Validating Non-invasive EEG Source Imaging Using Optimal Electrode Configurations on a Representative Rat Head Model

AbstractEpilepsy surgery is an important treatment modality for medically refractory focal epilepsy. The outcome of surgery usually depends on the localization accuracy of the epileptogenic zone (EZ) during pre-surgical evaluation. Good localization can be achieved with various electrophysiological and neuroimaging approaches. However, each approach has its own merits and limitations. Electroencephalography (EEG) Source Imaging (ESI) is an emerging model-based computational technique to localize cortical sources of electrical activity within the brain volume, three-dimensionally. ESI based pre-surgical evaluation gives an overall clinical yield of 73–91%, depending on choice of head model, inverse solution and EEG electrode density. It is a cost effective, non-invasive method which provides valuable additional information in presurgical evaluation due to its high localizing value specifically in MRI-negative cases, extra or basal temporal lobe epilepsy, multifocal lesions such as tuberous sclerosis or cases with multiple hypotheses. Unfortunately, less than 1% of surgical centers in developing countries use this method as a part of pre-surgical evaluation. This review promotes ESI as a useful clinical tool especially for patients with lesion-negative MRI to determine EZ cost-effectively with high accuracy under the optimized conditions.

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Incorporating and compensating cerebrospinal fluid in surface-based forward models of magneto– and electroencephalography

10.1101/037788 ◽

2016 ◽

Author(s):

Matti Stenroos ◽

Aapo Nummenmaa

Keyword(s):

Cerebrospinal Fluid ◽

Relative Error ◽

Reference Model ◽

Head Model ◽

Computationally Efficient ◽

Source Imaging ◽

Test Models ◽

Eeg Source Imaging ◽

Skull Conductivity ◽

S Model

AbstractMEG/EEG source imaging is usually done using a three-shell (3-S) or a simpler head model. Such models omit cerebrospinal fluid (CSF) that strongly affects the volume currents. We present a four-compartment (4-C) boundary-element (BEM) model that incorporates the CSF and is computationally efficient and straightforward to build using freely available software. We propose a way for compensating the omission of CSF by decreasing the skull conductivity of the 3-S model, and study the robustness of the 4-C and 3-S models to errors in skull conductivity.We generated dense boundary meshes using MRI datasets and automated SimNIBS pipeline. Then, we built a dense 4-C reference model using Galerkin BEM, and 4-C and 3-S test models using coarser meshes and both Galerkin and collocation BEMs. We compared field topographies of cortical sources, applying various skull conductivities and fitting conductivities that minimized the relative error in 4-C and 3-S models. When the CSF was left out from the EEG model, our compensated, unbiased approach improved the accuracy of the 3-S model considerably compared to the conventional approach, where CSF is neglected without any compensation (mean relative error < 20% vs. > 40%). The error due to the omission of CSF was of the same order in MEG and compensated EEG. EEG has, however, large overall error due to uncertain skull conductivity.Our results show that a realistic 4-C MEG/EEG model can be implemented using standard tools and basic BEM, without excessive workload or computational burden. If the CSF is omitted, compensated skull conductivity should be used in EEG.

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Imaging the Extent and Location of Spatiotemporally Distributed Epileptiform Sources from MEG Measurements

10.1101/2021.11.09.467915 ◽

2021 ◽

Author(s):

Xiyuan Jiang ◽

Shuai Ye ◽

Abbas Sohrabpour ◽

Anto Bagic ◽

Bin He

Keyword(s):

Focal Epilepsy ◽

Real Data ◽

Drug Resistant ◽

Interictal Spikes ◽

Source Imaging ◽

Non Invasive ◽

Eeg Source Imaging ◽

Surgical Evaluation ◽

Extended Sources ◽

Drug Resistant Epilepsy

Non-invasive MEG/EEG source imaging provides valuable information about the epileptogenic brain areas which can be used to aid presurgical planning in focal epilepsy patients suffering from drug-resistant seizures. However, the source extent estimation for electrophysiological source imaging remains to be a challenge and is usually largely dependent on subjective choice. Our recently developed algorithm, fast spatiotemporal iteratively reweighted edge sparsity minimization (FAST-IRES) strategy, has been shown to objectively estimate extended sources from EEG recording, while it has not been applied to MEG recordings. In this work, through extensive numerical experiments and real data analysis in a group of focal drug-resistant epilepsy patients interictal spikes, we demonstrated the ability of FAST-IRES algorithm to image the location and extent of underlying epilepsy sources from MEG measurements. Our results indicate the merits of FAST-IRES in imaging the location and extent of epilepsy sources for pre-surgical evaluation from MEG measurements.

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SimBSI: An open-source Simulink library for developing closed-loop brain signal interfaces in animals and humans

10.1101/769679 ◽

2019 ◽

Author(s):

Alejandro Ojeda ◽

Nathalie Buscher ◽

Pragathi Balasubramani ◽

Vojislav Maric ◽

Dhakshin Ramanathan ◽

...

