1932

Abstract

Multiple mental disorders have been associated with dysregulation of precise brain processes. However, few therapeutic approaches can correct such specific patterns of brain activity. Since the late 1960s and early 1970s, many researchers have hoped that this feat could be achieved by closed-loop brain imaging approaches, such as neurofeedback, that aim to modulate brain activity directly. However, neurofeedback never gained mainstream acceptance in mental health, in part due to methodological considerations. In this review, we argue that, when contemporary methodological guidelines are followed, neurofeedback is one of the few intervention methods in psychology that can be assessed in double-blind placebo-controlled trials. Furthermore, using new advances in machine learning and statistics, it is now possible to target very precise patterns of brain activity for therapeutic purposes. We review the recent literature in functional magnetic resonance imaging neurofeedback and discuss current and future applications to mental health.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-clinpsy-072220-014550
2022-05-09
2024-03-29
Loading full text...

Full text loading...

/deliver/fulltext/clinpsy/18/1/annurev-clinpsy-072220-014550.html?itemId=/content/journals/10.1146/annurev-clinpsy-072220-014550&mimeType=html&fmt=ahah

Literature Cited

  1. Abbott DF, Opdam HI, Briellmann RS, Jackson GD. 2005. Brief breath holding may confound functional magnetic resonance imaging studies. Hum. Brain Mapp. 24:4284–90
    [Google Scholar]
  2. Alegria AA, Wulff M, Brinson H, Barker GJ, Norman LJ et al. 2017. Real-time fMRI neurofeedback in adolescents with attention deficit hyperactivity disorder. Hum. Brain Mapp. 38:63190–209
    [Google Scholar]
  3. Amano K, Shibata K, Kawato M, Sasaki Y, Watanabe T. 2016. Learning to associate orientation with color in early visual areas by associative decoded fMRI neurofeedback. Curr. Biol. 26:141861–66
    [Google Scholar]
  4. APA Pres. Task Force Evid.-Based Pract 2006. Evidence-based practice in psychology. Am. Psychol. 61:4271–85
    [Google Scholar]
  5. Arns M, de Ridder S, Strehl U, Breteler M, Coenen A. 2009. Efficacy of neurofeedback treatment in ADHD: the effects on inattention, impulsivity and hyperactivity: a meta-analysis. Clin. EEG Neurosci. 40:3180–89
    [Google Scholar]
  6. Avena-Koenigsberger A, Misic B, Sporns O 2018. Communication dynamics in complex brain networks. Nat. Rev. Neurosci. 19:117–33
    [Google Scholar]
  7. Bagarinao E, Matsuo K, Nakai T, Sato S 2003. Estimation of general linear model coefficients for real-time application. NeuroImage 19:2 Part 1422–29
    [Google Scholar]
  8. Bashivan P, Kar K, DiCarlo JJ. 2019. Neural population control via deep image synthesis. Science 364:6439aav9436
    [Google Scholar]
  9. Bassett DS, Sporns O. 2017. Network neuroscience. Nat. Neurosci. 20:353–64
    [Google Scholar]
  10. Bazeille T, DuPre E, Richard H, Poline J-P, Thirion B. 2021. An empirical evaluation of functional alignment using inter-subject decoding. NeuroImage 245:118683
    [Google Scholar]
  11. Beatty J, Greenberg A, Deibler WP, O'Hanlon JF. 1974. Operant control of occipital theta rhythm affects performance in a radar monitoring task. Science 183:4127871–73
    [Google Scholar]
  12. Bu J, Young KD, Hong W, Ma R, Song H et al. 2019. Effect of deactivation of activity patterns related to smoking cue reactivity on nicotine addiction. Brain 142:61827–41
    [Google Scholar]
  13. Chambless DL, Hollon SD. 1998. Defining empirically supported therapies. J. Consult. Clin. Psychol. 66:17–18
    [Google Scholar]
  14. Chen P-H, Chen J, Yeshurun Y, Hasson U, Haxby JV, Ramadge PJ 2015. A reduced-dimension fMRI shared response model. Advances in Neural Information Processing Systems, Vol. 28 C Cortes, N Lawrence, D Lee, M Sugiyama, R Garnett 460–68 Red Hook, NY: Curran
