  This paper applies a hidden Markov model to the problem of Attention Deficit
Hyperactivity Disorder (ADHD) diagnosis from resting-state functional Magnetic
Resonance Image (fMRI) scans of subjects. The proposed model considers the
temporal evolution of fMRI voxel activations in the cortex, cingulate gyrus,
and thalamus regions of the brain in order to make a diagnosis. Four feature
dimen- sionality reduction methods are applied to the fMRI scan: voxel means,
voxel weighted means, principal components analysis, and kernel principal
components analysis. Using principal components analysis and kernel principal
components analysis for dimensionality reduction, the proposed algorithm
yielded an accu- racy of 63.01% and 62.06%, respectively, on the ADHD-200
competition dataset when differentiating between healthy control, ADHD
innattentive, and ADHD combined types.
