Advances in fMRI Real-Time Neurofeedback
Open accessAbstract
Functional magnetic resonance imaging (fMRI) neurofeedback is a type of biofeedback in which real-time online fMRI signals are used to self-regulate brain function. Since its advent in 2003 significant progress has been made in fMRI neurofeedback techniques. Specifically, the use of implicit protocols, external rewards, multivariate analysis, and connectivity analysis has allowed neuroscientists to explore a possible causal involvement of modified brain activity in modified behavior. These techniques have also been integrated into groundbreaking new neurofeedback technologies, specifically decoded neurofeedback (DecNef) and functional connectivity-based neurofeedback (FCNef). By modulating neural activity and behavior, DecNef and FCNef have substantially advanced both basic and clinical research.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
fMRI can be used as the treatment itself via real-time neurofeedback, with training adapted to personalized pathology.
“An additional possibility would be to use fMRI as the treatment itself, by using real-time measures of brain activity as a source of neurofeedback: patients could be trained to directly modulate their own brain activity, with the training technique adapted to their personalized version of pathology.113”
From Precision clinical trials: a framework for getting to precision medicine for neurobehavioural disorders (2021) · cites this paper as [113]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Distributed and overlapping representations of faces and objects in ventral temporal cortex
Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001) · Science · doi:10.1126/science.1063736 · Cited by 3,940 · see citing works
Encyclopedia of Machine Learning
Sammut, C., & Webb, G. I. (2010) · Springer eBooks · doi:10.1007/978-0-387-30164-8 · Cited by 3,499 · see citing works
Searching for a baseline: Functional imaging and the resting human brain
Gusnard, D. A., & Raichle, M. E. (2001) · Nature Reviews Neuroscience · doi:10.1038/35094500 · Cited by 3,371 · see citing works
Predicting human resting-state functional connectivity from structural connectivity
Honey, C. J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J. P., Meuli, R., & Hagmann, P. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0811168106 · Cited by 3,307 · see citing works
Operant Behavior: Areas of Research and Application,
Honig, W. K. (1966) · Medical Entomology and Zoology · Cited by 2,656 · see citing works
Decoding the visual and subjective contents of the human brain
Kamitani, Y., & Tong, F. (2005) · Nature Neuroscience · doi:10.1038/nn1444 · Cited by 1,829 · see citing works
Steps toward Artificial Intelligence
Minsky, M. (1961) · Proceedings of the IRE · doi:10.1109/jrproc.1961.287775 · Cited by 1,706 · see citing works
Closed-loop brain training: the science of neurofeedback
Sitaram, R., Ros, T., Stoeckel, L., Haller, S., Scharnowski, F., Lewis-Peacock, J., Weiskopf, N., Blefari, M. L., Rana, M., Oblak, E., et al. (2017) · Nature Reviews Neuroscience · doi:10.1038/nrn.2016.164 · Cited by 1,306 · see citing works
The Asynchronous State in Cortical Circuits
Renart, A., de la Rocha, J., Bartho, P., Hollender, L., Parga, N., Reyes, A., & Harris, K. D. (2010) · Science · doi:10.1126/science.1179850 · Cited by 1,106 · see citing works
Goebel, R., Esposito, F., & Formisano, E. (2006) · Human Brain Mapping · doi:10.1002/hbm.20249 · Cited by 1,053 · see citing works
A neuromarker of sustained attention from whole-brain functional connectivity
Rosenberg, M. D., Finn, E. S., Scheinost, D., Papademetris, X., Shen, X., Constable, R. T., & Chun, M. M. (2015) · Nature Neuroscience · doi:10.1038/nn.4179 · Cited by 1,004 · see citing works
Spontaneously emerging cortical representations of visual attributes
Kenet, T., Bibitchkov, D., Tsodyks, M., Grinvald, A., & Arieli, A. (2003) · Nature · doi:10.1038/nature02078 · Cited by 924 · see citing works
Control over brain activation and pain learned by using real-time functional MRI
deCharms, R. C., Maeda, F., Glover, G. H., Ludlow, D., Pauly, J. M., Soneji, D., Gabrieli, J. D. E., & Mackey, S. C. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0505210102 · Cited by 876 · see citing works
Predicting the orientation of invisible stimuli from activity in human primary visual cortex
Haynes, J., & Rees, G. E. (2005) · Nature Neuroscience · doi:10.1038/nn1445 · Cited by 866 · see citing works
Schnurr, P. P., Friedman, M. J., Engel, C. C., Foa, E. B., Shea, M. T., Chow, B. K., Resick, P. A., Thurston, V., Orsillo, S. M., Haug, R., et al. (2007) · JAMA · doi:10.1001/jama.297.8.820 · Cited by 813 · cited 1× · see citing works
“The repeated presentation of the aversive stimulus can cause overwhelming distress in the participant, and can therefore lead to a high dropout rate from the extinction therapy [42].”
Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment
Berkes, P., Orbán, G., Lengyel, M., & Fiser, J. (2011) · Science · doi:10.1126/science.1195870 · Cited by 782 · see citing works
Skinner, B. F. (1963) · American Psychologist · doi:10.1037/h0045185 · Cited by 675 · see citing works
Spontaneous Events Outline the Realm of Possible Sensory Responses in Neocortical Populations
Luczak, A., Barthó, P., & Harris, K. D. (2009) · Neuron · doi:10.1016/j.neuron.2009.03.014 · Cited by 593 · see citing works
Perceptual learning without perception
Watanabe, T., Náñez, J. E., & Sasaki, Y. (2001) · Nature · doi:10.1038/35101601 · Cited by 575 · see citing works
Self-citationMarzbani, H., Marateb, H., & Mansourian, M. (2016) · Basic and Clinical Neuroscience Journal · doi:10.15412/j.bcn.03070208 · Cited by 484 · see citing works
Perceptual Learning Incepted by Decoded fMRI Neurofeedback Without Stimulus Presentation
Shibata, K., Watanabe, T., Sasaki, Y., & Kawato, M. (2011) · Science · doi:10.1126/science.1212003 · Cited by 456 · see citing works
Self-citationNeuropsychosocial profiles of current and future adolescent alcohol misusers
Whelan, R., Watts, R., Orr, C., Althoff, R., Artiges, E., Banaschewski, T., Barker, G., Bokde, A., Büchel, C., Carvalho, F. M., et al. (2014) · Nature · doi:10.1038/nature13402 · Cited by 438 · see citing works
Haruno, M., & Kawato, M. (2006) · Journal of Neurophysiology · doi:10.1152/jn.00382.2005 · Cited by 433 · see citing works
Self-citationEcker, C., Marquand, A., Mourão-Miranda, J., Johnston, P., Daly, E. M., Brammer, M. J., Maltezos, S., Murphy, C. M., Robertson, D., Williams, S. C., et al. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.5413-09.2010 · Cited by 415 · see citing works
Weiskopf, N., Veit, R., Erb, M., Mathiak, K., Grodd, W., Goebel, R., & Birbaumer, N. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00145-9 · Cited by 413 · see citing works
Ecker, C., Rocha-Rego, V., Johnston, P., Mourão-Miranda, J., Marquand, A. F., Daly, E., Brammer, M. J., Murphy, C. M., & Murphy, D. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.08.024 · Cited by 387 · see citing works
Keogh, E., & Mueen, A. (2010) · Encyclopedia of Machine Learning · doi:10.1007/978-0-387-30164-8_192 · Cited by 358 · see citing works
McNally, R. J. (2007) · Clinical Psychology Review · doi:10.1016/j.cpr.2007.01.003 · Cited by 348 · see citing works
Young, K. D., Siegle, G. J., Zotev, V., Phillips, R., Misaki, M., Yuan, H., Drevets, W. C., & Bodurka, J. (2017) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2017.16060637 · Cited by 331 · see citing works
Closed-loop training of attention with real-time brain imaging
deBettencourt, M. T., Cohen, J. D., Lee, R. F., Norman, K. A., & Turk‐Browne, N. B. (2015) · Nature Neuroscience · doi:10.1038/nn.3940 · Cited by 324 · see citing works
Learned regulation of spatially localized brain activation using real-time fMRI
deCharms, R. C., Kalina Christoff, A., Glover, G. H., Pauly, J. M., Whitfield‐Gabrieli, S., & Gabrieli, J. D. E. (2003) · NeuroImage · doi:10.1016/j.neuroimage.2003.08.041 · Cited by 320 · see citing works
Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns
Yamashita, O., Sato, M.-A., Yoshioka, T., Tong, F., & Kamitani, Y. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.05.050 · Cited by 319 · see citing works
Real‐Time Functional Magnetic Resonance Imaging
Cox, R. W., Jesmanowicz, A., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910330213 · Cited by 314 · see citing works
Pattern-recognizing stochastic learning automata
Barto, A. G., & Anandan, P. (1985) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/tsmc.1985.6313371 · Cited by 308 · see citing works
A small number of abnormal brain connections predicts adult autism spectrum disorder
Yahata, N., Morimoto, J., Hashimoto, R., Lisi, G., Shibata, K., Kawakubo, Y., Kuwabara, H., Kuroda, M., Yamada, T., Megumi, F., et al. (2016) · Nature Communications · doi:10.1038/ncomms11254 · Cited by 305 · see citing works
Self-citationHaruno, M., Kuroda, T., Doya, K., Toyama, K., Kimura, M., Samejima, K., Imamizu, H., & Kawato, M. (2004) · The Journal of Neuroscience · doi:10.1523/jneurosci.3417-03.2004 · Cited by 300 · see citing works
Self-citationLearning: From Association to Cognition
Shanks, D. R. (2009) · Annual Review of Psychology · doi:10.1146/annurev.psych.093008.100519 · Cited by 260 · see citing works
Hanson, S. J., Matsuka, T., & Haxby, J. V. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.05.020 · Cited by 258 · see citing works
Real‐time fMRI using brain‐state classification
LaConte, S. M., Peltier, S., & Hu, X. P. (2006) · Human Brain Mapping · doi:10.1002/hbm.20326 · Cited by 256 · see citing works
Characterizing white matter with magnetization transfer andT2
Stanisz, G. J., Kecojević, A., Bronskill, M. J., & Henkelman, R. M. (1999) · Magnetic Resonance in Medicine · doi:10.1002/(sici)1522-2594(199912)42:6 · Cited by 249 · see citing works
Spontaneous and driven cortical activity: implications for computation.