    [Google Scholar]
  15. Chiba T, Kanazawa T, Koizumi A, Ide K, Taschereau-Dumouchel V et al. 2019. Current status of neurofeedback for post-traumatic stress disorder: a systematic review and the possibility of decoded neurofeedback. Front. Hum. Neurosci. 13:233
    [Google Scholar]
  16. Cohen JD, Daw N, Engelhardt B, Hasson U, Li K et al. 2017. Computational approaches to fMRI analysis. Nat. Neurosci. 20:3304–13
    [Google Scholar]
  17. Cohen MS. 2001. Real-time functional magnetic resonance imaging. Methods 25:2201–20
    [Google Scholar]
  18. Cortese A, Amano K, Koizumi A, Kawato M, Lau H. 2016. Multivoxel neurofeedback selectively modulates confidence without changing perceptual performance. Nat. Commun. 7:13669
    [Google Scholar]
  19. Cortese A, Lau H, Kawato M 2020. Unconscious reinforcement learning of hidden brain states supported by confidence. Nat. Commun. 11:4429
    [Google Scholar]
  20. Cortese A, Tanaka SC, Amano K, Koizumi A, Lau H et al. 2021. The DecNef collection, fMRI data from closed-loop decoded neurofeedback experiments. Sci. Data 8:165
    [Google Scholar]
  21. Cox RW, Jesmanowicz A. 1999. Real-time 3D image registration for functional MRI. Magn. Reson. Med. 42:61014–18
    [Google Scholar]
  22. Cox RW, Jesmanowicz A, Hyde JS. 1995. Real-time functional magnetic resonance imaging. Magn. Reson. Med. 33:2230–36
    [Google Scholar]
  23. deBettencourt MT, Cohen JD, Lee RF, Norman KA, Turk-Browne NB. 2015. Closed-loop training of attention with real-time brain imaging. Nat. Neurosci. 18:3470–75
    [Google Scholar]
  24. Eklund A, Dufort P, Villani M, Laconte S. 2014. BROCCOLI: software for fast fMRI analysis on many-core CPUs and GPUs. Front. Neuroinform. 8:24
    [Google Scholar]
  25. Emmert K, Kopel R, Sulzer J, Brühl AB, Berman BD et al. 2016. Meta-analysis of real-time fMRI neurofeedback studies using individual participant data: How is brain regulation mediated?. NeuroImage 124:A806–12
    [Google Scholar]
  26. Fetz EE. 1969. Operant conditioning of cortical unit activity. Science 163:3870955–58
    [Google Scholar]
  27. Fetz EE, Finocchio DV. 1971. Operant conditioning of specific patterns of neural and muscular activity. Science 174:4007431–35
    [Google Scholar]
  28. Fonov VS, Evans AC, McKinstry RC, Almli CR, Collins DL. 2011. Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage 54:1313–27
    [Google Scholar]
  29. Gembris D, Taylor JG, Schor S, Frings W, Suter D, Posse S. 2000. Functional magnetic resonance imaging in real time (FIRE): sliding-window correlation analysis and reference-vector optimization. Magn. Reson. Med. 43:2259–68
    [Google Scholar]
  30. Goddard N, Hood G, Cohen J, Eddy W, Genovese C et al. 1997. Online analysis of functional MRI datasets on parallel platforms. J. Supercomput. 11:3295–318
    [Google Scholar]
  31. Goebel R, Esposito F, Formisano E. 2006. Analysis of FIAC data with BrainVoyager QX: from single-subject to cortically aligned group GLM analysis and self-organizing group ICA. Hum. Brain Mapp. 27:5392–401
    [Google Scholar]
  32. Goodfellow I, Bengio Y, Courville A, Bengio Y. 2016. Deep Learning, Vol. 1 Cambridge, MA: MIT Press
  33. Greenberg G. 2013. The psychiatric drug crisis. New Yorker Sept. 3. https://www.betterdaysandnights.com/The%20Psychiatric%20Drug%20Crisis.pdf
    [Google Scholar]
  34. Griebel G, Holmes A. 2013. 50 years of hurdles and hope in anxiolytic drug discovery. Nat. Rev. Drug Discov. 12:9667–87
    [Google Scholar]
  35. Guan M, Ma L, Li L, Yan B, Zhao L et al. 2015. Self-regulation of brain activity in patients with postherpetic neuralgia: a double-blind randomized study using real-time fMRI neurofeedback. PLOS ONE 10:4e0123675
    [Google Scholar]
