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Cuadros J, Z-Rivera L, Castro C, Whitaker G, Otero M, Weinstein A, Martínez-Montes E, Prado P, Zañartu M. DIVA Meets EEG: Model Validation Using Formant-Shift Reflex. APPLIED SCIENCES (BASEL, SWITZERLAND) 2023; 13:7512. [PMID: 38435340 PMCID: PMC10906992 DOI: 10.3390/app13137512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
The neurocomputational model 'Directions into Velocities of Articulators' (DIVA) was developed to account for various aspects of normal and disordered speech production and acquisition. The neural substrates of DIVA were established through functional magnetic resonance imaging (fMRI), providing physiological validation of the model. This study introduces DIVA_EEG an extension of DIVA that utilizes electroencephalography (EEG) to leverage the high temporal resolution and broad availability of EEG over fMRI. For the development of DIVA_EEG, EEG-like signals were derived from original equations describing the activity of the different DIVA maps. Synthetic EEG associated with the utterance of syllables was generated when both unperturbed and perturbed auditory feedback (first formant perturbations) were simulated. The cortical activation maps derived from synthetic EEG closely resembled those of the original DIVA model. To validate DIVA_EEG, the EEG of individuals with typical voices (N = 30) was acquired during an altered auditory feedback paradigm. The resulting empirical brain activity maps significantly overlapped with those predicted by DIVA_EEG. In conjunction with other recent model extensions, DIVA_EEG lays the foundations for constructing a complete neurocomputational framework to tackle vocal and speech disorders, which can guide model-driven personalized interventions.
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Affiliation(s)
- Jhosmary Cuadros
- Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
- Grupo de Bioingeniería, Decanato de Investigación, Universidad Nacional Experimental del Táchira, San Cristóbal 5001, Venezuela
| | - Lucía Z-Rivera
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
- Escuela de Ingeniería Civil Biomédica, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso 2350026, Chile
| | - Christian Castro
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
- Escuela de Ingeniería Civil Biomédica, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso 2350026, Chile
| | - Grace Whitaker
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
| | - Mónica Otero
- Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Santiago 8420524, Chile
- Centro Basal Ciencia & Vida, Universidad San Sebastián, Santiago 8580000, Chile
| | - Alejandro Weinstein
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
- Escuela de Ingeniería Civil Biomédica, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso 2350026, Chile
| | | | - Pavel Prado
- Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago 7510602, Chile
| | - Matías Zañartu
- Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
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Legaz A, Prado P, Moguilner S, Báez S, Santamaría-García H, Birba A, Barttfeld P, García AM, Fittipaldi S, Ibañez A. Social and non-social working memory in neurodegeneration. Neurobiol Dis 2023; 183:106171. [PMID: 37257663 PMCID: PMC11177282 DOI: 10.1016/j.nbd.2023.106171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/08/2023] [Accepted: 05/24/2023] [Indexed: 06/02/2023] Open
Abstract
Although social functioning relies on working memory, whether a social-specific mechanism exists remains unclear. This undermines the characterization of neurodegenerative conditions with both working memory and social deficits. We assessed working memory domain-specificity across behavioral, electrophysiological, and neuroimaging dimensions in 245 participants. A novel working memory task involving social and non-social stimuli with three load levels was assessed across controls and different neurodegenerative conditions with recognized impairments in: working memory and social cognition (behavioral-variant frontotemporal dementia); general cognition (Alzheimer's disease); and unspecific patterns (Parkinson's disease). We also examined resting-state theta oscillations and functional connectivity correlates of working memory domain-specificity. Results in controls and all groups together evidenced increased working memory demands for social stimuli associated with frontocinguloparietal theta oscillations and salience network connectivity. Canonical frontal theta oscillations and executive-default mode network anticorrelation indexed non-social stimuli. Behavioral-variant frontotemporal dementia presented generalized working memory deficits related to posterior theta oscillations, with social stimuli linked to salience network connectivity. In Alzheimer's disease, generalized working memory impairments were related to temporoparietal theta oscillations, with non-social stimuli linked to the executive network. Parkinson's disease showed spared working memory performance and canonical brain correlates. Findings support a social-specific working memory and related disease-selective pathophysiological mechanisms.
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Affiliation(s)
- Agustina Legaz
- Cognitive Neuroscience Center (CNC), Universidad de San Andres, Buenos Aires, Argentina; National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Universidad Nacional de Córdoba, Facultad de Psicología, Córdoba, Argentina
| | - Pavel Prado
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Chile; Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago, Chile
| | - Sebastián Moguilner
- Cognitive Neuroscience Center (CNC), Universidad de San Andres, Buenos Aires, Argentina; Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Chile; Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, United States; Trinity College Dublin (TCD), Dublin, Ireland
| | | | - Hernando Santamaría-García
- Pontificia Universidad Javeriana, Medical School, Physiology and Psychiatry Departments, Memory and Cognition Center Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia
| | - Agustina Birba
- Cognitive Neuroscience Center (CNC), Universidad de San Andres, Buenos Aires, Argentina; Facultad de Psicología, Universidad de La Laguna, Tenerife, Spain; Instituto Universitario de Neurociencia, Universidad de La Laguna, Tenerife, Spain
| | - Pablo Barttfeld
- Cognitive Science Group. Instituto de Investigaciones Psicológicas (IIPsi), CONICET UNC, Facultad de Psicología, Universidad Nacional de Córdoba, Boulevard de la Reforma esquina Enfermera Gordillo, CP 5000. Córdoba, Argentina
| | - Adolfo M García
- Cognitive Neuroscience Center (CNC), Universidad de San Andres, Buenos Aires, Argentina; Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, United States; Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile; Trinity College Dublin (TCD), Dublin, Ireland
| | - Sol Fittipaldi
- Cognitive Neuroscience Center (CNC), Universidad de San Andres, Buenos Aires, Argentina; Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Chile; Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, United States; Trinity College Dublin (TCD), Dublin, Ireland.
| | - Agustín Ibañez
- Cognitive Neuroscience Center (CNC), Universidad de San Andres, Buenos Aires, Argentina; National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Chile; Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, United States; Trinity College Dublin (TCD), Dublin, Ireland.
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Delatolas T, Antonakakis M, Wolters CH, Zervakis M. EEG Source Analysis with a Convolutional Neural Network and Finite Element Analysis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083731 DOI: 10.1109/embc40787.2023.10340742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
To reconstruct the electrophysiological activity of brain responses, source analysis is performed through the solution of the forward and inverse problems. The former contains a unique solution while the latter is ill-posed. In this regard, many algorithms have been suggested relying on different prior information for solving the inverse problem. Recently, neural networks have been used to deal with source analysis. However, their underlying training for inverse solutions is based on suboptimal forward modeling. In this work, we propose a CNN that is able to reconstruct EEG brain activity. To train our proposed CNN, a skull-conductivity calibrated and white matter anisotropic head model. Based on this model, we generate simulated EEG data and used them to train our CNN. We first evaluate the performance of our CNN using the simulated EEG data while a realistic application with somatosensory evoked potentials follows. From the results, we observed that the CCN correctly localized the P20/N20 component at the subject-specific Brodmann area 3b and it can potentially localize deeper sources. A comparison is also presented with well-known inverse solutions (single dipole scans and sLORETA) showing similar localization performance. Through these results, an emerging potential for real applications appears on the basis of realistic head modeling.
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Liu C, Downey RJ, Mu Y, Richer N, Hwang J, Shah VA, Sato SD, Clark DJ, Hass CJ, Manini TM, Seidler RD, Ferris DP. Comparison of EEG Source Localization Using Simplified and Anatomically Accurate Head Models in Younger and Older Adults. IEEE Trans Neural Syst Rehabil Eng 2023; 31:2591-2602. [PMID: 37252873 PMCID: PMC10336858 DOI: 10.1109/tnsre.2023.3281356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Accuracy of electroencephalography (EEG) source localization relies on the volume conduction head model. A previous analysis of young adults has shown that simplified head models have larger source localization errors when compared with head models based on magnetic resonance images (MRIs). As obtaining individual MRIs may not always be feasible, researchers often use generic head models based on template MRIs. It is unclear how much error would be introduced using template MRI head models in older adults that likely have differences in brain structure compared to young adults. The primary goal of this study was to determine the error caused by using simplified head models without individual-specific MRIs in both younger and older adults. We collected high-density EEG during uneven terrain walking and motor imagery for 15 younger (22±3 years) and 21 older adults (74±5 years) and obtained [Formula: see text]-weighted MRI for each individual. We performed equivalent dipole fitting after independent component analysis to obtain brain source locations using four forward modeling pipelines with increasing complexity. These pipelines included: 1) a generic head model with template electrode positions or 2) digitized electrode positions, 3) individual-specific head models with digitized electrode positions using simplified tissue segmentation, or 4) anatomically accurate segmentation. We found that when compared to the anatomically accurate individual-specific head models, performing dipole fitting with generic head models led to similar source localization discrepancies (up to 2 cm) for younger and older adults. Co-registering digitized electrode locations to the generic head models reduced source localization discrepancies by ∼ 6 mm. Additionally, we found that source depths generally increased with skull conductivity for the representative young adult but not as much for the older adult. Our results can help inform a more accurate interpretation of brain areas in EEG studies when individual MRIs are unavailable.
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Cao J, Bulger E, Shinn-Cunningham B, Grover P, Kainerstorfer JM. Diffuse Optical Tomography Spatial Prior for EEG Source Localization in Human Visual Cortex. Neuroimage 2023:120210. [PMID: 37311535 DOI: 10.1016/j.neuroimage.2023.120210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 05/30/2023] [Indexed: 06/15/2023] Open
Abstract
Electroencephalography (EEG) and diffuse optical tomography (DOT) are imaging methods which are widely used for neuroimaging. While the temporal resolution of EEG is high, the spatial resolution is typically limited. DOT, on the other hand, has high spatial resolution, but the temporal resolution is inherently limited by the slow hemodynamics it measures. In our previous work, we showed using computer simulations that when using the results of DOT reconstruction as the spatial prior for EEG source reconstruction, high spatio-temporal resolution could be achieved. In this work, we experimentally validate the algorithm by alternatingly flashing two visual stimuli at a speed that is faster than the temporal resolution of DOT. We show that the joint reconstruction using both EEG and DOT clearly resolves the two stimuli temporally, and the spatial confinement is drastically improved in comparison to reconstruction using EEG alone.
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Affiliation(s)
- Jiaming Cao
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States
| | - Eli Bulger
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States
| | - Barbara Shinn-Cunningham
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States; Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States; Neuroscience Institute, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, 15213, Pennsylvania, United States; Department of Psychology, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States
| | - Pulkit Grover
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States; Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States; Neuroscience Institute, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, 15213, Pennsylvania, United States
| | - Jana M Kainerstorfer
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States; Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213, Pennsylvania, United States; Neuroscience Institute, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, 15213, Pennsylvania, United States.