Ringach, D. L. (2009) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2009.07.005 · Cited by 234 · see citing works
Learned regulation of brain metabolism
Birbaumer, N., Ruíz, S., & Sitaram, R. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.04.009 · Cited by 227 · see citing works
When Optimism Hurts: Inflated Predictions in Psychiatric Neuroimaging
Whelan, R., & Garavan, H. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2013.05.014 · Cited by 209 · see citing works
Scheinost, D., Stoica, T., Saksa, J., Papademetris, X., Constable, R. T., Pittenger, C., & Hampson, M. (2013) · Translational Psychiatry · doi:10.1038/tp.2013.24 · Cited by 201 · see citing works
Multivoxel neurofeedback selectively modulates confidence without changing perceptual performance
Cortese, A., Amano, K., Koizumi, A., Kawato, M., & Lau, H. (2016) · Nature Communications · doi:10.1038/ncomms13669 · Cited by 157 · see citing works
Self-citationLearning Control Over Emotion Networks Through Connectivity-Based Neurofeedback
Koush, Y., Meskaldji, D.-E., Pichon, S., Rey, G., Rieger, S. W., Linden, D. E. J., Ville, D. V. D., Vuilleumier, P. O., & Scharnowski, F. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv311 · Cited by 154 · see citing works
Connectivity-based neurofeedback: Dynamic causal modeling for real-time fMRI
Koush, Y., Rosa, M. J., Robineau, F., Heinen, K., Rieger, S. W., Weiskopf, N., Vuilleumier, P. O., Ville, D. V. D., & Scharnowski, F. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.010 · Cited by 153 · see citing works
Towards an unconscious neural reinforcement intervention for common fears
Taschereau-Dumouchel, V., Cortese, A., Chiba, T., Knotts, J. D., Kawato, M., & Lau, H. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1721572115 · Cited by 153 · see citing works
Self-citationMegumi, F., Yamashita, A., Kawato, M., & Imamizu, H. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00160 · Cited by 142 · see citing works
Self-citationVoyvodic, J. T. (1999) · NeuroImage · doi:10.1006/nimg.1999.0457 · Cited by 140 · see citing works
Improving Visual Perception through Neurofeedback
Scharnowski, F., Hutton, C., Josephs, O., Weiskopf, N., & Rees, G. E. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.6334-11.2012 · Cited by 137 · see citing works
Bray, S., Shinsuke Shimojo, A., & O’Doherty, J. P. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.2118-07.2007 · Cited by 137 · see citing works
A psychoengineering paradigm for the neurocognitive mechanisms of biofeedback and neurofeedback
Gaume, A., Vialatte, A., Mora-Sánchez, A., Ramdani, C., & Vialatte, F. B. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.06.012 · Cited by 133 · see citing works
Koizumi, A., Amano, K., Cortese, A., Shibata, K., Yoshida, W., Seymour, B., Kawato, M., & Lau, H. (2016) · Nature Human Behaviour · doi:10.1038/s41562-016-0006 · Cited by 133 · cited 7× · see citing works
“After repetitive trials with this procedure, participants learn to significantly enhance the degree of the similarity [10–12, 15, 19–21].”
InfluentialSelf-citationYamada, T., Hashimoto, R., Yahata, N., Ichikawa, N., Yoshihara, Y., Okamoto, Y., Kato, N., Takahashi, H., & Kawato, M. (2017) · The International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyx059 · Cited by 133 · see citing works
Self-citationComputational neuroscience approach to biomarkers and treatments for mental disorders
Yahata, N., Kasai, K., & Kawato, M. (2016) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.12502 · Cited by 126 · see citing works
Self-citationJohnson, K. A., Hartwell, K. J., LeMatty, T., Borckardt, J. J., Morgan, P. S., Govindarajan, K. A., Brady, K. T., & George, M. S. (2010) · Journal of Neuroimaging · doi:10.1111/j.1552-6569.2010.00529.x · Cited by 112 · see citing works
Liew, S., Rana, M., Cornelsen, S., Filho, M. F. D. B., Birbaumer, N., Sitaram, R., Cohen, L. G., & Soekadar, S. R. (2015) · Neurorehabilitation and neural repair · doi:10.1177/1545968315619699 · Cited by 107 · see citing works
Covert neurofeedback without awareness shapes cortical network spontaneous connectivity
Ramot, M., Grossman, S., Friedman, D. A., & Malach, R. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1516857113 · Cited by 104 · see citing works
Amano, K., Shibata, K., Kawato, M., Sasaki, Y., & Watanabe, T. (2016) · Current Biology · doi:10.1016/j.cub.2016.05.014 · Cited by 104 · see citing works
Self-citationHow feedback, motor imagery, and reward influence brain self‐regulation using real‐time fMRI
Sepulveda, P., Sitaram, R., Rana, M., Montalba, C., Tejos, C., & Ruiz, S. (2016) · Human Brain Mapping · doi:10.1002/hbm.23228 · Cited by 90 · see citing works
Emmert, K., Kopel, R., Koush, Y., Maire, R., Senn, P., Ville, D. V. D., & Haller, S. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2016.12.023 · Cited by 81 · see citing works
Toda, A., Imamizu, H., Kawato, M., & Sato, M.-A. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.057 · Cited by 78 · see citing works
Self-citationYamashita, A., Hayasaka, S., Kawato, M., & Imamizu, H. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx177 · Cited by 75 · see citing works
Self-citationA Neural Marker of Obsessive-Compulsive Disorder from Whole-Brain Functional Connectivity
Takagi, Y., Sakai, Y., Lisi, G., Yahata, N., Abe, Y., Nishida, S., Nakamae, T., Morimoto, J., Kawato, M., Narumoto, J., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-07792-7 · Cited by 72 · see citing works
Self-citationShared k‐space echo planar imaging with keyhole
