Harveysvane9836

To investigate the value of MRI-based radiomic model based on the radiomic features of different basal nuclei in differentiating idiopathic Parkinson's disease (IPD) from Parkinsonian variants of multiple system atrophy (MSA-P).

Radiomics was applied to the 3T susceptibility- weighted imaging (SWI) from 102 MSA-P patients and 83 IPD patients (allocated to a training and a testing cohort, 73 ratio). The substantia nigra (SN), caudate nucleus (CN), putamen (PUT), globus pallidus (GP), red nucleus (RN), and subthalamic nucleus (STN) were manually segmented, and 396 features were extracted. After feature selection, support vector machine (SVM) was generated, and its predictive performance was calculated in both the training and testing cohorts using the area under receiver operating characteristic curve (AUC).

Seven radiomic features were selected from the PUT, by which the SVM classifier achieved the best diagnostic performance with an AUC of 0.867 in the training cohort and an AUC of 0.862 in the testing cohort. MIRA-1 purchase Furthermore, the combined model, which incorporating part III of the Parkinson's Disease Rating Scale (UPDRSIII) scores into radiomic features of the PUT, further improved the diagnostic performance. However, radiomic features extracted from RN, SN, GP, CN, and STN had moderate to poor diagnostic performance, with AUC values that ranged from 0.610 to 0.788 in the training cohort and 0.583 to 0.766 in the testing cohort.

Radiomic features derived from the PUT had optimal value in differentiating IPD from MSA-P. A combined radiomic model, which contained radiomic features of the PUT and UPDRSIII scores, further improved performance and may represent a promising tool for distinguishing between IPD and MSA-P.

Radiomic features derived from the PUT had optimal value in differentiating IPD from MSA-P. A combined radiomic model, which contained radiomic features of the PUT and UPDRSIII scores, further improved performance and may represent a promising tool for distinguishing between IPD and MSA-P.Sleep complaints are the most prevalent syndromes in older adults, particularly in women. Moreover, they are frequently accompanied with a high level of depression and stress. Although several diffusion tensor imaging (DTI) studies reported associations between sleep quality and brain white matter (WM) microstructure, it is still unclear whether gender impacts the effect of sleep quality on structural alterations, and whether these alterations mediate the effects of sleep quality on emotional regulation. We included 389 older participants (176 females, age = 65.5 ± 5.5 years) from the 1000BRAINS project. Neuropsychological examinations covered the assessments of sleep quality, depressive symptomatology, current stress level, visual working memory, and selective attention ability. Based on the DTI dataset, the diffusion parameter maps, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), were calculated and normalized to a population-specific FA temp with current stress levels. Additionally, we found a significantly positive association between higher FA values in visual-related WM tracts and better outcomes in a visual pattern recognition test. Furthermore, a mediation analysis suggested that diffusion metrics within the corpus callosum partially mediated the associations between poor sleep quality/high stress and depressive symptomatology.The disease biology of frontotemporal lobe dementia (FTD) is complex and not fully understood, with limited translational value appreciated from animal models to date. Human cellular systems that can recapitulate phenotypic features of disease offer promise as translational tools to not only increase our understanding of disease processes but also increase the probability of success of translating novel treatment options to patients. However not all researchers may necessarily have access to well-characterized induced pluripotent stem cell (iPSC)-derived human neurons. As an example, we therefore comprehensively profiled phenotypic features over time in one commercially-available IPSC-derived human neuron cell line. This included systems-level assessments of neurite outgrowth dynamics, neuronal network function, and genome-wide gene expression. By investigating progranulin biology as an example we then demonstrated the utility of these cells as a tool to investigate human disease biology. For example, by using the siRNA-mediated knockdown of the progranulin (GRN) gene, we demonstrated the establishment of an isogenic human cellular model to facilitate translational FTD research. We reproduced findings from rodent neurons by demonstrating that recombinant progranulin (rPGRN) mediated neuroprotection. Contrary to previous rodent data, in our human cellular models, growth factor treatment showed no consistent sensitivity to modulate neurite outgrowth dynamics. Our study further provides the first evidence that rRPGRN modulated neuronal firing and synchrony in human neurons. Taken together, our datasets are a valuable systems-level resource demonstrating the utility of the tested commercially-available human iPSC neurons for investigating basic human neurobiology, translational neuroscience, and drug discovery applications in neurodegenerative and other CNS diseases.Autonomous trajectory and torque profile synthesis through modulation and generalization require a database of motion with accompanying dynamics, which is typically difficult and time-consuming to obtain. Inspired by adaptive control strategies, this paper presents a novel method for learning and synthesizing Periodic Compliant Movement Primitives (P-CMPs). P-CMPs combine periodic trajectories encoded as Periodic Dynamic Movement Primitives (P-DMPs) with accompanying task-specific Periodic Torque Primitives (P-TPs). The state-of-the-art approach requires to learn TPs for each variation of the task, e.g., modulation of frequency. Comparatively, in this paper, we propose a novel P-TPs framework, which is both frequency and phase-dependent. Thereby, the executed P-CMPs can be easily modulated, and consequently, the learning rate can be improved. Moreover, both the kinematic and the dynamic profiles are parameterized, thus enabling the representation of skills using corresponding parameters. The proposed framework was evaluated on two robot systems, i.