Simultaneous Skull Conductivity and Focal Source Imaging from EEG Recordings with the Help of Bayesian Uncertainty Modelling

ObjectiveTo determine whether seizure onset zone can be accurately localized prior to surgical planning in focal epilepsy patients, we performed non-invasive EEG recordings and source localization analyses on 39 patients.MethodsIn a total of 39 focal epilepsy patients, we recorded and extracted 138 seizures and 1,325 interictal epileptic discharges using high-density EEG. We have investigated a novel approach for directly imaging sources of seizures and interictal spikes from high density EEG recordings, and rigorously validated it for noninvasive localization of seizure onset zone (SOZ) determined from intracranial EEG findings and surgical resection volume. Conventional source imaging analyses were also performed for comparison.ResultsIctal source imaging showed a concordance rate of 95% when compared to intracranial EEG or resection results. The average distance from estimation to seizure onset (intracranial) electrodes is 1.35 cm in patients with concordant results, and 0.74 cm to surgical resection boundary in patients with successful surgery. About 41% of the patients were found to have multiple types of interictal activities; coincidentally, a lower concordance rate and a significantly worse performance in localizing SOZ were observed in these patients.ConclusionNoninvasive ictal source imaging with high-density EEG recording can provide highly concordant results with clinical decisions obtained by invasive monitoring or confirmed by resective surgery. By means of direct seizure imaging using high-density scalp EEG recordings, the added value of ictal source imaging is particularly high in patients with complex interictal activity patterns, who may represent the most challenging cases with poor prognosis.

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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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Consistency of EEG source localization and connectivity estimates

10.1101/071597 ◽

2016 ◽

Cited By ~ 1

Author(s):

Keyvan Mahjoory ◽

Vadim V. Nikulin ◽

Loïc Botrel ◽

Klaus Linkenkaer-Hansen ◽

Marco M. Fato ◽

...

Keyword(s):

Source Localization ◽

Effective Connectivity ◽

Head Model ◽

Source Imaging ◽

The Face ◽

Software Implementations ◽

Inverse Solutions ◽

Eeg Source Localization ◽

Eeg Recordings ◽

Forward And Inverse Modeling

AbstractAs the EEG inverse problem does not have a unique solution, the sources reconstructed from EEG and their connectivity properties depend on forward and inverse modeling parameters such as the choice of an anatomical template and electrical model, prior assumptions on the sources, and further implementational details. In order to use source connectivity analysis as a reliable research tool, there is a need for stability across a wider range of standard estimation routines. Using resting state EEG recordings of N=65 participants acquired within two studies, we present the first comprehensive assessment of the consistency of EEG source localization and functional/effective connectivity metrics across two anatomical templates (ICBM152 and Colin27), three electrical models (BEM, FEM and spherical harmonics expansions), three inverse methods (WMNE, eLORETA and LCMV), and three software implementations (Brainstorm, Fieldtrip and our own toolbox). Source localizations were found to be more stable across reconstruction pipelines than subsequent estimations of functional connectivity, while effective connectivity estimates where the least consistent. All results were relatively unaffected by the choice of the electrical head model, while the choice of the inverse method and source imaging package induced a considerable variability. In particular, a relatively strong difference was found between LCMV beamformer solutions on one hand and eLORETA/WMNE distributed inverse solutions on the other hand. We also observed a gradual decrease of consistency when results are compared between studies, within individual participants, and between individual participants. In order to provide reliable findings in the face of the observed variability, additional simulations involving interacting brain sources are required. Meanwhile, we encourage verification of the obtained results using more than one source imaging procedure.

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Deficit Versus Nondeficit Schizophrenia: An MEG-EEG Investigation of Resting State and Source Coherence—Preliminary Data

Clinical EEG and Neuroscience ◽

10.1177/1550059419867561 ◽

2019 ◽

Vol 51 (1) ◽

pp. 34-44 ◽

Cited By ~ 1

Author(s):

Klevest Gjini ◽

Susan M. Bowyer ◽

Frank Wang ◽

Nash N. Boutros

Keyword(s):