Zaitsev, M., Zilles, K., & Shah, N. J. (2001) · Magnetic Resonance in Medicine · doi:10.1002/1522-2594(200101)45:1 · Cited by 67 · see citing works
Stochastic transitions into silence cause noise correlations in cortical circuits
Mochol, G., Hermoso-Mendizabal, A., Sakata, S., Harris, K. D., & de la Rocha, J. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1410509112 · Cited by 62 · see citing works
Differential Activation Patterns in the Same Brain Region Led to Opposite Emotional States
Shibata, K., Watanabe, T., Kawato, M., & Sasaki, Y. (2016) · PLoS Biology · doi:10.1371/journal.pbio.1002546 · Cited by 59 · see citing works
Self-citationReal-time functional magnetic resonance imaging neurofeedback in motor neurorehabilitation
Linden, D. E. J., & Turner, D. L. (2016) · Current Opinion in Neurology · doi:10.1097/wco.0000000000000340 · Cited by 52 · see citing works
Oblak, E. F., Lewis‐Peacock, J. A., & Sulzer, J. S. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005681 · Cited by 52 · see citing works
Mendelsohn, A., Pine, A., & Schiller, D. (2013) · Neuron · doi:10.1016/j.neuron.2013.10.019 · Cited by 45 · see citing works
Decoded fMRI neurofeedback can induce bidirectional confidence changes within single participants
Cortese, A., Amano, K., Koizumi, A., Lau, H. C., & Kawato, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.069 · Cited by 45 · see citing works
Self-citationEXPERIMENTER EFFECTS IN PARAPSYCHOLOGICAL RESEARCH
Kennedy, J. E., & Taddonio, J. L. (2013) · Cited by 34 · see citing works
Intraoperative, real‐time, functional MRI
Gering, D. T., & Weber, D. M. (1998) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.1880080143 · Cited by 30 · see citing works
Shibata, K., Sasaki, Y., Watanabe, T., & Kawato, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.04234 · Cited by 4 · see citing works
Self-citationHebbian Plasticity for Improving Perceptual DecisionsPreprint
Huang, T.-R. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.03270 · Cited by 4 · see citing works
Identifying melancholic depression biomarker using whole-brain functional connectivityPreprint
Ichikawa, N., Lisi, G., Yahata, N., Okada, G., Takamura, M., Yamada, M., Suhara, T., Hashimoto, R., Yamada, T., Yoshihara, Y., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01039 · Cited by 2 · see citing works
Self-citationTowards an Unconscious Neurotherapy for Common FearsPreprint
Taschereau-Dumouchel, V., Cortese, A., Chiba, T., Knotts, J., Kawato, M., & Lau, H.-K. C. (2017) · bioRxiv · doi:10.1101/170183 · Cited by 2 · see citing works
Self-citation
Flagged first, then by citations
1 of the 79 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Ros, T., Enriquez-Geppert, S., Zotev, V., Young, K. D., Wood, G., Whitfield-Gabrieli, S., Wan, F., Vuilleumier, P., Vialatte, F., Van De Ville, D., et al. (2020) · Brain · doi:10.1093/brain/awaa009 · Cited by 399 · see citing works
Neurofeedback with fMRI: A critical systematic review
Thibault, R. T., MacPherson, A., Lifshitz, M., Roth, R. R., & Raz, A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.071 · Cited by 300 · see citing works
Treatment strategies for ADHD: an evidence-based guide to select optimal treatment
Caye, A., Swanson, J. M., Coghill, D., & Rohde, L. A. (2019) · Molecular Psychiatry · doi:10.1038/s41380-018-0116-3 · Cited by 303 · see citing works
Control freaks: Towards optimal selection of control conditions for fMRI neurofeedback studies
Sorger, B., Scharnowski, F., Linden, D. E. J., Hampson, M., & Young, K. D. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.11.004 · Cited by 215 · see citing works
Yamashita, A., Yahata, N., Itahashi, T., Lisi, G., Yamada, T., Ichikawa, N., Takamura, M., Yoshihara, Y., Kunimatsu, A., Okada, N., et al. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000042 · Cited by 205 · see citing works
Self-citationTowards an unconscious neural reinforcement intervention for common fears
Taschereau-Dumouchel, V., Cortese, A., Chiba, T., Knotts, J. D., Kawato, M., & Lau, H. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1721572115 · Cited by 153 · see citing works
Self-citationWeaverdyck, M. E., Lieberman, M. D., & Parkinson, C. M. (2020) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsaa057 · Cited by 136 · see citing works
Paret, C., Goldway, N., Zich, C., Keynan, J. N., Hendler, T., Linden, D. E. J., & Kadosh, K. C. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116107 · Cited by 118 · see citing works
Zhao, Z., Yao, S., Li, K., Sindermann, C., Zhou, F., Zhao, W., Li, J., Lührs, M., Goebel, R., Kendrick, K. M., et al. (2019) · Psychotherapy and Psychosomatics · doi:10.1159/000496057 · Cited by 102 · see citing works
Neurofeedback for cognitive enhancement and intervention and brain plasticity
Loriette, C., Ziane, C., & Hamed, S. B. (2021) · Revue Neurologique · doi:10.1016/j.neurol.2021.08.004 · Cited by 104 · see citing works
Chiba, T., Kanazawa, T., Koizumi, A., Ide, K., Taschereau‐Dumouchel, V., Boku, S., Hishimoto, A., Shirakawa, M. N., Sora, I., Lau, H. C., et al. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00233 · Cited by 97 · see citing works
Self-citationToward a comprehensive understanding of the neural mechanisms of decoded neurofeedback
Shibata, K., Lisi, G., Cortese, A., Watanabe, T., Sasaki, Y., & Kawato, M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.12.022 · Cited by 107 · see citing works
Self-citationDecoding and mapping task states of the human brain via deep learning
