Interactive Brain Stimulation Neurotherapy Based on BOLD Signal in Stroke Rehabilitation

  • Nadezhda A Khruscheva Federal Research Center of Fundamental and Translational Medicine (FRC FTM), Novosibirsk, Russia
  • Mikhail Ye Mel'nikov Federal Research Center of Fundamental and Translational Medicine (FRC FTM), Novosibirsk, Russia
  • Dmitriy D Bezmaternykh Federal Research Center of Fundamental and Translational Medicine (FRC FTM), Novosibirsk, Russia
  • Andrey A Savelov International Tomography Center Siberian Division of Russian Academy of Sciences, Novosibirsk
  • Konstantin V Kalgin Novosibirsk State University, NGU, Novosibirsk,
  • Yevgeny D Petrovsky International Tomography Center Siberian Division of Russian Academy of Sciences, Novosibirsk
  • Anastasia V Shurunova Novosibirsk State University, NGU, Novosibirsk,
  • Mark B Shtark Federal Research Center of Fundamental and Translational Medicine (FRC FTM), Novosibirsk, Russia
  • Estate M Sokhadze Duke University
Keywords: interactive brain stimulation, BOLD, cerebral networks, functional magnetic resonance imaging (fMRI), bimodal fMRI-EEG neurofeedback platform


Interactive brain stimulation is a new generation of neurofeedback characterized by a radical change in the targets of cognitive (volitional, adaptive) influence. These targets are represented by specific cerebral structures and neural networks, the reconstruction of which leads to the brain functions’ restoration and behavioral metamorphoses. Functional magnetic resonance imaging (fMRI) in the neurofeedback contour uses a natural intravascular tracer, a blood-oxygenation-level-dependent (BOLD) signal as feedback. The subject included into the "interactive brain contour" learns to modulate and modify his or her own cerebral networks, creating new ones or "awakening" pre-existing ones, in order to improve (or restore) mental, sensory, or motor functions. In this review we focus on interactive brain stimulation based on BOLD signal and its role in the motor rehabilitation of stroke, briefly introducing the basic concepts of the so-called “network vocabulary” and general biophysical basis of the BOLD signal. We also discuss a bimodal fMRI-EEG neurofeedback platform and the prospects of fMRI technology in controlling functional connectivity, a numerical assessment of neuroplasticity.

Author Biographies

Nadezhda A Khruscheva, Federal Research Center of Fundamental and Translational Medicine (FRC FTM), Novosibirsk, Russia

Dr Khruscheva is a senior research scientist at the Federal Research Center of Fundamental and Translational Medicine.

Mikhail Ye Mel'nikov, Federal Research Center of Fundamental and Translational Medicine (FRC FTM), Novosibirsk, Russia

Dr Mel'nikov is a junior researcher at the Federal Research Center of Fundamental and Translational Medicine (FRC FTM), Novosibirsk, Russia. He has multiple publications in this area.

Dmitriy D Bezmaternykh, Federal Research Center of Fundamental and Translational Medicine (FRC FTM), Novosibirsk, Russia

Bezmaternich is doctoral student at Federal Research Center of Fundamental and Translational Medicine (FRC FTM), Novosibirsk, Russia

Andrey A Savelov, International Tomography Center Siberian Division of Russian Academy of Sciences, Novosibirsk

Andrey Savelov is senior researcher at MRI center of Academy of Sciences in Novosibirsk

Konstantin V Kalgin, Novosibirsk State University, NGU, Novosibirsk,

Dr Kalgin is professor at Novosibirsk University, top 5 university in Russia

Yevgeny D Petrovsky, International Tomography Center Siberian Division of Russian Academy of Sciences, Novosibirsk

Dr Petrovsky is one of the leading MRI specialists, he is senior level research specialist

Anastasia V Shurunova, Novosibirsk State University, NGU, Novosibirsk,

Dr Shurunova is lecturer at Novosibirsk State University

Mark B Shtark, Federal Research Center of Fundamental and Translational Medicine (FRC FTM), Novosibirsk, Russia

Dr Shtark is academician, lead research specialist at Federal Research Center of Fundamental and Translational Medicine (FRC FTM), Novosibirsk, Russia. He is one of the most known East European specialists in neurofeedback

Estate M Sokhadze, Duke University

Dr Estate (Tato) Sokhadze is research scientist at Neurology department of Duke University in Durham, NC. He is specialist in neurofeedback, QEEG and neuromodulation. His current interests include treatment of stroke using neuromodulation and neurotherapy methods


Alionte, C., Notte, C., & Strubakos, C. D. (2022). From symmetry to chaos and back: Understanding and imaging the mechanisms of neural repair after stroke. Life Sciences, 288, Article 120161.

