Abstract
The exploration of neural activity patterns in motor imagery offers a new way of thinking for improving motor skills in normal individuals and for rehabilitating patients with motor disorders. In this paper, the influence relationship between the brain network of the brain motor system and the relevant motor intervals was investigated by collecting EEG signals during finger motor execution and motor imagery from 11 subjects. To address the problem that Granger causality can only reflect the interaction between two temporal variables, a conditional Granger causality analysis was introduced to analysis the brain network relationships between multiple motor compartments. The results showed that the brain network map of finger motor execution had more effective connections than that of finger motor imagination, and it was found that there were effective connection loops between left PMA and left MA, left MA and left SA, and left SA and right SA for both finger motor execution and motor imagination, and the most important connection in motor function was from premotor area to primary motor area.
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Boe, S., Gionfriddo, A., Kraeutner, S., Tremblay, A., Bardouille, T.: Laterality of brain activity during motor imagery is modulated by the provision of source level neurofeedback. In: NeuroImage, 2014, Conference 2016. LNCS, vol. 9999, pp. 1–13. Springer, Heidelberg (2016)
He, L., Hu, D., Meng, W., Ying, W., Deneen, K.M.V., Zhou, M.C.: Common bayesian network for classification of EEG-based multiclass motor imagery BCI. IEEE Trans. Syst. Man Cybern. Syst. (2017)
Arvaneh, M., et al.: Facilitating motor imagery-based brain–computer interface for stroke patients using passive movement. Neural Comput. Appl. 28(11), 3259–3272 (2016). https://doi.org/10.1007/s00521-016-2234-7
Fingelkurts, A.A., Kahkonen, S.: Functional connectivity in the brain is it an elusive concept. Neurosci. Biobehav. Rev. 28(8), 827–836 (2005)
Petit, L., Orssaud, C., Tzourio, N., Mazoyer, B., Berthoz, A.: Do Executed, Imagined and Suppressed Saccadic Eye Movements Share the Same Neuronal Mechanisms in Healthy Human ? Springer, Netherlands (1996)
Chen, H., Yang, Q., Liao, W., et al.: Evaluation of the effective connectivity of supplementary motor areas during motor imagery using Granger causality mapping. Neuroimage 47(4), 1844–1853 (2009)
Friston, K.J., Frith, C.D., Liddle, P.F., Frackowiak, R.S.J.: Functional connectivity: the principal-component analysis of large (PET) data sets. J. Cereb. Blood Flow Metab. 13(1), 5–14 (1993)
Friston, K.J., Frith, C.D., Frackowiak, R.S.J.: Time-dependent changes in effective connectivity measured with PET. Hum. Brain Mapp. 1, 69–79 (1993)
Lacourse, M.G., Orr, E.L.R., Cramer, S.C., Cohen, M.J.: Brain activation during execution and motor imagery of novel and skilled sequential hand movements. Neuroimage 27(3), 505–519 (2005)
Solodkin, A., Hlustik, P., Chen, E.E., Small, S.L.: Fine modulation in network activation during motor execution and motor imagery. Cereb. Cortex 14, 1246–1255 (2004)
Wang, S., Zhan, Y., Zhang, Y., et al.: Abnormal long- and short-range functional connectivity in adolescentonset schizophrenia patients: a resting-state fMRI study. Progress in Neuro-Psychopharmacol. Biol. Psychiatry 81, 445–451 (2018)
John, G.: Measurement of linear dependence and feedback between multiple time series. J. Am. Stat. Assoc. 77(378), 304–313 (1982)
Friston, K., Moran, R., Seth, A.K.: Analysing connectivity with Granger causality and dynamic causal modelling. Curr. Opin. Neurobiol. 23(2), 172–178 (2013)
Ding, M., Chen, Y., Bressler, S.L.: Granger Causality: Basic Theory and Application to Neuroscience. John Wiley & Sons, Ltd (2006)
Arvaneh, M., et al.: Facilitating motor imagery based brain computer interface for stroke patients using passive movement. Neural Comput. Appl. (2016)s
Rushworth, M.F.S., Johansen-Berg, H., GöBel, S.M., Devlin, J.T.: The left parietal and premotor cortices: motor attention and selection. Neuoimage 20, S89–S100 (2003)
Chouinard, P.A.: The primary motor and premotor areas of the human cerebral cortex. Neuroscientist 12(2), 143–152 (2006)
Ehrsson, H.: Imagery of voluntary movement of fingers, toes, and tongue activates corresponding body part specific motor representations. J. Neurophysiol. 90(5), 3304–3316 (2003)
Reis, J., Swayne, O.B., Vandermeeren, Y., Camus, M., Cohen, L.G.: Contribution of transcranial magnetic stimulation to the understanding of cortical mechanisms involved in motor control. J. Physiol. 586(2), 325–351 (2010)
Hammer, J., et al.: Predominance of movement speed over direction in neuronal population signals of motor cortex: intracranial EEG data and a simple explanatory model. Cereb. Cortex 26, 2863–2881 (2016)
Wise, S.P., Boussaoud, D., Johnson, P.B., et al.: Premotor and parietal cortex: corticocortical connectivity and combinatorial computations. Annu. Rev. Neurosci. 20(20), 25 (1997)
Dum, R.P.: Frontal lobe inputs to the digit representations of the motor areas on the lateral surface of the hemisphere. J. Neurosci. 25(6), 1375–1386 (2005)
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He, Y. et al. (2023). Brain Network Analysis of Hand Motor Execution and Imagery Based on Conditional Granger Causality. In: Ying, X. (eds) Human Brain and Artificial Intelligence. HBAI 2022. Communications in Computer and Information Science, vol 1692. Springer, Singapore. https://doi.org/10.1007/978-981-19-8222-4_11
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DOI: https://doi.org/10.1007/978-981-19-8222-4_11
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