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1

KESI-a novel method for spatial epileptic source localization in humans

100%
Chintaluri C., Wojcik D. K.
Acta Neurobiologiae Experimentalis
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2015
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tom 75
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nr Supl.
BACKGROUND AND AIMS: Develop a novel method to assist presurgical evaluation in epileptic patients with pharmacologically intractable epileptic seizures, by spatial source localization of epileptic epicenters using stereoencephalography (SEEG) and electrocorticography (EcoG) recordings. METHODS: We developed kernel Electrical Source Imaging (kESI), which takes into account realistic brain morphology and spatial variations in brain conductivity. This method is parameter free, can localize multiple sources, and is flexible to allow arbitrary electrode positions. To account for the patient specific brain morphology, a patient’s MR scan can be used to evaluate the measured potential in a forward model using Finite Element Method in FEniCS software. The inhomogeneous electrical conductivity of the gray and white matter, skin and skull etc. can also be included. kESI is an inverse method, which relies on the construction of kernel functions requiring computation of the potentials generated in the brain by numerous basis functions covering the probed volume. This approach is based on our previous approach of kernel Current Source Density in 3 dimensions, while utilizing the patient specific forward modeling scheme above. RESULTS: To show the proof-of-concept we generated dipolar ground truth data in a simplified spherical brain model with uniform conductivity. We assumed the electrodes on the surface of the sphere and inside the spherical volume emulating ECoG and SEEG style recordings respectively. We show that the proposed method works, and can help in deciding how different distributions of electrodes affect the quality of reconstruction. CONCLUSIONS: kESI method facilitates accurate localization of the seizure onset zones, and a possible procedure for prescribing optimal distributions of electrodes depending on available prior knowledge (e.g. dysfunction of specific brain structures) and clinical resources (availability of specific electrodes, etc.).
2

Kernel electrical source imaging (kESI) method for reconstruction of sources of brain electric activity in realistic brain geometries

80%
Kowalska M., Dzik J. M., Chintaluri C., Wojcik D. K.
Acta Neurobiologiae Experimentalis
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2019
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tom 79
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nr Suppl.1
INTRODUCTION: Around 50 million people worldwide are affected by epilepsy. Despite efficiency and steady development of pharmacological treatments, every third patient suffers from intractable seizures. A surgical intervention may be the only solution in these cases. To identify the region for resection, neurosurgeons implant intracranial and subdural electrodes which are used to localize the epileptogenic zone from the measured potentials. AIM(S): Providing better tools for reliable reconstruction of sources of brain activity may lead to more precise localization of the seizure’s origin and better surgical outcomes. To reconstruct sources of brain activity, we use kernel approximation methods for the inverse problem (the reconstruction itself). We model the electric field generated by the neural activity (the forward problem) with finite element method (FEM). We use FEM as it enables the inclusion of realistic head anatomy and tissue properties in the model. METHOD(S): Here we present a method – kernel Electrical Source Imaging (kESI) – of reconstruction of the activity underlying the measured potentials. kESI allows us to use information from arbitrarily placed electrodes and may integrate patient-specific anatomical information which increases precision of localization of epileptogenic zone for a specific patient. RESULTS: The preliminary results are promising. The major advantage of kESI over previous work is that it accounts for spatial variations of brain conductivity and can take into account patient-specific brain and skull anatomy. CONCLUSIONS: Nevertheless, further work is necessary to bring this method to the level of clinical application. FINANCIAL SUPPORT: Project funded from the Polish National Science Centre’s OPUS grant (2015/17/B/ ST7/04123).
3

Kernel current source density revisited

61%
Chintaluri C., Kowalska M., Czerwinski M. B., Sredniawa W., Jedrzejewska-Szmek J., Dzik J. M., Wojcik D. K.
Acta Neurobiologiae Experimentalis
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2019
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tom 79
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nr Suppl.1
INTRODUCTION: Extracellular recordings reflect transmembrane currents of neural and glial cells and thus have long been the foundation of measurements of neural activity. Recorded potential reflects activity of the underlying neural network and is directly related to the distribution of current sources along the active cells (current source density, CSD). The long‑range of the electric field leads to significant correlations between recordings at distant sites, which complicates the analysis. However, data interpretation can be facilitated by reconstruction of current sources. AIM(S): Facilitate reconstruction of sources of brain activity with open software. METHOD(S): The Kernel Current Source Density method (KCSD) is a general non-parametric framework for CSD estimation based on kernel techniques, which are widely used in machine‑learning. KCSD allows for current source estimation from potentials recorded by arbitrarily distributed electrodes. Overfitting is prevented by constraining complexity of the inferred CSD model. RESULTS: Here, we revisit KCSD to present a new, open-source implementation in the form of a package, which includes new functionality and several additional tools for kCSD analysis and for validation of the results of analysis accompanied by extensive tutorials implemented in Jupyter notebook. Specifically, we have added 1) analysis of spectral properties of the method; 2) error map generation for assessment of reconstruction accuracy; and 3) L‑curve, a method for estimation of optimum reconstruction parameters. The new implementation allows for CSD reconstruction from potentials measured by 1D, 2D, and 3D experimental setups for a) sources distributed in the entire tissue, b) in a slice, or c) in a single cell with known morphology, provided that the potential is generated by that cell. CONCLUSIONS: New Python implementation of kCSD facilitates CSD analysis and allows for estimation of errors. The toolbox and tutorials are available at https:// github.com/Neuroinflab/kCSD‑python.
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