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Abstract #5289

Individualized Functional Parcellation of Human Amygdala using a Semi-Supervised Clustering Method based on 7T Resting State fMRI Data

Xianchang Zhang1,2, Hewei Cheng3, Zhentao Zuo1, Ke Zhou1, Bo Wang1, Lin Chen1,2, Yong Fan4, and Rong Xue1,2,5

1State Key Laboratory of Brain and Cognitive Science, Beijing MR Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, People's Republic of China, 2University of Chinese Academy of Sciences, Beijing, People's Republic of China, 3Department of Biomedical Engineering, School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, People's Republic of China, 4Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States, 5Beijing Institute for Brain Disorders, Beijing, People's Republic of China

Functional subspecialization of human amygdala has been revealed in a variety of studies based on histological, in-vivo imaging, and meta-data. However, most of the existing studies identified functional subregions of amygdala at a group level. In this study, we investigated individualized functional neuroanatomy of amygdala based on 7T resting-state fMRI data with high spatiotemporal resolution. Our results have demonstrated that an improved semi-supervised clustering algorithm successfully parcellated individual subjects’ amygdala into 3 subregions, each of them having distinctive functional connectivity patterns. The individualized functional subregions of amygdala may better capture individual variability in functional neuroanatomy than their group level counterparts.

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