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Ferrand M, Baumann C, Aron O, Vignal JP, Jonas J, Tyvaert L, Colnat-Coulbois S, Koessler L, Maillard L. Intracerebral Correlates of Scalp EEG Ictal Discharges Based on Simultaneous Stereo-EEG Recordings. Neurology 2023; 100:e2045-e2059. [PMID: 36963841 PMCID: PMC10186237 DOI: 10.1212/wnl.0000000000207135] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 01/18/2023] [Indexed: 03/26/2023] Open
Abstract
BACKGROUND AND OBJECTIVES It remains unknown to what extent ictal scalp EEG can accurately predict the localization of the intracerebral seizure onset in presurgical evaluation of drug-resistant epilepsies. In this study, we aimed to define homogeneous ictal scalp EEG profiles (based on their first ictal abnormality) and assess their localizing value using simultaneously recorded scalp EEG and stereo-EEG. METHODS We retrospectively included consecutive patients with drug-resistant focal epilepsy who had simultaneous stereo-EEG and scalp EEG recordings of at least 1 seizure in the epileptology unit in Nancy, France. We analyzed 1 seizure per patient and used hierarchical cluster analysis to group similar seizure profiles on scalp EEG and then performed a descriptive analysis of their intracerebral correlates. RESULTS We enrolled 129 patients in this study. The hierarchical cluster analysis showed 6 profiles on scalp EEG first modification. None were specific to a single intracerebral localization. The "normal EEG" and "blurred EEG" clusters (early muscle artifacts) comprised only 5 patients each and corresponded to no preferential intracerebral localization. The "temporal discharge" cluster (n = 46) was characterized by theta or delta discharges on ipsilateral anterior temporal scalp electrodes and corresponded to a preferential mesial temporal intracerebral localization. The "posterior discharge" cluster (n = 42) was characterized by posterior ipsilateral or contralateral rhythmic alpha discharges or slow waves on scalp and corresponded to a preferential temporal localization. However, this profile was the statistically most frequent scalp EEG correlate of occipital and parietal seizures. The "diffuse suppression" cluster (n = 9) was characterized by a bilateral and diffuse background activity suppression on scalp and corresponded to mesial, and particularly insulo-opercular, localization. Finally, the "frontal discharge" cluster (n = 22) was characterized by bilateral frontal rhythmic fast activity or preictal spike on scalp and corresponded to preferential ventrodorsal frontal intracerebral localizations. DISCUSSION The hierarchical cluster analysis identified 6 seizure profiles regarding the first abnormality on scalp EEG. None of them were specific of a single intracerebral localization. Nevertheless, the strong relationships between the "temporal," "frontal," "diffuse suppression," and "posterior" profiles and intracerebral discharge localizations may contribute to hierarchize hypotheses derived from ictal scalp EEG analysis regarding intracerebral seizure onset.
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Affiliation(s)
- Mickaël Ferrand
- From the Department of Neurology (M.F., O.A., J.-P.V., J.J., L.T., L.M.), and University Hospital of Nancy, Lorraine University; Department of Epidemiology and Clinical Evaluation (C.B.), INSERM CIC-EC CIE6, Lorraine University, Vandoeuvre; Neurosciences of Systems and Cognition Project (O.A., J.J., L.T., L.K., L.M.), BioSiS Department (Department Biologie, Signaux et Systèmes en Cancérologie et Neurosciences), Research Center for Automatic Control of Nancy (CRAN), Lorraine University, CNRS, UMR 7039, Vandoeuvre; and Department of Neurosurgery (S.C.-C.), University Hospital of Nancy, Lorraine University, Nancy, France
| | - Cédric Baumann
- From the Department of Neurology (M.F., O.A., J.-P.V., J.J., L.T., L.M.), and University Hospital of Nancy, Lorraine University; Department of Epidemiology and Clinical Evaluation (C.B.), INSERM CIC-EC CIE6, Lorraine University, Vandoeuvre; Neurosciences of Systems and Cognition Project (O.A., J.J., L.T., L.K., L.M.), BioSiS Department (Department Biologie, Signaux et Systèmes en Cancérologie et Neurosciences), Research Center for Automatic Control of Nancy (CRAN), Lorraine University, CNRS, UMR 7039, Vandoeuvre; and Department of Neurosurgery (S.C.-C.), University Hospital of Nancy, Lorraine University, Nancy, France
| | - Olivier Aron
- From the Department of Neurology (M.F., O.A., J.-P.V., J.J., L.T., L.M.), and University Hospital of Nancy, Lorraine University; Department of Epidemiology and Clinical Evaluation (C.B.), INSERM CIC-EC CIE6, Lorraine University, Vandoeuvre; Neurosciences of Systems and Cognition Project (O.A., J.J., L.T., L.K., L.M.), BioSiS Department (Department Biologie, Signaux et Systèmes en Cancérologie et Neurosciences), Research Center for Automatic Control of Nancy (CRAN), Lorraine University, CNRS, UMR 7039, Vandoeuvre; and Department of Neurosurgery (S.C.-C.), University Hospital of Nancy, Lorraine University, Nancy, France
| | - Jean-Pierre Vignal
- From the Department of Neurology (M.F., O.A., J.-P.V., J.J., L.T., L.M.), and University Hospital of Nancy, Lorraine University; Department of Epidemiology and Clinical Evaluation (C.B.), INSERM CIC-EC CIE6, Lorraine University, Vandoeuvre; Neurosciences of Systems and Cognition Project (O.A., J.J., L.T., L.K., L.M.), BioSiS Department (Department Biologie, Signaux et Systèmes en Cancérologie et Neurosciences), Research Center for Automatic Control of Nancy (CRAN), Lorraine University, CNRS, UMR 7039, Vandoeuvre; and Department of Neurosurgery (S.C.-C.), University Hospital of Nancy, Lorraine University, Nancy, France
| | - Jacques Jonas
- From the Department of Neurology (M.F., O.A., J.-P.V., J.J., L.T., L.M.), and University Hospital of Nancy, Lorraine University; Department of Epidemiology and Clinical Evaluation (C.B.), INSERM CIC-EC CIE6, Lorraine University, Vandoeuvre; Neurosciences of Systems and Cognition Project (O.A., J.J., L.T., L.K., L.M.), BioSiS Department (Department Biologie, Signaux et Systèmes en Cancérologie et Neurosciences), Research Center for Automatic Control of Nancy (CRAN), Lorraine University, CNRS, UMR 7039, Vandoeuvre; and Department of Neurosurgery (S.C.-C.), University Hospital of Nancy, Lorraine University, Nancy, France
| | - Louise Tyvaert
- From the Department of Neurology (M.F., O.A., J.-P.V., J.J., L.T., L.M.), and University Hospital of Nancy, Lorraine University; Department of Epidemiology and Clinical Evaluation (C.B.), INSERM CIC-EC CIE6, Lorraine University, Vandoeuvre; Neurosciences of Systems and Cognition Project (O.A., J.J., L.T., L.K., L.M.), BioSiS Department (Department Biologie, Signaux et Systèmes en Cancérologie et Neurosciences), Research Center for Automatic Control of Nancy (CRAN), Lorraine University, CNRS, UMR 7039, Vandoeuvre; and Department of Neurosurgery (S.C.-C.), University Hospital of Nancy, Lorraine University, Nancy, France
| | - Sophie Colnat-Coulbois
- From the Department of Neurology (M.F., O.A., J.-P.V., J.J., L.T., L.M.), and University Hospital of Nancy, Lorraine University; Department of Epidemiology and Clinical Evaluation (C.B.), INSERM CIC-EC CIE6, Lorraine University, Vandoeuvre; Neurosciences of Systems and Cognition Project (O.A., J.J., L.T., L.K., L.M.), BioSiS Department (Department Biologie, Signaux et Systèmes en Cancérologie et Neurosciences), Research Center for Automatic Control of Nancy (CRAN), Lorraine University, CNRS, UMR 7039, Vandoeuvre; and Department of Neurosurgery (S.C.-C.), University Hospital of Nancy, Lorraine University, Nancy, France
| | - Laurent Koessler
- From the Department of Neurology (M.F., O.A., J.-P.V., J.J., L.T., L.M.), and University Hospital of Nancy, Lorraine University; Department of Epidemiology and Clinical Evaluation (C.B.), INSERM CIC-EC CIE6, Lorraine University, Vandoeuvre; Neurosciences of Systems and Cognition Project (O.A., J.J., L.T., L.K., L.M.), BioSiS Department (Department Biologie, Signaux et Systèmes en Cancérologie et Neurosciences), Research Center for Automatic Control of Nancy (CRAN), Lorraine University, CNRS, UMR 7039, Vandoeuvre; and Department of Neurosurgery (S.C.-C.), University Hospital of Nancy, Lorraine University, Nancy, France
| | - Louis Maillard
- From the Department of Neurology (M.F., O.A., J.-P.V., J.J., L.T., L.M.), and University Hospital of Nancy, Lorraine University; Department of Epidemiology and Clinical Evaluation (C.B.), INSERM CIC-EC CIE6, Lorraine University, Vandoeuvre; Neurosciences of Systems and Cognition Project (O.A., J.J., L.T., L.K., L.M.), BioSiS Department (Department Biologie, Signaux et Systèmes en Cancérologie et Neurosciences), Research Center for Automatic Control of Nancy (CRAN), Lorraine University, CNRS, UMR 7039, Vandoeuvre; and Department of Neurosurgery (S.C.-C.), University Hospital of Nancy, Lorraine University, Nancy, France.
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Hecker L, Tebartz van Elst L, Kornmeier J. Source localization using recursively applied and projected MUSIC with flexible extent estimation. Front Neurosci 2023; 17:1170862. [PMID: 37255753 PMCID: PMC10225686 DOI: 10.3389/fnins.2023.1170862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 04/17/2023] [Indexed: 06/01/2023] Open
Abstract
Magneto- and electroencephalography (M/EEG) are widespread techniques to measure neural activity in-vivo at a high temporal resolution but low spatial resolution. Locating the neural sources underlying the M/EEG poses an inverse problem, which is ill-posed. We developed a new method based on Recursive Application of Multiple Signal Classification (MUSIC). Our proposed method is able to recover not only the locations but, in contrast to other inverse solutions, also the extent of active brain regions flexibly (FLEX-MUSIC). This is achieved by allowing it to search not only for single dipoles but also dipole clusters of increasing extent to find the best fit during each recursion. FLEX-MUSIC achieved the highest accuracy for both single dipole and extended sources compared to all other methods tested. Remarkably, FLEX-MUSIC was capable to accurately estimate the level of sparsity in the source space (r = 0.82), whereas all other approaches tested failed to do so (r ≤ 0.18). The average computation time of FLEX-MUSIC was considerably lower compared to a popular Bayesian approach and comparable to that of another recursive MUSIC approach and eLORETA. FLEX-MUSIC produces only few errors and was capable to reliably estimate the extent of sources. The accuracy and low computation time of FLEX-MUSIC renders it an improved technique to solve M/EEG inverse problems both in neuroscience research and potentially in pre-surgery diagnostic in epilepsy.
