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Übersichtsarbeit

Real-Time Functional Magnetic Resonance Imaging as a Tool for Neurofeedback

Present and Future Applications

Published Online:https://doi.org/10.1024/2235-0977/a000300

Abstract. Neurofeedback allows participants to voluntarily control their own brain activity. Consequently, neurofeedback is a potential intervention tool in diverse clinical domains. Different brain signals can be fed back to the neurofeedback users, such as the hemodynamic response of the brain using functional magnetic resonance imaging (fMRI) and near-infrared spectroscopy (NIRS) or electrophysiological brain signals as measured with electroencephalography (EEG). Each of these neuroscientific methods has its advantages and disadvantages. For instance, using fMRI all brain regions can be targeted, while in EEG and NIRS signals from deeper regions cannot be precisely differentiated. Hence, fMRI-based neurofeedback allows treatment of mental and physical diseases, which are associated with activation patterns in deeper brain regions. Until now, only the blood oxygen level dependent signal (BOLD) has been used as feedback signal in fMRI-based neurofeedback studies. However, we have started to develop a neurofeedback pipeline using a different fMRI signal, namely arterial spin labeling (ASL), which will be introduced in this article. ASL neurofeedback enables a direct modulation of the cerebral blood flow and, consequently, might improve rehabilitation of disorders caused by perfusion imbalance in the future.


Echtzeit funktionelle Magnetresonanztomographie als Instrument für Neurofeedback – Gegenwärtige und zukünftige Anwendungen

Zusammenfassung. Neurofeedback ermöglicht die willentliche Kontrolle der eigenen Gehirnaktivität. Damit ist Neurofeedback ein potenzielles Interventionswerkzeug in verschiedenen klinischen Bereichen. Verschiedene Hirnsignale können an die Proband*innen rückgemeldet werden, wie z.B. die hämodynamische Reaktion des Gehirns mittels funktioneller Magnetresonanztomographie (fMRT) und Nahinfrarotspektroskopie (NIRS), oder mittels Elektroenzephalographie (EEG), welche die elektrophysiologischen Hirnsignale misst. Jede dieser neurowissenschaftlichen Methoden hat ihre Vor- und Nachteile. So können beispielsweise mit fMRT alle Hirnregionen erfasst werden, während bei EEG und NIRS Signale aus tiefer liegenden Regionen nicht genau unterschieden werden können. Daher ermöglicht fMRT-basiertes Neurofeedback die Behandlung von psychischen und physischen Erkrankungen, die mit Aktivierungsmustern in tieferen Hirnregionen einhergehen. Bisher wurde in fMRT-basierten Neurofeedback Studien nur das BOLD Signal als Feedbacksignal verwendet. Wir haben jedoch damit begonnen, eine Neurofeedback-Pipeline mit einem anderen fMRT-Signal zu entwickeln, nämlich Arterial Spin Labeling (ASL), welche in diesem Artikel vorgestellt wird. ASL Neurofeedback erlaubt die direkte Ansteuerung des zerebralen Blutflusses und könnte somit in Zukunft die Rehabilitation von Erkrankungen, die durch ein Durchblutungsungleichgewicht verursacht werden, verbessern.

References

  • Aguirre, G. K., Detre, J. A., Zarahn, E. & Alsop, D. C. (2002). Experimental design and the relative sensitivity of BOLD and perfusion fMRI. NeuroImage, 15(3), 488–500. First citation in articleCrossrefGoogle Scholar

  • Alegria, A. A., Wulff, M., Brinson, H., Barker, G. J., Norman, L. J., Brandeis, D. et al. (2017). Real-time f MRI neurofeedback in adolescents with attention deficit hyperactivity disorder. Human Brain Mapping, 38(6), 3190–3209. First citation in articleCrossrefGoogle Scholar

