  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 the 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
the brain and using features only from those regions is sufficient to
discriminate ADHD and control subjects. We propose a method to create a brain
mask that 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.
