Background: The etiology of freezing of gait in Parkinson's disease (PD) is yet to be clarified. Non-motor risk factors including cognitive impairment, sleep disturbance and mood disorders have been shown in freezing of gait. Research question: We aimed to determine the predictive value of non-motor features in freezing of gait development. Methods: Data were obtained from the Parkinson's Progression Markers Initiative. Fifty PD patients with self-reported freezing of gait, and 50 PD patients without freezing of gait at the fourth year visit were included. Groups were matched for Movement Disorders Society-Unified Parkinson's Disease Rating Scale Part III scores. Several cognitive and non-cognitive tests were used for non-motor features at baseline and over time. Executive function, visuospatial function, processing speed, learning and memory tests were used for cognition. Non-cognitive tests included sleepiness, REM sleep behavior disorder, depression and anxiety scales. Results: Patients with freezing of gait had higher scores on sleepiness, REM sleep behavior disorder, depression and anxiety scales. However, predictor model analysis revealed that baseline processing speed, learning and sleepiness scores were predictive of self-reported freezing of gait development over time.

IntroductionPrevious neuroimaging studies of Parkinson's disease (PD) patients have shown changes in whole-brain functional connectivity networks. Whether connectivity changes can be detected in the early stages (first 3 years) of PD by resting-state functional magnetic resonance imaging (fMRI) remains elusive. Research infrastructure including MRI and analytic capabilities is required to investigate this issue. The National Institutes of Health/National Institute of General Medical Sciences Center for Biomedical Research Excellence awards support infrastructure to advance research goals.MethodsStatic and dynamic functional connectivity analyses were conducted on early stage never-medicated PD subjects (N = 18) and matched healthy controls (N = 18) from the Parkinson's Progression Markers Initiative.ResultsAltered static and altered dynamic functional connectivity patterns were found in early PD resting-state fMRI data. Most static networks (with the exception of the default mode network) had a reduction in frequency and energy in specific low-frequency bands. Changes in dynamic networks in PD were associated with a decreased switching rate of brain states.DiscussionThis study demonstrates that in early PD, resting-state fMRI networks show spatial and temporal differences of fMRI signal characteristics. However, the default mode network was not associated with any measurable changes. Furthermore, by incorporating an optimum window size in a dynamic functional connectivity analysis, we found altered whole-brain temporal features in early PD, showing that PD subjects spend significantly more time than healthy controls in a specific brain state. These findings may help in improving diagnosis of early never-medicated PD patients. These key observations emerged in a Center for Biomedical Research Excellence–supported research environment.