Wang, X., Liang, X., Jiang, Z., Nguchu, B. A., Zhou, Y., Wang, Y., Wang, H., Li, Y., Zhu, Y., Wu, F., et al. (2019) · Human Brain Mapping · doi:10.1002/hbm.24891 · Cited by 93 · see citing works
Process-based framework for precise neuromodulation
Lubianiker, N., Goldway, N., Fruchtman-Steinbok, T., Paret, C., Keynan, J. N., Singer, N., Cohen, A., Kadosh, K. C., Linden, D. E. J., & Hendler, T. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0573-y · Cited by 84 · see citing works
Emotion Regulation Using Virtual Environments and Real-Time fMRI Neurofeedback
Lorenzetti, V., Melo, B., Basílio, R., Suo, C., Yücel, M., Tierra-Criollo, C. J., & Moll, J. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.00390 · Cited by 83 · see citing works
Mano, H., Kotecha, G. K., Leibnitz, K., Matsubara, T., Sprenger, C., Nakae, A., Shenker, N., Shibata, M., Voon, V., Yoshida, W., et al. (2018) · Wellcome Open Research · doi:10.12688/wellcomeopenres.14069.2 · Cited by 74 · see citing works
Self-citationA systematic review of fMRI neurofeedback reporting and effects in clinical populations
Tursic, A., Eck, J., Lührs, M., Linden, D. E. J., & Goebel, R. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102496 · Cited by 70 · see citing works
Trambaiolli, L. R., Kohl, S. H., Linden, D. E. J., & Mehler, D. M. A. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.02.015 · Cited by 72 · see citing works
Sendi, M. S. E., Zendehrouh, E., Miller, R. L., Fu, Z., Du, Y., Liu, J., Mormino, E. C., Salat, D. H., & Calhoun, V. D. (2021) · Frontiers in Neural Circuits · doi:10.3389/fncir.2020.593263 · Cited by 62 · see citing works
Kober, S. E., Grössinger, D., & Wood, G. M. D. O. (2019) · Dysphagia · doi:10.1007/s00455-019-09985-w · Cited by 62 · see citing works
Predicting the future of neuroimaging predictive models in mental health
Tejavibulya, L., Rolison, M. J., Gao, S., Liang, Q., Peterson, H., Dadashkarimi, J., Farruggia, M. C., Hahn, C. A., Noble, S., Lichenstein, S. D., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01635-2 · Cited by 57 · see citing works
Enhancing creative cognition with a rapid right-parietal neurofeedback procedure
Agnoli, S., Zanon, M., Mastria, S., Avenanti, A., & Corazza, G. E. (2018) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2018.02.015 · Cited by 57 · see citing works
Mehler, D. M. A., Williams, A. N., Krause, F., Lührs, M., Wise, R. G., Turner, D. L., Linden, D. E. J., & Whittaker, J. R. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.09.007 · Cited by 59 · see citing works
Taylor, J. E., Yamada, T., Kawashima, T., Kobayashi, Y., Yoshihara, Y., Miyata, J., Murai, T., Kawato, M., & Motegi, T. (2022) · Scientific Reports · doi:10.1038/s41598-022-05860-1 · Cited by 54 · see citing works
Self-citationKlein, F., Kohl, S. H., Lührs, M., Mehler, D. M. A., & Sorger, B. (2024) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2023.0087 · Cited by 57 · see citing works
Mental Imagery and Brain Regulation—New Links Between Psychotherapy and Neuroscience
Skottnik, L., & Linden, D. (2019) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2019.00779 · Cited by 52 · see citing works
Deep Neural Generative Model of Functional MRI Images for Psychiatric Disorder Diagnosis
Matsubara, T., Tashiro, T., & Uehara, K. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2895663 · Cited by 54 · see citing works
Kim, H., Tegethoff, M., Meinlschmidt, G., Stalujanis, E., Belardi, A., Jo, S., Lee, J., Kim, D., Yoo, S., & Lee, J. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.03.066 · Cited by 52 · see citing works
Li, H., & Yong Fan, A. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116059 · Cited by 53 · see citing works
Gong, A., Gu, F., Nan, W., Qu, Y., Jiang, C., & Fu, Y. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.638369 · Cited by 51 · see citing works
Unconscious reinforcement learning of hidden brain states supported by confidence
Cortese, A., Lau, H. C., & Kawato, M. (2020) · Nature Communications · doi:10.1038/s41467-020-17828-8 · Cited by 51 · see citing works
Self-citationHeunis, S., Lamerichs, R. M. J. N., Zinger, S., Caballero‐Gaudes, C., Jansen, J. F. A., Aldenkamp, A. P., & Breeuwer, M. M. (2020) · Human Brain Mapping · doi:10.1002/hbm.25010 · Cited by 49 · see citing works
Delineating implicit and explicit processes in neurofeedback learning
Muñoz-Moldes, S., & Cleeremans, A. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.09.003 · Cited by 48 · see citing works
Amygdala Modulation During Emotion Regulation Training With fMRI-Based Neurofeedback
Barreiros, A. R., Almeida, I. A. T., Baía, B. C., & Castelo-Branco, M. S. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00089 · Cited by 47 · see citing works
Connectome-based neurofeedback: A pilot study to improve sustained attention
Scheinost, D., Hsu, T. W., Avery, E. W., Hampson, M., Constable, R. T., Chun, M. M., & Rosenberg, M. D. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116684 · Cited by 45 · see citing works
Yaden, D., Eichstaedt, J., & Medaglia, J. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.00962 · Cited by 41 · see citing works
Li, K., Jiang, Y., Gong, Y., Zhao, W., Zhao, Z., Liu, X., Kendrick, K. M., Zhu, C., & Becker, B. (2019) · Neurophotonics · doi:10.1117/1.nph.6.2.025011 · Cited by 41 · see citing works
Lenze, E. J., Nicol, G. E., Barbour, D. L., Kannampallil, T., Wong, A. W. K., Piccirillo, J., Drysdale, A. T., Sylvester, C. M., Haddad, R., Miller, J. P., et al. (2021) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.200042 · Cited by 43 · see citing works
Can neurofeedback provide evidence of direct brain-behavior causality?
