P3-366: SUPPORT VECTOR MACHINE ANALYSIS OF RESTING-STATE FMRI IN COGNITIVE CONTINUUM

Support vector classification analysis of resting state functional connectivity fMRI

Random support vector machine cluster analysis of resting-state fMRI in Alzheimer's disease

Early diagnosis is critical for individuals with Alzheimer's disease (AD) in clinical practice because its progress is irreversible. In the existing literature, support vector machine (SVM) has always been applied to distinguish between AD and healthy controls (HC) based on neuroimaging data. But previous studies have only used a single SVM to classify AD and HC, and the accuracy is not very hi...

Support Vector Machine Classification of Resting State fMRI Datasets Using Dynamic Network Clusters

Support Vector Machine Classification of Resting State fMRI Datasets Using Clustered Dynamic Networks

On the generalizability of resting-state fMRI machine learning classifiers

Machine learning classifiers have become increasingly popular tools to generate single-subject inferences from fMRI data. With this transition from the traditional group level difference investigations to single-subject inference, the application of machine learning methods can be seen as a considerable step forward. Existing studies, however, have given scarce or no information on the generali...

Analysis of Resting-State fMRI Topological Graph Theory Properties in Methamphetamine Drug Users Applying Box-Counting Fractal Dimension

Introduction: Graph theoretical analysis of functional Magnetic Resonance Imaging (fMRI) data has provided new measures of mapping human brain in vivo. Of all methods to measure the functional connectivity between regions, Linear Correlation (LC) calculation of activity time series of the brain regions as a linear measure is considered the most ubiquitous one. The strength of the dependence obl...