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The human connectome has become the very frequent subject of study ofbrain-scientists, psychologists, and imaging experts in the last decade. Withdiffusion magnetic resonance imaging techniques, unified with advanced dataprocessing algorithms, today we are able to compute braingraphs with severalhundred, anatomically identified nodes and thousands of edges, corresponding tothe anatomical connections of the brain. The analysis of these graphs withoutrefined mathematical tools is hopeless. These tools need to address the higherror rate of the MRI processing workflow, and need to find structural causesor at least correlations of psychological properties and cerebral connections.Until now, structural connectomics was only rarely able identifying such causesor correlations. In the present work, we study the frequent neighbor sets ofthe most deeply investigated brain area, the hippocampus. By applying theFrequent Network Neighborhood mapping method, we identified frequentneighbor-sets of the hippocampus, which may influence numerous psychologicalparameters, including intelligence-related ones. We have found neighbor sets,which have significantly higher frequency in subjects with high-scored PennMatrix tests, and with low-scored Penn Word Memory tests. Our study utilizesthe braingraphs, computed from the imaging data of the Human ConnectomeProject's 414 subjects, each with 463 anatomically identified nodes.

Good Neighbors, Bad Neighbors: The Frequent Network Neighborhood Mapping  of the Hippocampus Enlightens Several Structural Factors of the Human  Intelligence on a 414-Subject Cohort