Identification of potential biomarkers in the hippocampus region for the diagnosis of ADHD using PBL-McRBFN approach

Attention Deficiency Hyperactivity Disorder (ADHD) as a disruptive behavior disorder is receiving lots of attention because of its complexity and need for early detection. This paper presents a study on identification of potential biomarkers in the diagnosis of ADHD based on the structural-MRI of the brain obtained through ADHD-200 competition data set. The region of the brain considered here is "hippocampus". The grey matter probability of the T1 images is segmented followed by tissue alignment and inter subject normalization. Then, the voxels of the hippocampus are segregated using a region-of-interest mask, and the grey matter tissue probability values are obtained. These values are then used as features to classify ADHD patients against typically developing controls using a projection based learning algorithm for a meta-cognitive radial basis function network (PBL-McRBFN) and compared the results with that of support vector machines. Initially we take all the voxels of hippocampus for our study and then we have selected the most relevant voxels as a biomarker using Chi-square approach and developed a classifier to diagnosis ADHD. The results clearly highlight that use of hippocampus from the structural-MRI is sufficient to diagnosis ADHD to certain degree of confidence.