A mathematical model for coregistered data from electroencephalography and diffusive optical tomography
Mathematical modelling of natural phenomena, Tome 20 (2025), article no. 4.

Voir la notice de l'article provenant de la source EDP Sciences

A mathematical model for coregistered time-dependent electroencephalography (EEG) and diffusive optical tomography (DOT) is developed and analysed. Evolution with time is introduced by considering time-dependent dipolar sources in the EEG model and time-dependent optical parameters for DOT. Dimensional analysis shows that time-derivatives can be neglected. A non-linear system of differential equations from literature is used to model the postsynaptic current and hemodynamic parameters at the neuron level. A key point of the full model is to explain how these quantities provide, at the level of the whole head, the moment of the dipolar source term of the EEG problem and the behaviour in time of the optical parameters of the DOT model. The well-posedness of the timedependent EEG problem is proved by the subtraction approach for moments with L2-regularity in time and continuous source trajectories. For the time-dependent DOT model with continuous optical parameters in time, standard results of functional analysis apply. We explain the full pipeline from the stimulation current up to the simulated signals recorded at the electroptodes. Numerical results for a three-dimensional realistic head model illustrate the capacity of simultaneous EEG/DOT measurements to attest neurovascular coupling between the neural activity and changes in the hemodynamic parameters.
DOI : 10.1051/mmnp/2025001

M. Darbas 1 ; S. Lohrengel 2 ; B. Sulis 2

1 LAGA UMR CNRS 7539, Université Sorbonne Paris Nord, France
2 LMR UMR CNRS 9008, Université de Reims-Champagne Ardenne, Reims, France
@article{MMNP_2025_20_a2,
     author = {M. Darbas and S. Lohrengel and B. Sulis},
     title = {A mathematical model for coregistered data from electroencephalography and diffusive optical tomography},
     journal = {Mathematical modelling of natural phenomena},
     eid = {4},
     publisher = {mathdoc},
     volume = {20},
     year = {2025},
     doi = {10.1051/mmnp/2025001},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.1051/mmnp/2025001/}
}
TY  - JOUR
AU  - M. Darbas
AU  - S. Lohrengel
AU  - B. Sulis
TI  - A mathematical model for coregistered data from electroencephalography and diffusive optical tomography
JO  - Mathematical modelling of natural phenomena
PY  - 2025
VL  - 20
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/articles/10.1051/mmnp/2025001/
DO  - 10.1051/mmnp/2025001
LA  - en
ID  - MMNP_2025_20_a2
ER  - 
%0 Journal Article
%A M. Darbas
%A S. Lohrengel
%A B. Sulis
%T A mathematical model for coregistered data from electroencephalography and diffusive optical tomography
%J Mathematical modelling of natural phenomena
%D 2025
%V 20
%I mathdoc
%U http://geodesic.mathdoc.fr/articles/10.1051/mmnp/2025001/
%R 10.1051/mmnp/2025001
%G en
%F MMNP_2025_20_a2
M. Darbas; S. Lohrengel; B. Sulis. A mathematical model for coregistered data from electroencephalography and diffusive optical tomography. Mathematical modelling of natural phenomena, Tome 20 (2025), article  no. 4. doi : 10.1051/mmnp/2025001. http://geodesic.mathdoc.fr/articles/10.1051/mmnp/2025001/

[1] J.M. Rennie, L.S. De Vries, M. Blennow, A. Foran, D.K. Shah, V. Livingstone, A.C. Van Huffelen, S.R. Mathieson, E. Pavlidis, L.C. Weeke, M.C. Toet, M. Finder, R.M. Pinnamaneni, D.M. Murray, A.C. Ryan, W.P. Marnane, G.B. Boylan Characterisation of neonatal seizures and their treatment using continuous EEG monitoring: a multicentre experience Arch. Dis. Child Fetal Neonatal Ed. 2019 F493 F501

[2] T. Koskela, G.S. Kendall, S. Memon, M. Sokolska, T. Mabuza, A. Huertas-Ceballos, S. Mitra, N.J. Robertson, J. Meek, K. Whitehead Prognostic value of neonatal EEG following therapeutic hypothermia in survivors of hypoxic-ischemic encephalopathy Clin. Neurophysiol. 2021 2091 2100

[3] C. Lee, R. Cooper, T. Austin Diffuse optical tomography to investigate the newborn brain Pediatr. Res. 2017 376 386

[4] M. Nourhashemi, M. Mahmoudzadeh, S. Goudjil, G. Kongolo, F. Wallois Neurovascular coupling in the developing neonatal brain at rest Hum. Brain Mapp. 2020 503 519

