Abstract #5289

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

Xianchang Zhang^1,2, Hewei Cheng³, Zhentao Zuo¹, Ke Zhou¹, Bo Wang¹, Lin Chen^1,2, Yong Fan⁴, and Rong Xue^1,2,5

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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