  36. Hamilton JP, Glover GH, Bagarinao E, Chang C, Mackey S et al. 2016. Effects of salience-network-node neurofeedback training on affective biases in major depressive disorder. Psychiatry Res. Neuroimaging 249:91–96
    [Google Scholar]
  37. Hartwell KJ, Hanlon CA, Li X, Borckardt JJ, Canterberry M et al. 2016. Individualized real-time fMRI neurofeedback to attenuate craving in nicotine-dependent smokers. J. Psychiatry Neurosci. 41:148–55
    [Google Scholar]
  38. Haxby JV, Connolly AC, Guntupalli JS. 2014. Decoding neural representational spaces using multivariate pattern analysis. Annu. Rev. Neurosci. 37:435–56
    [Google Scholar]
  39. Haxby JV, Gobbini MI, Furey ML, Ishai A, Schouten JL, Pietrini P. 2001. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293:55392425–30
    [Google Scholar]
  40. Haxby JV, Guntupalli JS, Connolly AC, Halchenko YO, Conroy BR et al. 2011. A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron 72:2404–16
    [Google Scholar]
  41. Hinds O, Ghosh S, Thompson TW, Yoo JJ, Whitfield-Gabrieli S et al. 2011. Computing moment-to-moment BOLD activation for real-time neurofeedback. NeuroImage 54:1361–68
    [Google Scholar]
  42. Holtmann M, Sonuga-Barke E, Cortese S, Brandeis D 2014. Neurofeedback for ADHD: a review of current evidence. Child Adolesc. Psychiatr. Clin. N. Am. 23:4789–806
    [Google Scholar]
  43. Hyman SE. 2012. Revolution stalled. Sci. Transl. Med. 4:155cm11
    [Google Scholar]
  44. Kamiya J. 1962. Conditioned discrimination of the EEG alpha rhythm in humans Paper presented at the 42nd Meeting of the Western Psychological Association San Francisco, CA: April 18–21
  45. Kamiya J. 1966. Trained control of EEG alpha frequency in humans Paper presented at Stanford University Palo Alto, CA:
  46. Kamiya J. 1968. Conscious control of brain waves. Psychol. Today 1:56–60
    [Google Scholar]
  47. Kamiya J. 2011. The first communications about operant conditioning of the EEG. J. Neurother. 15:165–73
    [Google Scholar]
  48. Kastrup A, Krüger G, Glover GH, Moseley ME. 1999. Assessment of cerebral oxidative metabolism with breath holding and fMRI. Magn. Reson. Med. 42:3608–11
    [Google Scholar]
  49. Keynan JN, Cohen A, Jackont G, Green N, Goldway N et al. 2019. Electrical fingerprint of the amygdala guides neurofeedback training for stress resilience. Nat. Hum. Behav. 3:163–73
    [Google Scholar]
  50. Keynan JN, Meir-Hasson Y, Gilam G, Cohen A, Jackont G et al. 2016. Limbic activity modulation guided by functional magnetic resonance imaging–inspired electroencephalography improves implicit emotion regulation. Biol. Psychiatry 80:6490–96
    [Google Scholar]
  51. Kim D-Y, Yoo S-S, Tegethoff M, Meinlschmidt G, Lee J-H. 2015. The inclusion of functional connectivity information into fMRI-based neurofeedback improves its efficacy in the reduction of cigarette cravings. J. Cogn. Neurosci. 27:81552–72
    [Google Scholar]
  52. Koizumi A, Amano K, Cortese A, Shibata K, Yoshida W et al. 2016. Fear reduction without fear through reinforcement of neural activity that bypasses conscious exposure. Nat. Hum. Behav. 1:6
    [Google Scholar]
  53. Koush Y, Ashburner J, Prilepin E, Sladky R, Zeidman P et al. 2017a. OpenNFT: an open-source Python/Matlab framework for real-time fMRI neurofeedback training based on activity, connectivity and multivariate pattern analysis. NeuroImage 156:489–503
    [Google Scholar]
  54. Koush Y, Masala N, Scharnowski F, Van De Ville D. 2019. Data-driven tensor independent component analysis for model-based connectivity neurofeedback. NeuroImage 184:214–26
    [Google Scholar]
  55. Koush Y, Meskaldji D-E, Pichon S, Rey G, Rieger SW et al. 2017b. Learning control over emotion networks through connectivity-based neurofeedback. Cereb. Cortex 27:21193–202
    [Google Scholar]