Spectral Analysis ◽

Resting State ◽

Low Frequency ◽

Source Analysis ◽

Beta Band ◽

Source Imaging ◽

Low Frequencies ◽

Deficit Syndrome ◽

Beta Frequency Band ◽

Eeg Recordings

This study investigated the magneto- and electroencephalography (MEG and EEG, respectively) resting state to identify the deviations closely associated with the deficit syndrome (DS) in schizophrenia patients. Ten subjects in each group (control, DS, and nondeficit schizophrenia [NDS]) were included. Subjects underwent MEG-EEG recordings during a resting state condition. MEG coherence source imaging (CSI) in source space and spectral analysis in sensor space were performed. Significant differences were found between the 2 patient groups: (1) MEG and EEG spectral analysis showed significantly higher power at low frequencies (delta band) at sensor space in DS compared with NDS patients; (2) source analysis revealed larger power in the DS compared with NDS group at low frequencies in the frontal region; (3) NDS patients showed significantly higher MEG signal relative power in beta bands in sensor space compared with DS patients; (4) both DS and NDS patients showed higher EEG absolute power at higher beta band compared to controls; and (5) patients with DS were found to have a significantly higher MEG CSI than controls in the beta frequency band. These data support the observation of increased power in the low-frequency EEG/MEG rhythms associated with the DS. Increased power in the beta rhythms was more associated with the NDS.

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Impact of skull sutures, spongiform bone distribution, and aging skull conductivities on the EEG forward and inverse problems

Journal of Neural Engineering ◽

10.1088/1741-2552/ac43f7 ◽

2021 ◽

Author(s):

Hannah May McCann ◽

Leandro Beltrachini

Keyword(s):

Inverse Problem ◽

Inverse Problems ◽

Clinical Diagnosis ◽

Electric Potential ◽

Potential Distribution ◽

Inhomogeneous Layer ◽

Source Imaging ◽

Bone Composition ◽

Source Localisation ◽

Skull Conductivity

Abstract Source imaging is a principal objective for electroencephalography (EEG), the solutions of which require forward problem (FP) computations characterising the electric potential distribution on the scalp due to known sources. Additionally, the EEG-FP is dependent upon realistic, anatomically correct volume conductors and accurate tissue conductivities, where the skull is particularly important. Skull conductivity, however, deviates according to bone composition and the presence of adult sutures. The presented study therefore analyses the effect the presence of adult sutures and differing bone composition have on the EEG-FP and inverse problem (IP) solutions. Utilising a well-established head atlas, detailed head models were generated including compact and spongiform bone and adult sutures. The true skull conductivity was considered as inhomogeneous according to spongiform bone proportion and sutures. The EEG-FP and EEG-IP were solved and compared to results employing homogeneous skull models, with varying conductivities and omitting sutures, as well as using a hypothesised aging skull conductivity model. Significant localised FP errors, with relative error up to 85%, were revealed, particularly evident along suture lines and directly related to the proportion of spongiform bone. This remained evident at various ages. Similar EEG-IP inaccuracies were found, with the largest (maximum 4.14 cm) across suture lines. It is concluded that modelling the skull as an inhomogeneous layer that varies according to spongiform bone proportion and includes differing suture conductivity is imperative for accurate EEG-FP and source localisation calculations. Their omission can result in significant errors, relevant for EEG research and clinical diagnosis.

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Improved EEG source localization with Bayesian uncertainty modelling of unknown skull conductivity

NeuroImage ◽

10.1016/j.neuroimage.2018.11.058 ◽

2019 ◽

Vol 188 ◽

pp. 252-260 ◽

Cited By ~ 2

Author(s):

Ville Rimpiläinen ◽

Alexandra Koulouri ◽

Felix Lucka ◽

Jari P. Kaipio ◽

Carsten H. Wolters

Keyword(s):

Source Localization ◽

Uncertainty Modelling ◽

Skull Conductivity ◽

Eeg Source Localization

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Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Journal of Visualized Experiments ◽

10.3791/52700 ◽

2015 ◽

Cited By ~ 5

Author(s):

Jihye Bae ◽

Abhay Deshmukh ◽

Yinchen Song ◽

Jorge Riera

Keyword(s):

High Resolution ◽

Focal Epilepsy ◽

Source Imaging ◽

Rat Models ◽

Eeg Recordings ◽

High Resolution Eeg

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Structural templates for imaging EEG cortical sources in infants

10.1101/2020.06.20.162131 ◽

2020 ◽

Author(s):

Christian O’Reilly ◽

Eric Larson ◽

John E. Richards ◽

Mayada Elsabbagh

Keyword(s):

