Modeling of the simulation artifact and brain evoked potential elicited by direct stimulation.
Poster No:
9
Submission Type:
Abstract Submission
Authors:
Emeline Manka1, Samuel Deslauriers-Gauthier1, Théodore Papadopoulo1, Petru Isan2, Fabien Almairac2
Institutions:
1University cote d'Azur, Inria, Sophia Antipolis, France, 2CHU de Nice, Nice, France
First Author:
Co-Author(s):
Introduction:
The main challenge of brain tumor (glioma) removal surgeries (resection) is to remove the maximum amount of lesion while preserving brain functional tissue (Duffau, 2015). To this end, direct electrostimulation (DES) of the brain in an awake patient is the gold standard for functional brain mapping. However, patients with contraindications (sleep apnea, obesity, etc.) cannot undergo awake surgeries (Prime, 2020). The present work is part of the connecTC project (CHU de Nice - Centre Inria d'Université Cote d'Azur), which aims to study electrophysiological signals recorded during surgeries after DES (Almairac, 2023). In this work, we developed a patient-specific conductivity model of the head that we validated using the data recorded during awake surgery. This physics approach of DES simulation allowed us to propose a one source model of a brain evoked potential (BEP). Unlike past works (Prime, 2020, Edwards, 2013), our proposition is a whole brain model adapted to the reality of surgery. It gave us promising preliminary results that could help to achieve a more long-term goal: improving brain mapping (Yamao, 2021) for patients who cannot undergo awake surgery.
Methods:
We focused on 2 subjects of the study. They underwent glioma removal using DES in awake condition at Nice University Hospital. During the awaking phase of the surgical procedure, the neurosurgeon identified functional cortical areas. These cortical sites were stimulated again after tumor removal was completed for research purposes, but under general anesthesia. Using electrode grids (8 up to 14 electrodes), we recorded the responses to stimulations (bipolar, 2-10 mA, 2Hz). For the 2 patients, we used anatomical T1 weighted MRI before and after surgery, and electrocortigraphy (ECoG) recordings. Segmentation of the cavity left by the resection of the tumor was performed manually. Using MRI data, we extracted the brain volume and created a tetrahedral mesh with or without cavity. We simulated the stimulation artifact propagation in the head by solving a forward problem using a finite element method, with a homogeneous conductivity of 1 S/m, for several stimulation sites as sources. To compare the results with real data, we extracted stimulation artifacts timings and assessed the amplitude recorded by each electrode of the grid. The distribution of artifact amplitudes across electrodes for each stimulation was compared with the simulations, and its quality was measured using Pearson correlation. To model BEP using one source, we simulated a dipole placed 10 mm under a given stimulation site and compared it with the corresponding recorded BEP. We computed the correlation of our simulation with the real signal depending on time.
Results:
Simulations performed on patients meshes (Fig. 1) showed a good fit of the data in terms of distribution of the artifact amplitudes between the electrodes (Fig. 2). We noticed that stimulations close to the electrodes were more difficult to simulate. We compared the fit of the data with, or without the presence of the cavity left by the tumor resection and found no significant difference (p-value 0.6). For the one source BEP model, we obtained different patterns of correlation depending on the simulated site. Representative results with high correlation at the peak of the BEP are shown in Fig. 2.
Conclusions:
Our model provided accurate simulations of the data recorded during surgery, thus validating the physical aspects of the model.
Some simulated BEP showed a good fit with real BEP (Fig. 2). However, other types of BEP patterns demonstrated more complexity, and indicate that our model has obvious limits. The next step will be to improve the accuracy of the model, such as differentiating white and grey matter with different conductivities, or improvement of mesh generation with automated cavity segmentation, and including propagation via the white matter.
Some simulated BEP showed a good fit with real BEP (Fig. 2). However, other types of BEP patterns demonstrated more complexity, and indicate that our model has obvious limits. The next step will be to improve the accuracy of the model, such as differentiating white and grey matter with different conductivities, or improvement of mesh generation with automated cavity segmentation, and including propagation via the white matter.
Brain Stimulation:
Direct Electrical/Optogenetic Stimulation 1
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 2
Keywords:
Computational Neuroscience
ELECTROCORTICOGRAPHY
Modeling
Other - Awake surgery, Brain Evoked Potentials, Direct Brain Stimulation
1|2Indicates the priority used for review
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Provide references using APA citation style.
Duffau, H. (2015). Resecting diffuse low-grade gliomas to the boundaries of brain functions: a new concept in surgical neuro-oncology. Journal of neurosurgical sciences, 59(4), 361-371.
Edwards, D., Cortes, M., Datta, A., Minhas, P., Wassermann, E.M., Bikson, M. (2013). Physiological & modeling evidence for focal transcranial electrical brain stimulation in humans: A basis for high-definition tDCS, NeuroImage, Volume 74, 266-275.
Prime, D., Woolfe, M., Rowlands, D., O’Keefe, S., & Dionisio, S. (2020). Comparing connectivity metrics in cortico-cortical evoked potentials using synthetic cortical response patterns. Journal of Neuroscience Methods, 334, 108559.
Prime, D., Woolfe, M., O’Keefe, S., Rowlands, D., & Dionisio, S. (2020). Quantifying volume conducted potential using stimulation artefact in cortico-cortical evoked potentials. Journal of neuroscience methods, 337, 108639.
Yamao, Y., Matsumoto, R., Kikuchi, T., Yoshida, K., Kunieda, T., & Miyamoto, S. (2021). Intraoperative brain mapping by cortico-cortical evoked potential. Frontiers in Human Neuroscience, 15, 635453.