Brain functional network modeling and analysis based on fMRI: a systematic review
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REVIEW PAPER
Brain functional network modeling and analysis based on fMRI: a systematic review Zhongyang Wang1 • Junchang Xin2,3
•
Zhiqiong Wang1 • Yudong Yao4 • Yue Zhao1 • Wei Qian5
Received: 16 January 2020 / Revised: 5 August 2020 / Accepted: 20 August 2020 Ó Springer Nature B.V. 2020
Abstract In recent years, the number of patients with neurodegenerative diseases (i.e., Alzheimer’s disease, Parkinson’s disease, mild cognitive impairment) and mental disorders (i.e., depression, anxiety and schizophrenia) have increased dramatically. Researchers have found that complex network analysis can reveal the topology of brain functional networks, such as smallworld, scale-free, etc. In the study of brain diseases, it has been found that these topologies have undergoed abnormal changes in different degrees. Therefore, the research of brain functional networks can not only provide a new perspective for understanding the pathological mechanism of neurological and psychiatric diseases, but also provide assistance for the early diagnosis. Focusing on the study of human brain functional networks, this paper reviews the research results in recent years. First, this paper introduces the background of the study of brain functional networks under complex network theory and the important role of topological properties in the study of brain diseases. Second, the paper describes how to construct a brain functional network using neural image data. Third, the common methods of functional network analysis, including network structure analysis and disease classification, are introduced. Fourth, the role of brain functional networks in pathological study, analysis and diagnosis of brain functional diseases is studied. Finally, the paper summarizes the existing studies of brain functional networks and points out the problems and future research directions. Keywords Brain functional networks Complex network Topological properties Neurological and psychiatric diseases Abbreviations AAL Anatomical automatic labeling BOLD Blood oxygenation level dependent CAD Computer aided diagnosis EEG Electroencephalogram fMRI Functional magnetic resonance imaging & Junchang Xin [email protected]
ICA MEG PCA ROI SVD SVM SPM TSCI WHO
Independent component analysis Magnetoencephalography Principal component analysis Region of interest Singular value decomposition Support vector machine statistical parametric mapping toolkit Traumatic complete spinal cord injury. World Health Organization
Zhongyang Wang [email protected] 1
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
Introduction
2
School of Computer Science and Engineering, Northeastern University, Shenyang, China
3
Key Laboratory of Big Data Management and Analytics (Liaoning Province), Northeastern University, Shenyang, China
4
Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA
5
College of Engineering, The University
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