fMRI-EEG correlations in an interactive study of the cerebral motor network in ischemic stroke

Neurofeedback (NF) technologies provide an interactive mode of self-regulation of the brain motor system in stroke rehabilitation. Simultaneous presentation of signals of two modalities, hemodynamic (functional MRI) and electrophysiological (EEG), in the feedback loop allows to compensate for the limitations of each neuroimaging technology and helps to reveal the cerebral mechanisms of motor recovery.

Aim of the study was to investigate coordinated changes in fMRI signal and EEG sensorimotor rhythm power during leg movement imagination in fMRIEEG-NF sessions in a patient with stroke.

Materials and methods. A patient with right-sided hemiparesis after a 6-week stroke was trained to imagine movements of the paretic foot in five fMRI-EEG-NF sessions. The feedback scale was 2/3 determined by the level of fMRI signal amplification from the supplementary motor area (SMA) and foot representation in the motor cortex (M1F) of the left hemisphere and 1/3 by desynchronization of EEG rhythms in the mu (8–13 Hz) and beta2 (18–26 Hz) frequency bands over the central midline region (electrode Cz according to the 10–20 system).

Results and discussion. In most of the NF runs, the patient achieved desynchronization of the mu and beta2 rhythms of the EEG and an increase in the fMRI signal of the regions of interest. Activation of the left M1F was associated with desynchronization of mu rhythms, which is consistent with the literature. Suppression of both mu and beta2 bands correlated (p<0.05) with activation of the premotor cortex bilaterally, the right primary motor cortex, and the anterior thalamus and anterior insula of the right hemisphere, indicating the involvement of the intact hemisphere in motor planning and control. The predominance of activation of homologous regions of the undamaged hemisphere, as well as the involvement of the nodes of the salience network and the dorsal attention network are consistent with the hypothesis of global functional reorganization of the brain after stroke; the same is evidenced by the strengthening of the intrahemispheric functional connectivity SMA–M1F bilaterally by the end of the course.

Conclusions. For the first time, an analysis of the relationships between fMRI and EEG signals in sessions on the bimodal fMRI-EEG NF platform was performed in a patient with post-stroke foot paresis. It was shown that volitional control of the activity of M1F and SMA of the affected hemisphere also activates homologous regions of the opposite hemisphere and recruits nodes of cognitive networks, demonstrating associations with the power of the muand beta2band of EEG in the central leads.

1. Ischemic stroke and transient ischemic attack: clinical guidelines. Moscow, 2024. 238 p. [In Russian].

2. Evans J.R., Dellinger M.B., Russell H.L. (eds.). Neurofeedback: the first fifty years. N.Y.; Academic Press, 2019. 536 p.

3. Pfurtscheller G., Neuper C., Andrew C., Edlinger G. Foot and hand area mu rhythms. Int. J. Psychophysiol. 1997;26(1–3):121–135. doi: 10.1016/s0167-8760(97)00760-5

4. McFarland D.J., Miner L.A., Vaughan T.M., Wolpaw J.R. Mu and beta rhythm topographies during motor imagery and actual movements. Brain topog. 2000;12(3):177–186. doi: 10.1023/a:1023437823106

5. Ogawa S., Menon R.S., Kim S.G., Ugurbil K. On the characteristics of functional magnetic resonance imaging of the brain. Annu. Rev. Biophys. Biomol. Struct. 1998;27(1):447–474. doi: 10.1146/annurev.biophys.27.1.447

6. Sitaram R., Veit R., Stevens B., Caria A., Gerloff C., Birbaumer N., Hummel F. Acquired control of ventral premotor cortex activity by feedback training: an exploratory real-time FMRI and TMS study. Neurorehabil. Neural Repair. 2012;26(3):256–265. doi: 10.1177/1545968311418345

7. 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. Graded fmri neurofeedback training of motor imagery in middle cerebral artery stroke patients: a preregistered proof-ofconcept study. Front. Hum. Neurosci. 2020;14:226. doi: 10.3389/fnhum.2020.00226

