Identification of a Gait Pattern for Detecting Mild Cognitive Impairment in Parkinson's Disease

: The aim of this study was to determine a gait pattern, i.e., a subset of spatial and temporal parameters, through a supervised machine learning (ML) approach, which could be used to reliably distinguish Parkinson's Disease (PD) patients with and without mild cognitive impairment (MCI). Thus, 80 PD patients underwent gait analysis and spatial-temporal parameters were acquired in three different conditions (normal gait, motor dual task and cognitive dual task). Statistical analysis was performed to investigate the data and, then, five ML algorithms and the wrapper method were implemented: Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM) and K-Nearest Neighbour (KNN). First, the algorithms for classifying PD patients with MCI were trained and validated on an internal dataset (sixty patients) and, then, the performance was tested by using an external dataset (twenty patients). Specificity, sensitivity, precision, accuracy and area under the receiver operating characteristic curve were calculated. SVM and RF showed the best performance and detected MCI with an accuracy of over 80.0%. The key features emerging from this study are stance phase, mean velocity, step length and cycle length; moreover, the major number of features selected by the wrapper belonged to the cognitive dual task, thus, supporting the close relationship between gait dysfunction and MCI in PD

Gait Analysis in Progressive Supranuclear Palsy Phenotypes

The objective of the present study was to describe gait parameters of progressive supranuclear palsy (PSP) phenotypes at early stage verifying the ability of gait analysis in discriminating between disease phenotypes and between the other variant syndromes of PSP (vPSP) and Parkinson's disease (PD). Nineteen PSP (10 PSP-Richardson's syndrome, five PSP-parkinsonism, and four PSP-progressive gait freezing) and nine PD patients performed gait analysis in single and dual tasks. Although phenotypes showed similar demographic and clinical variables, Richardson's syndrome presented worse cognitive functions. Gait analysis demonstrated worse parameters in Richardson's syndrome compared with the vPSP. The overall diagnostic accuracy of the statistical model during dual task was almost 90%. The correlation analysis showed a significant relationship between gait parameters and visuo-spatial, praxic, and attention abilities in PSP-Richardson's syndrome only. vPSP presented worse gait parameters than PD. Richardson's syndrome presents greater gait dynamic instability since the earliest stages than other phenotypes. Computerized gait analysis can differentiate between PSP phenotypes and between vPSP and PD

Cognitive contributions to gait and falls: Evidence and implications

Dementia and gait impairments often coexist in older adults and patients with neurodegenerative disease. Both conditions represent independent risk factors for falls. The relationship between cognitive function and gait has recently received increasing attention. Gait is no longer considered merely automated motor activity but rather an activity that requires executive function and attention as well as judgment of external and internal cues. In this review, we intend to: (1) summarize and synthesize the experimental, neuropsychological, and neuroimaging evidence that supports the role played by cognition in the control of gait; and (2) briefly discuss the implications deriving from the interplay between cognition and gait. In recent years, the dual task paradigm has been widely used as an experimental method to explore the interplay between gait and cognition. Several neuropsychological investigations have also demonstrated that walking relies on the use of several cognitive domains, including executive-attentional function, visuospatial abilities, and even memory resources. A number of morphological and functional neuroimaging studies have offered additional evidence supporting the relationship between gait and cognitive resources. Based on the findings from 3 lines of studies, it appears that a growing body of evidence indicates a pivotal role of cognition in gait control and fall prevention. The interplay between higher-order neural function and gait has a number of clinical implications, ranging from integrated assessment tools to possible innovative lines of interventions, including cognitive therapy for falls prevention on one hand and walking program for reducing dementia risk on the other