Title

Attributed graph distance measure for automatic detection of attention deficit hyperactive disordered subjects

Authors

Authors

S. Dey; A. R. Rao;M. Shah

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

Front. Neural Circuits

Keywords

attention deficit hyperactive disorder; functional magnetic resonance; imaging; support vector machine; multidimensional scaling; attributed; graph; ANTERIOR CINGULATE CORTEX; DEFICIT/HYPERACTIVITY DISORDER; FUNCTIONAL; CONNECTIVITY; CHILDREN; BRAIN; ADHD; ABNORMALITIES; DYSFUNCTION; ADOLESCENTS; ACTIVATION; Neurosciences

Abstract

Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention recently for two reasons. First, it is one of the most commonly found childhood disorders and second, the root cause of the problem is still unknown. Functional Magnetic Resonance Imaging (fMRI) data has become a popular tool for the analysis of ADHD, which is the focus of our current research. In this paper we propose a novel framework for the automatic classification of the ADHD subjects using their resting state fMRI (rs-fM RI) data of the brain. We construct brain functional connectivity networks for all the subjects. The nodes of the network are constructed with clusters of highly active voxels and edges between any pair of nodes represent the correlations between their average fM RI time series. The activity level of the voxels are measured based on the average power of their corresponding fM RI time-series. For each node of the networks, a local descriptor comprising of a set of attributes of the node is computed. Next, the Multi-Dimensional Scaling (MDS) technique is used to project all the subjects from the unknown graph-space to a low dimensional space based on their inter-graph distance measures. Finally, the Support Vector Machine (SVM) classifier is used on the low dimensional projected space for automatic classification of the ADHD subjects. Exhaustive experimental validation of the proposed method is performed using the data set released for the ADHD-200 competition. Our method shows promise as we achieve impressive classification accuracies on the training (70.49%) and test data sets (73.55%). Our results reveal that the detection rates are higher when classification is performed separately on the male and female groups of subjects.

Journal Title

Frontiers in Neural Circuits

Volume

8

Publication Date

1-1-2014

Document Type

Article

Language

English

First Page

11

WOS Identifier

WOS:000339264800001

ISSN

1662-5110

Share

COinS