ADHD classification using bag of words approach on network features
2012
Attention Deficit Hyperactivity Disorder (ADHD) is receiving lots of attention nowadays mainly because it is
one of the common brain disorders among children and not much information is known about the cause of this
disorder. In this study, we propose to use a novel approach for automatic classification of ADHD conditioned
subjects and control subjects using functional Magnetic Resonance Imaging (fMRI) data of resting state brains.
For this purpose, we compute the correlation between every possible voxel pairs within a subject and over the
time frame of the experimental protocol. A network of voxels is constructed by representing a high correlation
value between any two voxels as an edge. A Bag-of-Words (BoW) approach is used to represent each subject
as a histogram of network features; such as the number of degrees per voxel. The classification is done using
a Support Vector Machine (SVM). We also investigate the use of raw intensity values in the time series for
each voxel. Here, every subject is represented as a combined histogram of network and raw intensity features.
Experimental results verified that the classification accuracy improves when the combined histogram is used.
We tested our approach on a highly challenging dataset released by NITRC for ADHD-200 competition
and obtained promising results. The dataset not only has a large size but also includes subjects from different
demography and edge groups. To the best of our knowledge, this is the first paper to propose BoW approach in
any functional brain disorder classification and we believe that this approach will be useful in analysis of many
brain related conditions.
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