  • Alsop, D.C., Detre, J.A., Golay, X., Günther, M., Hendrikse, J., Hernandez-Garcia, L. et al. (2015) . Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: A consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia. Magnetic Resonance in Medicine, 73, 102–116. First citation in articleCrossrefGoogle Scholar

  • Binnewijzend, M. A., Kuijer, J. P., Benedictus, M. R., van der Flier, W. M., Wink, A. M., Wattjes, M. P. et al. (2013). Cerebral blood flow measured with 3D pseudocontinuous arterial spin-labeling MR imaging in Alzheimer disease and mild cognitive impairment: a marker for disease severity. Radiology, 267(1), 221–230. First citation in articleCrossrefGoogle Scholar

  • Birbaumer, N., Ruiz, S. & Sitaram, R. (2013). Learned regulation of brain metabolism. Trends in Cognitive Sciences, 17(6), 295–302. First citation in articleCrossrefGoogle Scholar

  • Blefari, M. L., Sulzer, J., Hepp-Reymond, M. C., Kollias, S. & Gassert, R. (2015). Improvement in precision grip force control with self-modulation of primary motor cortex during motor imagery. Frontiers in Behavioral Neuroscience, 9, 18. First citation in articleCrossrefGoogle Scholar

  • Borogovac, A., Habeck, C., Small, S. A. & Asllani, I. (2010). Mapping brain function using a 30-day interval between baseline and activation: a novel arterial spin labeling fMRI approach. Journal of Cerebral Blood Flow & Metabolism, 30(10), 1721–1733. First citation in articleCrossrefGoogle Scholar

  • Buxton, R. B., Frank, L. R., Wong, E. C., Siewert, B., Warach, S. & Edelman, R. R. (1998). A general kinetic model for quantitative perfusion imaging with arterial spin labeling. Magnetic Resonance in Medicine, 40(3), 383–396. First citation in articleCrossrefGoogle Scholar

  • Buxton, R. B., Uludağ, K., Dubowitz, D. J. & Liu, T. T. (2004). Modeling the hemodynamic response to brain activation. NeuroImage, 23, 220–233. First citation in articleCrossrefGoogle Scholar

  • Caria, A., Sitaram, R. & Birbaumer, N. (2012). Real-time fMRI: a tool for local brain regulation. The Neuroscientist, 18(5), 487–501. First citation in articleCrossrefGoogle Scholar

  • DeCharms, R. C., Maeda, F., Glover, G. H., Ludlow, D., Pauly, J. M., Soneji, D. et al. (2005). Control over brain activation and pain learned by using real-time functional MRI. Proceedings of the National Academy of Sciences, 102(51), 18626–18631. First citation in articleCrossrefGoogle Scholar

  • Detre, J. A., Leigh, J. S., Williams, D. S. & Koretsky, A. P. (1992). Perfusion imaging. Magnetic Resonance in Medicine, 23(1), 37–45. First citation in articleCrossrefGoogle Scholar

  • Emmert, K., Breimhorst, M., Bauermann, T., Birklein, F., Van De Ville, D. & Haller, S. (2014). Comparison of anterior cingulate vs. insular cortex as targets for real-time fMRI regulation during pain stimulation. Frontiers in Behavioral Neuroscience, 8, 350. First citation in articleCrossrefGoogle Scholar

  • Gruzelier, J. H. (2014). EEG-neurofeedback for optimising performance. I: a review of cognitive and affective outcome in healthy participants. Neuroscience & Biobehavioral Reviews, 44, 124–141. First citation in articleCrossrefGoogle Scholar

  • Guan, M., Li, L., Tong, L., Zhang, Y., Zheng, D., Yan, B. et al. (2015). Self-regulation of rACC activation in patients with postherpetic neuralgia: a preliminary study using real-time fMRI neurofeedback. PLoS One, 10(4), e0123675. First citation in articleCrossrefGoogle Scholar

