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Research progress on the regulation target of real-time functional magnetic resonance imaging neurofeedback
ZOU Zhi  LI Zhonglin  ZHOU Jing  WU Xiaoling  CHEN Bairu  WANG Caiyun  YAN Fengshan  MA Huimin  DOU Shewei  LI Yongli 

Cite this article as: Zou Z, Li ZL, Zhou J, et al. Research progress on the regulation target of real-time functional magnetic resonance imaging neurofeedback. Chin J Magn Reson Imaging, 2020, 11(8): 684-687. DOI:10.12015/issn.1674-8034.2020.08.022.


[Abstract] Real-time fMRI neurofeedback (rtfMRI-NF) technology can change the subject's neuroplasticity and learning ability. It has important clinical value for the possible treatments of neurological and psychiatric diseases and is the focus of current research. The principle is that by training subjects to regulate specific brain activity through rtfMRI-NF training and thereby change brain function and clinical behavior. Therefore, it is necessary to select appropriate regulation targets according to specific clinical applications before training. In recent years, with the rapid development of this technology, the analysis methods of rtfMRI-NF regulation target increase, so the choice is more and more diversified. According to the current research status of rtfMRI-NF, this paper divides the analysis methods of regulation targets into the following three types: based on region of interest activity, based on brain connections, and based on multi-voxel pattern analysis, which are introduced and discussed the existing problems and future prospects.
[Keywords] functional magnetic resonance imaging;neurofeedback;regulation target;region of interest;brain connectivity

ZOU Zhi Department of Imaging, Henan Provincial People’s Hospital, Zhengzhou 450000, China

LI Zhonglin Department of Imaging, Henan Provincial People’s Hospital, Zhengzhou 450000, China

ZHOU Jing Health Management Center, Henan Provincial People’s Hospital, Zhengzhou 450000, China

WU Xiaoling Department of Imaging, Henan Provincial People’s Hospital, Zhengzhou 450000, China

CHEN Bairu Department of Imaging, Henan Provincial People’s Hospital, Zhengzhou 450000, China

WANG Caiyun Department of Imaging, Henan Provincial People’s Hospital, Zhengzhou 450000, China

YAN Fengshan Department of Imaging, Henan Provincial People’s Hospital, Zhengzhou 450000, China

MA Huimin Health Management Center, Henan Provincial People’s Hospital, Zhengzhou 450000, China

DOU Shewei Department of Imaging, Henan Provincial People’s Hospital, Zhengzhou 450000, China

LI Yongli* Health Management Center, Henan Provincial People’s Hospital, Zhengzhou 450000, China

*Corresponding to: Li YL, E-mail: shyliyongli@126.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  This work was part of National Key R&D Program of China Under Grant No.2017YFB1002502 Henan Medical Science and Technology Plan No. SB201901077
Received  2020-03-03
Accepted  2020-04-12
DOI: 10.12015/issn.1674-8034.2020.08.022
Cite this article as: Zou Z, Li ZL, Zhou J, et al. Research progress on the regulation target of real-time functional magnetic resonance imaging neurofeedback. Chin J Magn Reson Imaging, 2020, 11(8): 684-687. DOI:10.12015/issn.1674-8034.2020.08.022.