Almeida, S. R. M., Vicentini, J., Bonilha, L., De Campos, B. M., Casseb, R. F., & Min, L. L. (2017). Brain connectivity and functional recovery in patients with ischemic stroke. Journal of Neuroimaging, 27(1), 65–70.

Alstott, J., Breakspear, M., Hagmann, P., Cammoun, L., & Sporns, O. (2009). Modeling the impact of lesions in the human brain. PLoS Computational Biology, 5(6), Article e1000408.

Alves, R., Henriques, R. N., Kerkelä, L., Chavarrías, C., Jespersen, S. N., & Shemesh, N. (2022). Correlation tensor MRI deciphers underlying kurtosis sources in stroke. NeuroImage, 247, Article 118833.

Bassett, D. S., Meyer-Lindenberg, A., Achard, S., Duke, T., & Bullmore, E. (2006). Adaptive reconfiguration of fractal small-world human brain functional networks. Proceedings of the National Academy of Sciences of the United States of America, 103(51), 19518–19523.

Bentley, W. J., Li, J. M., Snyder, A. Z., Raichle, M. E., & Snyder, L. H. (2016). Oxygen level and LFP in task-positive and task-negative areas: Bridging BOLD fMRI and electrophysiology. Cerebral Cortex, 26(1), 346–357.

Bezmaternykh, D. D., Kalgin, K. V., Maximova, P. E., Mel'nikov, M. Y., Petrovskii, E. D., Predtechenskaya, E. V., Savelov, A. A., Semenikhina, A. A., Tsaplina, T. N., Shtark, M. B., & Shurunova, A. V. (2021). Application of fMRI and simultaneous fMRI-EEG neurofeedback in post-stroke motor rehabilitation. Bulletin of Experimental Biology and Medicine, 171(3), 379–383.

Bezmaternykh, D. D., Mel'nikov, M. E., Petrovskii, E. D., Kozlova, L. I., Shtark, M. B., Savelov, A. A., Shubina, O. S., & Natarova, K. A. (2018). Spontaneous changes in functional connectivity of independent components of fMRI signal in healthy volunteers at rest and in subjects with mild depression. Bulletin of Experimental Biology and Medicine, 165(3), 325–330.

Birn, R. M., Bandettini, P. A., Cox, R. W., & Shaker, R. (1999). Event-related fMRI of tasks involving brief motion. Human Brain Mapping, 7(2), 106–114.<106::AID-HBM4>3.0.CO;2-O

Biswal, B., Yetkin, F. Z., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in Medicine, 34(4), 537–541.

Bonmassar, G., Anami, K., Ives, J., & Belliveau, J. W. (1999). Visual evoked potential (VEP) measured by simultaneous 64-channel EEG and 3T fMRI. NeuroReport, 10(9), 1893–1897.

Buckner, R. L. (1998). Event-related fMRI and the hemodynamic response. Human Brain Mapping, 6(5–6), 373–377<373::AID-HBM8>3.0.CO;2-P

Buckner, R. L., & DiNicola, L. M. (2019). The brain's default network: Updated anatomy, physiology and evolving insights. Nature Reviews Neuroscience, 20(10), 593–608.

Carter, A. R., Astafiev, S. V., Lang, C. E., Connor, L. T., Rengachary, J., Strube, M. J., Pope, D. L. W., Shulman, G. L., & Corbetta, M. (2010). Resting interhemispheric functional magnetic resonance imaging connectivity predicts performance after stroke. Annals of Neurology, 67(3), 365–375.

Carter, A. R., Shulman, G. L., & Corbetta, M. (2012). Why use a connectivity-based approach to study stroke and recovery of function? NeuroImage, 62(4), 2271–2280.

