Alzheimer's disease (AD) stands as a formidable neurodegenerative ailment that poses a
substantial threat to the elderly population, with no known curative or disease-slowing drugs …

Despite the high prevalence and clinical importance of comorbid psychosis in epilepsy, its
neurobiological mechanisms remain understudied. This narrative mini-review aims to …

This study examines the impact of sample size on predicting cognitive and mental health
phenotypes from brain imaging via machine learning. Our analysis shows a 3-to 9-fold …

The increasing incidence of epilepsy has led to the need for automatic systems that can
provide accurate diagnoses in order to improve the life quality of people suffering from this …

Academic research to the utilization of artificial intelligence (AI) has been proliferated over
the past few years. While AI and its subsets are continuously evolving in the fields of …

It has been a clinically important, long-standing challenge to accurately localize
epileptogenic focus in drug-resistant focal epilepsy because more intensive intervention to …

Big data analytics utilizes different techniques to transform large volumes of big datasets.
The analytics techniques utilize various computational methods such as Machine Learning …

Introduction The aim of the present study was to examine interhemispheric functional
connectivity (FC) and topological organization within the default-mode network (DMN) in …

Objective The main objective of this study was to determine the ability of double inversion
recovery (DIR) data coupled with machine-learning algorithms to distinguish normal …

Background and Objectives A new frontier in diagnostic radiology is the inclusion of
machine-assisted support tools that facilitate the identification of subtle lesions often not …