Globally, there is a substantial unmet need to diagnose various diseases effectively. The
complexity of the different disease mechanisms and underlying symptoms of the patient …

Neuroimaging has made it possible to measure pathological brain changes associated with
Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been …

Neuroimaging-based single subject prediction of brain disorders has gained increasing
attention in recent years. Using a variety of neuroimaging modalities such as structural …

Brain functional imaging data, especially functional magnetic resonance imaging (fMRI)
data, have been employed to reflect functional integration of the brain. Alteration in brain …

Characterizing the optimal fMRI paradigms for detecting behaviorally relevant functional
connectivity (FC) patterns is a critical step to furthering our knowledge of the neural basis of …

Abstract Machine learning techniques have gained prominence for the analysis of resting-
state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview …

In this study, a new predictive framework is proposed by integrating an improved grey wolf
optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO‐KELM …

With the increasing prevalence of autism spectrum disorder (ASD), it is important to identify
ASD patients for effective treatment and intervention, especially in early childhood …

Biomarkers in whichever modality are tremendously important in diagnosing of disease,
tracking disease progression and clinical trials. This applies in particular for disorders with a …

Resting‐state fMRI (rs‐fMRI) detects functional connectivity (FC) abnormalities that occur in
the brains of patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). FC …