Title
Attributed Graph Distance Measure For Automatic Detection Of Attention Deficit Hyperactive Disordered Subjects
Keywords
Attention deficit hyperactive disorder; Attributed graph; Functional magnetic resonance imaging; Multidimensional scaling; Support vector machine
Abstract
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-fMRI) 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 fMRI time series. The activity level of the voxels are measured based on the average power of their corresponding fMRI 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. © 2014 Dey, Rao and Shah.
Publication Date
6-16-2014
Publication Title
Frontiers in Neural Circuits
Volume
8
Issue
JUNE
Number of Pages
-
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.3389/fncir.2014.00064
Copyright Status
Unknown
Socpus ID
84902589544 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84902589544
STARS Citation
Dey, Soumyabrata; Rao, Ravishankar; and Shah, Mubarak, "Attributed Graph Distance Measure For Automatic Detection Of Attention Deficit Hyperactive Disordered Subjects" (2014). Scopus Export 2010-2014. 8590.
https://stars.library.ucf.edu/scopus2010/8590