Abstract
Neuroimaging techniques have had a major impact on medical science, allowing advances in the research of many neurological diseases and improving their diagnosis. In this context, multimodal neuroimaging approaches, based on the neurovascular coupling phenomenon, exploit their individual strengths to provide complementary information on the neural activity of the brain cortex. This work proposes a novel method for combining electroencephalography (EEG) and functional near–infrared spectroscopy (fNIRS) to explore the functional activity of the brain processes related to low-level language processing of skilled and dyslexic seven-year-old readers. We have transformed EEG signals into image sequences considering the interaction between different frequency bands by means of cross-frequency coupling (CFC), and applied an activation mask sequence obtained from the local functional brain activity inferred from simultaneously recorded fNIRS signals. Thus, the resulting image sequences preserve spatial and temporal information of the communication and interaction between different neural processes and provide discriminative information that enables differentiation between controls and dyslexic subjects.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
NITRC: Welcome. https://www.nitrc.org/
Bashivan, P., Rish, I., Yeasin, M., Codella, N.: Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks, February 2016. https://doi.org/10.48550/arXiv.1511.06448
Buxton, R.B.: Dynamic models of BOLD contrast. Neuroimage 62(2), 953–961 (2012). https://doi.org/10.1016/j.neuroimage.2012.01.012
Buxton, R.B., Uludağ, K., Dubowitz, D.J., Liu, T.T.: Modeling the hemodynamic response to brain activation. Neuroimage 23, S220–S233 (2004). https://doi.org/10.1016/j.neuroimage.2004.07.013
Canolty, R.T., Knight, R.T.: The functional role of cross-frequency coupling. Trends Cogn. Sci. 14(11), 506–515 (2010). https://doi.org/10.1016/j.tics.2010.09.001
Chiarelli, A.M., Zappasodi, F., Pompeo, F.D., Merla, A.: Simultaneous functional near-infrared spectroscopy and electroencephalography for monitoring of human brain activity and oxygenation: a review. NPh 4(4), 041411 (2017). https://doi.org/10.1117/1.NPh.4.4.041411
Cui, X., Bray, S., Reiss, A.L.: Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobin dynamics. Neuroimage 49(4), 3039–3046 (2010). https://doi.org/10.1016/j.neuroimage.2009.11.050
Devor, A., Dunn, A.K., Andermann, M.L., et al.: Coupling of total hemoglobin concentration, oxygenation, and neural activity in rat somatosensory cortex. Neuron 39(2), 353–359 (2003). https://doi.org/10.1016/S0896-6273(03)00403-3
Friedman, J.H.: Greedy function approximation: A gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001). https://doi.org/10.1214/aos/1013203451
Girouard, H., Iadecola, C.: Neurovascular coupling in the normal brain and in hypertension, stroke, and Alzheimer disease. J. Appl. Physiol. 100(1), 328–335 (2006). https://doi.org/10.1152/japplphysiol.00966.2005
Górriz, J.M., et al.: Computational approaches to explainable artificial intelligence: advances in theory, applications and trends. Inf. Fusion 100, 101945 (2023). https://doi.org/10.1016/j.inffus.2023.101945
Górriz, J.M., et al.: Artificial intelligence within the interplay between natural and artificial computation: advances in data science, trends and applications. Neurocomputing 410, 237–270 (2020). https://doi.org/10.1016/j.neucom.2020.05.078
Goswami, U.: A temporal sampling framework for developmental dyslexia. Trends Cogn. Sci. 15(1), 3–10 (2011). https://doi.org/10.1016/j.tics.2010.10.001
Li, R., Nguyen, T., Potter, T., Zhang, Y.: Dynamic cortical connectivity alterations associated with Alzheimer’s disease: an EEG and fNIRS integration study. NeuroImage: Clinical 21, 101622 (2019). https://doi.org/10.1016/j.nicl.2018.101622
Li, R., Yang, D., Fang, F., et al.: Concurrent fNIRS and EEG for brain function investigation: a systematic. Methodol. Focused Rev. Sens. (Basel) 22(15), 5865 (2022). https://doi.org/10.3390/s22155865
Ortiz, A., Martinez-Murcia, F.J., Luque, J.L.: ohers: dyslexia diagnosis by EEG temporal and spectral descriptors: an anomaly detection approach. Int. J. Neur. Syst. 30(07), 2050029 (2020). https://doi.org/10.1142/S012906572050029X
Peterson, R.L., Pennington, B.F.: Developmental Dyslexia, p. 27 (2015). https://doi.org/10.1146/annurev-clinpsy-032814-112842
Scholkmann, F., Kleiser, S., Metz, A.J., et al.: A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology. Neuroimage 85, 6–27 (2014). https://doi.org/10.1016/j.neuroimage.2013.05.004
Sheth, S.A., Nemoto, M., Guiou, M., et al.: Linear and nonlinear relationships between neuronal activity, oxygen metabolism, and hemodynamic responses. Neuron 42(2), 347–355 (2004). https://doi.org/10.1016/S0896-6273(04)00221-1
Shibasaki, H.: Human brain mapping: hemodynamic response and electrophysiology. Clin. Neurophysiol. 119(4), 731–743 (2008). https://doi.org/10.1016/j.clinph.2007.10.026
Tang, L., Avison, M.J., Gore, J.C.: Nonlinear blood oxygen level-dependent responses for transient activations and deactivations in V1 – insights into the hemodynamic response function with the balloon model. Magn. Reson. Imaging 27(4), 449–459 (2009). https://doi.org/10.1016/j.mri.2008.07.017
Yamada, T., Umeyama, S., Matsuda, K.: Separation of fNIRS signals into functional and systemic components based on differences in hemodynamic modalities. PLoS ONE 7(11), e50271 (2012). https://doi.org/10.1371/journal.pone.0050271
Acknowledgments
This research is part of the PID2022-137461NB-C32, PID2022-137629OA-I00 and PID2022-137451OB-I00 projects, funded by the MICIU/AEI/10.13039/501100011033 and by European Union NextGenerationEU/PRTR, as well as TIC251-G-FEDER project, funded by ERDF/EU. Marco A. Formoso grant PRE2019-087350 funded by MICIU/AEI/10.13039/501100011033 by “ESF Investing in your future”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Gallego-Molina, N.J., Ortiz, A., Formoso, M.A., Martínez-Murcia, F.J., Woo, W.L. (2024). Enhancing Neuronal Coupling Estimation by NIRS/EEG Integration. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Artificial Intelligence for Neuroscience and Emotional Systems. IWINAC 2024. Lecture Notes in Computer Science, vol 14674. Springer, Cham. https://doi.org/10.1007/978-3-031-61140-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-61140-7_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-61139-1
Online ISBN: 978-3-031-61140-7
eBook Packages: Computer ScienceComputer Science (R0)