Functional connectivity analysis of fMRI data can reveal synchronised activity between anatomically distinct brain regions. Here, we extract the characteristic connectivity signatures of different brain states to perform classification, allowing us to decode the different states based on the functional connectivity patterns. Our approach is based on polythetic decision trees, which combine powe...

Resting state functional connectivity MRI (fcMRI) has become a particularly useful tool for studying regional relationships in typical and atypical populations. Because many investigators have already obtained large data sets of task-related fMRI, the ability to use this existing task data for resting state fcMRI is of considerable interest. Two classes of data sets could potentially be modifie...

Human brain activity maps are produced by functional MRI (fMRI) research that describes the average level of engagement during a specific task various regions. Functional connectivity interrelationship, integrated performance, and organization these different This study investigates to quantify interactions between regions engaged concurrently in task. The key focus this was introduce demonstra...

Investigation of functional brain connectivity patterns using functional MRI has received significant interest in the neuroimaging domain. Brain functional connectivity alterations have widely been exploited for diagnosis and prediction of various brain disorders. Over the last several years, the research community has made tremendous advancements in constructing brain functional connectivity f...

Introduction: Many theories have been proposed about the etiology of autism. One is related to brain connectivity in patients with autism. Several studies have reported brain connectivity changes in autism disease. This study was performed on Electroencephalogram (EEG) studies that evaluated patients with autism, using functional brain connectivity, and compared them with typically-developing i...

Functional connectivity using resting-state fMRI has emerged as an important research tool for understanding normal brain function as well as changes occurring during brain development and in various brain disorders. Most prior work has examined changes in pairwise functional connectivity values using a multi-variate classification approach, such as Support Vector Machines (SVM). While it is po...

Rapid advances in neuroimaging techniques provide great potentials for study of Alzheimer's disease (AD). Existing findings have shown that AD is closely related to alteration in the functional brain network, i.e., the functional connectivity between different brain regions. In this paper, we propose a method based on sparse inverse covariance estimation (SICE) to identify functional brain conn...

Machine learning-based approaches are now able to examine functional magnetic resonance imaging data in a multivariate manner and extract features predictive of group membership. We applied support vector machine (SVM)-based classification to resting state functional connectivity (rsFC) data from nicotine-dependent smokers and healthy controls to identify brain-based features predictive of nico...