  56. Koush Y, Rosa MJ, Robineau F, Heinen K, Rieger SW et al. 2013. Connectivity-based neurofeedback: dynamic causal modeling for real-time fMRI. NeuroImage 81:422–30
    [Google Scholar]
  57. Kumar M, Ellis CT, Lu Q, Zhang H, Capotă M et al. 2020. BrainIAK tutorials: user-friendly learning materials for advanced fMRI analysis. PLOS Comput. Biol. 16:1e1007549
    [Google Scholar]
  58. Kwong KK, Belliveau JW, Chesler DA, Goldberg IE, Weisskoff RM et al. 1992. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. PNAS 89:125675–79
    [Google Scholar]
  59. LaConte SM, Peltier SJ, Hu XP. 2007. Real-time fMRI using brain-state classification. Hum. Brain Mapp. 28:101033–44
    [Google Scholar]
  60. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:7553436–44
    [Google Scholar]
  61. Lorenzetti V, Melo B, Basílio R, Suo C, Yücel M et al. 2018. Emotion regulation using virtual environments and real-time fMRI neurofeedback. Front. Neurol. 9:390
    [Google Scholar]
  62. MacInnes JJ, Adcock RA, Stocco A, Prat CS, Rao RPN, Dickerson KC 2020. Pyneal: open source real-time fMRI software. Front. Neurosci. 14:900
    [Google Scholar]
  63. MacInnes JJ, Dickerson KC, Chen N-K, Adcock RA. 2016. Cognitive neurostimulation: learning to volitionally sustain ventral tegmental area activation. Neuron 89:61331–42
    [Google Scholar]
  64. Mathiak K, Posse S. 2001. Evaluation of motion and realignment for functional magnetic resonance imaging in real time. Magn. Reson. Med. 45:1167–71
    [Google Scholar]
  65. Megumi F, Yamashita A, Kawato M, Imamizu H. 2015. Functional MRI neurofeedback training on connectivity between two regions induces long-lasting changes in intrinsic functional network. Front. Hum. Neurosci. 9:160
    [Google Scholar]
  66. Mehler DMA, Sokunbi MO, Habes I, Barawi K, Subramanian L et al. 2018. Targeting the affective brain—a randomized controlled trial of real-time fMRI neurofeedback in patients with depression. Neuropsychopharmacology 43:132578–85
    [Google Scholar]
  67. Mennen AC, Turk-Browne NB, Wallace G, Seok D, Jaganjac A et al. 2021. Cloud-based functional magnetic resonance imaging neurofeedback to reduce the negative attentional bias in depression: a proof-of-concept study. Cogn. Neurosci. Neuroimaging 6:4490–97
    [Google Scholar]
  68. Miller G. 2010. Is pharma running out of brainy ideas?. Science 329:5991502–4
    [Google Scholar]
  69. Misaki M, Phillips R, Zotev V, Wong C-K, Wurfel BE et al. 2018. Real-time fMRI amygdala neurofeedback positive emotional training normalized resting-state functional connectivity in combat veterans with and without PTSD: a connectome-wide investigation. NeuroImage Clin 20:543–55
    [Google Scholar]
  70. Moore G. 2021. Cramming more components onto integrated circuits (1965). Ideas That Created the Future: Classic Papers of Computer Science HR Lewis 261–66 Cambridge, MA: MIT Press
    [Google Scholar]
  71. Müller R-A, Shih P, Keehn B, Deyoe JR, Leyden KM, Shukla DK. 2011. Underconnected, but how? A survey of functional connectivity MRI studies in autism spectrum disorders. Cereb. Cortex 21:102233–43
    [Google Scholar]
  72. Oblak EF, Lewis-Peacock JA, Sulzer JS. 2017. Self-regulation strategy, feedback timing and hemodynamic properties modulate learning in a simulated fMRI neurofeedback environment. PLOS Comput. Biol. 13:7e1005681
    [Google Scholar]
  73. Oblak EF, Sulzer JS, Lewis-Peacock JA. 2019. A simulation-based approach to improve decoded neurofeedback performance. NeuroImage 195:300–10
    [Google Scholar]
  74. Ogawa S, Tank DW, Menon R, Ellermann JM, Kim SG et al. 1992. Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. PNAS 89:135951–55
    [Google Scholar]
  75. Orlov ND, Giampietro V, O'Daly O, Lam S-L, Barker GJ et al. 2018. Real-time fMRI neurofeedback to down-regulate superior temporal gyrus activity in patients with schizophrenia and auditory hallucinations: a proof-of-concept study. Transl. Psychiatry 8:46
