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Resting-state functional MRI (rs-fMRI) in functional neuroimaging techniqueshave improved in brain disorders, dysfunction studies via mapping the topologyof the brain connections, i.e. connectopic mapping. Since, there are the slightdifferences between healthy and unhealthy brain regions and functions,investigation into the complex topology of functional and structural brainnetworks in human is a complicated task with the growth of evaluation criteria.Irregular graph deep learning applications have widely spread to understandinghuman cognitive functions that are linked to gene expression and relateddistributed spatial patterns, because the neuronal networks of the brain canhold dynamically a variety of brain solutions with different activity patternsand functional connectivity, these applications might also be involved withboth node-centric and graph-centric tasks. In this paper, we performed a novelapproach of individual generative model and high order graph analysis for theregion of interest recognition areas of the brain which do not have a normalconnection during applying certain tasks. Here, we proposed a high orderframework of Graph Auto-Encoder (GAE) with a hypersphere distributer forfunctional data analysis in brain imaging studies that is underlyingnon-Euclidean structure in the learning of strong non-rigid graphs among largescale data. In addition, we distinguished the possible modes of correlations inabnormal brain connections. Our finding will show the degree of correlationbetween the affected regions and their simultaneous occurrence over time thatcan be used to diagnose brain diseases or revealing the ability of the nervoussystem to modify in brain topology at all angles, brain plasticity, accordingto input stimuli.

Graph-Based Method for Anomaly Prediction in Brain Network