  • Hammond, D. C. (2011). What is neurofeedback: An update. Journal of Neurotherapy, 15(4), 305–336. First citation in articleCrossrefGoogle Scholar

  • Hernandez-Garcia, L., Jahanian, H., Greenwald, M. K., Zubieta, J. K. & Peltier, S. J. (2011). Real-time functional MRI using pseudo-continuous arterial spin labeling. Magnetic Resonance in Medicine, 65(6), 1570–1577. First citation in articleCrossrefGoogle Scholar

  • Heunis, S., Lamerichs, R., Zinger, S., Caballero-Gaudes, C., Jansen, J. F., Aldenkamp, B. et al., (2018, June 6). Quality and denoising in real-time fMRI neurofeedback: a methods review. https://doi.org/10.31219/osf.io/xubhq First citation in articleGoogle Scholar

  • Hohenfeld, C., Nellessen, N., Dogan, I., Kuhn, H., Müller, C., Papa, F. et al. (2017). Cognitive improvement and brain changes after real-time functional MRI neurofeedback training in healthy elderly and prodromal Alzheimer's disease. Frontiers in Neurology, 8, 384. First citation in articleCrossrefGoogle Scholar

  • Ivanov, D., Gardumi, A., Haast, R. A. M., Pfeuffer, J., Poser, B. A., Uludag, K. (2017) Comparison of 3 T and 7 T ASL techniques for concurrent functional perfusion and BOLD studies. NeuroImage, 156, 363–376 First citation in articleCrossrefGoogle Scholar

  • Kadosh, K. C. & Staunton, G. (2019). A systematic review of the psychological factors that influence neurofeedback learning outcomes. NeuroImage, 185, 545–555. First citation in articleCrossrefGoogle Scholar

  • Kisler, K., Nelson, A. R., Montagne, A. & Zlokovic, B. V. (2017). Cerebral blood flow regulation and neurovascular dysfunction in Alzheimer disease. Nature Reviews Neuroscience, 18(7), 419. First citation in articleCrossrefGoogle Scholar

  • Kober, S. E., Grössinger, D. & Wood, G. (2019). Effects of motor imagery and visual neurofeedback on activation in the swallowing network: A real-time fMRI study. Dysphagia, 1–17. First citation in articleGoogle Scholar

  • Kober, S. E., Pinter, D., Enzinger, C., Damulina, A., Duckstein, H., Fuchs, S. et al. (2019). Self-regulation of brain activity and its effect on cognitive function in patients with multiple sclerosis–First insights from an interventional study using neurofeedback. Clinical Neurophysiology, 130(11), 2124–2131. First citation in articleCrossrefGoogle Scholar

  • Kober, S. E., Witte, M., Grinschgl, S., Neuper, C. & Wood, G. (2018). Placebo hampers ability to self-regulate brain activity: A double-blind sham-controlled neurofeedback study. Neuroimage, 181, 797–806. First citation in articleCrossrefGoogle Scholar

  • Kober, S. E., Witte, M., Ninaus, M., Koschutnig, K., Wiesen, D., Zaiser, G. et al. (2017). Ability to gain control over one's own brain activity and its relation to spiritual practice: A multimodal imaging study. Frontiers in human neuroscience, 11, 271. First citation in articleCrossrefGoogle Scholar

  • Kohl, S. H., Mehler, D. M. A., Lührs, M., Thibault, R. T., Konrad, K., & Sorger, B. (2019, December 24). The potential of functional near-infrared spectroscopy-based neurofeedback – a systematic review and recommendations for best practice. https://doi.org/10.31234/osf.io/yq3vj First citation in articleGoogle Scholar

  • Lawrence, E. J., Su, L., Barker, G. J., Medford, N., Dalton, J., Williams, S. C. et al. (2014). Self-regulation of the anterior insula: Reinforcement learning using real-time fMRI neurofeedback. NeuroImage, 88, 113–124. First citation in articleCrossrefGoogle Scholar