    [Google Scholar]
  76. Paret C, Kluetsch R, Ruf M, Demirakca T, Hoesterey S et al. 2014. Down-regulation of amygdala activation with real-time fMRI neurofeedback in a healthy female sample. Front. Behav. Neurosci. 8:299
    [Google Scholar]
  77. Paret C, Kluetsch R, Zaehringer J, Ruf M, Demirakca T et al. 2016a. Alterations of amygdala-prefrontal connectivity with real-time fMRI neurofeedback in BPD patients. Soc. Cogn. Affect. Neurosci. 11:6952–60
    [Google Scholar]
  78. Paret C, Ruf M, Gerchen MF, Kluetsch R, Demirakca T et al. 2016b. fMRI neurofeedback of amygdala response to aversive stimuli enhances prefrontal-limbic brain connectivity. NeuroImage 125:182–88
    [Google Scholar]
  79. Pindi P, Houenou J, Piguet C, Favre P. 2021. Real-time fMRI neurofeedback as a new treatment for psychiatric disorders: a systematic review. PsyArXiv r89sn. http://dx.doi.org/10.31234/osf.io/r89sn
    [Crossref]
  80. Ponce CR, Xiao W, Schade PF, Hartmann TS, Kreiman G, Livingstone MS 2019. Evolving images for visual neurons using a deep generative network reveals coding principles and neuronal preferences. Cell 177:4999–1009.e10
    [Google Scholar]
  81. Posse S, Binkofski F, Schneider F, Gembris D, Frings W et al. 2001. A new approach to measure single-event related brain activity using real-time fMRI: feasibility of sensory, motor, and higher cognitive tasks. Hum. Brain Mapp. 12:125–41
    [Google Scholar]
  82. Posse S, Fitzgerald D, Gao K, Habel U, Rosenberg D et al. 2003. Real-time fMRI of temporolimbic regions detects amygdala activation during single-trial self-induced sadness. NeuroImage 18:3760–68
    [Google Scholar]
  83. Ramot M, Gonzalez-Castillo J. 2019. A framework for offline evaluation and optimization of real-time algorithms for use in neurofeedback, demonstrated on an instantaneous proxy for correlations. NeuroImage 188:322–34
    [Google Scholar]
  84. Ramot M, Grossman S, Friedman D, Malach R. 2016. Covert neurofeedback without awareness shapes cortical network spontaneous connectivity. PNAS 113:17E2413–20
    [Google Scholar]
  85. Ramot M, Kimmich S, Gonzalez-Castillo J, Roopchansingh V, Popal H et al. 2017. Direct modulation of aberrant brain network connectivity through real-time neurofeedback. eLife 6:e28974
    [Google Scholar]
  86. Rance M, Walsh C, Sukhodolsky DG, Pittman B, Qiu M et al. 2018. Time course of clinical change following neurofeedback. NeuroImage 181:807–13
    [Google Scholar]
  87. Ringach DL. 2009. Spontaneous and driven cortical activity: implications for computation. Curr. Opin. Neurobiol. 19:4439–44
    [Google Scholar]
  88. Ros T, Enriquez-Geppert S, Zotev V, Young KD, Wood G et al. 2020. Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-NF checklist). Brain 143:61674–85
    [Google Scholar]
  89. Rubia K, Criaud M, Wulff M, Alegria A, Brinson H et al. 2019. Functional connectivity changes associated with fMRI neurofeedback of right inferior frontal cortex in adolescents with ADHD. NeuroImage 188:43–58
    [Google Scholar]
  90. Sadtler PT, Quick KM, Golub MD, Chase SM, Ryu SI et al. 2014. Neural constraints on learning. Nature 512:7515423–26
    [Google Scholar]
  91. Scharnowski F, Hutton C, Josephs O, Weiskopf N, Rees G. 2012. Improving visual perception through neurofeedback. J. Neurosci. 32:4917830–41
    [Google Scholar]
  92. Scheinost D, Stoica T, Saksa J, Papademetris X, Constable RT et al. 2013. Orbitofrontal cortex neurofeedback produces lasting changes in contamination anxiety and resting-state connectivity. Transl. Psychiatry 3:250
    [Google Scholar]
  93. Schott BH, Minuzzi L, Krebs RM, Elmenhorst D, Lang M et al. 2008. Mesolimbic functional magnetic resonance imaging activations during reward anticipation correlate with reward-related ventral striatal dopamine release. J. Neurosci. 28:5214311–19