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Affiliation(s)
- Lukas Hecker
- Department of Psychiatry and Psychotherapy, University of Freiburg Medical Center, Freiburg, Germany
- Department of Psychosomatic Medicine and Psychotherapy, University of Freiburg Medical Center, Freiburg, Germany
- Perception and Cognition Lab, Institute for Frontier Areas of Psychology and Mental Health, Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Ludger Tebartz van Elst
- Department of Psychiatry and Psychotherapy, University of Freiburg Medical Center, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jürgen Kornmeier
- Department of Psychiatry and Psychotherapy, University of Freiburg Medical Center, Freiburg, Germany
- Perception and Cognition Lab, Institute for Frontier Areas of Psychology and Mental Health, Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Ray KL, Griffin NR, Shumake J, Alario A, Allen JJB, Beevers CG, Schnyer DM. Altered electroencephalography resting state network coherence in remitted MDD. Brain Res 2023; 1806:148282. [PMID: 36792002 DOI: 10.1016/j.brainres.2023.148282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 02/10/2023] [Accepted: 02/11/2023] [Indexed: 02/16/2023]
Abstract
Individuals with remitted depression are at greater risk for subsequent depression and therefore may provide a unique opportunity to understand the neurophysiological correlates underlying the risk of depression. Research has identified abnormal resting-state electroencephalography (EEG) power metrics and functional connectivity patterns associated with major depression, however little is known about these neural signatures in individuals with remitted depression. We investigate the spectral dynamics of 64-channel EEG surface power and source-estimated network connectivity during resting states in 37 individuals with depression, 56 with remitted depression, and 49 healthy adults that did not differ on age, education, and cognitive ability across theta, alpha, and beta frequencies. Average reference spectral EEG surface power analyses identified greater left and midfrontal theta in remitted depression compared to healthy adults. Using Network Based Statistics, we also demonstrate within and between network alterations in LORETA transformed EEG source-space coherence across the default mode, fronto-parietal, and salience networks where individuals with remitted depression exhibited enhanced coherence compared to those with depression, and healthy adults. This work builds upon our currently limited understanding of resting EEG connectivity in depression, and helps bridge the gap between aberrant EEG power and brain network connectivity dynamics in this disorder. Further, our unique examination of remitted depression relative to both healthy and depressed adults may be key to identifying brain-based biomarkers for those at high risk for future, or subsequent depression.
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Affiliation(s)
| | | | | | - Alexandra Alario
- University of Texas, Austin, United States; University of Iowa, United States
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Jiang L, Li F, Chen Z, Zhu B, Yi C, Li Y, Zhang T, Peng Y, Si Y, Cao Z, Chen A, Yao D, Chen X, Xu P. Information transmission velocity-based dynamic hierarchical brain networks. Neuroimage 2023; 270:119997. [PMID: 36868393 DOI: 10.1016/j.neuroimage.2023.119997] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 02/09/2023] [Accepted: 02/27/2023] [Indexed: 03/05/2023] Open
Abstract
The brain functions as an accurate circuit that regulates information to be sequentially propagated and processed in a hierarchical manner. However, it is still unknown how the brain is hierarchically organized and how information is dynamically propagated during high-level cognition. In this study, we developed a new scheme for quantifying the information transmission velocity (ITV) by combining electroencephalogram (EEG) and diffusion tensor imaging (DTI), and then mapped the cortical ITV network (ITVN) to explore the information transmission mechanism of the human brain. The application in MRI-EEG data of P300 revealed bottom-up and top-down ITVN interactions subserving P300 generation, which was comprised of four hierarchical modules. Among these four modules, information exchange between visual- and attention-activated regions occurred at a high velocity, related cognitive processes could thus be efficiently accomplished due to the heavy myelination of these regions. Moreover, inter-individual variability in P300 was probed to be attributed to the difference in information transmission efficiency of the brain, which may provide new insight into the cognitive degenerations in clinical neurodegenerative disorders, such as Alzheimer's disease, from the transmission velocity perspective. Together, these findings confirm the capacity of ITV to effectively determine the efficiency of information propagation in the brain.
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Affiliation(s)
- Lin Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Zhaojin Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Bin Zhu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Chanlin Yi
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yuqin Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Tao Zhang
- School of science, Xihua University, Chengdu 610039, China
| | - Yueheng Peng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yajing Si
- School of Psychology, Xinxiang Medical University, Xinxiang 453003, China
| | - Zehong Cao
- STEM, University of South Australia, Adelaide, SA 5000, Australia
| | - Antao Chen
- Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China; School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, China.
| | - Xun Chen
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China; Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230026, China.
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan 611731, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China.
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60
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Riaz U, Razzaq FA, Areces-Gonzalez A, Piastra MC, Vega MLB, Paz-Linares D, Valdés-Sosa PA. Automatic Quality Control of the numerical accuracy of EEG Lead fields. Neuroimage 2023; 273:120091. [PMID: 37060935 DOI: 10.1016/j.neuroimage.2023.120091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 03/22/2023] [Accepted: 04/04/2023] [Indexed: 04/17/2023] Open
Abstract
Precise individualized EEG source localization is predicated on having accurate subject-specific Lead Fields (LFs) obtained from their Magnetic Resonance Images (MRI). LF calculation is a complex process involving several error-prone steps that start with obtaining a realistic head model from the MRI and finalizing with computationally expensive solvers such as the Boundary Element Method (BEM) or Finite Element Method (FEM). Current Big-Data applications require the calculation of batches of hundreds or thousands of LFs. LF. Quality Control is conventionally checked subjectively by experts, a procedure not feasible in practice for larger batches. To facilitate this step, we introduce the Lead Field Automatic-Quality Control Index (LF-AQI) that flags LF with potential errors. We base our LF-AQI on the assumption that LFs obtained from simpler head models, i.e., the homogeneous head model LF (HHM-LF) or spherical head model LF (SHM-LF), deviate only moderately from a "good" realistic test LF. Since these simpler LFs are easier to compute and check for errors, they may serve as "reference LF" to detect anomalous realistic test LF. We investigated this assumption by comparing correlation-based channel ρmin(ref,test)and source τmin(ref,test) similarity indices (SI) between "gold standards," i.e., very accurate FEM and BEM LFs, and the proposed references (HHM-LF and SHM-LF). Surprisingly we found that the most uncomplicated possible reference, HHM-LF had high SI values with the gold standards-leading us to explore further use of the channel ρmin(HHM-LF,test)and source τmin(HHM-LF,test)SI as a basis for our LF-AQI. Indeed, these SI successfully detected five simulated scenarios of LFs artifacts. This result encouraged us to evaluate the SI on a large dataset and thus define our LF-AQI. We thus computed the SI of 1251 LFs obtained from the Child Mind Institute (CMI) MRI dataset. When ρmin(HHM-LF,test)and source τmin(HHM-LF,test) were plotted for all test subjects on a 2D space, most were tightly clustered around the median of a high similarity centroid (HSC), except for a smaller proportion of outliers. We define the LF-AQI for a given LF as the log Euclidean distance between its SI and the HSC median. To automatically detect outliers, the threshold is at the 90th percentile of the CMI LF-AQIs (-0.9755). LF-AQI greater than this threshold flag individual LF to be checked. The robustness of this LF-AQI screening was checked by repeated out-of-sample validation. Strikingly, minor corrections in re-processing the flagged cases eliminated their status as outliers. Furthermore, the "doubtful" labels assigned by LF-AQI were validated by neuroscience students using a Likert scale questionnaire designed to manually check the LF's quality. Item Response Theory (IRT) analysis was applied to the questionnaire results to compute an optimized model and a latent variable θ for that model. A linear mixed model (LMM) between the θ and LF-AQI resulted in an effect with a Cohen's d value of 1.3 and a p-value <0.001, thus validating the correspondence of LF-AQI with the visual quality control. We provide an open-source pipeline to implement both LF calculation and its quality control to allow further evaluation of our index.
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Affiliation(s)
- Usama Riaz
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Fuleah A Razzaq
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Ariosky Areces-Gonzalez
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China; Department of Informatics, University of Pinar del Rio Hermanos Saiz Montes de Oca, Cuba
| | | | - Maria L Bringas Vega
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China; Cuban Neuroscience Center, Havana, Cuba
| | - Deirel Paz-Linares
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Pedro A Valdés-Sosa
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China; Cuban Neuroscience Center, Havana, Cuba.
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61
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Barborica A, Mindruta I, López-Madrona VJ, Alario FX, Trébuchon A, Donos C, Oane I, Pistol C, Mihai F, Bénar CG. Studying memory processes at different levels with simultaneous depth and surface EEG recordings. Front Hum Neurosci 2023; 17:1154038. [PMID: 37082152 PMCID: PMC10110965 DOI: 10.3389/fnhum.2023.1154038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 03/06/2023] [Indexed: 04/07/2023] Open
Abstract
Investigating cognitive brain functions using non-invasive electrophysiology can be challenging due to the particularities of the task-related EEG activity, the depth of the activated brain areas, and the extent of the networks involved. Stereoelectroencephalographic (SEEG) investigations in patients with drug-resistant epilepsy offer an extraordinary opportunity to validate information derived from non-invasive recordings at macro-scales. The SEEG approach can provide brain activity with high spatial specificity during tasks that target specific cognitive processes (e.g., memory). Full validation is possible only when performing simultaneous scalp SEEG recordings, which allows recording signals in the exact same brain state. This is the approach we have taken in 12 subjects performing a visual memory task that requires the recognition of previously viewed objects. The intracranial signals on 965 contact pairs have been compared to 391 simultaneously recorded scalp signals at a regional and whole-brain level, using multivariate pattern analysis. The results show that the task conditions are best captured by intracranial sensors, despite the limited spatial coverage of SEEG electrodes, compared to the whole-brain non-invasive recordings. Applying beamformer source reconstruction or independent component analysis does not result in an improvement of the multivariate task decoding performance using surface sensor data. By analyzing a joint scalp and SEEG dataset, we investigated whether the two types of signals carry complementary information that might improve the machine-learning classifier performance. This joint analysis revealed that the results are driven by the modality exhibiting best individual performance, namely SEEG.