Kvamme, T. L., Ros, T., & Overgaard, M. S. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119400 · Cited by 43 · see citing works
Agito ergo sum: Correlates of spatio-temporal motion characteristics during fMRI
Bolton, T. A. W., Kebets, V., Glerean, E., Zöller, D., Li, J., Yeo, B. T. T., Caballero‐Gaudes, C., & Ville, D. V. D. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116433 · Cited by 41 · see citing works
Lam, S.-L., Criaud, M., Lukito, S., Westwood, S. J., Agbedjro, D., Kowalczyk, O. S., Curran, S. R., Barret, N., Abbott, C., Liang, H., et al. (2022) · American Journal of Psychiatry · doi:10.1176/appi.ajp.21100999 · Cited by 40 · see citing works
Neurorights in the Constitution: from neurotechnology to ethics and politics
Ruíz, S., Valera, L., Ramos, P., & Sitaram, R. (2024) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2023.0098 · Cited by 38 · see citing works
Okano, K., Bauer, C. C. C., Ghosh, S., Lee, Y. J., Melero, H., Angeles, C. D. L., Nestor, P. G., del Re, E. C., Northoff, G., Whitfield, S., et al. (2020) · Psychiatry Research · doi:10.1016/j.psychres.2020.112862 · Cited by 38 · see citing works
Conducting decoded neurofeedback studies
Taschereau‐Dumouchel, V., Cortese, A., Lau, H. C., & Kawato, M. (2020) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsaa063 · Cited by 37 · see citing works
Self-citationNeurofeedback: Principles, appraisal, and outstanding issues
Papo, D. (2018) · European Journal of Neuroscience · doi:10.1111/ejn.14312 · Cited by 1 · see citing works
Dourron, H. M., Strauss, C. K., & Hendricks, P. S. (2022) · Pharmacological Reviews · doi:10.1124/pharmrev.121.000514 · Cited by 31 · see citing works
Neurofeedback for the Education of Children with ADHD and Specific Learning Disorders: A Review
Patil, A. U., Madathil, D., Fan, Y., Tzeng, O. J. L., Huang, C., & Huang, H.-W. (2022) · Brain Sciences · doi:10.3390/brainsci12091238 · Cited by 35 · see citing works
Closed-loop neuromodulation for studying spontaneous activity and causality
Ramot, M., & Martin, A. D. (2022) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2022.01.008 · Cited by 36 · see citing works
From internal models toward metacognitive AI
Kawato, M., & Cortese, A. (2021) · Biological Cybernetics · doi:10.1007/s00422-021-00904-7 · Cited by 35 · see citing works
Self-citationShort-delay neurofeedback facilitates training of the parietal alpha rhythm
Belinskaia, A., Smetanin, N., Lebedev, M., & Ossadtchi, A. E. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abc8d7 · Cited by 32 · see citing works
Real-Time Functional MRI in the Treatment of Mental Health Disorders
Taschereau‐Dumouchel, V., Cushing, C. A., & Lau, H. C. (2022) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-072220-014550 · Cited by 33 · see citing works
Resting-State Functional MRI of Healthy Adults: Temporal Dynamic Brain Coactivation Patterns
Liu, T., Wang, L., Suo, D., Zhang, J., Wang, K., Wang, J., Chen, D., & Yan, T. (2022) · Radiology · doi:10.1148/radiol.211762 · Cited by 31 · see citing works
Sreedharan, S., Arun, K., Sylaja, P., Kesavadas, C., & Sitaram, R. (2019) · Brain Connectivity · doi:10.1089/brain.2019.0674 · Cited by 29 · see citing works
Upregulation of Supplementary Motor Area Activation with fMRI Neurofeedback during Motor Imagery
Al‐Wasity, S., Vogt, S., Vučković, A., & Pollick, F. E. (2020) · eNeuro · doi:10.1523/eneuro.0377-18.2020 · Cited by 29 · see citing works
Misaki, M., Tsuchiyagaito, A., Zoubi, O. A., Paulus, M. P., Bodurka, J., & Tulsa 1000 Investigators (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102244 · Cited by 28 · see citing works
Unconscious psychological treatments for physiological survival circuits
Taschereau‐Dumouchel, V., Liu, K., & Lau, H. C. (2018) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2018.04.010 · Cited by 26 · see citing works
Green concept of neuromarketing based on a systematic review using the bibliometric method
Sangari, N., Khamseh, P. M., & Sana, S. S. (2023) · Green Finance · doi:10.3934/gf.2023016 · Cited by 26 · see citing works
Experience-dependent neurodevelopment of self-regulation in adolescence
Meredith, W. J., & Silvers, J. A. (2024) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2024.101356 · Cited by 38 · see citing works
Enhancing the Effects of Neurofeedback Training: The Motivational Value of the Reinforcers
Pérez-Elvira, R., Oltra‐Cucarella, J., Carrobles, J. A., Moltó, J., Flórez, M., Parra, S., Agudo, M., Saez, C., Guarino, S., Costea, R. M., et al. (2021) · Brain Sciences · doi:10.3390/brainsci11040457 · Cited by 25 · see citing works