Cheng, H.-J., Ng, K. K., Qian, X., Ji, F., Lu, Z. K., Teo, W. P., Hong, X., Nasrallah, F. A., Ang, K. K., Chuang, K.-H., Guan, C., Yu, H., Chew, E., & Zhou, J. H. (2021). Task-related brain functional network reconfigurations relate to motor recovery in chronic subcortical stroke. Scientific Reports, 11(1), Article 8442.

Duncan, P. W., Lai, S. M., & Keighley, J. (2000). Defining post-stroke recovery: Implications for design and interpretation of drug trials. Neuropharmacology, 39(5), 835–841.

Evans, J. R., Dellinger, M. B., & Russell, H. L. (Eds.). (2019). Neurofeedback: The first fifty years (1st ed.). Academic Press.

Feigin, V. L., Norrving, B., George, M. G., Foltz, J. L., Roth, G. A., & Mensah, G. A. (2016). Prevention of stroke: A strategic global imperative. Nature Reviews Neurology, 12(9), 501–512.

Feigin, V. L., Norrving, B., & Mensah, G. A. (2017). Global burden of stroke. Circulation Research, 120(3), 439–448.

Fornito, A., Zalesky, A., & Breakspear, M. (2015). The connectomics of brain disorders. Nature Reviews Neuroscience, 16(3), 159–172.

Fovet, T., Jardri, R., & Linden, D. (2015). Current issues in the use of fMRI-based neurofeedback to relieve psychiatric symptoms. Current Pharmaceutical Design, 21(23), 3384–3394.

Friston, K. J. (2011). Functional and effective connectivity: A review. Brain Connectivity, 1(1), 13–36.

Gauthier, C. J., & Fan, A. P. (2019). BOLD signal physiology: Models and applications. NeuroImage, 187, 116–127.

Golanov, E. V., Yamamoto, S., & Reis, D. J. (1994). Spontaneous waves of cerebral blood flow associated with a pattern of electrocortical activity. American Journal of Physiology, 266(1 Pt. 2), R204–R214.

Guggisberg, A. G., Koch, P. J., Hummel, F. C., & Buetefisch, C. M. (2019). Brain networks and their relevance for stroke rehabilitation. Clinical Neurophysiology, 130(7), 1098–1124.

Herrmann, C. S., & Debener, S. (2008). Simultaneous recording of EEG and BOLD responses: A historical perspective. International Journal of Psychophysiology, 67(3), 161–168.

Huster, R. J., Debener, S., Eichele, T., & Herrmann, C. S. (2012). Methods for simultaneous EEG-fMRI: An introductory review. The Journal of Neuroscience, 32(18), 6053–6060.

Ives, J. R., Warach, S., Schmitt, F., Edelman, R. R., & Schomer, D. L. (1993). Monitoring the patient's EEG during echo planar MRI. Electroencephalography and Clinical Neurophysiology, 87(6), 417–420.

Keynan, J. N., Cohen, A., Jackont, G., Green, N., Goldway, N., Davidov, A., Meir-Hasson, Y., Raz, G., Intrator, N., Fruchter, E., Ginat, K., Laska, E., Cavazza, M., & Hendler, T. (2019). Electrical fingerprint of the amygdala guides neurofeedback training for stress resilience. Nature Human Behaviour, 3(1), 63–73.

Keynan, J. N., Meir-Hasson, Y., Gilam, G., Cohen, A., Jackont, G., Kinreich, S., Ikar, L., Or-Borichev, A., Etkin, A., Gyurak, A., Klovatch, I., Intrator, N., & Hendler, T. (2016). Limbic activity modulation guided by functional magnetic resonance imaging-inspired electroencephalography improves implicit emotion regulation. Biological Psychiatry, 80(6), 490–496.

Kim, D. H., & Kang, H. (2022). Changes in bihemispheric structural connectivity following middle cerebral artery infarction. Journal of Personalized Medicine, 12(1), 81.

Koush, Y., Rosa, M. J., Robineau, F., Heinen, K., Rieger, S. W., Weiskopf, N., Vuilleumier, P., Van De Ville, D., & Scharnowski, F. (2013). Connectivity-based neurofeedback: Dynamic causal modeling for real-time fMRI. NeuroImage, 81, 422–430.

Kwakkel, G., & Kollen, B. J. (2013). Predicting activities after stroke: What is clinically relevant? International Journal of Stroke, 8(1), 25–32.

