  Resting-state functional MRI (rs-fMRI) in functional neuroimaging techniques
have improved in brain disorders, dysfunction studies via mapping the topology
of the brain connections, i.e. connectopic mapping. Since, there are the slight
differences between healthy and unhealthy brain regions and functions,
investigation into the complex topology of functional and structural brain
networks in human is a complicated task with the growth of evaluation criteria.
Irregular graph deep learning applications have widely spread to understanding
human cognitive functions that are linked to gene expression and related
distributed spatial patterns, because the neuronal networks of the brain can
hold dynamically a variety of brain solutions with different activity patterns
and functional connectivity, these applications might also be involved with
both node-centric and graph-centric tasks. In this paper, we performed a novel
approach of individual generative model and high order graph analysis for the
region of interest recognition areas of the brain which do not have a normal
connection during applying certain tasks. Here, we proposed a high order
framework of Graph Auto-Encoder (GAE) with a hypersphere distributer for
functional data analysis in brain imaging studies that is underlying
non-Euclidean structure in the learning of strong non-rigid graphs among large
scale data. In addition, we distinguished the possible modes of correlations in
abnormal brain connections. Our finding will show the degree of correlation
between the affected regions and their simultaneous occurrence over time that
can be used to diagnose brain diseases or revealing the ability of the nervous
system to modify in brain topology at all angles, brain plasticity, according
to input stimuli.