[5] A. Gallagher, F. Wallois, H. Obrig Functional near-infrared spectroscopy in pediatric clinical research: different pathophysiologies and promising clinical applications Neurophotonics 2023 023517

[6] F. Wallois, M. Mahmoudzadeh, A. Patil, R. Grebe Usefulness of simultaneous EEG-NIRS recording in language studies Brain Lang. 2012 110 123

[7] N. Roche-Labarbe, B. Zaaimi, P. Berquin, A. Nehlig, R. Grebe, F. Wallois NIRS-measured oxy- and deoxyhemoglobin changes associated with EEG spike-and-wave discharges in children Epilepsia 2008 1871 1880

[8] M. Chalia, C.W. Lee, L.A. Dempsey, A.D. Edwards, H. Singh, A.W. Michell, N.L. Everdell, R.W. Hill, J.C. Hebden, T. Austin, R.J. Cooper Hemodynamic response to burst-suppressed and discontinuous electroencephalography activity in infants with hypoxic ischemic encephalopathy Neurophotonics 2016 031408 031408

[9] M. Darbas, S. Lohrengel Review on mathematical modelling of electroencephalography (EEG) Jahresber. Dtsch. Math.-Ver. 2019 3 39

[10] S. Pursiainen, F. Lucka, C.H. Wolters Complete electrode model in EEG: relationship and differences to the point electrode model Phys. Med. Biol. 2012 999 1017

[11] J. Vorwerk, M. Clerc, M. Burger, C.H. Wolters Comparison of boundary element and finite element approaches to the EEG forward problem Biomed. Tech. 2012 795 798

[12] C.H. Wolters, H. Köstler, C. Möller, J. Härdtlein, L. Grasedyck, W. Hackbusch Numerical mathematics of the subtraction method for the modelling of a current dipole in EEG source reconstruction using finite element head models SIAM J. Sci. Comput. 2007 24 45

[13] B. Montcel, R. Chabrier, P. Poulet Time-resolved absorption and hemoglobin concentration difference maps: a method to retrieve depth-related information on cerebral hemodynamics Opt Express. 2006 12271 12287

[14] S.R. Arridge Optical tomography in medical imaging Inverse Probl. 1999 R41 R93

[15] S.R. Arridge, J.C. Schotland Optical tomography: forward and inverse problem Inverse Probl. 2009 123010

[16] J.P. Kaipio and E. Somersalo, Statistical and Computational Inverse Problems. Applied Mathematical Sciences, Vol. 160. Springer (2005).

[17] A. Aubert, R. Costalat A model of the coupling between brain electrical activity, metabolism, and hemodynamics: application to the interpretation of functional neuroimaging Neuroimage 2002 1162 1181

[18] R.B. Buxton, E.C. Wong, L.R. Frank Dynamics of blood flow and oxygenation changes during brain activation: the balloon model Magn. Reson. Med. 1998 855 864

[19] R.B. Buxton, K. Uludag, D.J. Dubowitz, T.T. Liu Modeling the hemodynamic response to brain activation Neuroimage 2004 S220 S233

[20] E.J. Mathias, Computational modelling of neurovascular coupling and the BOLD signal. Ph.D. Thesis, University of Canterbury, United Kingdom (2017).

[21] E.J. Mathias, A. Kenny, M.J. Plank, T. David Integrated models of neurovascular coupling and BOLD signals: responses for varying neural activations Neuroimage 2018 69 86

[22] S. Sten, H. Podéus, N. Sundqvist, F. Elinder, M. Engström, G. Cedersund A quantitative model for human neurovascular coupling with translated mechanisms from animals PLOS Comput. Biol. 2023 e1010818

[23] F. Rapetti, G. Rousseaux On quasi-static models hidden in Maxwell’s equations Appl. Num. Math. 2014 92 106

[24] O. Faugeras, F. Clément, R. Deriche, R. Keriven, T. Papadopoulo, J. Roberts, T. Viéville, F. Devernay, J. Gomes, G. Hermosillo, P. Kornprobst and D. Lingrand, The inverse EEG and MEG problems: the adjoint state approach. I. The continuous case. Inria, version 1 (1999). hal.inria.fr/docs/00/07/71/12/PDF/RR-3673.pdf.

[25] IT’IS Foundation, https://itis.swiss/virtual-population/tissue-properties/database/dielectric-properties/, visited on January 17th, 2025.

[26] C. Gabriel, Compilation of the Dielectric Properties of Body Tissues at RF and Microwave Frequencies (1996).