Larivière, S., Ward, N. S., & Boudrias, M.-H. (2018). Disrupted functional network integrity and flexibility after stroke: Relation to motor impairments. NeuroImage: Clinical, 19, 883–891.

Lecrux, C., & Hamel, E. (2016). Neuronal networks and mediators of cortical neurovascular coupling responses in normal and altered brain states. Philosophical Transactions of the Royal Society of B, Biological Sciences, 371(1705), Article 20150350.

Li, W., Li, Y., Zhu, W., & Chen, X. (2014). Changes in brain functional network connectivity after stroke. Neural Regeneration Research, 9(1), 51–60.

Liew, S.-L., Rana, M., Cornelsen, S., Fortunato De Barros Filho, M., Birbaumer, N., Sitaram, R., Cohen, L. G., & Soekadar, S. R. (2016). Improving motor corticothalamic communication after stroke using real-time fMRI connectivity-based neurofeedback. Neurorehabilitation and Neural Repair, 30(7), 671–675.

Lioi, G., Butet, S., Fleury, M., Bannier, E., Lécuyer, A., Bonan, I., & Barillot, C. (2020). A multi-target motor imagery training using bimodal EEG-fMRI neurofeedback: A pilot study in chronic stroke patients. Frontiers in Human Neuroscience, 14, Article 37.

Lioi, G., Fleury, M., Butet, S., Lécuyer, A., Barillot, C., & Bonan, I. (2018). Bimodal EEG-fMRI neurofeedback for stroke rehabilitation: A case report. Annals of Physical and Rehabilitation Medicine, 61(Suppl), e482–e483.

Lopes da Silva, F. (2013). EEG and MEG: Relevance to neuroscience. Neuron, 80(5), 1112–1128.

Luu, P., Tucker, D. M., Englander, R., Lockfeld, A., Lutsep, H., & Oken, B. (2001). Localizing acute stroke-related EEG changes: Assessing the effects of spatial undersampling. Journal of Clinical Neurophysiology, 18(4), 302–317.

Mano, M., Lécuyer, A., Bannier, E., Perronnet, L., Noorzadeh, S., & Barillot, C. (2017). How to build a hybrid neurofeedback platform combining EEG and fMRI. Frontiers in Neuroscience, 11, Article 140.

Marek, S., & Dosenbach, N. U. F. (2018). The frontoparietal network: Function, electrophysiology, and importance of individual precision mapping. Dialogues in Clinical Neuroscience, 20(2), 133–140.

Matthews, P. M., & Jezzard, P. (2004). Functional magnetic resonance imaging. Journal of Neurology, Neurosurgery, and Psychiatry, 75(1), 6–12.

Mehler, D. M. A., Williams, A. N., Krause, F., Lührs, M., Wise, R. G., Turner, D. L., Linden, D. E. J., & Whittaker, J. R. (2019). The BOLD response in primary motor cortex and supplementary motor area during kinesthetic motor imagery based graded fMRI neurofeedback. NeuroImage, 184, 36–44.

Mehler, D. M. A., Williams, A. N., Whittaker, J. R., Krause, F., Lührs, M., Kunas, S., Wise, R. G., Shetty, H. G. M., Turner, D. L., & Linden, D. E. J. (2020). Graded fMRI neurofeedback training of motor imagery in middle cerebral artery stroke patients: A preregistered proof-of-concept study. Frontiers in Human Neuroscience, 14, Article 226.

Meir-Hasson, Y., Keynan, J. N., Kinreich, S., Jackont, G., Cohen, A., Podlipsky-Klovatch, I., Hendler, T., & Intrator, N. (2016). One-class FMRI-inspired EEG model for self-regulation training. PLoS ONE, 11(5), Article e0154968.

Meir-Hasson, Y., Kinreich, S., Podlipsky, I., Hendler, T., & Intrator, N. (2014). An EEG finger-print of fMRI deep regional activation. NeuroImage, 102(Pt. 1), 128–141.

Mel'nikov, M. Y., Shtark, M. B., Savelov, A. A., & Bruhl, A. (2017). Real time fuctional magnetic resonance imaging biofeedback: A new generation of neurotherapy. Zhurnal Vysshei Nervnoi Deiatelnosti imeni I. P. Pavlova, 67(1), 3–32.

