fMRI-EEG Fingerprint Regression Model for Motor Cortex

  • Vitaly Rudnev Federal Research Center of Fundamental and Translational Medicine, Novosibirsk, Russia
  • Michael Melnikov
  • Michael Melnikov
  • Andrey Savelov International Tomographic Center, Siberian Branch of Russian Academy of Sciences
  • Mark Shtark Federal Research Center of Fundamental and Translational Medicine, Novosibirsk, Russia
  • Estate M Sokhadze Department of Biomedical Sciences University of South Carolina School of Medicine-Greenville, Greenvile, SC 29615
Keywords: EEG, fMRI BOLD, regression model, motor cortex, stroke

Abstract

The combination of modern machine learning and traditional statistical methods allows the construction of individual regression models for predicting the blood oxygenation level dependent (BOLD) signal of a selected region-of-interest within the brain using EEG signal. Among the many different models for motor cortex, we chose the EEG Fingerprint one-electrode approach, based on rigid regression model with Stockwell EEG signal transformation, used before only for the amygdala. In this study we demonstrate the way of finding suitable model parameters for the cases of BOLD signal reconstruction for five individuals: three of them were healthy, and two were after a hemorrhagic stroke with varying degrees of damage according to Medical Research Council (MRC) Weakness Scale. The principal possibility of BOLD restoring using regressor model was demonstrated for all the cases considered above. The results of direct and indirect comparisons of BOLD signal reconstruction at the motor region for healthy participants and for patients who suffered from a stroke are presented.

Author Biography

Estate M Sokhadze, Department of Biomedical Sciences University of South Carolina School of Medicine-Greenville, Greenvile, SC 29615

Research professor, Department of Biomedical Sciences, University of South Carolina School of Medicine-Greenville, Greenville, SC, 29615

Gratis associate professor, Department of Psychiatry & behavioral Sciences, University of Louisville, Louisville, KY 40202

References

Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005). Investigations into resting-state connectivity using independent component analysis. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 360(1457), 1001–1013. https://doi.org/10.1098/rstb.2005.1634

Bergstra, J., Yamins, D., & Cox, D. D. (2013). Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. Proceedings of the 30th International Conference on Machine Learning (ICML 2013), 28, 115–123.

Chang, C.-Y., Hsu, S.-H., Pion-Tonachini, L., & Jung, T.-P. (2018). Evaluation of artifact subspace reconstruction for automatic EEG artifact removal. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 1242–1245. https://doi.org/10.1109/EMBC.2018.8512547

Custo, A., Vulliemoz, S., Grouiller, F., Van De Ville, D., & Michel, C. (2014). EEG source imaging of brain states using spatiotemporal regression. NeuroImage, 96, 106–116. https://doi.org/10.1016/j.neuroimage.2014.04.002

de Munck, J. C., Gonçalves, S. I., Mammoliti, R., Heethaar, R. M., & Lopes da Silva, F. H. (2009). Interactions between different EEG frequency bands and their effect on alpha-fMRI correlations. NeuroImage, 47(1), 69–76. https://doi.org/10.1016/j.neuroimage.2009.04.029

Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21. https://doi.org/10.1016/j.jneumeth.2003.10.009

Emmert, K., Kopel, R., Sulzer, J., Brühl, A. B., Berman, B. D., Linden, D., Horovitz, S. G., Breimhorst, M., Caria, A., Frank, S., Johnston, S., Long, Z., Paret, C., Robineau, F., Veit, R., Bartsch, A., Beckmann, C. F., Van De Ville, D., & Haller, S. (2016). Meta-analysis of real-time fMRI neurofeedback studies using individual participant data: How is brain regulation mediated? NeuroImage, 124(Part A), 806–812. https://doi.org/10.1016/j.neuroimage.2015.09.042

Goense, J. B. M., & Logothetis, N. K. (2008). Neurophysiology of the BOLD fMRI signal in awake monkeys. Current Biology, 18(9), 631–640. https://doi.org/10.1016/j.cub.2008.03.054

Goldman, R. I., Stern, J. M., Engel, J., Jr., & Cohen, M. S. (2002). Simultaneous EEG and fMRI of the alpha rhythm. NeuroReport, 13(18), 2487–2492. https://doi.org/10.1097/01.wnr.0000047685.08940.d0

Gramfort, A., Luessi, M., Larson, E., Engemann, D. A., Strohmeier, D., Brodbeck, C., Goj, R., Jas, M., Brooks, T., Parkkonen, L., & Hämäläinen, M. (2013). MEG and EEG data analysis with MNE-Python. Frontiers in Neuroscience, 7, 267. https://doi.org/10.3389/fnins.2013.00267

Handwerker, D. A., Gonzalez-Castillo, J., D'Esposito, M., & Bandettini, P. A. (2012). The continuing challenge of understanding and modeling hemodynamic variation in fMRI. NeuroImage, 62(2), 1017–1023. https://doi.org/10.1016/j.neuroimage.2012.02.015

Iannetti, G. D., Niazy, R. K., Wise, R. G., Jezzard, P., Brooks, J. C. W., Zambreanu, L., Vennart, W., Matthews, P. M., & Tracey, I. (2005). Simultaneous recording of laser-evoked brain potentials and continuous, high-field functional magnetic resonance imaging in humans. NeuroImage, 28(3), 708–719. https://doi.org/10.1016/j.neuroimage.2005.06.060

Katan, M., & Luft, A. (2018). Global burden of stroke. Seminars in Neurology, 38(2), 208–211. https://doi.org/10.1055/s-0038-1649503

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). Author correction: Electrical fingerprint of the amygdala guides neurofeedback training for stress resilience. Nature Human Behaviour, 3(2), 194. https://doi.org/10.1038/s41562-019-0534-5