[1]
Samantha JF, Sarah FD, Thushini M, et al. A guide to literature informed decisions in the design of real time fMRI neurofeedback studies: A systematic review. Front Hum Neurosci, 2020, 14: 60.
[2]
Kazuhisa SG. Toward a comprehensive understanding of the neural mechanisms of decoded neurofeedback. NeuroImage, 2019, 188: 539-556.
[3]
Thibault RT, Macpherson A, Lifshitz M, et al. Neurofeedback with fMRI: A critical systematic review. NeuroImage, 2017, 172(2018): 786-807.
[4]
Watanabe T, Sasaki Y, Shibata K, et al. Advances in fMRI real-time neurofeedback. Trends in Cognitive Sciences, 2017, 21(12): 997-1010.
[5]
李晓陵,姚春丽,王丰,等.老年抑郁症影像学研究进展.磁共振成像, 2020, 11(3): 228-231.
[6]
Posse S, Fitzgerald D, Gao K, et al. Real-time fMRI of temporolimbic regions detects amygdala activation during single-trial self-induced sadness. NeuroImage, 2003, 18(3): 760-768.
[7]
Pavla L, Adéla L, Barbora K, et al. fMRI neurofeedback in emotion regulation: A literature review. NeuroImage, 2019, 193: 75-92.
[8]
Paret C, Hendler H. Live from the "Regulating Brain" : Harnessing the brain to change emotion. Am Psychol Associa, 2020, 20(1): 126-131.
[9]
Groenewold NA, Opmeer EM, Jonge PD, et al. Emotional valence modulates brain functional abnormalities in depression: Evidence from a meta-analysis of fMRI studies. Neurosci Biobehavioral Reviews, 2013, 37(2): 152-163.
[10]
Alegria AA, Wulff M, Brinson H, et al. Real-time fMRI neurofeedback in adolescents with attention deficit hyperactivity disorder. Hum Brain Mapping, 2017, 38(6): 3190-3209.
[11]
Dianne N, Noor T, Edwin T. Neurofeedback: self-regulation of pain using real-time fMRI a systematic review. Erasmus J Med, 2012, 2(2): 29-33.
[12]
Sherwood MS, Parker JG, Diller EE, et al. Self-directed down-regulation of auditory cortex activity mediated by real-time fMRI neurofeedback augments attentional processes, resting cerebral perfusion, and auditory activation. NeuroImage, 2019, 195: 475-489.
[13]
Emmert K, Kopel R, Koush Y, et al. Continuous vs intermittent neurofeedback to regulate auditory cortex activity of tinnitus patients using real-time fMRI-A pilot study. NeuroImage: Clinical, 2017, 14: 97-104.
[14]
Li X, Hartwell KJ, Borckardt J, et al. Volitional reduction of anterior cingulate cortex activity produces decreased cue craving in smoking cessation: a preliminary real-time fMRI study. Addiction Biology, 2013, 18(4): 739-748.
[15]
Hartwell KJ, Hanlon CA, Li X, et al. Individualized real-time fMRI neurofeedback to attenuate craving in nicotine-dependent smokers. J Psychiatry Neurosci, 2016, 41(1): 48-55.
[16]
Simon HK, Ralf V, Maartje SS, et al. Real-time fMRI neurofeedback training to improve eating behavior by self-regulation of the dorsolateral prefrontal cortex: A randomized controlled trial in overweight and obese subjects. NeuroImage, 2019, 191: 596-609.
[17]
Young KD, Siegle GJ, Misaki M, et al. Altered task-based and resting-state amygdala functional connectivity following real-time fMRI amygdala neurofeedback training in major depressive disorder. NeuroImage: Clinical, 2018, 17: 691-703.
[18]
Yamada T, Hashimoto RI, Yahata N, et al. Resting-state functional connectivity-based biomarkers and functional MRI-based neurofeedback for psychiatric disorders: A challenge for developing theranostic biomarkers. Inter J Neuropsychopharmacology, 2017, 20(10): 769-781.
[19]
Li ZL, Tong L, Guan M, et al. Altered resting-state amygdala functional connectivity after real-time fMRI emotion self-regulation training. Bio Med Res Inter, 2016, 2016: 2719895.
[20]
Paret C, Kluetsch R, Zaehringer J, et al. Alterations of amygdala-prefrontal connectivity with real-time fMRI neurofeedback in BPD patients. Soc Cognit Affective Neurosci, 2016, 11(6): 952-960.
[21]
Ruiz S, Rana M, Sass K, et al. Brain network connectivity and behaviour enhancement: a fMRI-BCI study//The 17th annual meeting of the organization for human brain mapping, Québec City, Canada, 2011.
[22]
Megumi F, Ayumu Y, Mitsuo K, 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.
[23]
Kim D, Yoo S, Tegethoff M, et al. The inclusion of functional connectivity information into fMRI-based neurofeedback improves its efficacy in the reduction of cigarette cravings. J Cognit Neurosci, 2015, 27(8): 1552-1572.
[24]
Spetter MS, Malekshahi R, Birbaumer N, et al. Volitional regulation of brain responses to food stimuli in overweight and obese subjects: a real-time fMRI feedback study. Appetite, 2017, 112: 188-195.
[25]
Yamashita A, Hayasaka S, Kawato M, et al. Connectivity neurofeedback training can differentially change functional connectivity and cognitive performance. Cerebral Cortex, 2017, 27(10): 4960-4970.
[26]
Misaki M, Tsuchiyagaito A, Zoubi OA, et al. Connectome-wide search for functional connectivity locus associated with pathological rumination as a target for real-time fMRI neurofeedback intervention. NeuroImage: Clinical, 2020, 26: 102244.
[27]
Weiss F, Zamoscik V, Schmidt SN, et al. Just a very expensive breathing training? Risk of respiratory artefacts in functional connectivity-based real-time fMRI neurofeedback. NeuroImage, 2020, 210: 116580.
[28]
Koush Y, Rosa MJ, Robineau F, et al. Connectivity-based neurofeedback: Dynamic causal modeling for real-time fMRI. NeuroImage, 2013, 81: 422-430.
[29]
Yury K, Nemanjia M, Frank S, et al. Data-driven tensor independent component analysis for model-based connectivity neurofeedback. NeuroImage, 2019, 184: 214-226.
[30]
Koush Y, Meskaldji DE, Pichon S, et al. Learning control over emotion networks through connectivity-based neurofeedback. Cerebral Cortex, 2015, 27(2): bhv311.
[31]
Yahata N, Morimoto J, Hashimoto R, et al. A small number of abnormal brain connections predicts adult autism spectrum disorder. Nature Communications, 2016, 7: 11254.
[32]
贺文颉,卜海兵,童莉,等.基于脑网络连接的实时功能磁共振成像神经反馈技术研究进展.生物医学工程学杂志, 2017, 34(3): 142-146.
[33]
Zeng LL, Shen H, Liu L, et al. Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain, 2012, 135(5): 1498-1507.
[34]
LaConte SM, Peltier SJ, Hu XP. Real-time fMRI using brain-state classification. Hum Brain Mapping, 2007, 28(10): 1033-1044.
[35]
Sitaram R, Lee S, Ruiz S, et al. Real-time support vector classification and feedback of multiple emotional brain states. Neuroimage, 2011, 56(2): 753-765.
[36]
Li ZL, Tong L, Wang LY, et al. Self-regulating positive emotion networks by feedback of multiple emotional brain states using real-time fMRI. Exper Brain Res, 2016, 234(12): 3575-3586.
[37]
Shibata K, Watanabe T, Kawato M, Sasaki Y. Differential activation patterns in the same brain region led to opposite emotional states. PLoS Biol, 2016, 14(9): e1002546.
[38]
Koizumi A, Amano K, Cortese A, et al. Fear reduction without fear through reinforcement of neural activity that bypasses conscious exposure. Nature Human Behaviour, 2016(1): 0006.
[39]
Cortese A, Amano K, Koizumi A, et al. Decoded fMRI neurofeedback can induce bidirectional confidence changes within single participants. NeuroImage, 2017, 149: 323-337.

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