Interactive Brain Stimulation Neurotherapy Based on BOLD Signal in Stroke Rehabilitation

Authors

  • 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

DOI:

https://doi.org/10.15540/nr.9.3.147

Keywords:

interactive brain stimulation, BOLD, cerebral networks, functional magnetic resonance imaging (fMRI), bimodal fMRI-EEG neurofeedback platform

Abstract

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

References

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. https://doi.org/10.1016/j.lfs.2021.120161

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. https://doi.org/10.1111/jon.12362

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. https://doi.org/10.1371/journal.pcbi.1000408

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. https://doi.org/10.1016/j.neuroimage.2021.118833

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. https://doi.org/10.1073/pnas.0606005103

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. https://doi.org/10.1093/cercor/bhu260

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. https://doi.org/10.1007/s10517-021-05232-1

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. https://doi.org/10.1007/s10517-018-4161-3

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. https://doi.org/10.1002/(SICI)1097-0193(1999)7:2<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. https://doi.org/10.1002/mrm.1910340409

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. https://doi.org/10.1097/00001756-199906230-00018

Buckner, R. L. (1998). Event-related fMRI and the hemodynamic response. Human Brain Mapping, 6(5–6), 373–377 https://doi.org/10.1002/(SICI)1097-0193(1998)6:5/6<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. https://doi.org/10.1038/s41583-019-0212-7

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. https://doi.org/10.1002/ana.21905

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. https://doi.org/10.1016/j.neuroimage.2012.02.070

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. https://doi.org/10.1038/s41598-021-87789-5

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. https://doi.org/10.1016/s0028-3908(00)00003-4

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. https://doi.org/10.1038/nrneurol.2016.107

Feigin, V. L., Norrving, B., & Mensah, G. A. (2017). Global burden of stroke. Circulation Research, 120(3), 439–448. https://doi.org/10.1161/CIRCRESAHA.116.308413

Fornito, A., Zalesky, A., & Breakspear, M. (2015). The connectomics of brain disorders. Nature Reviews Neuroscience, 16(3), 159–172. https://doi.org/10.1038/nrn3901

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. https://doi.org/10.2174/1381612821666150619092540

Friston, K. J. (2011). Functional and effective connectivity: A review. Brain Connectivity, 1(1), 13–36. https://doi.org/10.1089/brain.2011.0008

Gauthier, C. J., & Fan, A. P. (2019). BOLD signal physiology: Models and applications. NeuroImage, 187, 116–127. https://doi.org/10.1016/j.neuroimage.2018.03.018

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. https://doi.org/10.1152/ajpregu.1994.266.1.R204

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. https://doi.org/10.1016/j.clinph.2019.04.004

Herrmann, C. S., & Debener, S. (2008). Simultaneous recording of EEG and BOLD responses: A historical perspective. International Journal of Psychophysiology, 67(3), 161–168. https://doi.org/10.1016/j.ijpsycho.2007.06.006

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. https://doi.org/10.1523/JNEUROSCI.0447-12.2012

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. https://doi.org/10.1016/0013-4694(93)90156-p

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. https://doi.org/10.1038/s41562-018-0484-3

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. https://doi.org/10.1016/j.biopsych.2015.12.024

Kim, D. H., & Kang, H. (2022). Changes in bihemispheric structural connectivity following middle cerebral artery infarction. Journal of Personalized Medicine, 12(1), 81. https://doi.org/10.3390/jpm12010081

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. https://doi.org/10.1016/j.neuroimage.2013.05.010

Kwakkel, G., & Kollen, B. J. (2013). Predicting activities after stroke: What is clinically relevant? International Journal of Stroke, 8(1), 25–32. https://doi.org/10.1111/j.1747-4949.2012.00967.x

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. https://doi.org/10.1016/j.nicl.2018.06.010

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. https://doi.org/10.1098/rstb.2015.0350

Li, W., Li, Y., Zhu, W., & Chen, X. (2014). Changes in brain functional network connectivity after stroke. Neural Regeneration Research, 9(1), 51–60. https://doi.org/10.4103/1673-5374.125330

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. https://doi.org/10.1177/1545968315619699

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. https://doi.org/10.3389/fnhum.2020.00037

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. https://doi.org/10.1016/j.rehab.2018.05.1127

