An Overview of Identification Methods on Human Brain Effective Connectivity Networks Based on Functional Magnetic Resonance Imaging

被引：0
|
作者：
Ji J.-Z. [1 ]
Zou A.-X. [1 ]
Liu J.-D. [1 ]
机构：
[1] Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Artificial Intelligence Institute, Faculty of Information Technology, Beijing University of Technology, Beijing
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2021年 / 47卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Brain effective connectivity networks identification; Category system; Challenges and prospects; Functional magnetic resonance imaging (fMRI); Human brain connectome;
D O I：
10.16383/j.aas.c190491
中图分类号：
学科分类号：
摘要：
The brain effective connectivity networks characterize the causal interactions of neural activity between brain regions. Researches on brain effective connectivity networks of different populations can not only provide a new perspective for understanding the pathological mechanism of neuropsychiatric diseases, but also provide novel brain network imaging markers for the early diagnosis and evaluation for treatment of diseases, thus have very important theoretical and practical value. Using computational approaches to identify brain effective connectivity networks from functional magnetic resonance imaging (fMRI) data is currently an important subject in the human brain connectome. This paper firstly summarizes a workflow of identifying brain effective connectivity networks from fMRI data and illustrates its main processes and methods. Next, a comprehensive category system of identifying brain effective connectivity networks is presented, and several typical identifying algorithms in each category are described. Finally, by analyzing challenging problems in this area, we predict the further research directions in identifying brain effective connectivity networks and hope to present some references for related researches. Copyright © 2021 Acta Automatica Sinica. All rights reserved.
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页码：278 / 296
页数：18
相关论文
共 115 条
  • [1] Lehrer J., Neuroscience: Making connections, Nature, 457, 7229, pp. 524-527, (2009)
  • [2] Liang Xia, Wang Jin-Hui, He Yong, Human connectome: Structural and functional brain networks, Chinese Science Bulletin, 55, 16, pp. 1565-1583, (2010)
  • [3] Friston K J., Functional and effective connectivity: A review, Brain Connectivity, 1, 1, pp. 13-36, (2011)
  • [4] Song M, Jiang T Z., A review of functional magnetic resonance imaging for Brainnetome, Neuroscience Bulletin, 28, 4, pp. 389-398, (2012)
  • [5] Deshpande G, Li Z H, Santhanam P, Coles C D, Lynch M E, Hamann S, Et al., Recursive cluster elimination based support vector machine for disease state prediction using resting state functional and effective brain connectivity, PLoS One, 5, 12, (2010)
  • [6] Li X F, Coyle D, Maguire L, Watson D R, McGinnity T M., Gray matter concentration and effective connectivity changes in Alzheimer' s disease: A longitudinal structural MRI study, Neuroradiology, 53, 10, pp. 733-748, (2011)
  • [7] Friston K J., Functional and effective connectivity in neuroimaging: A synthesis, Human Brain Mapping, 2, 1-2, pp. 56-78, (1994)
  • [8] Smith S M, Miller K L, Salimi-Khorshidi G, Webster M, Beckmann C F, Nichols T E, Et al., Network modelling methods for fMRI, NeuroImage, 54, 2, pp. 875-891, (2011)
  • [9] Farahani F V, Karwowski W, Lighthall N R., Application of graph theory for identifying connectivity patterns in human brain networks: A systematic review, Frontiers in Neuroscience, 13, (2019)
  • [10] Zuo Xi-Nian, Zhang Zhe, He Yong, Zang Yu-Feng, The human functional connectome: Its methodology, developmental trajectory and behavioral association, Chinese Science Bulletin, 57, 35, pp. 3399-3413, (2012)
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