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Research progress of multimodal MRI of neuropsychiatric diseases based on linked independent component analysis
HU Bing  TAN Xuanyu  LIU Wanqing  LI Danyang  ZHANG Qing 

Cite this article as: Hu B, Tan XY, Liu WQ, et al. Research progress of multimodal MRI of neuropsychiatric diseases based on linked independent component analysis[J]. Chin J Magn Reson Imaging, 2022, 13(3): 129-133. DOI:10.12015/issn.1674-8034.2022.03.032.


[Abstract] Traditional multimodal MRI analysis is usually based on single-mode data, and the results are simply compared or correlated, but the a priori interaction information between modes is not fully utilized. Linked independent component analysis (LICA) is a multi-modal data fusion method, which can comprehensively and flexibly use independent component analysis to fuse and analyze multi-modal data, allow each modal group to have different units, signal-to-noise ratio, etc., and can automatically determine the optimal weight of each mode in the group, so as to make full use of the interactive information between modes, it has been widely used in the study of brain diseases. This paper reviews the research progress of MRI in the pathological mechanism, clinical diagnosis and classification of neuropsychiatric diseases.
[Keywords] linked independent component analysis；Alzheimer's disease；Parkinson's disease；depression；schizophrenia；magnetic resonance imaging；data fusion

HU Bing   TAN Xuanyu   LIU Wanqing   LI Danyang   ZHANG Qing*  

Department of Radiology, Zhongshan Hospital Affiliated to Dalian University, Dalian 116001, China

Zhang Q, E-mail: zhangqingsmile@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Scientific Research Fund of Liaoning Provincial Department of Education (No. jyt-dldxfw202006).
Received  2021-09-08
Accepted  2022-02-14
DOI: 10.12015/issn.1674-8034.2022.03.032
Cite this article as: Hu B, Tan XY, Liu WQ, et al. Research progress of multimodal MRI of neuropsychiatric diseases based on linked independent component analysis[J]. Chin J Magn Reson Imaging, 2022, 13(3): 129-133. DOI:10.12015/issn.1674-8034.2022.03.032.

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