[27] H. Azizollahi, A. Aarabi, F. Wallois Effects of uncertainty in head tissue conductivity and complexity on EEG forward modeling in neonates Hum. Brain Mapp. 2016 3604 3622

[28] S. Lew, D.D. Sliva, M. Choe, P.E. Grant, Y. Okada, C.H. Wolters, M.S. Hämäläinen Effects of sutures and fontanels on MEG and EEG source analysis in a realistic infant head model Neuroimage 2013 282 293

[29] H. Azizollahi, M. Darbas, M.M. Diallo, A. El Badia, S. Lohrengel EEG in neonates: forward modeling and sensitivity analysis with respect to variations of the conductivity Math. Biosci. Eng. 2018 905 932

[30] A. El Badia, T. Ha-Duong An inverse source problem in potential analysis Inverse Probl. 2000 651 663

[31] H. Dehghani, M.E. Eames, P.K. Yalavarthy, S.C. Davis, S. Srinivasan, C.M. Carpenter, B.W. Pogue, K.D. Paulsen Near infrared optical tomography using NIRFAST: algorithm for numerical model and image reconstruction Commun. Numer. Methods Eng. 2009 711 732

[32] N. Roche-Labarbe, F. Wallois, E. Ponchel, G. Kongolo, R. Grebe Coupled oxygenation oscillation measured by NIRS and intermittent cerebral activation on EEG in premature infants Neuroimage 2007 718 727

[33] M. Dehaes, K. Kazemi, M. Pélégrini-Issac, R. Grebe, H. Benali, F. Wallois Quantitative effect of the neonatal fontanel on synthetic near infrared spectroscopy measurements Hum. Brain Mapp. 2013 878 889

[34] A.H. Hielscher, R.E. Alcouffe, R.L. Barbour Comparison of finite-difference transport and diffusion calculations for photon migration in homogeneous and heterogeneous tissues Phys. Med. Biol. 1998 1285 1302

[35] E. Okada, D.T. Delpy Near-infrared light propagation in an adult head model. I. Modeling of low-level scattering in the cerebrospinal fluid layer Appl. Opt. 2003 2906 2914

[36] S. Lohrengel, F. Mahmoudzadeh, F. Oumri, S. Salmon, F. Wallois A homogenized cerebrospinal fluid model for diffuse optical tomography in the neonatal head Int. J. Numer. Method Biomed. Eng. 2022 e3538

[37] S.R. Arridge, J.C. Hebden Optical imaginig in medicine. II. Modelling and reconstruction Phys. Med. Biol. 1997 841

[38] D. Sterratt, B. Graham, A. Gillies and D. Willshaw, Principles of Computational Modelling in Neuroscience. Cambridge University Press (2011).

[39] A.L Hodgkin, A.F. Huxley A quantitative description of membrane current and its application to conduction and excitation in nerve J. Physiol. 1952 500 544

[40] R.D. Traub, J.G.R. Jefferys, R. Miles, M.A. Whittington, K. Tóth A branching dendritic model of a rodent CA3 pyramidal neurone J. Physiol. 1994 79 95

[41] A. Destexhe, Z.F. Mainen, T.J. Sejnowski Synthesis of models for excitable membranes, synaptic transmission and neuromodulation using a common kinetic formalism J. Comput. Neurosci. 1994 195 230

[42] H. Aurlien, I.O. Gjerde, J.H. Aarseth, G. Eldøoen, B. Karlsen, H. Skeidsvoll, N.E. Gilhus EEG background activity described by a large computerized database Clin. Neurophysiol. 2004 665 673

[43] L. Kocsis, P. Herman, A. Eke The modified Beer–Lambert law revisited Phys. Med. Biol. 2006 N91

[44] L.F. Shampine, M.W. Reichelt The MATLAB ODE Suite SIAM J. Sci. Comput. 1997 1 22

[45] F. Hecht New development in FreeFem++ J. Numer. Math. 2012 251 266

[46] H. Kager, W.J. Wadman, G.G. Somjen Simulated seizures and spreading depression in a neuron model incorporating interstitial space and ion concentrations J. Neurophysiol. 2000 495 512

[47] G.B. Ermentrout and D.H. Terman, Mathematical Foundations of Neurosciences. Springer (2010).

[48] H. Jiang, Diffuse Optical Tomography. Taylor and Francis (2011).

[49] M. Nourhashemi, Multimodal analysis of neurovascular coupling in the newborn. Ph.D. Thesis, University of Picardie Jules Verne, France (2018).

Cité par Sources :