Menon, V. (2011). Large-scale brain networks and psychopathology: A unifying triple network model. Trends in Cognitive Sciences, 15(10), 483–506.

Miller, K. L., Luh, W.-M., Liu, T. T., Martinez, A., Obata, T., Wong, E. C., Frank, L. R., & Buxton, R. B. (2001). Nonlinear temporal dynamics of the cerebral blood flow response. Human Brain Mapping, 13(1), 1–12.

Molnar-Szakacs, I., & Uddin, L. Q. (2013). Self-processing and the default mode network: Interactions with the mirror neuron system. Frontiers in Human Neuroscience, 7, Article 571.

Morgenroth, E., Saviola, F., Gilleen, J., Allen, B., Lührs, M., W Eysenck, M., & Allen, P. (2020). Using connectivity-based real-time fMRI neurofeedback to modulate attentional and resting state networks in people with high trait anxiety. NeuroImage: Clinical, 25, Article 102191.

Nudo R. J. (2003). Functional and structural plasticity in motor cortex: Implications for stroke recovery. Physical Medicine and Rehabilitation Clinics of North America, 14(1 Suppl.), S57–S76.

Nudo, R. J., Wise, B. M., SiFuentes, F., & Milliken, G. W. (1996). Neural substrates for the effects of rehabilitative training on motor recovery after ischemic infarct. Science, 272(5269), 1791–1794.

Ogawa, S., & Lee, T.-M. (1990). Magnetic resonance imaging of blood vessels at high fields: In vivo and in vitro measurements and image simulation. Magnetic Resonance in Medicine, 16(1), 9–18.

Ogawa, S., Menon, R. S., Kim, S. G., & Ugurbil, K. (1998). On the characteristics of functional magnetic resonance imaging of the brain. Annual Review of Biophysics and Biomolecular Structure, 27, 447–474.

Paret, C., Goldway, N., Zich, C., Keynan, J. N., Hendler, T., Linden, D., & Cohen Kadosh, K. (2019). Current progress in real-time functional magnetic resonance-based neurofeedback: Methodological challenges and achievements. NeuroImage, 202, 116107.

Petersen, S. E., & Sporns, O. (2015). Brain networks and cognitive architectures. Neuron, 88(1), 207–219.

Philiastides, M. G., Tu, T., & Sajda, P. (2021). Inferring macroscale brain dynamics via fusion of simultaneous EEG-fMRI. Annual Review of Neuroscience, 44, 315–334.

Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences of the United States of America, 98(2), 676–682.

Rance, M., Ruttorf, M., Nees, F., Schad, L. R., & Flor, H. (2014). Neurofeedback of the difference in activation of the anterior cingulate cortex and posterior insular cortex: Two functionally connected areas in the processing of pain. Frontiers in Behavioral Neuroscience, 8, Article 357.

Ritter, P., & Villringer, A. (2006). Simultaneous EEG-fMRI. Neuroscience & Biobehavioral Reviews, 30(6), 823–838.

Rowe, J. B. (2010). Connectivity analysis is essential to understand neurological disorders. Frontiers in Systems Neuroscience, 4, Article 144.

Rudnev, V., Melnikov, M., Savelov, A., Shtark, M., & Sokhadze, E. M. (2021). fMRI-EEG fingerprint regression model for motor cortex. NeuroRegulation, 8(3), 162–172.

Savelov, A. A., Shtark, M. B., Kozlova, L. I., Verevkin, E. G., Petrovskii, E. D., Pokrovskii, M. A., Rudych, P. D., & Tsyrkin, G. M. (2019). Dynamics of interactions between cerebral networks derived from fMRI data and motor rehabilitation during stokes. Bulletin of Experimental Biology and Medicine, 166(3), 399–403.

Savelov, A. A., Shtark, M. B., Mel'nikov, M. E., Kozlova, L. I., Bezmaternykh, D. D., Verevkin, E. G., Petrovskii, E. D., Pokrovskii, M. A., Tsirkin, G. M., & Rudych, P. D. (2019a). Prospects of synchronous fMRI-EEG recording as the basis for neurofeedback (exemplified on patient with stroke sequelae). Bulletin of Experimental Biology and Medicine, 166(3), 390–393.