Lopes da Silva, F. (2013). EEG and MEG: Relevance to neuroscience. Neuron, 80(5), 1112–1128. https://doi.org/10.1016/j.neuron.2013.10.017

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. https://doi.org/10.1097/00004691-200107000-00002

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. https://doi.org/10.3389/fnins.2017.00140

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. https://doi.org/10.31887/DCNS.2018.20.2/smarek

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. https://doi.org/10.1016/j.neuroimage.2018.09.007

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. https://doi.org/10.3389/fnhum.2020.00226

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. https://doi.org/10.1371/journal.pone.0154968

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. https://doi.org/10.1016/j.neuroimage.2013.11.004

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. https://doi.org/10.1016/j.tics.2011.08.003

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. https://doi.org/10.1002/hbm.1020

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. https://doi.org/10.3389/fnhum.2013.00571

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. https://doi.org/10.1016/j.nicl.2020.102191

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. https://doi.org/10.1016/s1047-9651(02)00054-2

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. https://doi.org/10.1126/science.272.5269.1791

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. https://doi.org/10.1002/mrm.1910160103

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. https://doi.org/10.1146/annurev.biophys.27.1.447

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. https://doi.org/10.1016/j.neuroimage.2019.116107

Petersen, S. E., & Sporns, O. (2015). Brain networks and cognitive architectures. Neuron, 88(1), 207–219. https://doi.org/10.1016/j.neuron.2015.09.027

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. https://doi.org/10.1146/annurev-neuro-100220-093239

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. https://doi.org/10.1073/pnas.98.2.676

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. https://doi.org/10.3389/fnbeh.2014.00357

Ritter, P., & Villringer, A. (2006). Simultaneous EEG-fMRI. Neuroscience & Biobehavioral Reviews, 30(6), 823–838. https://doi.org/10.1016/j.neubiorev.2006.06.008

Rowe, J. B. (2010). Connectivity analysis is essential to understand neurological disorders. Frontiers in Systems Neuroscience, 4, Article 144. https://doi.org/10.3389/fnsys.2010.00144

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. https://doi.org/10.15540/nr.8.3.162

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. https://doi.org/10.1007/s10517-019-04359-6

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. https://doi.org/10.1007/s10517-019-04357-8

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. https://doi.org/10.1007/s10517-019-04358-7

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. https://doi.org/10.1093/brain/awl214

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. https://doi.org/10.1016/j.biopsycho.2015.03.009

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. https://doi.org/10.1016/B978-0-12-817659-7.00046-4

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. https://doi.org/10.1007/s10517-015-2827-7

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. https://doi.org/10.1073/pnas.1521083113

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. https://doi.org/10.1177/1545968311418345

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. https://doi.org/10.1016/j.neuroscience.2016.09.026

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. https://doi.org/10.1523/JNEUROSCI.2511-16.2017

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. https://doi.org/10.1073/pnas.0609523103

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. https://doi.org/10.1016/j.nicl.2014.07.002

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. https://doi.org/10.1016/j.neuroimage.2013.03.033

Ullsperger, M., & Debener, S. (Eds.). (2010). Simultaneous EEG and fMRI: Recording, analysis, and application. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195372731.001.0001

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. https://doi.org/10.1371/journal.pone.0152875

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. https://doi.org/10.1523/JNEUROSCI.5709-09.2010

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. https://doi.org/10.1016/j.neurobiolaging.2005.10.008

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. https://doi.org/10.1093/brain/awq043

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. https://doi.org/10.1016/j.pmr.2009.07.003

Yoo, S.-S., & Jolesz, F. A. (2002). Functional MRI for neurofeedback: Feasibility study on a hand motor task. NeuroReport, 13(11), 1377–1381. https://doi.org/10.1097/00001756-200208070-00005

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. https://doi.org/10.1002/jmri.27066

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. https://doi.org/10.3390/brainsci11010056

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. https://doi.org/10.1161/STROKEAHA.112.676940

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. https://doi.org/10.1007/s00234-016-1646-5

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. https://doi.org/10.1007/s10517-022-05402-9

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. https://doi.org/10.1016/j.neuroimage.2013.04.126

Downloads

Published

2022-09-29

Issue

Vol. 9 No. 3 (2022)

Section

Review Articles

License

Authors who publish with this journal agree to the following terms:
  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-BY) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).