8. Liew S.L., Rana M., Cornelsen S., Fortunato de Barros Filho M., Birbaumer N., Sitaram R., Cohen L.G., Soekadar S.R. Improving motor corticothalamic communication after stroke using real-time fMRI connectivity-based neurofeedback. Neurorehabil. Neural Repair. 2016;30(7):671–675. doi: 10.1177/1545968315619699

9. Sanders Z.B., Fleming M.K., Smejka T., Marzolla M.C., Zich C., Rieger S.W., Lührs M., Goebel R., Sampaio-Baptista C., Johansen-Berg H. Self-modulation of motor cortex activity after stroke: a randomized controlled trial. Brain. 2022;145(10):3391–3404. doi: 10.1093/brain/awac239

10. Remsik A.B., Gjini K., Williams L. Jr., van Kan P.L.E., Gloe S., Bjorklund E., Rivera C.A., Romero S., Young B.M., Nair V.A., Caldera K.E., Williams J.C., Prabhakaran V. Ipsilesional mu rhythm desynchronization correlates with improvements in affected hand grip strength and functional connectivity in sensorimotor cortices following BCI-FES intervention for upper extremity in stroke survivors. Front. Hum. Neurosci. 2021;15:725645. doi: 10.3389/fnhum.2021.725645

11. Khrushcheva N.A., Kalgin K.V., Savelov A.A., Shurunova A.V., Predtechenskaya E.V., Shtark M.B. Changes in clinical and network functional connectivity parameters in motor networks and cerebellum based on resting-state functional magnetic resonance imaging data in patients with post-stroke hemiparesis receiving interactive brain stimulation neurotherapy. Annaly klinicheskoy i eksperimental’noy nevrologii = Annals of Clinical and Experimental Neurology. 2024;18(1):33–43. [In Russian]. doi: 10.54101/ACEN.2024.1.4

12. Warbrick T. Simultaneous EEG-fMRI: what have we learned and what does the future hold? Sensors. 2022;22(6):2262. doi: 10.3390/s22062262

13. Mano M., Lécuyer A., Bannier E., Perronnet L., Noorzadeh S., Barillot C. How to build a hybrid neurofeedback platform combining EEG and fMRI. Front. Neurosci. 2017;11:140. doi: 10.3389/fnins.2017.00140

14. Savelov A.A., Khrushcheva N.A., Kalgin K.V., Kozlova L.I., Bezmaternykh D.D., Melnikov M.E., Mazhirina K.G., Shurunova A.V., Predtechenskaya E.V., Shtark M.B. Structure, place, and clinical efficacy of the interactive brain therapy (stimulation) technology in cerebrovascular diseases. Kompleksnye problemy serdechno-sosudistykh zabolevaniy = Complex Issues of Cardiovascular Diseases. 2023;12(1):25–38. [In Russian]. doi: 10.17802/2306-1278-2023-12-1-25-38

15. Zotev V., Phillips R., Yuan H., Misaki M., Bodurka J. Self-regulation of human brain activity using simultaneous real-time fMRI and EEG neurofeedback. NeuroImage. 2014;85:985–995. doi: 10.1016/j.neuroimage.2013.04.126

16. Shtark M.B., Veryovkin E.G., Kozlova L.I., Mazhirina K.G., Pokrovsky M.A., Petrovsky E.D., Savelov A.A., Starostin A.S., Yarosh S.V. Synergetic fMRI-EEG brain mapping in alpha-rhythm voluntary control mode. Bull. Exp. Biol. Med. 2015;158(5):644– 649. doi: 10.1007/s10517-015-2827-7

17. Savelov A.A., Shtark M.B., Mel’nikov M.Ye., Kozlova L.I., Bezmaternykh D.D., Veryovkin E.G., Petrovsky E.D., Pokrovsky M.A., Tsirkin G.M., Rudych P.D. Prospects of synchronous fMRI-EEG recording as the basis for neurofeedback (exemplified on patient with stroke sequelae). Bull. Exp. Biol. Med. 2019;166(3):390-393. doi: 10.1007/s10517-019-04357-8