    [Google Scholar]
  94. Sepulveda P, Sitaram R, Rana M, Montalba C, Tejos C, Ruiz S. 2016. How feedback, motor imagery, and reward influence brain self-regulation using real-time fMRI. Hum. Brain Mapp. 37:93153–71
    [Google Scholar]
  95. Shibata K, Lisi G, Cortese A, Watanabe T, Sasaki Y, Kawato M 2019. Toward a comprehensive understanding of the neural mechanisms of decoded neurofeedback. NeuroImage 188:539–56
    [Google Scholar]
  96. Shibata K, Watanabe T, Kawato M, Sasaki Y 2016. Differential activation patterns in the same brain region led to opposite emotional states. PLOS Biol. 14:9e1002546
    [Google Scholar]
  97. Shibata K, Watanabe T, Sasaki Y, Kawato M. 2011. Perceptual learning incepted by decoded fMRI neurofeedback without stimulus presentation. Science 334:60611413–15
    [Google Scholar]
  98. Sirotin YB, Das A. 2009. Anticipatory haemodynamic signals in sensory cortex not predicted by local neuronal activity. Nature 457:7228475–79
    [Google Scholar]
  99. Sitaram R, Lee S, Ruiz S, Rana M, Veit R, Birbaumer N 2011. Real-time support vector classification and feedback of multiple emotional brain states. NeuroImage 56:2753–65
    [Google Scholar]
  100. Sitaram R, Ros T, Stoeckel L, Haller S, Scharnowski F et al. 2017. Closed-loop brain training: the science of neurofeedback. Nat. Rev. Neurosci. 18:286–100
    [Google Scholar]
  101. Smyser C, Grabowski TJ, Frank RJ, Haller JW, Bolinger L. 2001. Real-time multiple linear regression for fMRI supported by time-aware acquisition and processing. Magn. Reson. Med. 45:2289–98
    [Google Scholar]
  102. Sonuga-Barke EJS, Brandeis D, Cortese S, Daley D, Ferrin M et al. 2013. Nonpharmacological interventions for ADHD: systematic review and meta-analyses of randomized controlled trials of dietary and psychological treatments. Am. J. Psychiatry 170:3275–89
    [Google Scholar]
  103. Sorger B, Scharnowski F, Linden DEJ, Hampson M, Young KD 2019. Control freaks: towards optimal selection of control conditions for fMRI neurofeedback studies. NeuroImage 186:256–65
    [Google Scholar]
  104. Srinivasan R, Nunez PL, Tucker DM, Silberstein RB, Cadusch PJ. 1996. Spatial sampling and filtering of EEG with spline Laplacians to estimate cortical potentials. Brain Topogr. 8:4355–66
    [Google Scholar]
  105. Sterman MB, Howe RC, Macdonald LR. 1970. Facilitation of spindle-burst sleep by conditioning of electroencephalographic activity while awake. Science 167:39211146–48
    [Google Scholar]
  106. Stoeckel LE, Garrison KA, Ghosh S, Wighton P, Hanlon CA et al. 2014. Optimizing real time fMRI neurofeedback for therapeutic discovery and development. NeuroImage Clin. 5:245–55
    [Google Scholar]
  107. Sulzer J, Haller S, Scharnowski F, Weiskopf N, Birbaumer N et al. 2013. Real-time fMRI neurofeedback: progress and challenges. NeuroImage 76:386–99
    [Google Scholar]
  108. Takamura M, Okamoto Y, Shibasaki C, Yoshino A, Okada G et al. 2020. Antidepressive effect of left dorsolateral prefrontal cortex neurofeedback in patients with major depressive disorder: a preliminary report. J. Affect. Disord. 271:224–27
    [Google Scholar]
  109. Taschereau-Dumouchel V, Cortese A, Chiba T, Knotts JD, Kawato M, Lau H 2018a. Towards an unconscious neural reinforcement intervention for common fears. PNAS 115:133470–75
    [Google Scholar]
  110. Taschereau-Dumouchel V, Cortese A, Lau H, Kawato M 2021. Conducting decoded neurofeedback studies. Soc. Cogn. Affect. Neurosci. 16:8838–48
    [Google Scholar]
  111. Taschereau-Dumouchel V, Kawato M, Lau H. 2020. Multivoxel pattern analysis reveals dissociations between subjective fear and its physiological correlates. Mol. Psychiatry 25:2342–54
    [Google Scholar]
