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

Psychometric properties of the Caregiver's inventory neuropsychological diagnosis dementia (CINDD) in mild cognitive impairment and dementia

Objectives: The Caregiver's Inventory Neuropsychological Diagnosis Dementia (CINDD) is an easy tool designed to quantify cognitive, behavioural and functional deficits of patients with cognitive impairment. Aim of the present study was to analyse the psychometric properties of the CINDD in Mild Cognitive Impairment (MCI) and Dementia (D). Design, setting and participants: The CINDD, composed by 9 sub-domains, was administered to fifty-six caregivers of patients with different types of dementia (D) and 44 caregivers of patients with MCI. All patients underwent an extensive neuropsychological assessment, the Neuropsychiatric Inventory (NPI) and functional autonomy scales. The reliability, convergent construct validity and possible cut-off of CINND were measured by Cronbach's alpha (α), Pearson's correlation and ROC analysis, respectively. Results: The D and MCI patients differed only for age (p=0.006). The internal consistency of CINDD was high (α= 0.969). The α-value for each CINDD domain was considered acceptable, except the mood domain (α=0.209). The CINDD total score correlated with cognitive screening tests; each domain of the CINDD correlated with the corresponding score from either tests or NPI (p<0.05), except for visuo-spatial perception skills and apathy. A screening cut-off equal to 59, can be used discriminate D from MCI (Sensitivity=0.70, Specificity=0.57). Conclusion: The CINDD is a feasible, accurate and reliable tool for the assessment of cognitive and behavioural difficulties in patients with different degree of cognitive impairment. It may be used to quantify and monitor caregiver-reported ecological data in both clinical and research settings

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