  • Leontiev, O. & Buxton, R. B. (2007). Reproducibility of BOLD, perfusion, and CMRO2 measurements with calibrated-BOLD fMRI. Neuroimage, 35(1), 175–184. First citation in articleCrossrefGoogle Scholar

  • Liew, S. L., Rana, M., Cornelsen, S., Fortunato de Barros Filho, M., Birbaumer, N., Sitaram, R. et al. (2016). Improving motor corticothalamic communication after stroke using real-time fMRI connectivity-based neurofeedback. Neurorehabilitation and Neural Repair, 30(7), 671–675. First citation in articleCrossrefGoogle Scholar

  • Linden, D. E., Habes, I., Johnston, S. J., Linden, S., Tatineni, R., Subramanian, L. et al. (2012). Real-time self-regulation of emotion networks in patients with depression. PloS One, 7(6), e38115. First citation in articleCrossrefGoogle Scholar

  • Linhartová, P., Látalová, A., Kóša, B., Kašpárek, T., Schmahl, C. & Paret, C. (2019). fMRI neurofeedback in emotion regulation: a literature review. NeuroImage, 193, 75–92. First citation in articleCrossrefGoogle Scholar

  • Logothetis, N. K. & Wandell, B. A. (2004). Interpreting the BOLD signal. Annual Review of Physiology, 66, 735–769. First citation in articleCrossrefGoogle Scholar

  • Mehler, D. M., Sokunbi, M. O., Habes, I., Barawi, K., Subramanian, L., Range, M. et al. (2018). Targeting the affective brain – a randomized controlled trial of real-time fMRI neurofeedback in patients with depression. Neuropsychopharmacology, 43(13), 2578. First citation in articleCrossrefGoogle Scholar

  • Pantano, P., Formisano, R., Ricci, M., Piero, V. D., Sabatini, U., Pofi, B. D. et al. (1996). Motor recovery after stroke: morphological and functional brain alterations. Brain, 119(6), 1849–1857. First citation in articleCrossrefGoogle Scholar

  • Paret, C., Goldway, N., Zich, C., Keynan, J. N., Hendler, T., Linden, D. et al. (2019). Current progress in real-time functional magnetic resonance-based neurofeedback: Methodological challenges and achievements. NeuroImage, 202, 116107. First citation in articleCrossrefGoogle Scholar

  • Raoult, H., Petr, J., Bannier, E., Stamm, A., Gauvrit, J. Y., Barillot, C. et al. (2011). Arterial spin labeling for motor activation mapping at 3T with a 32-channel coil: reproducibility and spatial accuracy in comparison with BOLD fMRI. NeuroImage, 58(1), 157–167. First citation in articleCrossrefGoogle Scholar

  • Robineau, F., Saj, A., Neveu, R., Van De Ville, D., Scharnowski, F. & Vuilleumier, P. (2019). Using real-time fMRI neurofeedback to restore right occipital cortex activity in patients with left visuo-spatial neglect: proof-of-principle and preliminary results. Neuropsychological Rehabilitation, 29(3), 339–360. First citation in articleCrossrefGoogle Scholar

  • Ros, T., Enriquez-Geppert, S., Zotev, V., Young, K., Wood, G., Whitfield-Gabrieli, S. et al. (2019). Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist). First citation in articleGoogle Scholar

  • Rubia, K., Criaud, M., Wulff, M., Alegria, A., Brinson, H., Barker, G. et al. (2019). Functional connectivity changes associated with fMRI neurofeedback of right inferior frontal cortex in adolescents with ADHD. NeuroImage, 188, 43–58. First citation in articleCrossrefGoogle Scholar

  • Scharnowski, F., Hutton, C., Josephs, O., Weiskopf, N. & Rees, G. (2012). Improving visual perception through neurofeedback. Journal of Neuroscience, 32(49), 17830–17841. First citation in articleCrossrefGoogle Scholar