  112. Taschereau-Dumouchel V, Liu K-Y, Lau H. 2018b. Unconscious psychological treatments for physiological survival circuits. Curr. Opin. Behav. Sci. 24:62–68
    [Google Scholar]
  113. Taschereau-Dumouchel V, Roy M 2020. Could brain decoding machines change our minds?. Trends Cogn. Sci. 24:11856–58
    [Google Scholar]
  114. Thibault RT, MacPherson A, Lifshitz M, Roth RR, Raz A 2018. Neurofeedback with fMRI: a critical systematic review. NeuroImage 172:786–807
    [Google Scholar]
  115. Thomason ME, Burrows BE, Gabrieli JDE, Glover GH. 2005. Breath holding reveals differences in fMRI BOLD signal in children and adults. NeuroImage 25:3824–37
    [Google Scholar]
  116. Tursic A, Eck J, Lührs M, Linden DEJ, Goebel R. 2020. A systematic review of fMRI neurofeedback reporting and effects in clinical populations. NeuroImage Clin. 28:102496
    [Google Scholar]
  117. Varoquaux G. 2018. Cross-validation failure: Small sample sizes lead to large error bars. NeuroImage 180:68–77
    [Google Scholar]
  118. Victor TA, Furey ML, Fromm SJ, Ohman A, Drevets WC 2010. Relationship between amygdala responses to masked faces and mood state and treatment in major depressive disorder. Arch. Gen. Psychiatry 67:111128–38
    [Google Scholar]
  119. Voyvodic JT. 1999. Real-time fMRI paradigm control, physiology, and behavior combined with near real-time statistical analysis. NeuroImage 10:291–106
    [Google Scholar]
  120. Wager TD, Atlas LY, Lindquist MA, Roy M, Woo C-W, Kross E. 2013. An fMRI-based neurologic signature of physical pain. N. Engl. J. Med. 368:151388–97
    [Google Scholar]
  121. Wang Y, Keller B, Capota M, Anderson MJ, Sundaram N et al. 2016. Real-time full correlation matrix analysis of fMRI data. 2016 IEEE International Conference on Big Data1242–51 Piscataway, NJ: IEEE
    [Google Scholar]
  122. Watanabe T, Sasaki Y, Shibata K, Kawato M. 2017. Advances in fMRI real-time neurofeedback. Trends Cogn. Sci. 21:12997–1010
    [Google Scholar]
  123. Weiskopf N. 2012. Real-time fMRI and its application to neurofeedback. NeuroImage 62:2682–92
    [Google Scholar]
  124. Weiskopf N, Mathiak K, Bock SW, Scharnowski F, Veit R et al. 2004a. Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI). IEEE Trans. Biomed. Eng. 51:6966–70
    [Google Scholar]
  125. Weiskopf N, Scharnowski F, Veit R, Goebel R, Birbaumer N, Mathiak K. 2004b. Self-regulation of local brain activity using real-time functional magnetic resonance imaging (fMRI). J. Physiol. 98:4–6357–73
    [Google Scholar]
  126. Weiskopf N, Veit R, Erb M, Mathiak K, Grodd W et al. 2003. Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data. NeuroImage 19:3577–86
    [Google Scholar]
  127. Weiss F, Zamoscik V, Schmidt SNL, Halli P, Kirsch P, Gerchen MF 2020. Just a very expensive breathing training? Risk of respiratory artefacts in functional connectivity-based real-time fMRI neurofeedback. NeuroImage 210:116580
    [Google Scholar]
  128. Whitfield-Gabrieli S, Ford JM 2012. Default mode network activity and connectivity in psychopathology. Annu. Rev. Clin. Psychol. 8:49–76
    [Google Scholar]
  129. Woo C-W, Schmidt L, Krishnan A, Jepma M, Roy M et al. 2017. Quantifying cerebral contributions to pain beyond nociception. Nat. Commun. 8:14211
    [Google Scholar]
  130. Yamashita A, Hayasaka S, Kawato M, Imamizu H. 2017. Connectivity neurofeedback training can differentially change functional connectivity and cognitive performance. Cereb. Cortex 27:104960–70
    [Google Scholar]
  131. Yao S, Becker B, Geng Y, Zhao Z, Xu X et al. 2016. Voluntary control of anterior insula and its functional connections is feedback-independent and increases pain empathy. NeuroImage 130:230–40
    [Google Scholar]
  132. Yoo S-S, Fairneny T, Chen N-K, Choo S-E, Panych LP et al. 2004. Brain-computer interface using fMRI: spatial navigation by thoughts. NeuroReport 15:101591–95