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Affiliation(s)
- Andrei Barborica
- Department of Physics, University of Bucharest, Bucharest, Romania
- *Correspondence: Andrei Barborica
| | - Ioana Mindruta
- Epilepsy Monitoring Unit, Department of Neurology, Emergency University Hospital Bucharest, Bucharest, Romania
- Department of Neurology, Medical Faculty, Carol Davila University of Medicine and Pharmacy Bucharest, Bucharest, Romania
| | | | | | - Agnès Trébuchon
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- APHM, Timone Hospital, Functional and Stereotactic Neurosurgery, Marseille, France
| | - Cristian Donos
- Department of Physics, University of Bucharest, Bucharest, Romania
| | - Irina Oane
- Epilepsy Monitoring Unit, Department of Neurology, Emergency University Hospital Bucharest, Bucharest, Romania
| | | | - Felicia Mihai
- Department of Physics, University of Bucharest, Bucharest, Romania
| | - Christian G. Bénar
- Aix Marseille University, INSERM, INS, Institute of Neuroscience System, Marseille, France
- Christian G. Bénar
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62
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López-Madrona VJ, Villalon SM, Velmurugan J, Semeux-Bernier A, Garnier E, Badier JM, Schön D, Bénar CG. Reconstruction and localization of auditory sources from intracerebral SEEG using independent component analysis. Neuroimage 2023; 269:119905. [PMID: 36720438 DOI: 10.1016/j.neuroimage.2023.119905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 01/11/2023] [Accepted: 01/26/2023] [Indexed: 01/30/2023] Open
Abstract
Stereo-electroencephalography (SEEG) is the surgical implantation of electrodes in the brain to better localize the epileptic network in pharmaco-resistant epileptic patients. This technique has exquisite spatial and temporal resolution. Still, the number and the position of the electrodes in the brain is limited and determined by the semiology and/or preliminary non-invasive examinations, leading to a large number of unexplored brain structures in each patient. Here, we propose a new approach to reconstruct the activity of non-sampled structures in SEEG, based on independent component analysis (ICA) and dipole source localization. We have tested this approach with an auditory stimulation dataset in ten patients. The activity directly recorded from the auditory cortex served as ground truth and was compared to the ICA applied on all non-auditory electrodes. Our results show that the activity from the auditory cortex can be reconstructed at the single trial level from contacts as far as ∼40 mm from the source. Importantly, this reconstructed activity is localized via dipole fitting in the proximity of the original source. In addition, we show that the size of the confidence interval of the dipole fitting is a good indicator of the reliability of the result, which depends on the geometry of the SEEG implantation. Overall, our approach allows reconstructing the activity of structures far from the electrode locations, partially overcoming the spatial sampling limitation of intracerebral recordings.
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Affiliation(s)
| | - Samuel Medina Villalon
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille 13005, France; APHM, Timone Hospital, Epileptology and cerebral rhythmology, Marseille 13005, France
| | - Jayabal Velmurugan
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille 13005, France
| | | | - Elodie Garnier
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille 13005, France
| | - Jean-Michel Badier
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille 13005, France
| | - Daniele Schön
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille 13005, France
| | - Christian-G Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille 13005, France.
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63
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Prado P, Moguilner S, Mejía JA, Sainz-Ballesteros A, Otero M, Birba A, Santamaria-Garcia H, Legaz A, Fittipaldi S, Cruzat J, Tagliazucchi E, Parra M, Herzog R, Ibáñez A. Source space connectomics of neurodegeneration: One-metric approach does not fit all. Neurobiol Dis 2023; 179:106047. [PMID: 36841423 PMCID: PMC11170467 DOI: 10.1016/j.nbd.2023.106047] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 02/05/2023] [Accepted: 02/15/2023] [Indexed: 02/25/2023] Open
Abstract
Brain functional connectivity in dementia has been assessed with dissimilar EEG connectivity metrics and estimation procedures, thereby increasing results' heterogeneity. In this scenario, joint analyses integrating information from different metrics may allow for a more comprehensive characterization of brain functional interactions in different dementia subtypes. To test this hypothesis, resting-state electroencephalogram (rsEEG) was recorded in individuals with Alzheimer's Disease (AD), behavioral variant frontotemporal dementia (bvFTD), and healthy controls (HCs). Whole-brain functional connectivity was estimated in the EEG source space using 101 different types of functional connectivity, capturing linear and nonlinear interactions in both time and frequency-domains. Multivariate machine learning and progressive feature elimination was run to discriminate AD from HCs, and bvFTD from HCs, based on joint analyses of i) EEG frequency bands, ii) complementary frequency-domain metrics (e.g., instantaneous, lagged, and total connectivity), and iii) time-domain metrics with different linearity assumption (e.g., Pearson correlation coefficient and mutual information). <10% of all possible connections were responsible for the differences between patients and controls, and atypical connectivity was never captured by >1/4 of all possible connectivity measures. Joint analyses revealed patterns of hypoconnectivity (patientsHCs) in both groups was mainly identified in frontotemporal regions. These atypicalities were differently captured by frequency- and time-domain connectivity metrics, in a bandwidth-specific fashion. The multi-metric representation of source space whole-brain functional connectivity evidenced the inadequacy of single-metric approaches, and resulted in a valid alternative for the selection problem in EEG connectivity. These joint analyses reveal patterns of brain functional interdependence that are overlooked with single metrics approaches, contributing to a more reliable and interpretable description of atypical functional connectivity in neurodegeneration.
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Affiliation(s)
- Pavel Prado
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago, Chile
| | - Sebastian Moguilner
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina
| | - Jhony A Mejía
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Departamento de Ingeniería Biomédica, Universidad de Los Andes, Bogotá, Colombia
| | | | - Mónica Otero
- Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Santiago, Chile; Centro BASAL Ciencia & Vida, Universidad San Sebastián, Santiago, Chile
| | - Agustina Birba
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina
| | - Hernando Santamaria-Garcia
- PhD Neuroscience Program, Physiology and Psychiatry Departments, Pontificia Universidad Javeriana, Bogotá, Colombia; Memory and Cognition Center Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia; Global Brain Health Institute, University of California San Francisco, San Francisco, California; Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Agustina Legaz
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina; National Scientific and Technical Research Council, Buenos Aires, Argentina
| | - Sol Fittipaldi
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina; Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland; National Scientific and Technical Research Council, Buenos Aires, Argentina
| | - Josephine Cruzat
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Enzo Tagliazucchi
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Departamento de Física, Universidad de Buenos Aires and Instituto de Física de Buenos Aires (IFIBA -CONICET), Buenos Aires, Argentina
| | - Mario Parra
- School of Psychological Sciences and Health, University of Strathclyde, Glasgow, United Kingdom
| | - Rubén Herzog
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Fundación para el Estudio de la Conciencia Humana (EcoH), Chile
| | - Agustín Ibáñez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina; PhD Neuroscience Program, Physiology and Psychiatry Departments, Pontificia Universidad Javeriana, Bogotá, Colombia; Memory and Cognition Center Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia; Trinity College Dublin (TCD), Dublin, Ireland.
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64
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Lamontagne SJ, Duda JM, Madarasmi S, Rogers VA, Yu E, Pizzagalli DA, Schroder HS. Limited impacts of biogenetic messaging on neural correlates of cognitive control and beliefs about depression. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2023; 23:383-399. [PMID: 36869258 PMCID: PMC9984246 DOI: 10.3758/s13415-023-01073-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/26/2023] [Indexed: 03/05/2023]
Abstract
During the past 60 years, perceptions about the origins of mental illness have shifted toward a biomedical model, depicting depression as a biological disorder caused by genetic abnormalities and/or chemical imbalances. Despite benevolent intentions to reduce stigma, biogenetic messages promote prognostic pessimism, reduce feelings of agency, and alter treatment preferences, motivations, and expectations. However, no research has examined how these messages influence neural markers of ruminative activity or decision-making, a gap this study sought to fill. In this pre-registered, clinical trial (NCT03998748), 49 participants with current or past depressive experiences completed a sham saliva test and were randomly assigned to receive feedback that they either have (gene-present; n = 24) or do not have (gene-absent; n = 25) a genetic predisposition to depression. Before and after receiving the feedback, resting-state activity and neural correlates of cognitive control (error-related negativity [ERN] and error positivity [Pe]) were measured using high-density electroencephalogram (EEG). Participants also completed self-report measures of beliefs about the malleability and prognosis of depression and treatment motivation. Contrary to hypotheses, biogenetic feedback did not alter perceptions or beliefs about depression, nor did it alter EEG markers of self-directed rumination nor neurophysiological correlates of cognitive control. Explanations of these null findings are discussed in the context of prior studies.
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Affiliation(s)
| | | | | | | | - Esther Yu
- Harvard University, Cambridge, MA, USA
| | | | - Hans S Schroder
- Department of Psychiatry, University of Michigan Medical School, 4250 Plymouth Road, Office 1752, Ann Arbor, MI, 48109, USA.
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65
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Borra D, Fantozzi S, Bisi MC, Magosso E. Modulations of Cortical Power and Connectivity in Alpha and Beta Bands during the Preparation of Reaching Movements. SENSORS (BASEL, SWITZERLAND) 2023; 23:3530. [PMID: 37050590 PMCID: PMC10099070 DOI: 10.3390/s23073530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 03/24/2023] [Accepted: 03/25/2023] [Indexed: 06/19/2023]
Abstract
Planning goal-directed movements towards different targets is at the basis of common daily activities (e.g., reaching), involving visual, visuomotor, and sensorimotor brain areas. Alpha (8-13 Hz) and beta (13-30 Hz) oscillations are modulated during movement preparation and are implicated in correct motor functioning. However, how brain regions activate and interact during reaching tasks and how brain rhythms are functionally involved in these interactions is still limitedly explored. Here, alpha and beta brain activity and connectivity during reaching preparation are investigated at EEG-source level, considering a network of task-related cortical areas. Sixty-channel EEG was recorded from 20 healthy participants during a delayed center-out reaching task and projected to the cortex to extract the activity of 8 cortical regions per hemisphere (2 occipital, 2 parietal, 3 peri-central, 1 frontal). Then, we analyzed event-related spectral perturbations and directed connectivity, computed via spectral Granger causality and summarized using graph theory centrality indices (in degree, out degree). Results suggest that alpha and beta oscillations are functionally involved in the preparation of reaching in different ways, with the former mediating the inhibition of the ipsilateral sensorimotor areas and disinhibition of visual areas, and the latter coordinating disinhibition of the contralateral sensorimotor and visuomotor areas.
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Affiliation(s)
- Davide Borra
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi” (DEI), University of Bologna, Cesena Campus, 47521 Cesena, Italy; (D.B.); (M.C.B.); (E.M.)
| | - Silvia Fantozzi
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi” (DEI), University of Bologna, Cesena Campus, 47521 Cesena, Italy; (D.B.); (M.C.B.); (E.M.)
- Interdepartmental Center for Industrial Research on Health Sciences & Technologies, University of Bologna, 40064 Bologna, Italy
| | - Maria Cristina Bisi
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi” (DEI), University of Bologna, Cesena Campus, 47521 Cesena, Italy; (D.B.); (M.C.B.); (E.M.)
- Interdepartmental Center for Industrial Research on Health Sciences & Technologies, University of Bologna, 40064 Bologna, Italy
| | - Elisa Magosso
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi” (DEI), University of Bologna, Cesena Campus, 47521 Cesena, Italy; (D.B.); (M.C.B.); (E.M.)
