Exploiting The Brain’s Network Structure in Identifying ADHD

• Main Authors: Soumyabrata eDey, A. Ravishankar eRao, Mubarak eShah
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2012-11-01
• Series: Frontiers in Systems Neuroscience
• Subjects: Principal Component Analysis; Default Mode Network; Attention Deficit Hyperactive Disorder; Functional Magnetic Resonance Image; Linear Discriminant Analysis
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fnsys.2012.00075/full

Attention Deficit Hyperactive Disorder (ADHD) is a common behavioral problem affecting children. In this work, we investigate the automatic classification of ADHD subjects using the resting state Functional Magnetic Resonance Imaging (fMRI) sequences of the brain. We show that brain can be modeled as a functional network, and certain properties of the networks differ in ADHD subjects from control subjects. We compute the pairwise correlation of brain voxels' activity over the time frame of the experimental protocol which helps to model the function of a brain as a network. Different network features are computed for each of the voxels constructing the network. The concatenation of the network features of all the voxels in a brain serves as the feature vector. Feature vectors from a set of subjects are then used to train a PCA-LDA (principal component analysis-linear discriminant analysis) based classifier. We hypothesized that ADHD related differences lie in some specific regions of brain and using features only from those regions are sufficient to discriminate ADHD and control subjects. We propose a method to create a brain mask which includes the useful regions only and demonstrate that using the feature from the masked regions improves classification accuracy on the test data set. We train our classifier with 776 subjects, and test on 171 subjects provided by The Neuro Bureau for the ADHD-200 challenge. We demonstrate the utility of graph-motif features, specifically the maps that represent the frequency of participation of voxels in network cycles of length 3. The best classification performance (69.59%) is achieved using 3-cycle map features with masking. Our proposed approach holds promise in being able to diagnose and understand the disorder.