18. Butet S., Fleury M., Duché Q., Bannier E., Lioi G., Scotto di Covella L., Lévêque‑Le Bars E., Lécuyer A., Maurel P., Bonan I. EEG-fMRI neurofeedback versus motor imagery after stroke, a randomized controlled trial. J. Neuroeng. Rehabil. 2025;22(1):67. doi: 10.1186/s12984-025-01598-9

19. Seghier M.L. Laterality index in functional MRI: methodological issues. Magn. Reson. Imaging. 2008;26(5):594–601. doi: 10.1016/j.mri.2007.10.010

20. Grefkes C., Ward N.S. Cortical reorganization after stroke: how much and how functional? Neuroscientist. 2014;20(1):56–70. doi: 10.1177/1073858413491147

21. Zich C., Debener S., Kranczioch C., Bleichner M.G., Gutberlet I., de Vos M. Real-time EEG feedback during simultaneous EEG–fMRI identifies the cortical signature of motor imagery. NeuroImage. 2015;114:438–447. doi: 10.1016/j.neuroimage.2015.04.020

22. Moosmann M., Schönfelder V.H., Specht K., Scheeringa R., Nordby H., Hugdahl K. Realignment parameter-informed artefact correction for simultaneous EEG–fMRI recordings. NeuroImage. 2009;45(4):1144– 1150. doi: 10.1016/j.neuroimage.2009.01.024

23. Oostenveld R., Fries P., Maris E., Schoffelen J.M. FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. 2011(1):156869. doi: 10.1155/2011/156869

24. Kleifges K., Bigdely-Shamlo N., Kerick S.E., Robbins K.A. BLINKER: Automated extraction of ocular indices from EEG enabling large-scale analysis. Front. Neurosci. 2017;11:12. doi: 10.3389/fnins.2017.00012

25. Ciccarelli G., Federico G., Mele G., di Cecca A., Migliaccio M., Ilardi C.R., Alfano V., Salvatore M., Cavaliere C. Simultaneous real-time EEG-fMRI neurofeedback: A systematic review. Front. Hum. Neurosci. 2023;17:1123014. doi: 10.3389/fnhum.2023.1123014

26. Buetefisch, C.M. Role of the contralesional hemisphere in post-stroke recovery of upper extremity motor function. Front. Neurol. 2015;6:214. doi: 10.3389/fneur.2015.00214

27. Machado T.C., Carregosa A.A., Santos M.S., Ribeiro N.M.S., Melo A. Efficacy of motor imagery additional to motor-based therapy in the recovery of motor function of the upper limb in post-stroke individuals: a systematic review. Top Stroke Rehabil. 2019;26(7):548–553. doi: 10.1080/10749357.2019.1627716.

28. Yuan H., Liu T., Szarkowski R., Rios C., Ashe J., He B. Negative covariation between task-related responses in alpha/beta-band activity and BOLD in human sensorimotor cortex: an EEG and fMRI study of motor imagery and movements. NeuroImage. 2010;49(3):2596–2606. doi: 10.1016/j.neuroimage.2009.10.028

29. Yin S., Liu Y., Ding M. Amplitude of sensorimotor mu rhythm is correlated with BOLD from multiple brain regions: a simultaneous EEG-fMRI study. Front. Hum. Neurosci. 2016;10:364. doi: 10.3389/fnhum.2016.00364

30. Sitaram R., Ros T., Stoeckel L., Haller S., Scharnowski F., Lewis-Peacock J., Weiskopf N., Blefari M.L., Rana M., Oblak E., Birbaumer N., Sulzer J. Closed-loop brain training: the science of neurofeedback. Nat. Rev. Neurosci. 2017;18(2):86–100. doi:10.1038/nrn.2016.164

31. Barone J., Rossiter H. E. Understanding the role of sensorimotor beta oscillations. Front. Syst. Neurosci. 2021;15:655886. doi: 10.3389/fnsys.2021.655886