|
|
|
| Advances in Real-Time Functional Magnetic Resonance Imaging Neurofeedback |
| Yang Dongmei1, Zhang Wenhai2,3#*, Ding Qiang4#* |
1(School of Fundamental Science,Jiangsu Vocational College of Medicine,Yancheng 224051,Jiangsu,China)
2(Mental Health Center,Yancheng Institute of Technology,Yancheng 224051,Jiangsu,China)
3(Research Center of Brain and Cognitive Neuroscience,Liaoning Normal University,Dalian 116029,Liaoning,China)
4(Department of Psychological Medicine,Children’s Hospital of Fudan University,Shanghai 201102,China) |
|
|
|
|
Abstract Neurofeedback refers to a self-regulation technique that provides individuals with feedback about specific brain activity in connection with a related behavior. Real-time Functional Magnetic Resonance Imaging Neurofeedback (rtfMRI-nf) is a novel neurofeedback technique,requiring participants to adjust blood oxygen level-dependent signal index during training to regulate their brain activity. Recently,rtfMRI-nf has made significant progress in data acquisition and analysis. Two new rtfMRI-nf technologies have begun available,decoded neurofeedback and functional connectivity-based neurofeedback. This paper introduced the two new rtfMRI-nf technologies and summarizes their progress in key method areas such as implicit protocol,multivariate analysis,and connectivity analysis. At the same time,this paper reviewed the current status of the two rtfMRI-nf technologies in basic research fields such as perceptual learning and metacognition,as well as clinical research fields such as fear elimination,depression,autism spectrum disorder and nicotine addiction. Finally,we discussed the two potential problems of “one to many” relationship and dimension curse of these two rtfMRI-nf technologies,and propose solutions.
|
|
Received: 21 October 2019
|
|
|
|
|
|
[1] Thibault RT,Lifshitz M,Raz A.The self-regulating brain and neurofeedback:Experimental science and clinical promise[J].Cortex,2016,74:247-261.
[2] Kadosh KC,Staunton G.A systematic review of the psychological factors that influence neurofeedback learning outcomes[J].NeuroImage,2019,185:545-555.
[3] Rance M,Walsh C,Sukhodolsky DG,et al.Time course of clinical change following neurofeedback[J].NeuroImage,2018,181:807-813.
[4] Herwig U,Lutz J,Scherpiet S,et al.Training emotion regulation through real-time fMRI neurofeedback of amygdala activity[J].NeuroImage,2019,184:687-696.
[5] Sulzer J,Haller S,Scharnowski F,et al.Real-time fMRI neurofeedback:Progress and challenges[J].NeuroImage,2013,76:386-399.
[6] Thibault RT,MacPherson A,Lifshitz M,et al.Neurofeedback with fMRI:A critical systematic review[J].NeuroImage,2018,172:786-807.
[7] Watanabe T,Sasaki Y,Shibata K,et al.Advances in fMRI real-time neurofeedback[J].Trends in Cognitive Sciences,2017,21(12):997-1010.
[8] Shibata K,Watanabe T,Sasaki Y,et al.Perceptual learning incepted by decoded fMRI neurofeedback without stimulus presentation[J].Science,2011,334(6061):1413-1415.
[9] 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].Journal of Cognitive Neuroscience,2015,27(8):1552-1572.
[10] 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[J].Frontiers in Human Neuroscience,2015,9:160.
[11] Amano K,Shibata K,Kawato M,et al.Learning to associate orientation with color in early visual areas by associative decoded fMRI neurofeedback[J].Current Biology,2016,26(14):1861-1866.
[12] Shibata K,Watanabe T,Kawato M,et al.Differential activation patterns in the same brain region led to opposite emotional states[J].PLoS Biology,2016,14(9):e1002546.
[13] Koizumi A,Amano K,Cortese A,et al.Fear reduction without fear through reinforcement of neural activity that bypasses conscious exposure[J].Nature Human Behaviour,2017,1(1):0006.
[14] Cortese A,Amano K,Koizumi A,et al.Decoded fMRI neurofeedback can induce bidirectional confidence changes within single participants[J].NeuroImage,2017,149:323-337.
[15] Koush Y,Meskaldji DE,Pichon S,et al.Learning control over emotion networks through connectivity-based neurofeedback[J].Cerebral Cortex,2017,27(2):1193-1202.
[16] Yamada T,Hashimoto R,Yahata N,et al.Resting-state functional connectivity-based biomarkers and functional MRI-based neurofeedback for psychiatric disorders:A Challenge for developing theranostic biomarkers[J].International Journal of Neuropsychopharmacology,2017,20(10):769-781.
