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Brain functional network modeling and analysis based on fMRI: a systematic review

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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 small-world, 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.

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Abbreviations

AAL:

Anatomical automatic labeling

BOLD:

Blood oxygenation level dependent

CAD:

Computer aided diagnosis

EEG:

Electroencephalogram

fMRI:

Functional magnetic resonance imaging

ICA:

Independent component analysis

MEG:

Magnetoencephalography

PCA:

Principal component analysis

ROI:

Region of interest

SVD:

Singular value decomposition

SVM:

Support vector machine

SPM:

statistical parametric mapping toolkit

TSCI:

Traumatic complete spinal cord injury.

WHO:

World Health Organization

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Acknowledgements

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Funding

This work was supported by the National Natural Science Foundation of China (61472069, 61402089, U1401256, 61672146), and the Fundamental Research Funds for the Central Universities (N2019007, N180101028, N180408019), and the China Postdoctoral Science Foundation (2019T120216 and 2018M641705), and the CETC Joint Fund, and the Recruitment Program of Global Experts under Grant (01270021814101/022).

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Authors and Affiliations

  1. College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China

    Zhongyang Wang, Zhiqiong Wang & Yue Zhao

  2. School of Computer Science and Engineering, Northeastern University, Shenyang, China

    Junchang Xin

  3. Key Laboratory of Big Data Management and Analytics (Liaoning Province), Northeastern University, Shenyang, China

    Junchang Xin

  4. Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA

    Yudong Yao

  5. College of Engineering, The University of Texas at El Paso, El Paso, TX, USA

    Wei Qian

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  1. Zhongyang Wang
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ZYW and JCX collected the background information. ZYW, JCX and ZQW analyzed and compared the current research situation. ZW, JX had the major responsibility for preparing the paper, ZYW, ZQW and YDY wrote part of the paper. JCX, YZ and WQ. supervised the project. All authors read and approved the final manuscript.

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Correspondence to Junchang Xin.

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Wang, Z., Xin, J., Wang, Z. et al. Brain functional network modeling and analysis based on fMRI: a systematic review. Cogn Neurodyn 15, 389–403 (2021). https://doi.org/10.1007/s11571-020-09630-5

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