Application of the Chinese Version of the Montreal Cognitive Assessment-Basic for Assessing Mild Cognitive Impairment in Parkinson's Disease.

Mild cognitive impairment (MCI) is a common and pivotal non-motor symptom in Parkinson's disease (PD). It is necessary to use the appropriate tools to characterize the cognitive profiles and identify the subjects at risk of MCI in clinical practice. A cohort of 207 non-demented patients with PD and 52 age- and gender-matched cognitively normal controls (NCs) underwent the Chinese Version of Montreal Cognitive Assessment-Basic (MoCA-BC) evaluation. Patients with PD also received detailed motor and non-motor evaluation by serial scales. Cognitive profiles were investigated in patients with PD-MCI, relative to patients with normal cognition (PD-NC) and cognitively NCs. In addition, differences in demography, major motor and non-motor symptoms were compared between patients with PD-MCI and PD-NC. There were 70 patients with PD-MCI, occupying 33.8% of the total patients. Patients with PD-MCI had impairment in multiple cognitive domains, especially in executive function, memory and visuospatial function on MoCA-BC, relative to cognitively NCs or PD-NC. Compared with PD-NC patients, PD-MCI patients were older (p = 0.002) and had a later onset age (p = 0.007) and higher score of the Unified Parkinson's Disease Rating Scale (UPDRS) part III (p = 0.001). The positive rate of clinical possible rapid eye movement sleep behavior disorder (cpRBD) in the PD-MCI group was significantly increased relative to the PD-NC group (p = 0.003). Multivariate logistic analysis showed that older age (OR = 1.06; p = 0.012), higher score of UPDRS-III (OR = 1.03; p = 0.018) and the presence of cpRBD (OR = 2.10; p = 0.037) were independently associated factors of MCI in patients with PD. In conclusion, executive function, memory and visuospatial function are the main impaired cognitive profiles in PD-MCI via MoCA-BC. Aging, motor severity and RBD may be independently related factors of MCI in PD.

Walking stability in patients with benign paroxysmal positional vertigo: an objective assessment using wearable accelerometers and machine learning.

BACKGROUND: Benign paroxysmal positional vertigo (BPPV) is one of the most common peripheral vestibular disorders leading to balance difficulties and increased fall risks. This study aims to investigate the walking stability of BPPV patients in clinical settings and propose a machine-learning-based classification method for determining the severity of gait disturbances of BPPV. METHODS: Twenty-seven BPPV outpatients and twenty-seven healthy subjects completed level walking trials at self-preferred speed in clinical settings while wearing two accelerometers on the head and lower trunk, respectively. Temporo-spatial variables and six walking stability related variables [root mean square (RMS), harmonic ratio (HR), gait variability, step/stride regularity, and gait symmetry] derived from the acceleration signals were analyzed. A support vector machine model (SVM) based on the gait variables of BPPV patients were developed to differentiate patients from healthy controls and classify the handicapping effects of dizziness imposed by BPPV. RESULTS: The results showed that BPPV patients employed a conservative gait and significantly reduced walking stability compared to the healthy controls. Significant different mediolateral HR at the lower trunk and anteroposterior step regularity at the head were found in BPPV patients among mild, moderate, and severe DHI (dizziness handicap inventory) subgroups. SVM classification achieved promising accuracies with area under the curve (AUC) of 0.78, 0.83, 0.85 and 0.96 respectively for differentiating patients from healthy controls and classifying the three stages of DHI subgroups. Study results suggest that the proposed gait analysis that is based on the coupling of wearable accelerometers and machine learning provides an objective approach for assessing gait disturbances and handicapping effects of dizziness imposed by BPPV.

Assessment of gray and white matter structural alterations in migraineurs without aura.

BACKGROUND: Migraine constitute a disorder characterized by recurrent headaches, and have a high prevalence, a high socio-economic burden and severe effects on quality of life. Our previous fMRI study demonstrated that some brain regions are functional alterations in migraineurs. As the function of the human brain is related to its structure, we further investigated white and gray matter structural alterations in migraineurs. METHODS: In current study, we used surface-based morphometry, voxel-based morphometry and diffusion tensor imaging analyses to detect structural alterations of the white matter and gray matter in 32 migraineurs without aura compared with 32 age- and gender-matched healthy controls. RESULTS: We found that migraineurs without aura exhibited significantly increased gray matter volume in the bilateral cerebellar culmen, increased cortical thickness in the lateral occipital-temporal cortex, decreased cortical thickness in the right insula, increased gyrification index in left postcentral gyrus, superior parietal lobule and right lateral occipital cortex, and decreased gyrification index in the left rostral middle frontal gyrus compared with controls. No significant change in white matter microstructure was found in DTI analyses. CONCLUSION: The significantly altered gray matter brain regions were known to be associated with sensory discrimination of pain, multi-sensory integration and nociceptive information processing and were consistent with our previous fMRI study, and may be involved in the pathological mechanism of migraine without aura.