Age Differences ◽

Brain Activity ◽

Multiple Time ◽

List Type ◽

Source Reconstruction ◽

Pia Mater ◽

Source Imaging ◽

Cortical Sources ◽

Topological Errors ◽

Eeg Recordings

AbstractElectroencephalographic (EEG) source reconstruction is a powerful approach that helps to unmix scalp signals, mitigates volume conduction issues, and allows anatomical localization of brain activity. Algorithms used to estimate cortical sources require an anatomical model of the head and the brain, generally reconstructed using magnetic resonance imaging (MRI). When such scans are unavailable, a population average can be used for adults, but no average surface template is available for cortical source imaging in infants. To address this issue, this paper introduces a new series of 12 anatomical models for subjects between zero and 24 months of age. These templates are built from MRI averages and volumetric boundary element method segmentation of head tissues available as part of the Neurodevelopmental MRI Database. Surfaces separating the pia mater, the gray matter, and the white matter were estimated using the Infant FreeSurfer pipeline. The surface of the skin as well as the outer and inner skull surfaces were extracted using a cube marching algorithm followed by Laplacian smoothing and mesh decimation. We post-processed these meshes to correct topological errors and ensure watertight meshes. The use of these templates for source reconstruction is demonstrated and validated using 100 high-density EEG recordings in 7-month-old infants. Hopefully, these templates will support future studies based on EEG source reconstruction and functional connectivity in healthy infants as well as in clinical pediatric populations. Particularly, they should make EEG-based neuroimaging more feasible in longitudinal neurodevelopmental studies where it may not be possible to scan infants at multiple time points.Graphical abstractHighlightsTwelve surface templates for infants in the 0-2 years old range are proposedThese templates can be used for EEG source reconstruction using existing toolboxesA relatively modest impact of age differences was found in this age rangeCorrelation analysis confirms increasing source differences with age differencesSources reconstructed with infants versus adult templates significantly differ

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Bayesian Modelling of Skull Conductivity Uncertainties in EEG Source Imaging

EMBEC & NBC 2017 - IFMBE Proceedings ◽

10.1007/978-981-10-5122-7_223 ◽

2017 ◽

pp. 892-895 ◽

Cited By ~ 1

Author(s):

Ville Rimpiläinen ◽

Alexandra Koulouri ◽

Felix Lucka ◽

Jari P. Kaipio ◽

Carsten H. Wolters

Keyword(s):

Bayesian Modelling ◽

Source Imaging ◽

Eeg Source Imaging ◽

Skull Conductivity

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Efficient but less mobilized monitoring system in individuals with high aggressive predispositions

10.31234/osf.io/ya26b ◽

2019 ◽

Author(s):

Fanny Grisetto ◽

Yvonne N. Delevoye-Turrell ◽

Clémence Roger

Keyword(s):

Cognitive Control ◽

Monitoring System ◽

Congruency Effect ◽

Performance Monitoring ◽

Simon Task ◽

Aggressive Behaviors ◽

Eeg Recordings ◽

Global Reduction

Aggressive behaviors in pathological and healthy populations have been largely related to poor cognitive control functioning. However, few studies investigated the influence of aggressive traits (i.e., aggressiveness) on cognitive control. In the current study, we investigated the effects of aggressiveness on cognitive control abilities and particularly, on performance monitoring. Thirty-two participants performed a Simon task while electroencephalography (EEG) and electromyography (EMG) were recorded. Participants were classified as high and low aggressive using the BPAQ questionnaire (Buss & Perry, 1992). EMG recordings were used to reveal three response types by uncovering small incorrect muscular activations in ~15% of correct trials (i.e., partial-errors) that have to be distinguished from full-error and pure-correct responses. For these three response types, EEG recordings were used to extract fronto-central negativities indicative of performance monitoring, the error and correct (-related) negativities (ERN/Ne and CRN/Nc). Behavioral results indicated that the high aggressiveness group had a larger congruency effect compared to the low aggressiveness group, but there were no differences in accuracy. EEG results revealed a global reduction in performance-related negativities amplitudes in all the response types in the high aggressiveness group compared to the low aggressiveness group. Interestingly, the distinction between the ERN/Ne and the CRN/Nc components was preserved both in high and low aggressiveness groups. In sum, high aggressive traits did not affect the capacity to self-evaluate erroneous from correct actions but are associated with a decrease in the importance given to one’s own performance. The implication of these findings are discussed in relation to pathological aggressiveness.

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