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Objective: Parkinson's disease (PD) is a common neurological disorder with variable clinical manifestations and magnetic resonance imaging (MRI) findings. We propose a handcrafted image classification model that can accurately (i) classify different PD stages, (ii) detect comorbid dementia, and (iii) discriminate PD-related motor symptoms., Methods: Selected image datasets from three PD studies were used to develop the classification model. Our proposed novel automated system was developed in four phases: (i) texture features are extracted from the non-fixed size patches. In the feature extraction phase, a pyramid histogram-oriented gradient (PHOG) image descriptor is used. (ii) In the feature selection phase, four feature selectors: neighborhood component analysis (NCA), Chi2, minimum redundancy maximum relevancy (mRMR), and ReliefF are used to generate four feature vectors. (iii) Two classifiers: k-nearest neighbor (kNN) and support vector machine (SVM) are used in the classification step. A ten-fold cross-validation technique is used to validate the results. (iv) Eight predicted vectors are generated using four selected feature vectors and two classifiers. Finally, iterative majority voting (IMV) is used to attain general classification results. Therefore, this model is named nested patch-PHOG-multiple feature selectors and multiple classifiers-IMV (NP-PHOG-MFSMCIMV)., Results: Our presented NP-PHOG-MFSMCIMV model achieved 99.22, 98.70, and 99.53% accuracies for the collected PD stages, PD dementia, and PD symptoms classification datasets, respectively., Significance: The obtained accuracies (over 98% for all states) demonstrated the performance of developed NP-PHOG-MFSMCIMV model in automated PD state classification., Competing Interests: Declaration of Competing Interest The authors of this manuscript declare no conflicts of interest., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background: Coronavirus disease 2019 (COVID-19) pandemic and lockdown period may induce an impairment in quality of life (QoL), disruption in treatment (DIT), and posttraumatic stress disorder (PTSD) in chronic neurological diseases (CNDs). To reach this information, a multicenter, cross-sectional study (COVQoL-CND) was planned. Parkinson's disease (PD), headache (HA), multiple sclerosis (MS), epilepsy (EP), polyneuropathy (PNP), and cerebrovascular disease (CVD) were selected as the CND., Methods: The COVQoL-CND study includes demographic data, the World Health Organization Quality of Life short form (WHOQOL-BREF), and Impact of Event Scale-Revised (IES-R) forms., Results: The mean age of a total of 577 patients was 49 ± 17 (19-87 years), and the ratio of female/male was 352/225. The mean age of patients with PD, HA, MS, EP, PNP, and CVD were 65 ± 11, 39 ± 12, 38 ± 10, 47 ± 17, 61 ± 12, and 60 ± 15 years, respectively. The IES-R scores were found to be higher in the younger group, those with comorbid disease, contacted with CO-VID-19 patients, or diagnosed with COVID-19. In the group with a high IES-R score, the rate of DIT was found to be high. IES-R scores were negatively correlated with QoL. IES-R total scores were found highest in the CVD group and lowest in the PD group. The ratio of DIT was found highest in the PNP group and the lowest in the EP group. Contact with CO-VID-19 patients was high in the EP and HA group., Conclusions: The results of the COVQoL-CND study showed that lockdown causes posttraumatic stress and deterioration in the QoL in CND., (© 2021 S. Karger AG, Basel.)