- Interdepartmental Center for Industrial Research on Health Sciences & Technologies, University of Bologna, 40064 Bologna, Italy
- Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, 40121 Bologna, Italy
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66
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Paz-Linares D, Gonzalez-Moreira E, Areces-Gonzalez A, Wang Y, Li M, Vega-Hernandez M, Wang Q, Bosch-Bayard J, Bringas-Vega ML, Martinez-Montes E, Valdes-Sosa MJ, Valdes-Sosa PA. Minimizing the distortions in electrophysiological source imaging of cortical oscillatory activity via Spectral Structured Sparse Bayesian Learning. Front Neurosci 2023; 17:978527. [PMID: 37008210 PMCID: PMC10050575 DOI: 10.3389/fnins.2023.978527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 02/07/2023] [Indexed: 03/17/2023] Open
Abstract
Oscillatory processes at all spatial scales and on all frequencies underpin brain function. Electrophysiological Source Imaging (ESI) is the data-driven brain imaging modality that provides the inverse solutions to the source processes of the EEG, MEG, or ECoG data. This study aimed to carry out an ESI of the source cross-spectrum while controlling common distortions of the estimates. As with all ESI-related problems under realistic settings, the main obstacle we faced is a severely ill-conditioned and high-dimensional inverse problem. Therefore, we opted for Bayesian inverse solutions that posited a priori probabilities on the source process. Indeed, rigorously specifying both the likelihoods and a priori probabilities of the problem leads to the proper Bayesian inverse problem of cross-spectral matrices. These inverse solutions are our formal definition for cross-spectral ESI (cESI), which requires a priori of the source cross-spectrum to counter the severe ill-condition and high-dimensionality of matrices. However, inverse solutions for this problem were NP-hard to tackle or approximated within iterations with bad-conditioned matrices in the standard ESI setup. We introduce cESI with a joint a priori probability upon the source cross-spectrum to avoid these problems. cESI inverse solutions are low-dimensional ones for the set of random vector instances and not random matrices. We achieved cESI inverse solutions through the variational approximations via our Spectral Structured Sparse Bayesian Learning (ssSBL) algorithm https://github.com/CCC-members/Spectral-Structured-Sparse-Bayesian-Learning. We compared low-density EEG (10-20 system) ssSBL inverse solutions with reference cESIs for two experiments: (a) high-density MEG that were used to simulate EEG and (b) high-density macaque ECoG that were recorded simultaneously with EEG. The ssSBL resulted in two orders of magnitude with less distortion than the state-of-the-art ESI methods. Our cESI toolbox, including the ssSBL method, is available at https://github.com/CCC-members/BC-VARETA_Toolbox.
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Affiliation(s)
- Deirel Paz-Linares
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
| | - Eduardo Gonzalez-Moreira
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
- Research Unit for Neurodevelopment, Institute of Neurobiology, Autonomous University of Mexico, Querétaro, Mexico
- Faculty of Electrical Engineering, Central University “Marta Abreu” of Las Villas, Santa Clara, Cuba
| | - Ariosky Areces-Gonzalez
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Faculty of Technical Sciences, University of Pinar del Río “Hermanos Saiz Montes de Oca”, Pinar del Rio, Cuba
| | - Ying Wang
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Li
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Qing Wang
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- McGill Centre for Integrative Neurosciences MCIN, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Ludmer Centre for Mental Health, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Jorge Bosch-Bayard
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- McGill Centre for Integrative Neurosciences MCIN, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Ludmer Centre for Mental Health, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Maria L. Bringas-Vega
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
| | | | - Mitchel J. Valdes-Sosa
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
| | - Pedro A. Valdes-Sosa
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
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67
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Namazifard S, Subbarao K. Multiple Dipole Source Position and Orientation Estimation Using Non-Invasive EEG-like Signals. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23052855. [PMID: 36905060 PMCID: PMC10006898 DOI: 10.3390/s23052855] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/28/2023] [Accepted: 03/01/2023] [Indexed: 05/25/2023]
Abstract
The problem of precisely estimating the position and orientation of multiple dipoles using synthetic EEG signals is considered in this paper. After determining a proper forward model, a nonlinear constrained optimization problem with regularization is solved, and the results are compared with a widely used research code, namely EEGLAB. A thorough sensitivity analysis of the estimation algorithm to the parameters (such as the number of samples and sensors) in the assumed signal measurement model is conducted. To confirm the efficacy of the proposed source identification algorithm on any category of data sets, three different kinds of data-synthetic model data, visually evoked clinical EEG data, and seizure clinical EEG data are used. Furthermore, the algorithm is tested on both the spherical head model and the realistic head model based on the MNI coordinates. The numerical results and comparisons with the EEGLAB show very good agreement, with little pre-processing required for the acquired data.
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68
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Kang J, Huang C, Perkins C, Alvarez A, Kunyansky L, Witte RS, O'Donnell M. Current Source Density Imaging Using Regularized Inversion of Acoustoelectric Signals. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:739-749. [PMID: 36260574 PMCID: PMC10081961 DOI: 10.1109/tmi.2022.3215748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Acoustoelectric (AE) imaging can potentially image biological currents at high spatial (~mm) and temporal (~ms) resolution. However, it does not directly map the current field distribution due to signal modulation by the acoustic field and electric lead fields. Here we present a new method for current source density (CSD) imaging. The fundamental AE equation is inverted using truncated singular value decomposition (TSVD) combined with Tikhonov regularization, where the optimal regularization parameter is found based on a modified L-curve criterion with TSVD. After deconvolution of acoustic fields, the current field can be directly reconstructed from lead field projections and the CSD image computed from the divergence of that field. A cube phantom model with a single dipole source was used for both simulation and bench-top phantom studies, where 2D AE signals generated by a 0.6 MHz 1.5D array transducer were recorded by orthogonal leads in a 3D Cartesian coordinate system. In simulations, the CSD reconstruction had significantly improved image quality and current source localization compared to AE images, and performance further improved as the fractional bandwidth (BW) increased. Similar results were obtained in the phantom with a time-varying current injected. Finally, a feasibility study using an in vivo swine heart model showed that optimally reconstructed CSD images better localized the current source than AE images over the cardiac cycle.
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69
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Ternisien E, Cecchin T, Colnat-Coulbois S, Maillard LG, Koessler L. Extracting the Invisible: Mesial Temporal Source Detection in Simultaneous EEG and SEEG Recordings. Brain Topogr 2023; 36:192-209. [PMID: 36732440 DOI: 10.1007/s10548-023-00940-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 01/19/2023] [Indexed: 02/04/2023]
Abstract
Epileptic source detection relies mainly on visual expertise of scalp EEG signals, but it is recognised that epileptic discharges can escape to this expertise due to a deep localization of the brain sources that induce a very low, even negative, signal to noise ratio. In this methodological study, we aimed to investigate the feasibility of extracting deep mesial temporal sources that were invisible in scalp EEG signals using blind source separation (BSS) methods (infomax ICA, extended infomax ICA, and JADE) combined with a statistical measure (kurtosis). We estimated the effect of different methodological and physiological parameters that could alter or improve the extraction. Using nine well-defined mesial epileptic networks (1949 spikes) obtained from seven patients and simultaneous EEG-SEEG recordings, the first independent component extracted from the scalp EEG signals was validated in mean from 46 to 80% according to the different parameters. The three BSS methods equally performed (no significant difference) and no influence of the number of scalp electrodes used was found. At the opposite, the number and amplitude of spikes included in the averaging before the extraction modified the performance. Anyway, despite their invisibility in scalp EEG signals, this study demonstrates that deep source extraction is feasible under certain conditions and with the use of common signal analysis toolboxes. This finding confirms the crucial need to continue the signal analysis of scalp EEG recordings which contains subcortical signals that escape to expert visual analysis but could be found by signal processing.
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Affiliation(s)
| | | | - Sophie Colnat-Coulbois
- Université de Lorraine, CNRS, CRAN, Nancy, France.,Université de Lorraine, CHRU-Nancy, Service de Neurochirurgie, Nancy, France
| | - Louis Georges Maillard
- Université de Lorraine, CNRS, CRAN, Nancy, France.,Université de Lorraine, CHRU-Nancy, Service de Neurologie, Nancy, France
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70
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Shi LJ, Li CC, Lin YC, Ding CT, Wang YP, Zhang JC. The association of magnetoencephalography high-frequency oscillations with epilepsy types and a ripple-based method with source-level connectivity for mapping epilepsy sources. CNS Neurosci Ther 2023; 29:1423-1433. [PMID: 36815318 PMCID: PMC10068465 DOI: 10.1111/cns.14115] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 01/09/2023] [Accepted: 01/25/2023] [Indexed: 02/24/2023] Open
Abstract
OBJECTIVE To explore the association between high-frequency oscillations (HFOs) and epilepsy types and to improve the accuracy of source localization. METHODS Magnetoencephalography (MEG) ripples of 63 drug-resistant epilepsy patients were detected. Ripple rates, distribution, spatial complexity, and the clustering coefficient of ripple channels were used for the preliminary classification of lateral temporal lobe epilepsy (LTLE), mesial temporal lobe epilepsy (MTLE), and nontemporal lobe epilepsy (NTLE), mainly frontal lobe epilepsy (FLE). Furthermore, the seizure site identification was improved using the Tucker LCMV method and source-level betweenness centrality. RESULTS Ripple rates were significantly higher in MTLE than in LTLE and NTLE (p < 0.05). The LTLE and MTLE were mainly distributed in the temporal lobe, followed by the parietal lobe, occipital lobe, and frontal lobe, whereas MTLE ripples were mainly distributed in the frontal lobe, then parietal lobe and occipital lobe. Nevertheless, the NTLE ripples were primarily in the frontal lobe and partially in the occipital lobe (p < 0.05). Meanwhile, the spatial complexity of NTLE was significantly higher than that of LTLE and MTLE and was lowest in MTLE (p < 0.01). However, an opposite trend was observed for the standardized clustering coefficient compared with spatial complexity (p < 0.01). Finally, the tucker algorithm showed a higher percentage of ripples at the surgical site when the betweenness centrality was added (p < 0.01). CONCLUSION This study demonstrated that HFO rates, distribution, spatial complexity, and clustering coefficient of ripple channels varied considerably among the three epilepsy types. Additionally, tucker MEG estimation combined with ripple rates based on the source-level functional connectivity is a promising approach for presurgical epilepsy evaluation.
