  Objective: Brain networks have gained increasing recognition as potential
biomarkers in mental health studies, but there are limited approaches that can
leverage complex brain networks for accurate classification. Our goal is to
develop a novel Bayesian Support Vector Machine (SVM) approach that
incorporates high-dimensional networks as covariates and is able to overcome
limitations of existing penalized methods. Methods: We develop a novel
Dirichlet process mixture of double exponential priors on the coefficients in
the Bayesian SVM model that is able to perform feature selection and
uncertainty quantification, by pooling information across edges to determine
differential sparsity levels in an unsupervised manner. We develop different
versions of the model that incorporates static and dynamic connectivity
features, as well as an integrative analysis that jointly includes features
from multiple scanning sessions. We perform classification of intelligence
levels using resting state fMRI data from the Human Connectome Project (HCP),
and a second Attention Deficiency Hyperactivity Disorder (ADHD) classification
task. Results: Our results clearly reveal the considerable greater
classification accuracy under the proposed approach over state-of-the-art
methods. The multi-session analysis results in the highest classification
accuracy in the HCP data analysis. Conclusion: We provide concrete evidence
that the novel Bayesian SVMs provides an unsupervised and automated approach
for network-based classification, that results in considerable improvements
over penalized methods and parametric Bayesian approaches. Significance: Our
work is one of the first to conclusively demonstrate the advantages of a
Bayesian SVM in network-based classification of mental health outcomes, and the
importance of multi-session network analysis.