[17] Yamashita A,Hayasaka S,Kawato M,et al.Connectivity neurofeedback training can differentially change functional connectivity and cognitive performance[J].Cerebral Cortex,2017,27(10):4960-4970.
[18] Taschereau-Dumouchel V,Cortese A,Chiba T,et al.Towards an unconscious neural reinforcement intervention for common fears[J].Proceedings of the National Academy of Sciences,2018,115(13):3470-3475.
[19] Whelan R,Garavan H.When optimism hurts:Inflated predictions in psychiatric neuroimaging.[J].Biological Psychiatry,2014,75(9):746-748.
[20] Yahata N,Kasai K,Kawato M.Computational neuroscience approach to biomarkers and treatments for mental disorders[J].Psychiatry and Clinical Neurosciences,2017,71(4):215-237.
[21] Shibata K,Lisi G,Cortese A,et al.Toward a comprehensive understanding of the neural mechanisms of decoded neurofeedback[J].NeuroImage,2019,188:539-556.
[22] Bryant RA,Kenny L,Rawson N,et al.Efficacy of exposure-based cognitive behaviour therapy for post-traumatic stress disorder in emergency service personnel:A randomised clinical trial[J].Psychological Medicine,2019,49(9):1565-1573.
[23] Cai Chang,Ogawa K,Kochiyama T,et al.Temporal recalibration of motor and visual potentials in lag adaptation in voluntary movement[J].NeuroImage,2018,172:654-662.
[24] Oblak EF,Sulzer JS,Lewis-Peacock JA.A simulation-based approach to improve decoded neurofeedback performance[J].NeuroImage,2019,195:300-310.
[25] Rosenberg MD,Finn ES,Scheinost D,et al.A neuromarker of sustained attention from whole-brain functional connectivity[J].Nature Neuroscience,2016,19(1):165-171.
[26] Koush Y,Rosa MJ,Robineau F,et al.Connectivity-based neurofeedback:Dynamic causal modeling for real-time fMRI[J].NeuroImage,2013,81:422-430.
[27] Chiba T,Kanazawa T,Koizumi A,et al.Current Status of Neurofeedback for Post-traumatic Stress Disorder:A systematic review and the possibility of decoded neurofeedback[J].Frontiers in Human Neuroscience,2019,13:233.
[28] Sun Hailun,Jiang Rongtao,Qi Shile,et al.Preliminary prediction of individual response to electroconvulsive therapy using whole-brain functional magnetic resonance imaging data[J].NeuroImage:Clinical,2020,26:102080.
[29] Sorger B,Scharnowski F,Linden DEJ,et al.Control freaks:Towards optimal selection of control conditions for fMRI neurofeedback studies[J].NeuroImage,2019,186:256-265.
[30] Paret C,Goldway N,Zich C,et al.Current progress in real-time functional magnetic resonance-based neurofeedback:Methodological challenges and achievements[J].NeuroImage,2019,202:116107.
[31] Fovet T,Micoulaud-Franchi J A,Vialatte F B,et al.On assessing neurofeedback effects:should double-blind replace neurophysiological mechanisms?[J].Brain,2017,140(10):e63-e63.
[32] Lubianiker N,Goldway N,Fruchtman-Steinbok T,et al.Process-based framework for precise neuromodulation[J].Nature human Human Behaviour,2019,3(5):436-445.
[33] Nakazawa E,Yamamoto K,Tachibana K,et al.Ethics of decoded neurofeedback in clinical research,treatment,and moral enhancement[J].AJOB Neuroscience,2016,7(2):110-117.
[34] Cohen JD,Daw N,Engelhardt B,et al.Computational approaches to fMRI analysis[J].Nature Neuroscience,2017,20(3):304-313.
[35] Toda A,Imamizu H,Kawato M,et al.Reconstruction of two-dimensional movement trajectories from selected magnetoencephalography cortical currents by combined sparse Bayesian methods[J].NeuroImage,2011,54(2):892-905.
[36] Yamashita O,Sato MA,Yoshioka T,et al.Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns[J].NeuroImage,2008,42(4):1414-1429.
[37] Seyyedi S H,Minaei-Bidgoli B.Using learning automata to determine proper subset size in high-dimensional spaces[J].Journal of Experimental &Theoretical Artificial Intelligence,2017,29(2):415-432.
[38] Oblak EF,Sulzer JS,Lewis-Peacock JA.A simulation-based approach to improve decoded neurofeedback performance[J].NeuroImage,2019,195:300-310.
[39] Li Hongming,Fan Yong.Interpretable,highly accurate brain decoding of subtly distinct brain states from functional MRI using intrinsic functional networks and long short-term memory recurrent neural networks[J].NeuroImage,2019,202:116059. |
|
|
|
