Research development of real-time functional magnetic resonance imaging neuro-feedback technology based on brain network connectivity_Journal of Biomedical Engineering

Authors：

HE Wenjie ¹ , BU Haibing ¹ , TONG Li ¹ , LIU Fuquan ² , ZENG Ying ¹ , WANG Linyuan ¹ ,  YAN Bin ¹

1. Information Engineering University, Zhengzhou 450002, P.R.China;
2. Troops 65021, Shenyang 110162, P.R.China;

Corresponding author：

YAN Bin, Email: ybspace@hotmail.com

Keywords：

neurofeedback; real-time functional magnetic resonance imaging; self-regulation; brain network connectivity

DOI：

10.7507/1001-5515.201605037

Video：

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The emergence of real-time functional magnetic resonance imaging (rt-fMRI) has provided foundations for neurofeedback based on brain hemodynamics and has given the new opportunity and challenge to cognitive neuroscience research. Along with the study of advanced brain neural mechanisms, the regulation goal of rt-fMRI neurofeedback develops from the early specific brain region activity to the brain network connectivity more accordant with the brain functional activities, and the study of the latter may be a trend in the area. Firstly, this paper introduces basic principle and development of rt-fMRI neurofeedback. Then, it specifically discusses the current research status of brain connectivity neurofeedback technology, including research approaches, experimental methods, conclusions, and so on. Finally, it discusses the problems in this field in the future development.

Citation： HE Wenjie, BU Haibing, TONG Li, LIU Fuquan, ZENG Ying, WANG Linyuan, YAN Bin. Research development of real-time functional magnetic resonance imaging neuro-feedback technology based on brain network connectivity. Journal of Biomedical Engineering, 2017, 34(3): 456-460. doi: 10.7507/1001-5515.201605037 Copy

