Postural control,falls and Parkinson’s disease: Are fallers more asymmetric than non-fallers?

Postural control asymmetry is an important aspect of Parkinson’s disease (PD) that may be associated with falls. The aim of this study was to compare the postural control asymmetry during postural tasks between fallers and non-fallers in people with PD and neurological healthy age-matched controls (CG). Individuals with idiopathic PD (n = 24) and CG (n = 24) were sub-divided into groups of fallers and non-fallers based on their fall history over the past year. Participants performed blocks of three 30-s trials of quiet standing with feet in a side-by-side and semi-tandem stance position. The center of pressure parameters for each limb were measured and used to calculate the symmetry index. Fallers compared to non-fallers had decreased asymmetry of vertical force in the side-by-side condition. During the tandem-front leg condition, PD non-fallers increased asymmetry of the medial-lateral velocity of sway compared to CG non-fallers. In addition, for the tandem–back leg condition, PD non-fallers increased asymmetry of total displacement and medial-lateral root mean square and mean velocity of sway compared to PD fallers. The results of the study did not support the hypothesis that PD fallers are more asymmetric than PD non-fallers. On the contrary, our results indicated that PD non-fallers had higher postural control asymmetry, especially during the more challenging (semi-tandem standing) postural task.     

Rhythmic priming across effector systems: A randomized controlled trial with Parkinson’s disease patients

This study investigated the immediate effects of auditory-motor entrainment across effector systems by examining whether Rhythmic Auditory Stimulation training of arm or finger movements would modulate gait speed. Forty-one participants with idiopathic Parkinson’s Disease were randomly assigned to 3 groups. Participants in the finger-tapping group tapped in synchrony with a metronome set to 20% faster pace than the pre-training walking cadence, whereas participants in the other group were asked to swing both arms in an alternating motion in synchrony with the metronome. Participants in the control condition did not receive training. To assess gait parameters pre- and post-training, participants walked on a 14-meter flat walkway at his/her preferred walking cadence with no auditory cueing. Results indicated that there was a significant increase in gait velocity after the finger tapping training (p < .005), whereas no differences were observed in the arm swing (p = .802) and in the control conditions (p = .525). Similarly, there were significant changes in gait cadence post-training in the finger tapping group (p < .005), but not after arm swing training (p = .879) or control (p = .759). There were no significant changes in stride length post-training in none of the groups. These findings suggest that auditory-motor entrainment in one effector system may prime a second effector system. Interestingly, however, the priming effect on gait was only observed in the finger tapping condition and not with synchronized arm swing movements. These findings have significant implications for motor rehabilitation and open new avenues for further investigation of the mechanisms underlying cross-effector coupling.     

The impact of cognitive interventions on cognitive symptoms in idiopathic Parkinson’s disease: a systematic review

This systematic review addressed efficacy of cognitive stimulation (CS), cognitive training (CT), and cognitive rehabilitation (CR) to improve cognitive functions in Parkinson’s disease (PD) with (PD-MCI) and without mild cognitive impairment (PD-H). Five databases were searched. Twelve CT, four CS, and a combination of CT with CR were found. PD-H benefited from CT or CS compared to active or passive controls in 42.1% of cognitive tests, and in 33.3% of psychological and functional measures. PD-MCI alone, compared with controls, only improved in 6.9% of cognitive measures after CT. PD-H and PD-MCI, alone or together, somehow improved information processing speed, attention, working memory, executive functions, and visual episodic memory. PD-MCI improved better than PD-H in global cognition and planning abilities. The outcomes suggest some efficacy of cognitive interventions in PD. However, small samples, lack of information regarding standardization of interventions, and poor methodological quality limit results validity and prevent firm conclusions.     

Examining multiple feature evaluation and classification methods for improving the diagnosis of Parkinson’s disease

An accurate diagnosis of Parkinson’s disease by specialists involves many neurological, psychological and physical examinations. The specialists investigate a number of symptoms and signs when examining the nervous system conditions of a person. The diagnosis involves reviewing the medical history and genetic factor of the person. The recent diagnosis methodology to Parkinson’s disease relies on voice disorders analysis. This methodology entails extracting feature sets of a recorded person’s voice then utilizing a machine learning technique to identify the healthy and Parkinson’s cases from the voice. This paper attempts to improve the diagnoses of Parkinson’s disease by testing multiple feature evaluation and classification machine learning methods based on the voice disorders analysis. The aim of this paper is to find the optimal solution to the problem by (i) proposing a new Multiple Feature Evaluation Approach (MFEA) of a multi-agent system (ii) implementing five independent classification schemas which are Decision Tree, Naïve Bayes, Neural Network, Random Forests, and Support Vector Machine on the Parkinson’s diagnosis before and after applying the MFEA, and (iii) evaluating the diagnosis accuracy of the results. The methodology of the tests encompasses 10-fold cross-validation to evaluate the learning of methods and track variation in their performance. The test results show that the MFEA of the multi-agent system finds the best set of features and improves the performance of the classifiers. The average rate of improvement in the diagnostic accuracy of the classifiers are Decision Tree 10.51%, Naïve Bayes 15.22%, Neural Network 9.19%, Random Forests 12.75%, and Support Vector Machine 9.13%. These results show that the MFEA makes a significant improvement to the classifiers’ diagnosis results.