    [Google Scholar]
  133. Yoo S-S, Jolesz FA. 2002. Functional MRI for neurofeedback: feasibility study on a hand motor task. NeuroReport 13:111377–81
    [Google Scholar]
  134. Young KD, Misaki M, Harmer CJ, Victor T, Zotev V et al. 2017a. Real-time functional magnetic resonance imaging amygdala neurofeedback changes positive information processing in major depressive disorder. Biol. Psychiatry 82:8578–86
    [Google Scholar]
  135. Young KD, Siegle GJ, Zotev V, Phillips R, Misaki M et al. 2017b. Randomized clinical trial of real-time fMRI amygdala neurofeedback for major depressive disorder: effects on symptoms and autobiographical memory recall. Am. J. Psychiatry 174:8748–55
    [Google Scholar]
  136. Young KD, Zotev V, Phillips R, Misaki M, Yuan H et al. 2014. Real-time FMRI neurofeedback training of amygdala activity in patients with major depressive disorder. PLOS ONE 9:2e88785
    [Google Scholar]
  137. Yuan H, Young KD, Phillips R, Zotev V, Misaki M, Bodurka J 2014. Resting-state functional connectivity modulation and sustained changes after real-time functional magnetic resonance imaging neurofeedback training in depression. Brain Connect. 4:9690–701
    [Google Scholar]
  138. Zaehringer J, Ende G, Santangelo P, Kleindienst N, Ruf M et al. 2019. Improved emotion regulation after neurofeedback: a single-arm trial in patients with borderline personality disorder. NeuroImage Clin. 24:102032
    [Google Scholar]
  139. Zhang S, Yoshida W, Mano H, Yanagisawa T, Mancini F et al. 2020. Pain control by co-adaptive learning in a brain–machine interface. Curr. Biol. 30:203935–44.e7
    [Google Scholar]
  140. Zhao Z, Yao S, Li K, Sindermann C, Zhou F et al. 2019. Real-time functional connectivity-informed neurofeedback of amygdala-frontal pathways reduces anxiety. Psychother. Psychosom. 88:15–15
    [Google Scholar]
  141. Zhou F, Zhao W, Qi Z, Geng Y, Yao S et al. 2020. Beyond fear centers—a distributed fMRI-based neuromarker for the subjective experience of fear. bioRxiv 2020.11.23.394973. https://doi.org/10.1101/2020.11.23.394973
    [Crossref]
  142. Zilverstand A, Sorger B, Sarkheil P, Goebel R. 2015. fMRI neurofeedback facilitates anxiety regulation in females with spider phobia. Front. Behav. Neurosci. 9:148
    [Google Scholar]
  143. Zilverstand A, Sorger B, Slaats-Willemse D, Kan CC, Goebel R, Buitelaar JK. 2017. fMRI neurofeedback training for increasing anterior cingulate cortex activation in adult attention deficit hyperactivity disorder: an exploratory randomized, single-blinded study. PLOS ONE 12:1e0170795
    [Google Scholar]
  144. Zotev V, Mayeli A, Misaki M, Bodurka J. 2020. Emotion self-regulation training in major depressive disorder using simultaneous real-time fMRI and EEG neurofeedback. NeuroImage Clin. 27:102331
    [Google Scholar]
  145. Zotev V, Phillips R, Misaki M, Wong CK, Wurfel BE et al. 2018. Real-time fMRI neurofeedback training of the amygdala activity with simultaneous EEG in veterans with combat-related PTSD. NeuroImage Clin. 19:106–21
    [Google Scholar]
  146. Zotev V, Yuan H, Misaki M, Phillips R, Young KD et al. 2016. Correlation between amygdala BOLD activity and frontal EEG asymmetry during real-time fMRI neurofeedback training in patients with depression. NeuroImage Clin. 11:224–38
    [Google Scholar]
  147. Zweerings J, Hummel B, Keller M, Zvyagintsev M, Schneider F et al. 2019. Neurofeedback of core language network nodes modulates connectivity with the default-mode network: a double-blind fMRI neurofeedback study on auditory verbal hallucinations. NeuroImage 189:533–42
    [Google Scholar]
/content/journals/10.1146/annurev-clinpsy-072220-014550
Loading
/content/journals/10.1146/annurev-clinpsy-072220-014550
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error