1.	Bargmann C I, Newsome W T. The brain research through advancing innovative neurotechnologies (BRAIN) initiative and neurology. JAMA Neurol, 2014, 71(6): 675-676.
2.	Brühl A B. Making sense of real-time functional magnetic resonance imaging (rtfMRI) and rtfMRI neurofeedback. Int J Neuropsychopharmacol, 2015, 18(6): pyv020..
3.	Thibault R T, Lifshitz M, Raz A. The self-regulating brain and neurofeedback: Experimental science and clinical promise. Cortex, 2016, 74: 247-261.
4.	Stoeckel L E, Garrison K A, Ghosh S, et al. Optimizing real time fMRI neurofeedback for therapeutic discovery and development. Neuroimage Clin, 2014, 5: 245-255.
5.	Birbaumer N, Ruiz S, Sitaram R. Learned regulation of brain metabolism. Trends Cogn Sci, 2013, 17(6): 295-302.
6.	Scheinost D, Stoica T, Saksa J, et al. Orbitofrontal cortex neurofeedback produces lasting changes in contamination anxiety and resting-state connectivity. Transl Psychiatry, 2013, 3(4): e250.
7.	Robineau F, Rieger S W, Mermoud C, et al. Self-regulation of inter-hemispheric visual cortex balance through real-time fMRI neurofeedback training. Neuroimage, 2014, 100: 1-14.
8.	Arns M, Heinrich H, Strehl U. Evaluation of neurofeedback in ADHD: The long and winding road. Biol Psychol, 2014, 95(SI): 108-115.
9.	Esmail S, Linden D E J. Neural networks and neurofeedback in Parkinson's disease. NeuroRegulation, 2014, 1(3/4): 240.
10.	Thibault R T, Lifshitz M, Birbaumer N, et al. Neurofeedback, self-regulation, and brain imaging: clinical science and fad in the service of mental disorders. Psychother Psychosom, 2015, 84(4): 193-207.
11.	Cannon R L. Editorial perspective: Defining neurofeedback and its functional processes. NeuroRegulation, 2015, 2(2): 60-69.
12.	Linden D E. Neurofeedback and networks of depression. Dialogues Clin Neurosci, 2014, 16(1): 103-112.
13.	Ruiz S, Lee S, Soekadar S R, et al. Acquired self-control of insula cortex modulates emotion recognition and brain network connectivity in schizophrenia. Hum Brain Mapp, 2013, 34(1): 200-212.
14.	Mulders P C, van Eijndhoven P F, Schene A H, et al. Resting-state functional connectivity in major depressive disorder: A review. Neurosci Biobehav Rev, 2015, 56: 330-344.
15.	Jie Biao, Zhang Daoqiang, Wee C Y, et al. Topological graph kernel on multiple thresholded functional connectivity networks for mild cognitive impairment classification. Hum Brain Mapp, 2014, 35(7): 2876-2897.
16.	吕柄江, 赵小杰, 姚力, 等. 实时功能磁共振成像及其应用. 科学通报, 2014, 59(2): 195-209.
17.	Cox R W, Jesmanowicz A, Hyde J S. Real-time functional magnetic resonance imaging. Magn Reson Med, 1995, 33(2): 230-236.
18.	Weiskopf N, Veit R, Erb M, et al. Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): Methodology and exemplary data. Neuroimage, 2003, 19(3): 577-586.
