  The human connectome has become the very frequent subject of study of
brain-scientists, psychologists, and imaging experts in the last decade. With
diffusion magnetic resonance imaging techniques, unified with advanced data
processing algorithms, today we are able to compute braingraphs with several
hundred, anatomically identified nodes and thousands of edges, corresponding to
the anatomical connections of the brain. The analysis of these graphs without
refined mathematical tools is hopeless. These tools need to address the high
error rate of the MRI processing workflow, and need to find structural causes
or at least correlations of psychological properties and cerebral connections.
Until now, structural connectomics was only rarely able identifying such causes
or correlations. In the present work, we study the frequent neighbor sets of
the most deeply investigated brain area, the hippocampus. By applying the
Frequent Network Neighborhood mapping method, we identified frequent
neighbor-sets of the hippocampus, which may influence numerous psychological
parameters, including intelligence-related ones. We have found neighbor sets,
which have significantly higher frequency in subjects with high-scored Penn
Matrix tests, and with low-scored Penn Word Memory tests. Our study utilizes
the braingraphs, computed from the imaging data of the Human Connectome
Project's 414 subjects, each with 463 anatomically identified nodes.
