  Modern neuroimaging technologies, combined with state-of-the-art data
processing pipelines, have made it possible to collect longitudinal
observations of an individual's brain connectome at different ages. It is of
substantial scientific interest to study how brain connectivity varies over
time in relation to human cognitive traits. In brain connectomics, the
structural brain network for an individual corresponds to a set of
interconnections among brain regions. We propose a symmetric bilinear logistic
regression to learn a set of small subgraphs relevant to a binary outcome from
longitudinal brain networks as well as estimating the time effects of the
subgraphs. We enforce the extracted signal subgraphs to have clique structure
which has appealing interpretations as they can be related to neurological
circuits. The time effect of each signal subgraph reflects how its predictive
effect on the outcome varies over time, which may improve our understanding of
interactions between the aging of brain structure and neurological disorders.
Application of this method on longitudinal brain connectomics and cognitive
capacity data shows interesting discovery of relevant interconnections among a
small set of brain regions in frontal and temporal lobes with better predictive
performance than competitors.
