机构：[1]School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, People’s Republic of China[2]Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging and Informatics, University of Southern California, Marina del Rey, CA, USA[3]Department of Radiology, Zhejiang Provincial People’s Hospital, Hangzhou, People’s Republic of China
Brain metastases are the most prevalent cerebral tumors. Resting state networks (RSNs) are involved in multiple perceptual and cognitive functions. Therefore, precisely localizing multiple RSNs may be extremely valuable before surgical resection of metastases, to minimize neurocognitive impairments. Here we aimed to investigate the reliability of independent component analysis (ICA) for localizing multiple RSNs from resting-state functional MRI (rs-fMRI) data in individual patients, and further evaluate lesion-related spatial shifts of the RSNs. Twelve patients with brain metastases and 14 healthy controls were recruited. Using an improved automatic component identification method, we successfully identified seven common RSNs, including: the default mode network (DMN), executive control network (ECN), dorsal attention network (DAN), language network (LN), sensorimotor network (SMN), auditory network (AN) and visual network (VN), in both individual patients and controls. Moreover, the RSNs in the patients showed a visible spatial shift compared to those in the controls, and the spatial shift of some regions was related to the tumor location, which may reflect a complicated functional mechanism - functional disruptions and reorganizations - caused by metastases. Besides, higher cognitive networks (DMN, ECN, DAN and LN) showed significantly larger spatial shifts than perceptual networks (SMN, AN and VN), supporting a functional dichotomy between the two network groups even in pathologic alterations associated with metastases. Overall, our findings provide evidence that ICA is a promising approach for presurgical localization of multiple RSNs from rs-fMRI data in individual patients. More attention should be paid to the spatial shifts of the RSNs before surgical resection.
基金：
the National Natural
Science Foundation of China (No. 81401482) and the Educational
Commission of Sichuan Province of China (No. 17ZA0269). P.
Thompson is funded in part by the NIH, under grant U54 EB020403 from
the Big Data to Knowledge (BD2K) program.
语种：
外文
PubmedID：
中科院(CAS)分区：
出版当年[2019]版：
大类|3 区医学
小类|4 区神经成像
最新[2023]版：
大类|3 区医学
小类|3 区神经成像
第一作者：
第一作者机构：[1]School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, People’s Republic of China[2]Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging and Informatics, University of Southern California, Marina del Rey, CA, USA
通讯作者：
通讯机构：[1]School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, People’s Republic of China[2]Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging and Informatics, University of Southern California, Marina del Rey, CA, USA
推荐引用方式(GB/T 7714)：
Ju-Rong Ding,Fangmei Zhu,Bo Hua,et al.Presurgical localization and spatial shift of resting state networks in patients with brain metastases.[J].Brain imaging and behavior.2019,13(2):408-420.doi:10.1007/s11682-018-9864-6.
APA：
Ju-Rong Ding,Fangmei Zhu,Bo Hua,Xingzhong Xiong,Yuqiao Wen...&Paul M. Thompson.(2019).Presurgical localization and spatial shift of resting state networks in patients with brain metastases..Brain imaging and behavior,13,(2)
MLA：
Ju-Rong Ding,et al."Presurgical localization and spatial shift of resting state networks in patients with brain metastases.".Brain imaging and behavior 13..2(2019):408-420