19.	Sulzer J, Haller S, Scharnowski F, et al. Real-time fMRI neurofeedback: Progress and challenges. Neuroimage, 2013, 76(1): 386-399.
20.	李椋. 基于实时功能磁共振成像的脑机接口技术研究. 郑州: 信息工程大学, 2014.
21.	Brühl A B, Scherpiet S, Sulzer J, et al. Real-time neurofeedback using functional MRI could improve down-regulation of amygdala activity during emotional stimulation: A proof-of-concept study. Brain Topogr, 2014, 27(1): 138-148.
22.	deCharms R C, Christoff K, Glover G H, et al. Learned regulation of spatially localized brain activation using real-time fMRI. Neuroimage, 2004, 21(1): 436-443.
23.	Li Baojuan, Liu Li, Friston K J, et al. A treatment-resistant default mode subnetwork in major depression. Biol Psychiatry, 2013, 74(1): 48-54.
24.	胡德文, 沈辉. 脑磁共振影像数据时空分析. 北京: 科学出版社, 2014: 133-176.
25.	Zotev V, Phillips R, Young K D, et al. Prefrontal control of the amygdala during real-time fMRI neurofeedback training of emotion regulation. PLoS One, 2013, 8(11): e79184.
26.	Paret C, Ruf M, Gerchen M F, et al. fMRI neurofeedback of amygdala response to aversive stimuli enhances prefrontal-limbic brain connectivity. Neuroimage, 2016, 125: 182-188.
27.	Kadosh K C, Luo Qiang, de Burca C, et al. Using real-time fMRI to influence effective connectivity in the developing emotion regulation network. Neuroimage, 2016, 125: 616-626.
28.	Ruiz S, Rana M, Sass K, et al. Brain network connectivity and behaviour enhancement: a fMRI-BCI study//17th Annual Meeting of the Organization for Human Brain Mapping. Quebec City: The Organization for Human Brain Mapping, 2011.
29.	Megumi F, Yamashita A, Kawato M, et al. Functional MRI neurofeedback training on connectivity between two regions induces long-lasting changes in intrinsic functional network. Front Hum Neurosci, 2015, 9: 160.
30.	Koush Y, Rosa M J, Robineau F, et al. Connectivity-based neurofeedback: Dynamic causal modeling for real-time fMRI. Neuroimage, 2013, 81: 422-430.
31.	Koush Y, Meskaldji D E, Pichon S, et al. Learning control over emotion networks through connectivity-based neurofeedback. Cereb Cortex, 2017, 27(2): 1193-1202.
32.	Ruiz S, Buyukturkoglu K, Rana M, et al. Real-time fMRI brain computer interfaces: Self-regulation of single brain regions to networks. Biol Psychol, 2014, 95(SI): 4-20.
33.	童莉, 王理军, 郑载舟, 等. 基于多体素模式分析的 fMRI 视觉解码研究综述. 信息工程大学学报, 2015, 16(1): 66-72.
34.	Li Zhonglin, Tong Li, Guan Min, et al. Altered resting-state amygdala functional connectivity after real-time fMRI emotion self-regulation training. Biomed Res Int, 2016, 2016: 1-8.
35.	Li Zhonglin, Tong Li, Wang Linyuan, et al. Self-regulating positive emotion networks by feedback of multiple emotional brain states using real-time fMRI. Exp Brain Res, 2016, 234(12): 3575-3586.
36.	Li Xiaofei, Yao Li, Ye Qing, et al. Online spatial normalization for real-time fMRI. PLoS One, 2014, 9(7): e103302.

