Presurgical localization and spatial shift of resting state networks in patients with brain metastases

ORIGINAL RESEARCH
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Abstract

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.

Keywords

Brain metastases Resting state networks Independent component analysis Resting-state functional MRI 
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Notes

Acknowledgements

This work was supported by 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.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

11682_2018_9864_MOESM1_ESM.docx (6.4 mb)
ESM 1 (DOCX 6518 kb)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Automation and Information EngineeringSichuan University of Science and EngineeringZigongPeople’s Republic of China
  2. 2.Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging and InformaticsUniversity of Southern CaliforniaMarina del ReyUSA
  3. 3.Department of RadiologyZhejiang Provincial People’s HospitalHangzhouPeople’s Republic of China

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