  Convolution Neural Network (CNN) recently have been adopted in several
neuroimaging studies for diagnosis capturing disease-specific changes in the
brain. While many of these methods are designed to work with images in $\mathbb
R^n$ exploiting regular structure of the domain, they are not well-suited to
analyze data with irregular structure such as brain connectivity. As there is
significant interest in understanding the altered interactions between
different brain regions that lead to neuro-disorders, it is important to
develop data-driven methods that work with a population of graph data for
traditional prediction tasks. In this regime, we propose a novel CNN-based
framework with adaptive graph transforms to learn the most disease-relevant
connectome feature maps which have the highest discrimination power across
diagnostic categories. The backbone of our framework is a multi-resolution
representation of the graph matrix which is steered by a set of wavelet-like
graph transforms. In this context, our supervised graph learning framework
outperforms conventional graph methods that predict diagnostic label only based
on the underlying individual graph. Our extensive experiments on two real
datasets of functional and structural brain networks show that our
multi-resolution framework achieves significantly higher accuracy, precision
and recall in predicting diagnostic labels and identifying disease-specific
brain connectivities that are associated with brain disorders such as
Attention-Deficit/Hyperactivity Disorder (ADHD) and Alzheimer's Disease (AD).
