  Human brain development is a complex and dynamic process that is affected by
several factors such as genetics, sex hormones, and environmental changes. A
number of recent studies on brain development have examined functional
connectivity (FC) defined by the temporal correlation between time series of
different brain regions. We propose to add the directional flow of information
during brain maturation. To do so, we extract effective connectivity (EC)
through Granger causality (GC) for two different groups of subjects, i.e.,
children and young adults. The motivation is that the inclusion of causal
interaction may further discriminate brain connections between two age groups
and help to discover new connections between brain regions. The contributions
of this study are threefold. First, there has been a lack of attention to
EC-based feature extraction in the context of brain development. To this end,
we propose a new kernel-based GC (KGC) method to learn nonlinearity of complex
brain network, where a reduced Sine hyperbolic polynomial (RSP) neural network
was used as our proposed learner. Second, we used causality values as the
weight for the directional connectivity between brain regions. Our findings
indicated that the strength of connections was significantly higher in young
adults relative to children. In addition, our new EC-based feature outperformed
FC-based analysis from Philadelphia neurocohort (PNC) study with better
discrimination of the different age groups. Moreover, the fusion of these two
sets of features (FC + EC) improved brain age prediction accuracy by more than
4%, indicating that they should be used together for brain development studies.
