计算机科学
连接体
人类连接体项目
人工智能
聚类分析
静息状态功能磁共振成像
模式识别(心理学)
正规化(语言学)
功能连接
连贯性(哲学赌博策略)
图形
神经影像学
神经科学
理论计算机科学
数学
心理学
统计
标识
DOI:10.1109/isbi.2016.7493432
摘要
We propose a novel brain parcellation method to define individualized, groupwise consistent brain network nodes based on integrated functional and morphological information. Particularly, our method is built under a collaborative multi-view clustering framework in conjunction with graph-regularization techniques. Our method is able to integrate multimodality imaging data in the brain parcellation to comprehensively capture inter-subject variability in functional anatomy. Our method has been evaluated based on resting state functional MRI and structural MRI data obtained from the WU-Minn Human Connectome Project. The experimental results have demonstrated that our method could obtain groupwise consistent and subject-specific parcellation results with better functional and structural coherence.
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