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re values of the CN and PU in the PD patients than healthy controls (P less then 0.05). Conclusion Neostriatum radiomics signatures achieved good diagnostic performance for PD and potentially could serve as a basis for the clinical diagnosis of PD. Copyright © 2020 Liu, Wang, Zheng, Zhang and Zhang.Motivation Brain morphometry from magnetic resonance imaging (MRI) is a promising neuroimaging biomarker for the non-invasive diagnosis and monitoring of neurodegenerative and neurological disorders. [https://www.selleckchem.com/products/otx015.html https://www.selleckchem.com/products/otx015.html] Current tools for brain morphometry often come with a high computational burden, making them hard to use in clinical routine, where time is often an issue. We propose a deep learning-based approach to predict the volumes of anatomically delineated subcortical regions of interest (ROI), and mean thicknesses and curvatures of cortical parcellations directly from T1-weighted MRI. Advantages are the timely availability of results while maintaining a clinically relevant accuracy. Materials and Methods An anonymized dataset of 574 subjects (443 healthy controls and 131 patients with epilepsy) was used for the supervised training of a convolutional neural network (CNN). A silver-standard ground truth was generated with FreeSurfer 6.0. Results The CNN predicts a total of 165 morphometric measures directly from raw MR images. Analysis of the results using intraclass correlation coefficients showed, in general, good correlation with FreeSurfer generated ground truth data, with some of the regions nearly reaching human inter-rater performance (ICC &gt; 0.75). Cortical thicknesses predicted by the CNN showed cross-sectional annual age-related gray matter atrophy rates both globally (thickness change of -0.004 mm/year) and regionally in agreement with the literature. A statistical test to dichotomize patients with epilepsy from healthy controls revealed similar effect sizes for structures affecting all subtypes as reported in a large-scale epilepsy study. Conclusions We demonstrate the general feasibility of using deep learning to estimate human brain morphometry directly from T1-weighted MRI within seconds. A comparison of the results to other publications shows accuracies of comparable magnitudes for the subcortical volumes and cortical thicknesses. Copyright © 2020 Rebsamen, Suter, Wiest, Reyes and Rummel.Background Previous studies have indicated that non-motor symptoms are primary problems in focal dystonia, but limited data are available about non-motor problems and their correlation with motor severity in generalized dystonia (GD). Methods In the present study, we performed a case-control study and enrolled isolated inherited or idiopathic GD patients and age- and sex-matched healthy controls (HC). Clinical characteristics, motor symptoms, non-motor problems, including psychiatric co-morbidity, sleep problems, fatigue, and quality of life (QoL) were assessed in both groups using various rating scales and assessments. Results Thirty-three patients with GD and 33 controls were enrolled. Significant higher scores on depression and anxiety (p less then 0.001) were shown in GD compared with HC, whereas the frequency of obsessive-compulsive disorders approached that of HC (p = 0.238). Patients with GD also had significantly higher Pittsburg Sleep Quality Index (PSQI) and fatigue scores than HC, whereas no difference was observed in excessive daytime somnolence. In GD, QoL was more impaired, with statistically lower scores in both physical and mental components. Psychiatric rating scales did not correlate to motor severity or disease duration but might influence quality of sleep. Subgroup analysis suggests non-motor manifestations differ with different etiologies in GD. Conclusion This study suggests that non-motor symptoms in GD, such as psychiatric problems, are likely to be primary determinants not correlated to motor severity, which may also affect quality of sleep and fatigue. Copyright © 2020 Li, Wang, Yang, Qiao, Zhang and Wan.Background and Purpose This study tests the hypothesis that middle school and high school students can improve their stroke knowledge using Stroke 1-2-0, a stroke educational tool, and pass this knowledge on to their family members. Methods A total of 625 students and 198 parents/grandparents participated in learning about stroke using Stroke 1-2-0. After a group training session for the students by a neurologist at school, the students took educational material to home and educated their parents/grandparents. A questionnaire was given to students, parents/grandparents before, immediately after, and 1 year after the educational event. Results All participants agreed that Stroke 1-2-0 was a much easier tool to remember than FAST. Almost all the students (96.4%) remembered the meaning of Stroke 1-2-0 as compared to 7.3% from the base line (p less then 0.001). The rate of complete Stroke 1-2-0 mastery from 96.3% fell to 84.4% at 3 months and 63.8% at 1 year after training (p less then 0.001). Following education from children, the proportion of parents/grandparents who mastered Stroke 1-2-0 was significantly higher than baseline (79.9 vs. 24.8%). Conclusion Middle school and high school students can effectively use Stroke 1-2-0 to improve their stroke knowledge and pass this knowledge to their family members. Sustained educational efforts and repeated educational events are needed though. Copyright © 2020 Li, Liu, Vrudhula, Liu and Zhao.Two studies examined whether people could identify rich false memories. Each participant in both studies was presented with two videos, one of a person recalling a true emotional memory, and one of the same person recalling a false memory. These videos were filmed during a study which involved implanting rich false memories (Shaw and Porter, 2015). The false memories in the videos either involved committing a crime (assault, or assault with a weapon) or other highly emotional events (animal attack, or losing a large sum of money) during adolescence. In study 1, participants (n = 124) were no better than chance at accurately classifying false memories (61.29% accurate), or false memories of committing crime (53.33% accurate). In study 2, participants (n = 82) were randomly assigned to one of three conditions, where they only had access to the (i) audio account of the memory with no video, (ii) video account with no audio, or (iii) the full audio-visual accounts. False memories were classified correctly by 32.14% of the audio-only group, 45.