Abstract Machine learning leverages statistical and computer science principles to develop
algorithms capable of improving performance through interpretation of data rather than …

The human brain is a complex, interconnected network par excellence. Accurate and
informative map** of this human connectome has become a central goal of neuroscience …

Graph theoretical analysis of neuroimaging data is growing rapidly and could potentially
provide a relatively simple but powerful quantitative framework to describe and compare …

Idiopathic generalized epilepsy (IGE) has been linked with disrupted intra‐network
connectivity of multiple resting‐state networks (RSNs); however, whether impairment is …

Brain functioning is increasingly seen as a complex interplay of dynamic neural systems that
rely on the integrity of structural and functional networks. Recent studies that have …

Central to the development of clinical applications of functional connectomics for neurology
and psychiatry is the discovery and validation of biomarkers. Resting state fMRI (R-fMRI) is …

Background Resting state fMRI has emerged as a popular neuroimaging method for
automated recognition and classification of brain disorders. Attention Deficit Hyperactivity …

Epilepsy, a neurological disease characterized by recurrent seizures, is highly
heterogeneous in nature. Based on the prevalence, epilepsy is classified into two types …

There is a growing body of evidence pointing toward large-scale networks underlying the
core phenomena in epilepsy, from seizure generation to cognitive dysfunction or response …

Automated recognition and classification of brain diseases are of tremendous value to
society. Attention deficit hyperactivity disorder (ADHD) is a diverse spectrum disorder whose …