Alzheimer’s disease (AD) is a progressive chronic illness that leads to cognitive decline and dementia. Neuroimaging technologies, such as functional magnetic resonance imaging (fMRI), deep learning approaches offer promising avenues for AD classification. In this study, we investigate the use of fMRI-based connectivity (FC) measures, including Pearson correlation coefficient (PCC), maximal inf...

INTRODUCTION In combination with multivariate pattern analysis algorithms such as Support Vector Machines (SVM) [1] or Support Vector Regression (SVR) [2], functional connectivity offers a powerful tool for brain classification. With this approach, several groups have successfully identified and predicted brain characteristics using resting-state MRI data [35]. Specifically, Dosenbach et al. ha...

BACKGROUND Epilepsy is one of the most prevalent neurological disorders. It remains medically intractable for about one-third of patients with focal epilepsy, for whom precise localization of the epileptogenic zone responsible for seizure initiation may be critical for successful surgery. Existing fMRI literature points to widespread network disturbances in functional connectivity. Per previous...

Background: Schizophrenia is a mental disorder that severely affects the perception and relations of individuals. Nowadays, this disease is diagnosed by psychiatrists based on psychiatric tests, which is highly dependent on their experience and knowledge. This study aimed to design a fully automated framework for the diagnosis of schizophrenia from electroencephalogram signals using advanced de...

Decoding specific cognitive states from brain activity constitutes a major goal of neuroscience. Previous studies of brain-state classification have focused largely on decoding brief, discrete events and have required the timing of these events to be known. To date, methods for decoding more continuous and purely subject-driven cognitive states have not been available. Here, we demonstrate that...