Savelov, A. A., Shtark, M. B., Mel'nikov, M. E., Kozlova, L. I., Bezmaternykh, D. D., Verevkin, E. G., Petrovskii, E. D., Pokrovskii, M. A., Tsirkin, G. M., & Rudych, P. D. (2019b). Dynamics of fMRI and EEG parameters in a stroke patient assessed during a neurofeedback course focused on Brodmann area 4 (M1). Bulletin of Experimental Biology and Medicine, 166(3), 394–398.

Schaechter, J. D., Moore, C. I., Connell, B. D., Rosen, B. R., & Dijkhuizen, R. M. (2006). Structural and functional plasticity in the somatosensory cortex of chronic stroke patients. Brain, 129(10), 2722–2733.

Scharnowski, F., Veit, R., Zopf, R., Studer, P., Bock, S., Diedrichsen, J., Goebel, R., Mathiak, K., Birbaumer, N., & Weiskopf, N. (2015). Manipulating motor performance and memory through real-time fMRI neurofeedback. Biological Psychology, 108, 85–97.

Shtark, M. B. (2019). Neurofeedback: A scarce resource at the mental market. In J. R. Evans, M. B. Dellinger, & H. L. Russell (Eds.), Neurofeedback: The first fifty years (pp. 353–358). Academic Press.

Shtark, M. B., Korostyshevskaia, A. M., Rezakova, M. V., & Savelov, A. A. (2012). Functional magnetic resonance imaging and neuroscience Uspekhi Fiziologicheskikh Nauk, 43(1), 3–29.

Shtark, M. B., Verevkin, E. G., Kozlova, L. I., Mazhirina, K. G., Pokrovskii, M. A., Petrovskii, E. D., Savelov, A. A., Starostin, A. S., & Yarosh, S. V. (2015). Synergetic fMRI-EEG brain mapping in alpha-rhythm voluntary control mode. Bulletin of Experimental Biology and Medicine, 158(5), 644–649.

Siegel, J. S., Ramsey, L. E., Snyder, A. Z., Metcalf, N. V., Chacko, R. V., Weinberger, K., Baldassarre, A., Hacker, C. D., Shulman, G. L., & Corbetta, M. (2016). Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke. Proceedings of the National Academy of Sciences of the United States of America, 113(30), E4367–E4376.

Sitaram, R., Veit, R., Stevens, B., Caria, A., Gerloff, C., Birbaumer, N., & Hummel, F. (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.

Sokolova, O. O., Shtark, M. B., & Lisachev, P. D. (2010). Neuronal plasticity and gene expression. Uspekhi Fiziologicheskikh Nauk, 41(1), 26–44.

Sorger, B., Kamp, T., Weiskopf, N., Peters, J. C., & Goebel, R. (2018). When the brain takes 'BOLD' steps: Real-time fMRI neurofeedback can further enhance the ability to gradually self-regulate regional brain activation. Neuroscience, 378, 71–88.

Spampinato, D. A., Block, H. J., & Celnik, P. A. (2017). Cerebellar–M1 connectivity changes associated with motor learning are somatotopic specific. Journal of Neuroscience, 37(9), 2377–2386.

Sporns, O., & Honey, C. J. (2006). Small worlds inside big brains. Proceedings of the National Academy of Sciences of the United States of America, 103(51), 19219–19220.

Stoeckel, L. E., Garrison, K. A., Ghosh, S., Wighton, P., Hanlon, C. A., Gilman, J. M., Greer, S., Turk-Browne, N. B., deBettencourt, M. T., Scheinost, D., Craddock, C., Thompson, T., Calderon, V., Bauer, C. C., George, M., Breiter, H. C., Whitfield-Gabrieli, S., Gabrieli, J. D., LaConte, S. M., Hirshberg, L., … Evins, A. E. (2014). Optimizing real time fMRI neurofeedback for therapeutic discovery and development. NeuroImage: Clinical, 5, 245–255.

Sulzer, J., Haller, S., Scharnowski, F., Weiskopf, N., Birbaumer, N., Blefari, M. L., Bruehl, A. B., Cohen, L. G., deCharms, R. C., Gassert, R., Goebel, R., Herwig, U., LaConte, S., Linden, D., Luft, A., Seifritz, E., & Sitaram, R. (2013). Real-time fMRI neurofeedback: Progress and challenges. NeuroImage, 76, 386–399.