Neurofeedback and neural self-regulation: a new perspective based on allostasis
Mirifar, A., Keil, A., & Ehrlenspiel, F. (2022) · Reviews in the Neurosciences · doi:10.1515/revneuro-2021-0133 · Cited by 26 · see citing works
Targeted neurorehabilitation strategies in post-stroke aphasia
Shah‐Basak, P. P., Boukrina, O. V., Li, X. R., Jebahi, F., & Kielar, A. (2023) · Restorative Neurology and Neuroscience · doi:10.3233/rnn-231344 · Cited by 23 · see citing works
A simulation-based approach to improve decoded neurofeedback performance
Oblak, E. F., Sulzer, J. S., & Lewis‐Peacock, J. A. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.03.062 · Cited by 21 · see citing works
Georgopoulou, A., Hardman, D. K., Thuruthel, T. G., Fumiya Iida, A., & Clemens, F. J. (2023) · Advanced Science · doi:10.1002/advs.202301590 · Cited by 21 · see citing works
Real-Time Decoding of Attentional States Using Closed-Loop EEG Neurofeedback
Tuckute, G., Hansen, S. T., Kjær, T. W., & Hansen, L. K. (2021) · Neural Computation · doi:10.1162/neco_a_01363 · Cited by 21 · see citing works
Krause, F., Kogias, N., Krentz, M., Lührs, M., Goebel, R., & Hermans, E. J. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118527 · Cited by 21 · see citing works
Neurotherapeutics for ADHD : Do they work?
Rubia, K. (2022) · PsyCh Journal · doi:10.1002/pchj.544 · Cited by 19 · see citing works
Lieberman, J. M., Rabellino, D., Densmore, M., Frewen, P., Steyrl, D., Scharnowski, F., Théberge, J., Hosseini‐Kamkar, N., Neufeld, R. W. J., Jetly, R., et al. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1229729 · Cited by 20 · see citing works
Neuroethical Issues of the Brain/MINDS Project of Japan
Sadato, N., Morita, K., Kasai, K., Fukushi, T., Nakamura, K., Nakazawa, E., Okano, H., & Okabe, S. (2019) · Neuron · doi:10.1016/j.neuron.2019.01.006 · Cited by 20 · see citing works
Xue, A. M., Foerde, K., Walsh, B. T., STEINGLASS, J. E., Daphna Shohamy, A., & Bakkour, A. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.0958-21.2021 · Cited by 20 · see citing works
Chikhi, S., Matton, N., Sanna, M., & Blanchet, S. (2023) · Biological Psychology · doi:10.1016/j.biopsycho.2023.108521 · Cited by 21 · see citing works
A Novel Brain–Computer Interface Virtual Environment for Neurofeedback During Functional MRI
Baqapuri, H. I., Roes, L. D., Zvyagintsev, M., Ramadan, S., Keller, M., Roecher, E., Zweerings, J., Klasen, M., Gur, R. C., & Mathiak, K. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.593854 · Cited by 19 · see citing works
Effects of Neurofeedback training on performance in laboratory tasks: A systematic review
Chiasson, P., Boylan, M. R., Elhamiasl, M., Pruitt, J., Ranjan, S., Riels, K. M., Sahoo, A. K., Mirifar, A., & Keil, A. (2022) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2023.04.005 · Cited by 18 · see citing works
Yang, X., Zeng, Y., Jiao, G., Gan, X., Linden, D. E. J., Hernaus, D., Zhu, C., Li, K., Yao, D., Yao, S., et al. (2024) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2024.110968 · Cited by 16 · see citing works
Xia, M., Xu, P., Yang, Y., Jiang, W., Wang, Z., Gu, X., Yang, M., Li, D., Li, S., Dong, G., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3074220 · Cited by 17 · see citing works
Ramot, M., & González-Castillo, J. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.12.006 · Cited by 17 · see citing works
The DecNef collection, fMRI data from closed-loop decoded neurofeedback experiments
Cortese, A., Tanaka, S., Amano, K., Koizumi, A., Lau, H. C., Sasaki, Y., Shibata, K., Taschereau‐Dumouchel, V., Watanabe, T., & Kawato, M. (2021) · Scientific Data · doi:10.1038/s41597-021-00845-7 · Cited by 17 · see citing works
Self-citationXia, Z., Yang, P., Chen, S., Zhou, H., & Yan, C. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae252 · Cited by 16 · see citing works
Advances in biofeedback and neurofeedback studies on smoking
Pandria, N., Athanasiou, A., Konstantara, L., Karagianni, M. A., & Bamidis, P. D. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102397 · Cited by 16 · see citing works
Getting stress-related disorders under control: the untapped potential of neurofeedback
Krause, F., Linden, D. E. J., & Hermans, E. J. (2024) · Trends in Neurosciences · doi:10.1016/j.tins.2024.08.007 · Cited by 16 · see citing works
Fukuma, R., Yanagisawa, T., Yokoi, H., Hirata, M., Yoshimine, T., Saitoh, Y., Kamitani, Y., & Kishima, H. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00478 · Cited by 16 · see citing works
What would a synthetic connectome look like?
