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Mild cognitive impairment (MCI) is characterized as a transitional phase between cognitive decline associated with normal aging and Alzheimer's disease (AD). Resting-state functional magnetic resonance imaging (fMRI) measuring blood oxygenation level-dependent (BOLD) signals provides complementary information considered essential for understanding disease progression. Previous studies suggested that multi-scale entropy (MSE) analysis quantifying the complexity of BOLD signals is a novel and promising method for investigating neurodegeneration associated with cognitive decline in different stages of MCI. Therefore, the current study used MSE to explore the changes in the complexity of resting-state brain BOLD signals in patients with early MCI (EMCI) and late MCI (LMCI). We recruited 345 participants' data from the Alzheimer's Disease Neuroimaging Initiative database, including 176 normal control (NC) subjects, 87 patients with EMCI and 82 patients with LMCI. We observed a significant reduction of brain signal complexity toward regularity in the left fusiform gyrus region in the EMCI group and in the rostral anterior cingulate cortex in the LMCI group. Our results extend prior work by revealing that significant reductions of brain BOLD signal complexity can be detected in different stages of MCI independent of age, sex and regional atrophy. Notably, the reduction of BOLD signal complexity in the rostral anterior cingulate cortex was significantly associated with greater risk of progression to AD. The present study thus identified MSE as a potential imaging biomarker for the early diagnosis of pre-clinical Alzheimer's disease and provides further insights into the neuropathology of cognitive decline in prodromal AD. Copyright © 2020 Zheng, Onoda, Nagai and Yamaguchi.Accurate segmentation of different sub-regions of gliomas such as peritumoral edema, necrotic core, enhancing, and non-enhancing tumor core from multimodal MRI scans has important clinical relevance in diagnosis, prognosis and treatment of brain tumors. However, due to the highly heterogeneous appearance and shape of these tumors, segmentation of the sub-regions is challenging. Recent developments using deep learning models has proved its effectiveness in various semantic and medical image segmentation tasks, many of which are based on the U-Net network structure with symmetric encoding and decoding paths for end-to-end segmentation due to its high efficiency and good performance. In brain tumor segmentation, the 3D nature of multimodal MRI poses challenges such as memory and computation limitations and class imbalance when directly adopting the U-Net structure. In this study we aim to develop a deep learning model using a 3D U-Net with adaptations in the training and testing strategies, network structures, and model parameters for brain tumor segmentation. Furthermore, instead of picking one best model, an ensemble of multiple models trained with different hyper-parameters are used to reduce random errors from each model and yield improved performance. Preliminary results demonstrate the effectiveness of this method and achieved the 9th place in the very competitive 2018 Multimodal Brain Tumor Segmentation (BraTS) challenge. In addition, to emphasize the clinical value of the developed segmentation method, a linear model based on the radiomics features extracted from segmentation and other clinical features are developed to predict patient overall survival. Evaluation of these innovations shows high prediction accuracy in both low-grade glioma and glioblastoma patients, which achieved the 1st place in the 2018 BraTS challenge. Copyright © 2020 Feng, Tustison, Patel and Meyer.[This corrects the article DOI 10.3389/fnhum.2019.00463.]. Copyright © 2020 Gooderham, Ho and Handy.Aging is a condition that may be characterized by a decline in physical, sensory, and mental capacities, while increased morbidity and multimorbidity may be associated with disability. A wide range of clinical conditions (e.g., frailty, mild cognitive impairment, metabolic syndrome) and age-related diseases (e.g., Alzheimer's and Parkinson's disease, cancer, sarcopenia, cardiovascular and respiratory diseases) affect older people. Virtual reality (VR) is a novel and promising tool for assessment and rehabilitation in older people. Usability is a crucial factor that must be considered when designing virtual systems for medicine. We conducted a systematic review with Preferred Reporting Items for Systematic reviews and Meta-analysis (PRISMA) guidelines concerning the usability of VR clinical systems in aging and provided suggestions to structure usability piloting. Findings show that different populations of older people have been recruited to mainly assess usability of non-immersive VR, with particular attention paid to motor/physical rehabilitation. Mixed approach (qualitative and quantitative tools together) is the preferred methodology; technology acceptance models are the most applied theoretical frameworks, however senior adapted models are the best within this context. Despite minor interaction issues and bugs, virtual systems are rated as usable and feasible. We encourage usability and user experience pilot studies to ameliorate interaction and improve acceptance and use of VR clinical applications in older people with the aid of suggestions (VR-USOP) provided by our analysis. Copyright © 2020 Tuena, Pedroli, Trimarchi, Gallucci, Chiappini, Goulene, Gaggioli, Riva, Lattanzio, Giunco and Stramba-Badiale.The positive relationship between socioeconomic status (SES) and cognitive performance is mediated, in part, by differences in brain structure in typically developing youth. Associations between brain regions that relate to SES overlap with brain regions known to be sensitive to prenatal alcohol exposure (PAE). Animal models demonstrate that PAE attenuates neural and cognitive benefits of early life enrichment. However, whether or not environmental factors related to SES are associated with brain development in youth affected by PAE remains unknown in humans. Methods T1-weighted magnetic resonance imaging (MRI) scans were obtained in participants with PAE and compared to age- and sex- matched Controls (n = 197, 48% with PAE, 44% girls, 6.5-17.7 years old). General linear modeling was utilized to examine associations between SES and subcortical brain volumes for youth with PAE compared to Controls. Epigenetic screening Results Group by SES interactions were observed within the hippocampus (HPC), nucleus accumbens (NAc) and ventral diencephalon (vDC) (corrected p values less then 0.