  • Simmons, A., Strigo, I., Matthews, S. C., Paulus, M. P. & Stein, M. B. (2006). Anticipation of aversive visual stimuli is associated with increased insula activation in anxiety-prone subjects. Biological Psychiatry, 60(4), 402–409. First citation in articleCrossrefGoogle Scholar

  • Sitaram, R., Ros, T., Stoeckel, L., Haller, S., Scharnowski, F., Lewis-Peacock, J. et al. (2017). Closed-loop brain training: the science of neurofeedback. Nature Reviews Neuroscience, 18(2), 86. First citation in articleCrossrefGoogle Scholar

  • Sitaram, R., Veit, R., Stevens, B., Caria, A., Gerloff, C., Birbaumer, N. et al. (2012). Acquired control of ventral premotor cortex activity by feedback training: an exploratory real-time FMRI and TMS study. Neurorehabilitation and Neural Repair, 26(3), 256–265. First citation in articleCrossrefGoogle Scholar

  • Spann, S. M., Kazimierski, K. S., Aigner, C. S., Kraiger, M., Bredies, K. & Stollberger, R. (2017). Spatio-temporal TGV denoising for ASL perfusion imaging. NeuroImage, 157, 81–96. First citation in articleCrossrefGoogle Scholar

  • Sreedharan, S., Arun, K. M., Sylaja, P. N., Kesavadas, C. & Sitaram, R. (2019). Functional connectivity of language regions of stroke patients with expressive aphasia during real-time functional magnetic resonance imaging based neurofeedback. Brain Connectivity, 9(8), 613–626. First citation in articleCrossrefGoogle Scholar

  • Sterman, M. B. (2000). Basic concepts and clinical findings in the treatment of seizure disorders with EEG operant conditioning. Clinical Electroencephalography, 31(1), 45–55. First citation in articleCrossrefGoogle Scholar

  • Subramanian, L., Hindle, J. V., Johnston, S., Roberts, M. V., Husain, M., Goebel, R. et al. (2011). Real-time functional magnetic resonance imaging neurofeedback for treatment of Parkinson's disease. Journal of Neuroscience, 31(45), 16309–16317. First citation in articleCrossrefGoogle Scholar

  • Sulzer, J., Haller, S., Scharnowski, F., Weiskopf, N., Birbaumer, N., Blefari, M. L. et al. (2013). Real-time fMRI neurofeedback: progress and challenges. NeuroImage, 76, 386–399. First citation in articleCrossrefGoogle Scholar

  • Thibault, R. T., Lifshitz, M. & Raz, A. (2016). The self-regulating brain and neurofeedback: experimental science and clinical promise. Cortex, 74, 247–261. First citation in articleCrossrefGoogle Scholar

  • Thibault, R. T., Lifshitz, M. & Raz, A. (2017). Neurofeedback or neuroplacebo?. Brain, 140(4), 862–864. First citation in articleCrossrefGoogle Scholar

  • Thibault, R. T., MacPherson, A., Lifshitz, M., Roth, R. R. & Raz, A. (2018). Neurofeedback with fMRI: a critical systematic review. Neuroimage, 172, 786–807. First citation in articleCrossrefGoogle Scholar

  • Tjandra, T., Brooks, J. C., Figueiredo, P., Wise, R., Matthews, P. M. & Tracey, I. (2005). Quantitative assessment of the reproducibility of functional activation measured with BOLD and MR perfusion imaging: implications for clinical trial design. NeuroImage, 27(2), 393–401. First citation in articleCrossrefGoogle Scholar

  • Van Gelderen, P., Wu, C., de Zwart, J. A., Cohen, L., Hallett, M. & Duyn, J. H. (2005). Resolution and reproducibility of BOLD and perfusion functional MRI at 3.0 Tesla. Magnetic Resonance in Medicine, 54(3), 569–576. First citation in articleCrossrefGoogle Scholar