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Affiliation(s)
- Li-Juan Shi
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Can-Cheng Li
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Yi-Cong Lin
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Brain Functional Disease and Neuromodulation of Beijing Key Laboratory, Beijing, China
| | - Cheng-Tao Ding
- Hefei Innovation Research Institute, Beihang University, Hefei, Anhui, China
| | - Yu-Ping Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Brain Functional Disease and Neuromodulation of Beijing Key Laboratory, Beijing, China
| | - Ji-Cong Zhang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China.,Hefei Innovation Research Institute, Beihang University, Hefei, Anhui, China
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71
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Wabina RS, Silpasuwanchai C. Neural stochastic differential equations network as uncertainty quantification method for EEG source localization. Biomed Phys Eng Express 2023; 9. [PMID: 36368029 DOI: 10.1088/2057-1976/aca20b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 11/11/2022] [Indexed: 11/13/2022]
Abstract
EEG source localization remains a challenging problem given the uncertain conductivity values of the volume conductor models (VCMs). As uncertain conductivities vary across people, they may considerably impact the forward and inverse solutions of the EEG, leading to an increase in localization mistakes and misdiagnoses of brain disorders. Calibration of conductivity values using uncertainty quantification (UQ) techniques is a promising approach to reduce localization errors. The widely-known UQ methods involve Bayesian approaches, which utilize prior conductivity values to derive their posterior inference and estimate their optimal calibration. However, these approaches have two significant drawbacks: solving for posterior inference is intractable, and choosing inappropriate priors may lead to increased localization mistakes. This study used the Neural Stochastic Differential equations Network (SDE-Net), a combination of dynamical systems and deep learning techniques that utilizes the Wiener process to minimize conductivity uncertainties in the VCM and improve the inverse problem. Results revealed that SDE-Net generated a lower localization error rate in the inverse problem compared to Bayesian techniques. Future studies may employ new stochastic dynamical systems-based techniques as a UQ technique to address further uncertainties in the EEG Source Localization problem. Our code can be found here:https://github.com/rrwabina/SDENet-UQ-ESL.
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Affiliation(s)
- R S Wabina
- Center for Health and Wellness Technology, Asian Institute of Technology (AIT), Khlong Luang, Pathum Thani, Thailand
| | - C Silpasuwanchai
- Center for Health and Wellness Technology, Asian Institute of Technology (AIT), Khlong Luang, Pathum Thani, Thailand
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72
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Nguyen KH, Ebbatson M, Tran Y, Craig A, Nguyen H, Chai R. Source-Space Brain Functional Connectivity Features in Electroencephalogram-Based Driver Fatigue Classification. SENSORS (BASEL, SWITZERLAND) 2023; 23:2383. [PMID: 36904587 PMCID: PMC10007183 DOI: 10.3390/s23052383] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/10/2023] [Accepted: 02/19/2023] [Indexed: 06/18/2023]
Abstract
This study examined the brain source space functional connectivity from the electroencephalogram (EEG) activity of 48 participants during a driving simulation experiment where they drove until fatigue developed. Source-space functional connectivity (FC) analysis is a state-of-the-art method for understanding connections between brain regions that may indicate psychological differences. Multi-band FC in the brain source space was constructed using the phased lag index (PLI) method and used as features to train an SVM classification model to classify driver fatigue and alert conditions. With a subset of critical connections in the beta band, a classification accuracy of 93% was achieved. Additionally, the source-space FC feature extractor demonstrated superiority over other methods, such as PSD and sensor-space FC, in classifying fatigue. The results suggested that source-space FC is a discriminative biomarker for detecting driving fatigue.
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Affiliation(s)
- Khanh Ha Nguyen
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia
| | - Matthew Ebbatson
- School of Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia
| | - Yvonne Tran
- Department of Linguistics, Macquarie University Hearing, Macquarie University, Sydney, NSW 2109, Australia
| | - Ashley Craig
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
- John Walsh Centre for Rehabilitation Research, Kolling Institute, Northern Sydney Local Health District, St Leonards, Sydney, NSW 2065, Australia
| | - Hung Nguyen
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia
| | - Rifai Chai
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia
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73
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Ziogas A, Habermeyer E, Santtila P, Poeppl TB, Mokros A. Neuroelectric Correlates of Human Sexuality: A Review and Meta-Analysis. ARCHIVES OF SEXUAL BEHAVIOR 2023; 52:497-596. [PMID: 32016814 DOI: 10.1007/s10508-019-01547-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 07/17/2019] [Accepted: 09/04/2019] [Indexed: 05/15/2023]
Abstract
Many reviews on sexual arousal in humans focus on different brain imaging methods and behavioral observations. Although neurotransmission in the brain is mainly performed through electrochemical signals, there are no systematic reviews of the electrophysiological correlates of sexual arousal. We performed a systematic search on this subject and reviewed 255 studies including various electrophysiological methods. Our results show how neuroelectric signals have been used to investigate genital somatotopy as well as basic genital physiology during sexual arousal and how cortical electric signals have been recorded during orgasm. Moreover, experiments on the interactions of cognition and sexual arousal in healthy subjects and in individuals with abnormal sexual preferences were analyzed as well as case studies on sexual disturbances associated with diseases of the nervous system. In addition, 25 studies focusing on brain potentials during the interaction of cognition and sexual arousal were eligible for meta-analysis. The results showed significant effect sizes for specific brain potentials during sexual stimulation (P3: Cohen's d = 1.82, N = 300, LPP: Cohen's d = 2.30, N = 510) with high heterogeneity between the combined studies. Taken together, our review shows how neuroelectric methods can consistently differentiate sexual arousal from other emotional states.
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Affiliation(s)
- Anastasios Ziogas
- Department of Forensic Psychiatry, University Hospital of Psychiatry Zurich, Alleestrasse 61A, 8462, Rheinau, Switzerland.
| | - Elmar Habermeyer
- Department of Forensic Psychiatry, University Hospital of Psychiatry Zurich, Zurich, Switzerland
| | - Pekka Santtila
- Department of Arts & Sciences, New York University-Shanghai, Shanghai, China
| | - Timm B Poeppl
- Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, RWTH Aachen University, Aachen, Germany
| | - Andreas Mokros
- Department of Forensic Psychiatry, University Hospital of Psychiatry Zurich, Zurich, Switzerland
- Faculty of Psychology, Fern Universität in Hagen (University of Hagen), Hagen, Germany
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74
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Gross J, Junghöfer M, Wolters C. Bioelectromagnetism in Human Brain Research: New Applications, New Questions. Neuroscientist 2023; 29:62-77. [PMID: 34873945 PMCID: PMC9902961 DOI: 10.1177/10738584211054742] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Bioelectromagnetism has contributed some of the most commonly used techniques to human neuroscience such as magnetoencephalography (MEG), electroencephalography (EEG), transcranial magnetic stimulation (TMS), and transcranial electric stimulation (TES). The considerable differences in their technical design and practical use give rise to the impression that these are quite different techniques altogether. Here, we review, discuss and illustrate the fundamental principle of Helmholtz reciprocity that provides a common ground for all four techniques. We show that, more than 150 years after its discovery by Helmholtz in 1853, reciprocity is important to appreciate the strengths and limitations of these four classical tools in neuroscience. We build this case by explaining the concept of Helmholtz reciprocity, presenting a methodological account of this principle for all four methods and, finally, by illustrating its application in practical clinical studies.
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Affiliation(s)
- Joachim Gross
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany,Joachim Gross, Institute for Biomagnetism and Biosignalanalysis, University of Münster, Malmedyweg 15, Münster, 48149, Germany.
| | - Markus Junghöfer
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
| | - Carsten Wolters
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
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75
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Applying correlation analysis to electrode optimization in source domain. Med Biol Eng Comput 2023; 61:1225-1238. [PMID: 36719563 DOI: 10.1007/s11517-023-02770-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 12/30/2022] [Indexed: 02/01/2023]
Abstract
In brain computer interface-based neurorehabilitation system, a large number of electrodes may increase the difficulty of signal acquisition and the time consumption of decoding algorithm for motor imagery EEG (MI-EEG). The traditional electrode optimization methods were limited by the low spatial resolution of scalp EEG. EEG source imaging (ESI) was further applied to reduce the number of electrodes, in which either the electrodes covering activated cortical areas were selected, or the reconstructed electrodes of EEGs with higher Fisher scores were retained. However, the activated dipoles do not all contribute equally to decoding, and the Fisher score cannot represent the correlations between electrodes and dipoles. In this paper, based on ESI and correlation analysis, a novel electrode optimization method, denoted ECCEO, was developed. The scalp MI-EEG was mapped to cortical regions by ESI, and the dipoles with larger amplitudes were chosen to designate a region of interest (ROI). Then, Pearson correlation coefficients between each dipole of the ROI and the corresponding electrode were calculated, averaged, and ranked to obtain two average correlation coefficient sequences. A small but important group of electrodes for each class were alternately added to the predetermined basic electrode set to form a candidate electrode set. Their features were extracted and evaluated to determine the optimal electrode set. Experiments were conducted on two public datasets, the average decoding accuracies achieved 95.99% and 88.30%, and the reduction of computational cost were 65% and 56%, respectively; statistical significance was examined as well.
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76
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Marchesotti S, Bernasconi F, Rognini G, De Lucia M, Bleuler H, Blanke O. Neural signatures of visuo-motor integration during human-robot interactions. Front Neurorobot 2023; 16:1034615. [PMID: 36776553 PMCID: PMC9908758 DOI: 10.3389/fnbot.2022.1034615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 11/23/2022] [Indexed: 01/28/2023] Open
Abstract
Visuo-motor integration shapes our daily experience and underpins the sense of feeling in control over our actions. The last decade has seen a surge in robotically and virtually mediated interactions, whereby bodily actions ultimately result in an artificial movement. But despite the growing number of applications, the neurophysiological correlates of visuo-motor processing during human-machine interactions under dynamic conditions remain scarce. Here we address this issue by employing a bimanual robotic interface able to track voluntary hands movement, rendered in real-time into the motion of two virtual hands. We experimentally manipulated the visual feedback in the virtual reality with spatial and temporal conflicts and investigated their impact on (1) visuo-motor integration and (2) the subjective experience of being the author of one's action (i.e., sense of agency). Using somatosensory evoked responses measured with electroencephalography, we investigated neural differences occurring when the integration between motor commands and visual feedback is disrupted. Our results show that the right posterior parietal cortex encodes for differences between congruent and spatially-incongruent interactions. The experimental manipulations also induced a decrease in the sense of agency over the robotically-mediated actions. These findings offer solid neurophysiological grounds that can be used in the future to monitor integration mechanisms during movements and ultimately enhance subjective experience during human-machine interactions.
