PIT Bioinformatics Group, Eötvös University, Budapest, 1117, Hungary.
Uratim Ltd., Budapest, 1118, Hungary.
Sci Rep. 2020 Jul 20;10(1):11967. doi: 10.1038/s41598-020-68914-2.
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, united 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 of 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 "Good Neighbor" sets, which correlate with better test results and also "Bad Neighbor" sets, which correlate with worse test results. Our study utilizes the braingraphs, computed from the imaging data of the Human Connectome Project's 414 subjects, each with 463 anatomically identified nodes.
在过去的十年中，人类连接组已成为脑科学家、心理学家和成像专家经常研究的课题。通过扩散磁共振成像技术，结合先进的数据处理算法，我们现在能够计算出具有数百个解剖学上可识别节点和数千个边缘的脑图谱，这些边缘对应于大脑的解剖连接。如果没有精细的数学工具，对这些图谱进行分析是毫无希望的。这些工具需要解决 MRI 处理工作流程中的高错误率问题，并需要找到结构原因，或者至少找到心理特性和大脑连接的相关性。到目前为止，结构连接组学很少能够确定这些原因或相关性。在本研究中，我们研究了研究最深入的大脑区域——海马体的常见邻集。通过应用频繁网络邻域映射方法，我们确定了海马体的常见邻集，这些邻集可能影响包括与智力相关的许多心理参数。我们发现了与更好的测试结果相关的“好邻居”集，也发现了与更差的测试结果相关的“坏邻居”集。我们的研究利用了来自人类连接组计划的 414 名受试者的成像数据计算出的脑图谱，每个受试者都有 463 个解剖学上可识别的节点。
