) and conflicting results regarding non-motor symptoms. Compliance to exercise regimes is challenging for PD patients but may be improved by adding exergaming elements to the training program. Aerobic exercise seems a safe intervention for people with PD, although care must be taken to avoid falls in at-risk individuals. Further studies are needed to establish the long term of aerobic exercise, including a focus on non-motor symptoms and health-related quality of life.

Project description:The clinical applications of the association of cortical thickness and white matter fiber with freezing of gait (FoG) are limited in patients with Parkinson's disease (PD). In this retrospective study, using white matter fiber from diffusion-weighted imaging and cortical thickness from structural-weighted imaging of magnetic resonance imaging, we investigated whether a machine learning-based model can help assess the risk of FoG at the individual level in patients with PD. Data from the Parkinson's Disease Progression Marker Initiative database were used as the discovery cohort, whereas those from the Fujian Medical University Union Hospital Parkinson's Disease database were used as the external validation cohort. Clinical variables, white matter fiber, and cortical thickness were selected by random forest regression. The selected features were used to train the support vector machine(SVM) learning models. The median area under the receiver operating characteristic curve (AUC) was calculated. Model performance was validated using the external validation cohort. In the discovery cohort, 25 patients with PD were defined as FoG converters (15 men, mean age 62.1 years), whereas 60 were defined as FoG nonconverters (38 men, mean age 58.5 years). In the external validation cohort, 18 patients with PD were defined as FoG converters (8 men, mean age 66.9 years), whereas 37 were defined as FoG nonconverters (21 men, mean age 65.1 years). In the discovery cohort, the model trained with clinical variables, cortical thickness, and white matter fiber exhibited better performance (AUC, 0.67-0.88). More importantly, SVM-radial kernel models trained using random over-sampling examples, incorporating white matter fiber, cortical thickness, and clinical variables exhibited better performance (AUC, 0.88). This model trained using the above mentioned features was successfully validated in an external validation cohort (AUC, 0.91). Furthermore, the following minimal feature sets that were used: fractional anisotropy value and mean diffusivity value for right thalamic radiation, age at baseline, and cortical thickness for left precentral gyrus and right dorsal posterior cingulate gyrus. Therefore, machine learning-based models using white matter fiber and cortical thickness can help predict the risk of FoG conversion at the individual level in patients with PD, with improved performance when combined with clinical variables.

Dataset Information

Cortical thickness as predictor of response to exercise in people with Parkinson's disease.

Publications

Cortical thickness as predictor of response to exercise in people with Parkinson's disease.

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