Center of pressure responses to unpredictable external perturbations indicate low accuracy in predicting fall risk in people with Parkinson's disease

Distinguishing features of Parkinson's disease fallers based on wireless insole plantar pressure monitoring

Postural instability is one of the most disabling motor signs of Parkinson's disease (PD) and often underlies an increased likelihood of falling and loss of independence. Current clinical assessments of PD-related postural instability are based on a retropulsion test, which introduces human error and only evaluates reactive balance. There is an unmet need for objective, multi-dimensional assessments of postural instability that directly reflect activities of daily living in which individuals may experience postural instability. In this study, we trained machine-learning models on insole plantar pressure data from 111 participants (44 with PD and 67 controls) as they performed simulated static and active postural tasks of activities that often occur during daily living. Models accurately classified PD from young controls (area under the curve (AUC) 0.99+/- 0.00), PD from age-matched controls (AUC 0.99+/- 0.01), and PD fallers from PD non-fallers (AUC 0.91+/- 0.08). Utilizing features from both static and active postural tasks significantly improved classification performances, and all tasks were useful for separating PD from controls; however, tasks with higher postural threats were preferred for separating PD fallers from PD non-fallers.

Electrocortical Dynamics of Usual Walking and the Planning to Step over Obstacles in Parkinson's Disease

The neural correlates of locomotion impairments observed in people with Parkinson's disease (PD) are not fully understood. We investigated whether people with PD present distinct brain electrocortical activity during usual walking and the approach phase of obstacle avoidance when compared to healthy individuals. Fifteen people with PD and fourteen older adults walked overground in two conditions: usual walking and obstacle crossing. Scalp electroencephalography (EEG) was recorded using a mobile 64-channel EEG system. Independent components were clustered using a k-means clustering algorithm. Outcome measures included absolute power in several frequency bands and alpha/beta ratio. During the usual walk, people with PD presented a greater alpha/beta ratio in the left sensorimotor cortex than healthy individuals. While approaching obstacles, both groups reduced alpha and beta power in the premotor and right sensorimotor cortices (balance demand) and increased gamma power in the primary visual cortex (visual demand). Only people with PD reduced alpha power and alpha/beta ratio in the left sensorimotor cortex when approaching obstacles. These findings suggest that PD affects the cortical control of usual walking, leading to a greater proportion of low-frequency (alpha) neuronal firing in the sensorimotor cortex. Moreover, the planning for obstacle avoidance changes the electrocortical dynamics associated with increased balance and visual demands. People with PD rely on increased sensorimotor integration to modulate locomotion.

Should we use both clinical and mobility measures to identify fallers in Parkinson's disease?

BACKGROUND

Although much is known about the multifactorial nature of falls in Parkinson's disease (PD), optimal classification of fallers remains unclear.

OBJECTIVE

To identify clinical (demographic, motor, cognitive and patient-reported) and objective mobility (balance and gait) measures that best discriminate fallers from non-fallers in PD.

METHODS

People with mild-to-moderate idiopathic PD were classified as fallers (at least one fall; n = 54) or non-fallers (n = 90) based on previous six months falls. Clinical characteristics included demographic, motor and cognitive status and patient-reported outcomes. Mobility (balance and gait) characteristics were derived from body-worn, inertial sensors while performing walking and standing tasks. To investigate the combinations of (up to four) measures that best discriminate fallers from non-fallers in each scenario (i.e., clinical-only, mobility-only and combined clinical + mobility models), we applied logistic regression employing a 'best subsets selection strategy' with a 5-fold cross validation, and calculated the area under the curve (AUC).

RESULTS

The highest AUCs for the clinical-only, mobility-only and clinical + mobility models were 0.89, 0.88, and 0.94, respectively. The most consistently selected measures in the top-10 ranked models were freezing of gait status (8x), the root mean square of anterior-posterior trunk acceleration while standing on a foam with eyes open (5x), gait double support duration (4x) and the postural instability and gait disorders score from the MDS UPDRS (4x).

CONCLUSIONS

Findings highlight the importance of considering multiple aspects of clinical as well as objective balance and gait characteristics for the classification of fallers and non-fallers in PD.

tDCS application for postural control in Parkinson's disease: Effects are associated with baseline characteristics

INTRODUCTION

Transcranial direct current stimulation (tDCS) improves postural response to perturbation in patients with Parkinson's disease (PwPD). However, the influence of baseline characteristics such as clinical/cognitive and postural performance on the response to tDCS remains unclear.

OBJECTIVE

To investigate whether baseline level of postural control (performance during sham condition) and clinical/cognitive characteristics are associated with tDCS-related changes in postural responses to external perturbations in PwPD.

METHODS

Twenty-four PwPD participated in this study. Clinical assessment included disease severity, disease duration, levodopa equivalent dose and global cognition. Anodal tDCS protocols targeting the primary motor cortex were applied in two separate sessions (at least 2 weeks apart): active (2 mA for 20 min) and sham stimulation. Seven trials with the backward translation of the support base (20 cm/s and 5 cm) were performed after tDCS. Postural outcomes included the recovery time to stable position and onset latency of the medial gastrocnemius (MG). Pearson and Spearman correlation tests were performed.

RESULTS

No significant correlations were observed between clinical/cognitive characteristics and tDCS-related changes in postural responses. Negative associations were observed between the baseline level of postural control and tDCS-related changes in postural responses for the recovery time (r = -0.657; p < 0.001) and the MG onset latency (rs = -0.539; p = 0.007). PwPD with worse baseline postural control demonstrated greater improvement after active stimulation.

CONCLUSIONS

Findings suggest that tDCS-related effects on postural response to perturbation are related to the baseline level of postural control, but not to clinical characteristics in PwPD. Those with worse baseline postural control responded better to tDCS.