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Affiliation(s)
- Silvia Marchesotti
- Laboratory of Cognitive Neuroscience, Center for Neuroprosthetics and Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland,Laboratory of Robotic Systems, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland,*Correspondence: Silvia Marchesotti
| | - Fosco Bernasconi
- Laboratory of Cognitive Neuroscience, Center for Neuroprosthetics and Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Giulio Rognini
- Laboratory of Cognitive Neuroscience, Center for Neuroprosthetics and Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland,Laboratory of Robotic Systems, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Marzia De Lucia
- Laboratoire de Recherche en Neuroimagerie, University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Hannes Bleuler
- Laboratory of Robotic Systems, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Olaf Blanke
- Laboratory of Cognitive Neuroscience, Center for Neuroprosthetics and Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland,Department of Clinical Neurosciences, Faculty of Medicine, University Hospital, Geneva, Switzerland,Olaf Blanke
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77
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McFadden J. Consciousness: Matter or EMF? Front Hum Neurosci 2023; 16:1024934. [PMID: 36741784 PMCID: PMC9889563 DOI: 10.3389/fnhum.2022.1024934] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 12/28/2022] [Indexed: 01/20/2023] Open
Abstract
Conventional theories of consciousness (ToCs) that assume that the substrate of consciousness is the brain's neuronal matter fail to account for fundamental features of consciousness, such as the binding problem. Field ToC's propose that the substrate of consciousness is the brain's best accounted by some kind of field in the brain. Electromagnetic (EM) ToCs propose that the conscious field is the brain's well-known EM field. EM-ToCs were first proposed only around 20 years ago primarily to account for the experimental discovery that synchronous neuronal firing was the strongest neural correlate of consciousness (NCC). Although EM-ToCs are gaining increasing support, they remain controversial and are often ignored by neurobiologists and philosophers and passed over in most published reviews of consciousness. In this review I examine EM-ToCs against established criteria for distinguishing between ToCs and demonstrate that they outperform all conventional ToCs and provide novel insights into the nature of consciousness as well as a feasible route toward building artificial consciousnesses.
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78
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Eggert E, Ghin F, Stock AK, Mückschel M, Beste C. The role of visual association cortices during response selection processes in interference-modulated response stopping. Cereb Cortex Commun 2023; 4:tgac050. [PMID: 36654911 PMCID: PMC9837466 DOI: 10.1093/texcom/tgac050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 12/27/2022] [Accepted: 12/28/2022] [Indexed: 01/11/2023] Open
Abstract
Response inhibition and the ability to navigate distracting information are both integral parts of cognitive control and are imperative to adaptive behavior in everyday life. Thus far, research has only inconclusively been able to draw inferences regarding the association between response stopping and the effects of interfering information. Using a novel combination of the Simon task and a stop signal task, the current study set out to investigate the behavioral as well as the neurophysiological underpinnings of the relationship between response stopping and interference processing. We tested n = 27 healthy individuals and combined temporal EEG signal decomposition with source localization methods to delineate the precise neurophysiological dynamics and functional neuroanatomical structures associated with conflict effects on response stopping. The results showed that stopping performance was compromised by conflicts. Importantly, these behavioral effects were reflected by specific aspects of information coded in the neurophysiological signal, indicating that conflict effects during response stopping are not mediated via purely perceptual processes. Rather, it is the processing of specific, stop-relevant stimulus features in the sensory regions during response selection, which underlies the emergence of conflict effects in response stopping. The findings connect research regarding response stopping with overarching theoretical frameworks of perception-action integration.
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Affiliation(s)
| | - Filippo Ghin
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Fetscherstrasse 74, 01309 Dresden, Germany,Faculty of Medicine, University Neuropsychology Center, TU Dresden, Fetscherstrasse 74, 01309 Dresden, Germany
| | - Ann-Kathrin Stock
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Fetscherstrasse 74, 01309 Dresden, Germany,Faculty of Medicine, University Neuropsychology Center, TU Dresden, Fetscherstrasse 74, 01309 Dresden, Germany
| | | | - Christian Beste
- Corresponding author: Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Fetscherstrasse 74, Dresden 01307, Germany.
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79
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Lupenko S, Butsiy R, Shakhovska N. Advanced Modeling and Signal Processing Methods in Brain-Computer Interfaces Based on a Vector of Cyclic Rhythmically Connected Random Processes. SENSORS (BASEL, SWITZERLAND) 2023; 23:760. [PMID: 36679557 PMCID: PMC9866141 DOI: 10.3390/s23020760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/04/2023] [Accepted: 01/06/2023] [Indexed: 06/17/2023]
Abstract
In this study is substantiated the new mathematical model of vector of electroencephalographic signals, registered under the conditions of multiple repetitions of the mental control influences of brain-computer interface operator, in the form of a vector of cyclic rhythmically connected random processes, which, due to taking into account the stochasticity and cyclicity, the variability and commonality of the rhythm of the investigated signals have a number of advantages over the known models. This new model opens the way for the study of multidimensional distribution functions; initial, central, and mixed moment functions of higher order such as for each electroencephalographic signal separately; as well as for their respective compatible probabilistic characteristics, among which the most informative characteristics can be selected. This provides an increase in accuracy in the detection (classification) of mental control influences of the brain-computer interface operators. Based on the developed mathematical model, the statistical processing methods of vector of electroencephalographic signals are substantiated, which consist of statistical evaluation of its probabilistic characteristics and make it possible to conduct an effective joint statistical estimation of the probability characteristics of electroencephalographic signals. This provides the basis for coordinated integration of information from different sensors. The use of moment functions of higher order and their spectral images in the frequency domain, as informative characteristics in brain-computer interface systems, are substantiated. Their significant sensitivity to the mental controlling influence of the brain-computer interface operator is experimentally established. The application of Bessel's inequality to the problems of reducing the dimensions (from 500 to 20 numbers) of the vectors of informative features makes it possible to significantly reduce the computational complexity of the algorithms for the functioning of brain-computer interface systems. Namely, we experimentally established that only the first 20 values of the Fourier transform of the estimation of moment functions of higher-order electroencephalographic signals are sufficient to form the vector of informative features in brain-computer interface systems, because these spectral components make up at least 95% of the total energy of the corresponding statistical estimate of the moment functions of higher-order electroencephalographic signals.
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Affiliation(s)
- Serhii Lupenko
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
- Institute of Telecommunications and Global Information Space, National Academy of Sciences of Ukraine, 02000 Kyiv, Ukraine
| | - Roman Butsiy
- Institute of Telecommunications and Global Information Space, National Academy of Sciences of Ukraine, 02000 Kyiv, Ukraine
| | - Nataliya Shakhovska
- Institute of Computer Sciences and Information Technologies, Lviv Polytechnic National University, 79000 Lviv, Ukraine
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80
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Yarici MC, Thornton M, Mandic DP. Ear-EEG sensitivity modeling for neural sources and ocular artifacts. Front Neurosci 2023; 16:997377. [PMID: 36699519 PMCID: PMC9868963 DOI: 10.3389/fnins.2022.997377] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 12/09/2022] [Indexed: 01/12/2023] Open
Abstract
The ear-EEG has emerged as a promising candidate for real-world wearable brain monitoring. While experimental studies have validated several applications of ear-EEG, the source-sensor relationship for neural sources from across the brain surface has not yet been established. In addition, modeling of the ear-EEG sensitivity to sources of artifacts is still missing. Through volume conductor modeling, the sensitivity of various configurations of ear-EEG is established for a range of neural sources, in addition to ocular artifact sources for the blink, vertical saccade, and horizontal saccade eye movements. Results conclusively support the introduction of ear-EEG into conventional EEG paradigms for monitoring neural activity that originates from within the temporal lobes, while also revealing the extent to which ear-EEG can be used for sources further away from these regions. The use of ear-EEG in scenarios prone to ocular artifacts is also supported, through the demonstration of proportional scaling of artifacts and neural signals in various configurations of ear-EEG. The results from this study can be used to support both existing and prospective experimental ear-EEG studies and applications in the context of sensitivity to both neural sources and ocular artifacts.
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81
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Thio BJ, Aberra AS, Dessert GE, Grill WM. Ideal current dipoles are appropriate source representations for simulating neurons for intracranial recordings. Clin Neurophysiol 2023; 145:26-35. [PMID: 36403433 PMCID: PMC9772254 DOI: 10.1016/j.clinph.2022.11.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 09/20/2022] [Accepted: 11/01/2022] [Indexed: 11/10/2022]
Abstract
OBJECTIVE To determine whether dipoles are an appropriate simplified representation of neural sources for stereo-EEG (sEEG). METHODS We compared the distributions of voltages generated by a dipole, biophysically realistic cortical neuron models, and extended regions of cortex to determine how well a dipole represented neural sources at different spatial scales and at electrode to neuron distances relevant for sEEG. We also quantified errors introduced by the dipole approximation of neural sources in sEEG source localization using standardized low-resolution electrotomography (sLORETA). RESULTS For pyramidal neurons, the coefficient of correlation between voltages generated by a dipole and neuron model were > 0.9 for distances > 1 mm. For small regions of cortex (∼0.1 cm2), the error in voltages between a dipole and region was < 100 µV for all distances. However, larger regions of active cortex (>5 cm2) yielded > 50 µV errors within 1.5 cm of an electrode when compared to single dipoles. Finally, source localization errors were < 5 mm when using dipoles to represent realistic neural sources. CONCLUSIONS Single dipoles are an appropriate source model to represent both single neurons and small regions of active cortex, while multiple dipoles are required to represent large regions of cortex. SIGNIFICANCE Dipoles are computationally tractable and valid source models for sEEG.
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Affiliation(s)
- Brandon J Thio
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Aman S Aberra
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Grace E Dessert
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Durham, NC, United States; Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States; Department of Neurobiology, Duke University, Durham, NC, United States; Department of Neurosurgery, Duke University, Durham, NC, United States.
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82
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Kumar A, Lyzhko E, Hamid L, Srivastav A, Stephani U, Japaridze N. Neuronal networks underlying ictal and subclinical discharges in childhood absence epilepsy. J Neurol 2023; 270:1402-1415. [PMID: 36370186 PMCID: PMC9971098 DOI: 10.1007/s00415-022-11462-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 10/25/2022] [Accepted: 10/26/2022] [Indexed: 11/13/2022]
Abstract
Childhood absence epilepsy (CAE), involves 3 Hz generalized spikes and waves discharges (GSWDs) on the electroencephalogram (EEG), associated with ictal discharges (seizures) with clinical symptoms and impairment of consciousness and subclinical discharges without any objective clinical symptoms or impairment of consciousness. This study aims to comparatively characterize neuronal networks underlying absence seizures and subclinical discharges, using source localization and functional connectivity (FC), to better understand the pathophysiological mechanism of these discharges. Routine EEG data from 12 CAE patients, consisting of 45 ictal and 42 subclinical discharges were selected. Source localization was performed using the exact low-resolution electromagnetic tomography (eLORETA) algorithm, followed by FC based on the imaginary part of coherency. FC based on the thalamus as the seed of interest showed significant differences between ictal and subclinical GSWDs (p < 0.05). For delta (1-3 Hz) and alpha bands (8-12 Hz), the thalamus displayed stronger connectivity towards other brain regions for ictal GSWDs as compared to subclinical GSWDs. For delta band, the thalamus was strongly connected to the posterior cingulate cortex (PCC), precuneus, angular gyrus, supramarginal gyrus, parietal superior, and occipital mid-region for ictal GSWDs. The strong connections of the thalamus with other brain regions that are important for consciousness, and with components of the default mode network (DMN) suggest the severe impairment of consciousness in ictal GSWDs. However, for subclinical discharges, weaker connectivity between the thalamus and these brain regions may suggest the prevention of impairment of consciousness. This may benefit future therapeutic targets and improve the management of CAE patients.