1. Bargmann C I, Newsome W T. The brain research through advancing innovative neurotechnologies (BRAIN) initiative and neurology. JAMA Neurol, 2014, 71(6): 675-676.
2. Brühl A B. Making sense of real-time functional magnetic resonance imaging (rtfMRI) and rtfMRI neurofeedback. Int J Neuropsychopharmacol, 2015, 18(6): pyv020..
3. Thibault R T, Lifshitz M, Raz A. The self-regulating brain and neurofeedback: Experimental science and clinical promise. Cortex, 2016, 74: 247-261.
4. Stoeckel L E, Garrison K A, Ghosh S, et al. Optimizing real time fMRI neurofeedback for therapeutic discovery and development. Neuroimage Clin, 2014, 5: 245-255.
5. Birbaumer N, Ruiz S, Sitaram R. Learned regulation of brain metabolism. Trends Cogn Sci, 2013, 17(6): 295-302.
6. Scheinost D, Stoica T, Saksa J, et al. Orbitofrontal cortex neurofeedback produces lasting changes in contamination anxiety and resting-state connectivity. Transl Psychiatry, 2013, 3(4): e250.
7. Robineau F, Rieger S W, Mermoud C, et al. Self-regulation of inter-hemispheric visual cortex balance through real-time fMRI neurofeedback training. Neuroimage, 2014, 100: 1-14.
8. Arns M, Heinrich H, Strehl U. Evaluation of neurofeedback in ADHD: The long and winding road. Biol Psychol, 2014, 95(SI): 108-115.
9. Esmail S, Linden D E J. Neural networks and neurofeedback in Parkinson's disease. NeuroRegulation, 2014, 1(3/4): 240.
10. Thibault R T, Lifshitz M, Birbaumer N, et al. Neurofeedback, self-regulation, and brain imaging: clinical science and fad in the service of mental disorders. Psychother Psychosom, 2015, 84(4): 193-207.
11. Cannon R L. Editorial perspective: Defining neurofeedback and its functional processes. NeuroRegulation, 2015, 2(2): 60-69.
12. Linden D E. Neurofeedback and networks of depression. Dialogues Clin Neurosci, 2014, 16(1): 103-112.
13. Ruiz S, Lee S, Soekadar S R, et al. Acquired self-control of insula cortex modulates emotion recognition and brain network connectivity in schizophrenia. Hum Brain Mapp, 2013, 34(1): 200-212.
14. Mulders P C, van Eijndhoven P F, Schene A H, et al. Resting-state functional connectivity in major depressive disorder: A review. Neurosci Biobehav Rev, 2015, 56: 330-344.
15. Jie Biao, Zhang Daoqiang, Wee C Y, et al. Topological graph kernel on multiple thresholded functional connectivity networks for mild cognitive impairment classification. Hum Brain Mapp, 2014, 35(7): 2876-2897.
16. 吕柄江, 赵小杰, 姚力, 等. 实时功能磁共振成像及其应用. 科学通报, 2014, 59(2): 195-209.
17. Cox R W, Jesmanowicz A, Hyde J S. Real-time functional magnetic resonance imaging. Magn Reson Med, 1995, 33(2): 230-236.
18. Weiskopf N, Veit R, Erb M, et al. Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): Methodology and exemplary data. Neuroimage, 2003, 19(3): 577-586.
19. Sulzer J, Haller S, Scharnowski F, et al. Real-time fMRI neurofeedback: Progress and challenges. Neuroimage, 2013, 76(1): 386-399.
20. 李椋. 基于实时功能磁共振成像的脑机接口技术研究. 郑州: 信息工程大学, 2014.
21. Brühl A B, Scherpiet S, Sulzer J, et al. Real-time neurofeedback using functional MRI could improve down-regulation of amygdala activity during emotional stimulation: A proof-of-concept study. Brain Topogr, 2014, 27(1): 138-148.
22. deCharms R C, Christoff K, Glover G H, et al. Learned regulation of spatially localized brain activation using real-time fMRI. Neuroimage, 2004, 21(1): 436-443.
23. Li Baojuan, Liu Li, Friston K J, et al. A treatment-resistant default mode subnetwork in major depression. Biol Psychiatry, 2013, 74(1): 48-54.
24. 胡德文, 沈辉. 脑磁共振影像数据时空分析. 北京: 科学出版社, 2014: 133-176.
25. Zotev V, Phillips R, Young K D, et al. Prefrontal control of the amygdala during real-time fMRI neurofeedback training of emotion regulation. PLoS One, 2013, 8(11): e79184.
26. Paret C, Ruf M, Gerchen M F, et al. fMRI neurofeedback of amygdala response to aversive stimuli enhances prefrontal-limbic brain connectivity. Neuroimage, 2016, 125: 182-188.
27. Kadosh K C, Luo Qiang, de Burca C, et al. Using real-time fMRI to influence effective connectivity in the developing emotion regulation network. Neuroimage, 2016, 125: 616-626.
28. Ruiz S, Rana M, Sass K, et al. Brain network connectivity and behaviour enhancement: a fMRI-BCI study//17th Annual Meeting of the Organization for Human Brain Mapping. Quebec City: The Organization for Human Brain Mapping, 2011.
29. Megumi F, Yamashita A, Kawato M, et al. Functional MRI neurofeedback training on connectivity between two regions induces long-lasting changes in intrinsic functional network. Front Hum Neurosci, 2015, 9: 160.
30. Koush Y, Rosa M J, Robineau F, et al. Connectivity-based neurofeedback: Dynamic causal modeling for real-time fMRI. Neuroimage, 2013, 81: 422-430.
31. Koush Y, Meskaldji D E, Pichon S, et al. Learning control over emotion networks through connectivity-based neurofeedback. Cereb Cortex, 2017, 27(2): 1193-1202.
32. Ruiz S, Buyukturkoglu K, Rana M, et al. Real-time fMRI brain computer interfaces: Self-regulation of single brain regions to networks. Biol Psychol, 2014, 95(SI): 4-20.
33. 童莉, 王理军, 郑载舟, 等. 基于多体素模式分析的 fMRI 视觉解码研究综述. 信息工程大学学报, 2015, 16(1): 66-72.
34. Li Zhonglin, Tong Li, Guan Min, et al. Altered resting-state amygdala functional connectivity after real-time fMRI emotion self-regulation training. Biomed Res Int, 2016, 2016: 1-8.
35. Li Zhonglin, Tong Li, Wang Linyuan, et al. Self-regulating positive emotion networks by feedback of multiple emotional brain states using real-time fMRI. Exp Brain Res, 2016, 234(12): 3575-3586.
36. Li Xiaofei, Yao Li, Ye Qing, et al. Online spatial normalization for real-time fMRI. PLoS One, 2014, 9(7): e103302.

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