Neuroimagingbased approaches have been extensively applied to study mental illness in
recent years and have deepened our understanding of both cognitively healthy and …

Abstract Functional Magnetic Resonance Imaging (fMRI) is presently one of the most
popular techniques for analysing the dynamic states in brain images using various kinds of …

There has been growing interest in studying the temporal reconfiguration of brain functional
connectivity to understand the role of dynamic interaction (eg, integration and segregation) …

Applying network science approaches to investigate the functions and anatomy of the
human brain is prevalent in modern medical imaging analysis. Due to the complex network …

Electroconvulsive therapy (ECT) has been shown to be effective in schizophrenia,
particularly when rapid symptom reduction is needed or in cases of resistance to drug …

The purpose of this study is to apply graph convolutional networks (GCNs) for feature
extraction and classification of patients with autism spectrum disorder (ASD). The number of …

Abstract Background Restricted Boltzmann machines (RBMs), including greedy layer-wise
trained RBMs as part of a deep belief network (DBN), have the ability to identify spatial …

Joint structural and functional modelling of the brain based on multimodal imaging
increasingly show potential in elucidating the underpinnings of human cognition. In the …

The increasing performance of feature extraction and regression modeling in various
domains raises the hope for machine and deep learning to assist clinicians in numerous …

Precision medicine is an emerging research field that proposes personalized diagnosis,
prognosis, and treatment based on the analysis of individual health data and …