  Due to the spontaneous nature of resting-state fMRI (rs-fMRI) signals,
cross-subject comparison and therefore, group studies of rs-fMRI are
challenging. Most existing group comparison methods use features extracted from
the fMRI time series, such as connectivity features, independent component
analysis (ICA), and functional connectivity density (FCD) methods. However, in
group studies, especially in the case of spectrum disorders, distances to a
single atlas or a representative subject do not fully reflect the differences
between subjects that may lie on a multi-dimensional spectrum. Moreover, there
may not exist an individual subject or even an average atlas in such cases that
is representative of all subjects. Here we describe an approach that measures
pairwise distances between the synchronized rs-fMRI signals of pairs of
subjects instead of to a single reference point. We also present a method for
fMRI data comparison that leverages this generated pairwise feature to
establish a radial basis function kernel matrix. This kernel matrix is used in
turn to perform kernel regression of rs-fMRI to a clinical variable such as a
cognitive or neurophysiological performance score of interest. This method
opens a new pointwise analysis paradigm for fMRI data. We demonstrate the
application of this method by performing a pointwise analysis on the cortical
surface using rs-fMRI data to identify cortical regions associated with
variability in ADHD index. While pointwise analysis methods are common in
anatomical studies such as cortical thickness analysis and voxel- and
tensor-based morphometry and its variants, such a method is lacking for rs-fMRI
and could improve the utility of rs-fMRI for group studies. The method
presented in this paper is aimed at filling this gap.