Keyword(s):

Closed Loop ◽

Multiple Scales ◽

Cognitive Task ◽

Field Potential ◽

Software Framework ◽

Source Imaging ◽

Non Invasive ◽

Eeg Source Imaging ◽

Therapeutic Tools ◽

And Behavior

AbstractObjectiveA promising application of BCI technology is in the development of personalized therapies that can target neural circuits linked to mental or physical disabilities. Typical BCIs, however, offer limited value due to simplistic designs and poor understanding of the conditions being treated. Building BCIs on more solid grounds may require the characterization of the brain dynamics supporting cognition and behavior at multiple scales, from single-cell and local field potential (LFP) recordings in animals to non-invasive electroencephalography (EEG) in humans. Despite recent efforts, a unifying software framework to support closed-loop studies in both animals and humans, is still lacking. The objective of this paper is to develop such a neurotechnology software framework.ApproachHere we develop the Simulink for Brain Signal Interfaces library (SimBSI). Simulink is a mature graphical programming environment within MATLAB that has gained traction for processing electrophysiological data. SimBSI adds to this ecosystem: 1) advanced human EEG source imaging, 2) cross-species multimodal data acquisition based on the Lab Streaming Layer library, and 3) a graphical experimental design platform.Main resultsWe used several examples to demonstrate the capabilities of the library, ranging from simple signal processing, to online EEG source imaging, cognitive task design, and closed-loop neuromodulation. We further demonstrate the simplicity of developing a sophisticated experimental environment for rodents within this environment.SignificanceWith the SimBSI library we hope to aid BCI practitioners of dissimilar backgrounds in the development of, much needed, single and cross-species closed-loop neuroscientific experiments. These experiments may provide the necessary mechanistic data for BCIs to become effective therapeutic tools.

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Faculty Opinions recommendation of Automated EEG source imaging: A retrospective, blinded clinical validation study.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.734143825.793565898 ◽

2019 ◽

Author(s):

Christoph Baumgartner

Keyword(s):

Validation Study ◽

Clinical Validation ◽

Source Imaging ◽

Eeg Source Imaging

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ConvDip: A convolutional neural network for better M/EEG Source Imaging

10.1101/2020.04.09.033506 ◽

2020 ◽

Cited By ~ 2

Author(s):

Lukas Hecker ◽

Rebekka Rupprecht ◽

Ludger Tebartz van Elst ◽

Juergen Kornmeier

Keyword(s):

Neural Network ◽

Inverse Problem ◽

Convolutional Neural Network ◽

Brain Activity ◽

Clinical Diagnostics ◽

Dipole Model ◽

Source Imaging ◽

Eeg Source Imaging ◽

Eeg Data ◽

Inverse Solutions

AbstractEEG and MEG are well-established non-invasive methods in neuroscientific research and clinical diagnostics. Both methods provide a high temporal but low spatial resolution of brain activity. In order to gain insight about the spatial dynamics of the M/EEG one has to solve the inverse problem, which means that more than one configuration of neural sources can evoke one and the same distribution of EEG activity on the scalp. Artificial neural networks have been previously used successfully to find either one or two dipoles sources. These approaches, however, have never solved the inverse problem in a distributed dipole model with more than two dipole sources. We present ConvDip, a novel convolutional neural network (CNN) architecture that solves the EEG inverse problem in a distributed dipole model based on simulated EEG data. We show that (1) ConvDip learned to produce inverse solutions from a single time point of EEG data and (2) outperforms state-of-the-art methods (eLORETA and LCMV beamforming) on all focused performance measures. (3) It is more flexible when dealing with varying number of sources, produces less ghost sources and misses less real sources than the comparison methods. (4) It produces plausible inverse solutions for real-world EEG recordings and needs less than 40 ms for a single forward pass. Our results qualify ConvDip as an efficient and easy-to-apply novel method for source localization in EEG and MEG data, with high relevance for clinical applications, e.g. in epileptology and real time applications.

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Head model and electrical source imaging: A study of 38 epileptic patients

NeuroImage Clinical ◽

10.1016/j.nicl.2014.06.005 ◽

2014 ◽

Vol 5 ◽

pp. 77-83 ◽

Cited By ~ 64

Author(s):

Gwénael Birot ◽

Laurent Spinelli ◽

Serge Vulliémoz ◽

Pierre Mégevand ◽

Denis Brunet ◽

...

Keyword(s):

Head Model ◽

Source Imaging ◽

Epileptic Patients ◽

Electrical Source

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Influence of Patient-Specific Head Modeling on EEG Source Imaging

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/5076865 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Yohan Céspedes-Villar ◽

Juan David Martinez-Vargas ◽

G. Castellanos-Dominguez

Keyword(s):

Neurological Diseases ◽

Structural Data ◽

Problem Formulation ◽

Patient Specific ◽

Source Imaging ◽

Eeg Source Imaging ◽

Deep Sources ◽

The Individual ◽

Tissue Compartments ◽

The Brain

Electromagnetic source imaging (ESI) techniques have become one of the most common alternatives for understanding cognitive processes in the human brain and for guiding possible therapies for neurological diseases. However, ESI accuracy strongly depends on the forward model capabilities to accurately describe the subject’s head anatomy from the available structural data. Attempting to improve the ESI performance, we enhance the brain structure model within the individual-defined forward problem formulation, combining the head geometry complexity of the modeled tissue compartments and the prior knowledge of the brain tissue morphology. We validate the proposed methodology using 25 subjects, from which a set of magnetic-resonance imaging scans is acquired, extracting the anatomical priors and an electroencephalography signal set needed for validating the ESI scenarios. Obtained results confirm that incorporating patient-specific head models enhances the performed accuracy and improves the localization of focal and deep sources.

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