Rabinowitch, I. (2019) · Physics of Life Reviews · doi:10.1016/j.plrev.2019.06.005 · Cited by 16 · see citing works
Educational fMRI: From the Lab to the Classroom
Seghier, M. L., Fahim, M. A., & Habak, C. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.02769 · Cited by 19 · see citing works
Decoded EEG neurofeedback-guided cognitive reappraisal training for emotion regulation
Li, L., Gui, X., Huang, G., Zhang, L., Wan, F., Han, X., Wang, J., Ni, D., Liang, Z., & Zhang, Z. (2024) · Cognitive Neurodynamics · doi:10.1007/s11571-024-10108-x · Cited by 15 · see citing works
Carlson, H. N., & Weiner, J. L. (2020) · International review of neurobiology · doi:10.1016/bs.irn.2020.09.006 · Cited by 14 · see citing works
Feasibility of training the dorsolateral prefrontal-striatal network by real-time fMRI neurofeedback
Weiß, F., Zhang, J., Aslan, A., Kirsch, P., & Gerchen, M. F. (2022) · Scientific Reports · doi:10.1038/s41598-022-05675-0 · Cited by 15 · see citing works
Wang, Z., Tamaki, M., Frank, S., Shibata, K., Worden, M. S., Yamada, T., Kawato, M., Sasaki, Y., & Watanabe, T. (2021) · Journal of Vision · doi:10.1167/jov.21.8.24 · Cited by 14 · see citing works
Self-citationFrontostriatal circuitry as a target for fMRI-based neurofeedback interventions: A systematic review
Orth, L., Meeh, J., Gur, R. C., Neuner, I., & Sarkheil, P. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.933718 · Cited by 14 · see citing works
Mechanisms of brain self-regulation: psychological factors, mechanistic models and neural substrates
Sitaram, R., Sánchez‐Corzo, A., Vargas, G. S., Cortese, A., El‐Deredy, W., Jackson, A., & Fetz, E. E. (2024) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2023.0093 · Cited by 17 · see citing works
Kober, S. E., Neuper, C., & Wood, G. M. D. O. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.606684 · Cited by 14 · see citing works
A double‐blind trial of decoded neurofeedback intervention for specific phobias
Cushing, C. A., Lau, H. C., Kawato, M., Craske, M. G., & Taschereau‐Dumouchel, V. (2024) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.13726 · Cited by 14 · see citing works
Self-citationComputationally Informed Interventions for Targeting Compulsive Behaviors
Kahnt, T. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.08.028 · Cited by 13 · see citing works
Attentional set to safety recruits the ventral medial prefrontal cortex
Yao, S., Qi, S., Kendrick, K. M., & Mobbs, D. (2018) · Scientific Reports · doi:10.1038/s41598-018-33953-3 · Cited by 14 · see citing works
Sundar, L. K. S., Baajour, S., Beyer, T., Lanzenberger, R. R., Traub‐Weidinger, T., Rausch, I., Pataraia, E., Hahn, A., Rischka, L., Hienert, M., et al. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00252 · Cited by 12 · see citing works
Polley, D. B., & Schiller, D. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104652 · Cited by 13 · see citing works
Li, X., Li, Z., Zou, Z., Wu, X., Gao, H., Wang, C., Zhou, J., Qi, F., Zhang, M., He, J., et al. (2022) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2022.825286 · Cited by 12 · see citing works
Li, Y., Li, S., Li, H., Tang, Y., & Zhang, D. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121079 · Cited by 12 · see citing works
Zhang, Y., Becker, B., Kendrick, K. M., Zhang, Q., & Yao, S. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03382-8 · Cited by 12 · see citing works
Russo, A. G., Lührs, M., Salle, F. D., Esposito, F., & Goebel, R. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abecc3 · Cited by 12 · see citing works
Genetically influenced externalizing and internalizing risk pathways as novel prevention targets.
Dick, D. M., Saunders, T. R., Balcke, E., Driver, M. N., Neale, Z. E., Vassileva, J., & Langberg, J. M. (2021) · Psychology of Addictive Behaviors · doi:10.1037/adb0000759 · Cited by 12 · see citing works
Li, K., Yang, J., Becker, B., & Li, X. (2023) · Neurophotonics · doi:10.1117/1.nph.10.2.025011 · Cited by 10 · see citing works
- Authors
- Watanabe, T., Sasaki, Y., Shibata, K., & Kawato, M.
- Affiliations
- Brown University (US)
- Advanced Telecommunications Research Institute International (JP)
- Corresponding author
- Kawato, Mitsuo
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2017-12
- Volume
- 21
- Issue
- 12
- Pages
- 997-1010
- ISSN
- 1364-6613 (ISSN-L)
- PMC
- PMC5694350
- OpenAlex
- W2763386556
- Cited by
- 242
- References
- 79
- Citations by year
- 2026: 12 · 2025: 26 · 2024: 27 · 2023: 23 · 2022: 40 · 2021: 33 · 2020: 29 · 2019: 27 · 2018: 21 · 2017: 2 · 2016: 1 · 2014: 1
- FWCI
- 2.42
- Type
- journal article
- Language
- en
- Update notices
- erratum (2018-08-01)
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Trends in Cognitive Sciences (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Japan Agency for Medical Research and Development
- Japan Society for the Promotion of Science (17H04789)
- National Science Foundation (BCS 1539717)
- Cabinet Office, Government of Japan
- Council for Science, Technology and Innovation
- National Institutes of Health (R01EY019466)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- fMRI neurofeedback, biofeedback, extrinsic rewards, multivariate analysis, functional connectivity, behavior modification
- MeSH terms
- Brain, Brain Mapping, Humans, Magnetic Resonance Imaging, Neurofeedback
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review