Dataset Information

Brain activation underlying turning in Parkinson's disease patients with and without freezing of gait: a virtual reality fMRI study.

ABSTRACT:

Background

Freezing of gait is a debilitating symptom affecting many patients with Parkinson's disease (PD), causing severe immobility and decreased quality of life. Turning is known to be the most common trigger for freezing and also causes the highest rates of falls. However, the pathophysiological basis for these effects is not well understood.

Methods

This study used a virtual reality paradigm in combination with functional magnetic resonance imaging to explore the neural correlates underlying turning in 17 PD patients with freezing of gait (FOG) and 10 PD patients without FOG while off their dopaminergic medication. Participants used foot pedals to navigate a virtual environment, which allowed for blood oxygen level-dependent (BOLD) responses and footstep latencies to be compared between periods of straight "walking" and periods of turning through 90°. BOLD data were then analyzed using a mixed effects analysis.

Results

Within group similarities revealed that overall, PD patients with freezing relied heavily on cortical control to enable effective stepping with increased visual cortex activation during turning. Between groups differences showed that when turning, patients with freezing preferentially activated inferior frontal regions that have been implicated in the recruitment of a putative stopping network. In addition, freezers failed to activate premotor and superior parietal cortices. Finally, increased task-based functional connectivity was found in subcortical regions associated with gait and stopping within the freezers group during turning.

Conclusions

These findings suggest that an increased propensity towards stopping in combination with reduced sensorimotor integration may underlie the neurobiology of freezing of gait during turning.

SUBMITTER: Gilat M

PROVIDER: S-EPMC5516618 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Brain activation underlying turning in Parkinson's disease patients with and without freezing of gait: a virtual reality fMRI study.

Gilat Moran M Shine James M JM Walton Courtney C CC O'Callaghan Claire C Hall Julie M JM Lewis Simon J G SJG

NPJ Parkinson's disease 20151022

<h4>Background</h4>Freezing of gait is a debilitating symptom affecting many patients with Parkinson's disease (PD), causing severe immobility and decreased quality of life. Turning is known to be the most common trigger for freezing and also causes the highest rates of falls. However, the pathophysiological basis for these effects is not well understood.<h4>Methods</h4>This study used a virtual reality paradigm in combination with functional magnetic resonance imaging to explore the neural corr ...[more]

PMID: 28725687

Similar Datasets

Project description:BackgroundFreezing of gait (FOG) is a sensitive problem, which is caused by motor control deficits and requires greater attention during postural transitions such as turning in people with Parkinson's disease (PD). However, the turning characteristics have not yet been extensively investigated to distinguish between people with PD with and without FOG (freezers and non-freezers) based on full-body kinematic analysis during the turning task. The objectives of this study were to identify the machine learning model that best classifies people with PD and freezers and reveal the associations between clinical characteristics and turning features based on feature selection through stepwise regression.MethodsThe study recruited 77 people with PD (31 freezers and 46 non-freezers) and 34 age-matched older adults. The 360° turning task was performed at the preferred speed for the inner step of the more affected limb. All experiments on the people with PD were performed in the "Off" state of medication. The full-body kinematic features during the turning task were extracted using the three-dimensional motion capture system. These features were selected via stepwise regression.ResultsIn feature selection through stepwise regression, five and six features were identified to distinguish between people with PD and controls and between freezers and non-freezers (PD and FOG classification problem), respectively. The machine learning model accuracies revealed that the random forest (RF) model had 98.1% accuracy when using all turning features and 98.0% accuracy when using the five features selected for PD classification. In addition, RF and logistic regression showed accuracies of 79.4% when using all turning features and 72.9% when using the six selected features for FOG classification.ConclusionWe suggest that our study leads to understanding of the turning characteristics of people with PD and freezers during the 360° turning task for the inner step of the more affected limb and may help improve the objective classification and clinical assessment by disease progression using turning features.

Dataset Information

Brain activation underlying turning in Parkinson's disease patients with and without freezing of gait: a virtual reality fMRI study.

Background

Methods

Results

Conclusions

Publications

Brain activation underlying turning in Parkinson's disease patients with and without freezing of gait: a virtual reality fMRI study.

Similar Datasets

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