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

Kilner, J. M., Mattout, J., Henson, R., & Friston, K. J. (2005). Hemodynamic correlates of EEG: A heuristic. NeuroImage, 28(1), 280–286. https://doi.org/10.1016/j.neuroimage.2005.06.008

Laufs, H., Krakow, K., Sterzer, P., Eger, E., Beyerle, A., Salek-Haddadi, A., & Kleinschmidt, A. (2003). Electroencephalographic signatures of attentional and cognitive default modes in spontaneous brain activity fluctuations at rest. Proceedings of the National Academy of Sciences of the United States of America, 100(19), 11053–11058. https://doi.org/10.1073/pnas.1831638100

Logothetis, N. K. (2002). The neural basis of the blood-oxygen-level-dependent functional magnetic resonance imaging signal. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 357(1424), 1003–1037. https://doi.org/10.1098/rstb.2002.1114

Mantini, D., Perrucci, M. G., Del Gratta, C., Romani, G. L., & Corbetta, M. (2007). Electrophysiological signatures of resting state networks in the human brain. Proceedings of the National Academy of Sciences of the United States of America, 104(32), 13170–13175. https://doi.org/10.1073/pnas.0700668104

Marecek, R., Lamos, M., Mikl, M., Barton, M., Fajkus, J., Rektor, I., & Brazdil, M. (2016). What can be found in scalp EEG spectrum beyond common frequency bands. EEG-fMRI study. Journal of Neural Engineering, 13(4), 046026. https://doi.org/10.1088/1741-2560/13/4/046026

Meir-Hasson, Y., Kinreich, S., Podlipsky, I., Hendler, T., & Intrator, N. (2014). An EEG Finger-Print of fMRI deep regional activation. NeuroImage, 102(Part 1), 128–141. https://doi.org/10.1016/j.neuroimage.2013.11.004

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), e0154968. https://doi.org/10.1371/journal.pone.0154968

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

Murta, T., Chaudhary, U. J., Tierney, T. M., Dias, A., Leite, M., Carmichael, D. W., Figueiredo, P., & Lemieux, L. (2017). Phase-amplitude coupling and the BOLD signal: A simultaneous intracranial EEG (icEEG) - fMRI study in humans performing a finger-tapping task. NeuroImage, 146, 438–451. https://doi.org/10.1016/j.neuroimage.2016.08.036

Murta, T., Leite, M., Carmichael, D. W., Figueiredo, P., & Lemieux, L. (2015). Electrophysiological correlates of the BOLD signal for EEG-informed fMRI. Human Brain Mapping, 36(1), 391–414. https://doi.org/10.1002/hbm.22623

Niazy, R. K., Beckmann, C. F., Iannetti, G. D., Brady, J. M., & Smith, S. M. (2005). Removal of FMRI environment artifacts from EEG data using optimal basis sets. NeuroImage, 28(3), 720–737. https://doi.org/10.1016/j.neuroimage.2005.06.067

Ogawa, S., Lee, T. M., Kay, A. R., & Tank, D. W. (1990). Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proceedings of the National Academy of Sciences of the United States of America, 87(24), 9868–9872. https://doi.org/10.1073/pnas.87.24.9868

Pion-Tonachini, L., Kreutz-Delgado, K., & Makeig, S. (2019). ICLabel: An automated electroencephalographic independent component classifier, dataset, and website. NeuroImage, 198, 181–197. https://doi.org/10.1016/j.neuroimage.2019.05.026

Rosa, M. J., Daunizeau, J., & Friston, K. J. (2010). EEG-fMRI integration: A critical review of biophysical modeling and data analysis approaches. Journal of Integrative Neuroscience, 9(4), 453–476. https://doi.org/10.1142/s0219635210002512

Swartz Center for Computational Neuroscience. (n.d.). Makoto’s preprocessing pipeline. https://sccn.ucsd.edu/wiki/Makoto%27s_preprocessing_pipeline

Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., & Beckmann, C. F. (2009). Correspondence of the brain's functional architecture during activation and rest. Proceedings of the National Academy of Sciences of the United States of America, 106(31), 13040–13045. https://doi.org/10.1073/pnas.0905267106

Stockwell, G. (2007). Why use the S-Transform? In L. Rodino, B.-W. Schulze, & M. W. Wong (Eds.), Pseudo-differential operators: Partial differential equations and time-frequency analysis (pp. 279–309). Providence, RI: American Mathematical Society.

UCL Queen Square Institute of Neurology. (n.d.). Statistical Parametric Mapping. Software. The Wellcome Centre for Human Neuroimaging. https://www.fil.ion.ucl.ac.uk/spm/software/

University of Oregon. (n.d.). MRIConvert files. Lewis Center for Neuroimaging. https://lcni.uoregon.edu/download/mriconvert

Wan, X., Riera, J., Iwata, K., Takahashi, M., Wakabayashi, T., & Kawashima, R. (2006). The neural basis of the hemodynamic response nonlinearity in human primary visual cortex: Implications for neurovascular coupling mechanism. NeuroImage, 32(2), 616–625. https://doi.org/10.1016/j.neuroimage.2006.03.040

Wei, C.-S., Lin, Y.-P., Wang, Y.-T., Lin, C.-T., & Jung, T.-P. (2018). A subject-transfer framework for obviating inter- and intra-subject variability in EEG-based drowsiness detection. NeuroImage, 174, 407–419. https://doi.org/10.1016/j.neuroimage.2018.03.032

Yin, S., Liu, Y., & Ding, M. (2016). Amplitude of sensorimotor Mu rhythm is correlated with BOLD from multiple brain regions: A simultaneous EEG-fMRI study. Frontiers in Human Neuroscience, 10, 364. https://doi.org/10.3389/fnhum.2016.00364

Published
2021-09-30
Section
Research Papers