  • Veit, R., Flor, H., Erb, M., Hermann, C., Lotze, M., Grodd, W. et al. (2002). Brain circuits involved in emotional learning in antisocial behavior and social phobia in humans. Neuroscience Letters, 328(3), 233–236. First citation in articleCrossrefGoogle Scholar

  • Vidorreta, M., Wang, Z., Rodriguez, I., Pastor, M., Detre, J. A., Fernandez-Seara, M. A. (2013) Comparison of 2D and 3D single-shot ASL perfusion fMRI sequences; NeuroImage, 66, 662–671. First citation in articleCrossrefGoogle Scholar

  • Watanabe, T., Sasaki, Y., Shibata, K. & Kawato, M. (2017). Advances in fMRI real-time neurofeedback. Trends in cognitive sciences, 21(12), 997–1010. First citation in articleCrossrefGoogle Scholar

  • Weiskopf, N. (2012). Real-time fMRI and its application to neurofeedback. NeuroImage, 62(2), 682–692. First citation in articleCrossrefGoogle Scholar

  • Wiest, R., Abela, E., Missimer, J., Schroth, G., Hess, C. W., Sturzenegger, M. et al. (2014). Interhemispheric cerebral blood flow balance during recovery of motor hand function after ischemic stroke—a longitudinal MRI study using arterial spin labeling perfusion. PLoS One, 9(9), e106327. First citation in articleCrossrefGoogle Scholar

  • Wood, G., Kober, S. E., Witte, M. & Neuper, C. (2014). On the need to better specify the concept of “control” in brain-computer-interfaces/neurofeedback research. Frontiers in Systems Neuroscience, 8, 171. First citation in articleCrossrefGoogle Scholar

  • Yeo, S. H., Lim, Z. J. I., Mao, J. & Yau, W. P. (2017). Effects of central nervous system drugs on recovery after stroke: a systematic review and meta-analysis of randomized controlled trials. Clinical Drug Investigation, 37(10), 901–928. First citation in articleCrossrefGoogle Scholar

  • Young, K. D., Misaki, M., Harmer, C. J., Victor, T., Zotev, V., Phillips, R. et al., (2017). Real-time functional magnetic resonance imaging amygdala neurofeedback changes positive information processing in major depressive disorder. Biological Psychiatry, 82(8), 578–586. First citation in articleCrossrefGoogle Scholar

  • Young, K. D., Zotev, V., Phillips, R., Misaki, M., Yuan, H., Drevets, W. C. et al. (2014). Real-time FMRI neurofeedback training of amygdala activity in patients with major depressive disorder. PloS One, 9(2), e88785. First citation in articleCrossrefGoogle Scholar

  • Zaehringer, J., Ende, G., Santangelo, P., Kleindienst, N., Ruf, M., Bertsch, K. et al. (2019). Improved emotion regulation after neurofeedback: A single-arm trial in patients with borderline personality disorder. NeuroImage: Clinical, 102032. First citation in articleCrossrefGoogle Scholar

  • Zhang, G., Yao, L., Zhang, H., Long, Z., & Zhao, X. (2013). Improved working memory performance through self-regulation of dorsal lateral prefrontal cortex activation using real-time fMRI. PloS One, 8(8), e73735. First citation in articleCrossrefGoogle Scholar

  • Zhang, N., Gordon, M. L., Ma, Y., Chi, B., Gomar, J. J., Peng, S. et al. (2018). The age-related perfusion pattern measured with arterial spin labeling MRI in healthy subjects. Frontiers in Aging Neuroscience, 10, 214. First citation in articleCrossrefGoogle Scholar

  • Zilverstand, A., Sorger, B., Sarkheil, P., & Goebel, R. (2015). fMRI neurofeedback facilitates anxiety regulation in females with spider phobia. Frontiers in Behavioral Neuroscience, 9, 148. First citation in articleCrossrefGoogle Scholar