Ullsperger, M., & Debener, S. (Eds.). (2010). Simultaneous EEG and fMRI: Recording, analysis, and application. Oxford University Press.

Tang, C., Zhao, Z., Chen, C., Zheng, X., Sun, F., Zhang, X., Tian, J., Fan, M., Wu, Y., & Jia, J. (2016). Decreased functional connectivity of homotopic brain regions in chronic stroke patients: A resting state fMRI study. PLoS ONE, 11(4), Article e0152875.

van Meer, M. P. A., van der Marel, K., Wang, K., Otte, W. M., el Bouazati, S., Roeling, T. A. P., Viergever, M. A., Berkelbach van der Sprenkel, J. W., & Dijkhuizen, R. M. (2010). Recovery of sensorimotor function after experimental stroke correlates with restoration of resting-state interhemispheric functional connectivity. The Journal of Neuroscience, 30(11), 3964–3972.

Wang, C., Stebbins, G. T., Nyenhuis, D. L., deToledo-Morrell, L., Freels, S., Gencheva, E., Pedelty, L., Sripathirathan, K., Moseley, M. E., Turner, D. A., Gabrieli, J. D. E., & Gorelick, P. B. (2006). Longitudinal changes in white matter following ischemic stroke: A three-year follow-up study. Neurobiology of Aging, 27(12), 1827–1833.

Wang, L., Yu, C., Chen, H., Qin, W., He, Y., Fan, F., Zhang, Y., Wang, M., Li, K., Zang, Y., Woodward, T. S., & Zhu, C. (2010). Dynamic functional reorganization of the motor execution network after stroke. Brain, 133(4), 1224–1238.

Wang, W., Collinger, J. L., Perez, M. A., Tyler-Kabara, E. C., Cohen, L. G., Birbaumer, N., Brose, S. W., Schwartz, A. B., Boninger, M. L., & Weber, D. J. (2010). Neural interface technology for rehabilitation: Exploiting and promoting neuroplasticity. Physical Medicine and Rehabilitation Clinics of North America, 21(1), 157–178.

Yoo, S.-S., & Jolesz, F. A. (2002). Functional MRI for neurofeedback: Feasibility study on a hand motor task. NeuroReport, 13(11), 1377–1381.

Yu, X., Jiaerken, Y., Wang, S., Hong, H., Jackson, A., Yuan, L., Lou, M., Jiang, Q., Zhang, M., & Huang, P. (2020). Changes in the corticospinal tract beyond the ischemic lesion following acute hemispheric stroke: A diffusion kurtosis imaging study. Journal of Magnetic Resonance Imaging, 52(2), 512–519.

Yuan, K., Chen, C., Wang, X., Chu, W. C.-W., & Tong, R. K.-Y. (2021). BCI training effects on chronic stroke correlate with functional reorganization in motor-related regions: A concurrent EEG and fMRI study. Brain Sciences, 11(1), 56.

Zeiler, S. R., Gibson, E. M., Hoesch, R. E., Li, M. Y., Worley, P. F., O'Brien, R. J., & Krakauer, J. W. (2013). Medial premotor cortex shows a reduction in inhibitory markers and mediates recovery in a mouse model of focal stroke. Stroke, 44(2), 483–489.

Zhang, Y., Liu, H., Wang, L., Yang, J., Yan, R., Zhang, J., Sang, L., Li, P., Wang, J., & Qiu, M. (2016). Relationship between functional connectivity and motor function assessment in stroke patients with hemiplegia: A resting-state functional MRI study. Neuroradiology, 58(5), 503–511.

Zhuravleva, K. V., Savelov, A. A., Korostyshevskaya, A. M., & Shtark, M. B. (2022). Diffusional characteristics of brain matter after stroke. Bulletin of Experimental Biology and Medicine, 172(4), 402–406.

Zotev, V., Phillips, R., Yuan, H., Misaki, M., & Bodurka, J. (2014). Self-regulation of human brain activity using simultaneous real-time fMRI and EEG neurofeedback. NeuroImage, 85(Pt. 3), 985–995.

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