Machine learning and computer vision methods are showing good performance in medical
imagery analysis. Yet only a few applications are now in clinical use and one of the reasons …

Schizophrenia is a socially significant mental disorder resulting frequently in severe forms of
disability. Diagnosis, choice of treatment tactics, and rehabilitation in clinical psychiatry are …

Predicting accuracy in cognitively challenging tasks has potential applications in education
and industry. Task demand has been linked with increases in response time and variations …

In the field of psychoneurology, analysis of neuroimaging data aimed at extracting distinctive
patterns of pathologies, such as epilepsy and depression, is well known to represent a …

Major depressive disorder (MDD) is a common mental disorder and is amongst the most
prevalent psychiatric disorders. MDD remains challenging to diagnose and predict its onset …

Electroencephalography (EEG)-based measurements of fine tactile sensation produce large
amounts of data, with high costs for manual evaluation. In this study, an EEG-based machine …

Automatic segmentation methods based on deep learning have recently demonstrated state-
of-the-art performance, outperforming the ordinary methods. Nevertheless, these methods …

Deep learning convolution neural networks have proved to be a powerful tool for MRI
analysis. In current work, we explore the potential of the deformable convolution deep neural …

It has long been known that patients with depression exhibit abnormal brain functional
connectivity patterns, that are often studied from a graph-theoretic perspective. However …

Rett Syndrome (RTT) is a rare neurodevelopmental disorder caused by mutation in the
MECP2 gene. No cures are still available, but several clinical trials are ongoing. Here we …