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Affiliation(s)
- Ami Kumar
- Department of Neuropediatrics, Children's Hospital, University Medical Center Schleswig-Holstein, University of Kiel, Kiel, Germany. .,Faculty of Mathematics and Natural Sciences, University of Kiel, Kiel, Germany. .,Department of Neurology, Columbia University Irving Medical Center, New York, USA.
| | - Ekaterina Lyzhko
- Department of Neuropediatrics, Children’s Hospital, University Medical Center Schleswig-Holstein, University of Kiel, Kiel, Germany
| | - Laith Hamid
- Institute of Medical Psychology and Medical Sociology, University of Kiel, Kiel, Germany ,Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Anand Srivastav
- Faculty of Mathematics and Natural Sciences, University of Kiel, Kiel, Germany
| | - Ulrich Stephani
- Department of Neuropediatrics, Children’s Hospital, University Medical Center Schleswig-Holstein, University of Kiel, Kiel, Germany
| | - Natia Japaridze
- Department of Neuropediatrics, Children’s Hospital, University Medical Center Schleswig-Holstein, University of Kiel, Kiel, Germany
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83
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Gomes BA, Plaska CR, Ortega J, Ellmore TM. A simultaneous EEG-fMRI study of thalamic load-dependent working memory delay period activity. Front Behav Neurosci 2023; 17:1132061. [PMID: 36910125 PMCID: PMC9997713 DOI: 10.3389/fnbeh.2023.1132061] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 02/10/2023] [Indexed: 02/25/2023] Open
Abstract
Introduction Working memory (WM) is an essential component of executive functions which depend on maintaining task-related information online for brief periods in both the presence and absence of interfering stimuli. Active maintenance occurs during the WM delay period, the time between stimulus encoding and subsequent retrieval. Previous studies have extensively documented prefrontal and posterior parietal cortex activity during the WM delay period, but the role of subcortical structures including the thalamus remains to be fully elucidated, especially in humans. Methods Using a simultaneous electroencephalogram (EEG)-functional magnetic resonance imaging (fMRI) approach, we investigated the role of the thalamus during the WM delay period in a modified Sternberg paradigm following low and high memory load encoding of naturalistic scenes. During the delay, participants passively viewed scrambled scenes containing similar color and spatial frequency to serve as a perceptual baseline. Individual source estimation was weighted by the location of the thalamic fMRI signal relative to the WM delay period onset. Results The effects memory load on maintenance were observed bilaterally in thalamus with higher EEG source amplitudes in the low compared to high load condition occurring 160-390 ms after the onset of the delay period. Conclusion The main finding that thalamic activation was elevated during the low compared to high condition despite similar duration of perceptual input and upcoming motor requirements suggests a capacity-limited role for sensory filtering of the thalamus during consolidation of stimuli into WM, where the highest activity occurs when fewer stimuli need to be maintained in the presence of interfering perceptual stimuli during the delay. The results are discussed in the context of theories regarding the role of the thalamus in sensory gating during working memory.
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Affiliation(s)
- Bernard A Gomes
- Program in Cognitive Neuroscience, The Graduate Center of the City University of New York, New York, NY, United States
| | - Chelsea Reichert Plaska
- Doctoral Program in Behavioral and Cognitive Neuroscience, The Graduate Center of the City University of New York, New York, NY, United States
| | - Jefferson Ortega
- Department of Psychology, The City College of the City University of New York, New York, NY, United States
| | - Timothy M Ellmore
- Program in Cognitive Neuroscience, The Graduate Center of the City University of New York, New York, NY, United States.,Doctoral Program in Behavioral and Cognitive Neuroscience, The Graduate Center of the City University of New York, New York, NY, United States.,Department of Psychology, The City College of the City University of New York, New York, NY, United States
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84
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Bao Z, Frewen P. Sense of self in mind and body: an eLORETA-EEG study. Neurosci Conscious 2022; 2022:niac017. [PMID: 36530551 PMCID: PMC9748806 DOI: 10.1093/nc/niac017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 10/06/2022] [Accepted: 11/03/2022] [Indexed: 12/23/2022] Open
Abstract
The human brain engages the sense of self through both semantic and somatic self-referential processing (SRP). Alpha and theta oscillations have been found to underlie SRP but have not been compared with respect to semantic and somatic SRP. We recorded electroencephalography (EEG) from 50 participants during focused internal attention on life roles (e.g. "friend") and outer body (e.g. "arms") compared to resting state and an external attention memory task and localized the sources of on-scalp alpha (8-12 Hz) and theta (4-8 Hz) EEG signals with exact low-resolution tomography. Logarithm of F-ratios was calculated to compare differences in alpha and theta power between SRP conditions, resting state, and external attention. Results indicated that compared to resting state, semantic SRP induced lower theta in the frontal cortex and higher theta in the parietal cortex, whereas somatic SRP induced lower alpha in the frontal and insula cortex and higher alpha in the parietal cortex. Furthermore, results indicated that compared to external attention, both semantic and somatic SRP induced higher alpha in the dorsolateral prefrontal cortex with lateralized patterns based on task condition. Finally, an analysis directly comparing semantic and somatic SRP indicated frontal-parietal and left-right lateralization of SRP in the brain. Our results suggest the alpha and theta oscillations in the frontal, parietal, and the insula cortex may play crucial roles in semantic and somatic SRP.
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Affiliation(s)
| | - Paul Frewen
- *Correspondence address. Department of Psychiatry, Schulich School of Medicine and Dentistry, 339 Windermere Rd., London, ON N6A 5A5, Canada. Tel: +519 685 8500 E-mail:
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85
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Accurate Emotion Recognition Utilizing Extracted EEG Sources as Graph Neural Network Nodes. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10077-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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86
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Fogarty JS, Barry RJ, Steiner-Lim GZ. Auditory equiprobable NoGo P3: A single-trial latency-adjusted ERP analysis. Int J Psychophysiol 2022; 182:90-104. [DOI: 10.1016/j.ijpsycho.2022.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 10/01/2022] [Accepted: 10/04/2022] [Indexed: 11/07/2022]
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87
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Cortical electrical activity changes in healthy aging using EEG-eLORETA analysis. NEUROIMAGE: REPORTS 2022. [DOI: 10.1016/j.ynirp.2022.100143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
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88
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González-López M, Gonzalez-Moreira E, Areces-González A, Paz-Linares D, Fernández T. Who's driving? The default mode network in healthy elderly individuals at risk of cognitive decline. Front Neurol 2022; 13:1009574. [DOI: 10.3389/fneur.2022.1009574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 11/08/2022] [Indexed: 12/02/2022] Open
Abstract
IntroductionAge is the main risk factor for the development of neurocognitive disorders, with Alzheimer's disease being the most common. Its physiopathological features may develop decades before the onset of clinical symptoms. Quantitative electroencephalography (qEEG) is a promising and cost-effective tool for the prediction of cognitive decline in healthy older individuals that exhibit an excess of theta activity. The aim of the present study was to evaluate the feasibility of brain connectivity variable resolution electromagnetic tomography (BC-VARETA), a novel source localization algorithm, as a potential tool to assess brain connectivity with 19-channel recordings, which are common in clinical practice.MethodsWe explored differences in terms of functional connectivity among the nodes of the default mode network between two groups of healthy older participants, one of which exhibited an EEG marker of risk for cognitive decline.ResultsThe risk group exhibited increased levels of delta, theta, and beta functional connectivity among nodes of the default mode network, as well as reversed directionality patterns of connectivity among nodes in every frequency band when compared to the control group.DiscussionWe propose that an ongoing pathological process may be underway in healthy elderly individuals with excess theta activity in their EEGs, which is further evidenced by changes in their connectivity patterns. BC-VARETA implemented on 19-channels EEG recordings appears to be a promising tool to detect dysfunctions at the connectivity level in clinical settings.
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89
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Conti M, Stefani A, Bovenzi R, Cerroni R, Garasto E, Placidi F, Liguori C, Schirinzi T, Mercuri NB, Pierantozzi M. STN-DBS Induces Acute Changes in β-Band Cortical Functional Connectivity in Patients with Parkinson's Disease. Brain Sci 2022; 12:brainsci12121606. [PMID: 36552066 PMCID: PMC9775160 DOI: 10.3390/brainsci12121606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 11/18/2022] [Accepted: 11/22/2022] [Indexed: 11/25/2022] Open
Abstract
Subthalamic nucleus deep-brain stimulation (STN-DBS), in addition to a rapid improvement of Parkinson's disease (PD) motor symptoms, can exert fast, local, neuromodulator activity, reducing β-synchronous oscillations between STN and the motor cortex with possible antikinetic features. However, STN-DBS modulation of β-band synchronization in extramotor cortical areas has been scarcely explored. For this aim, we investigated DBS-induced short-term effects on EEG-based cortical functional connectivity (FC) in β bands in six PD patients who underwent STN-DBS within the past year. A 10 min, 64-channel EEG recording was performed twice: in DBS-OFF and 60 min after DBS activation. Seven age-matched controls performed EEG recordings as the control group. A source-reconstruction method was used to identify brain-region activity. The FC was calculated using a weighted phase-lag index in β bands. Group comparisons were made using the Wilcoxon test. The PD patients showed a widespread cortical hyperconnectivity in β bands in both DBS-OFF and -ON states compared to the controls. Moreover, switching on STN-DBS determined an acute reduction in β FC, primarily involving corticocortical links of frontal, sensorimotor and limbic lobes. We hypothesize that an increase in β-band connectivity in PD is a widespread cortical phenomenon and that STN-DBS could quickly reduce it in the cortical regions primarily involved in basal ganglia-cortical circuits.
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Affiliation(s)
- Matteo Conti
- Parkinson Centre, Department of Systems Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy
| | - Alessandro Stefani
- Parkinson Centre, Department of Systems Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy
- Correspondence:
| | - Roberta Bovenzi
- Parkinson Centre, Department of Systems Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy
| | - Rocco Cerroni
- Parkinson Centre, Department of Systems Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy
| | - Elena Garasto
- Parkinson Centre, Department of Systems Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy
| | - Fabio Placidi
- Neurology Unit, Department of Systems Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy
| | - Claudio Liguori
- Neurology Unit, Department of Systems Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy
| | - Tommaso Schirinzi
- Parkinson Centre, Department of Systems Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy
| | - Nicola B. Mercuri
- Neurology Unit, Department of Systems Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy
| | - Mariangela Pierantozzi
- Parkinson Centre, Department of Systems Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy
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90
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Sorrentino P, Petkoski S, Sparaco M, Troisi Lopez E, Signoriello E, Baselice F, Bonavita S, Pirozzi MA, Quarantelli M, Sorrentino G, Jirsa V. Whole-Brain Propagation Delays in Multiple Sclerosis, a Combined Tractography-Magnetoencephalography Study. J Neurosci 2022; 42:8807-8816. [PMID: 36241383 PMCID: PMC9698668 DOI: 10.1523/jneurosci.0938-22.2022] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 09/13/2022] [Accepted: 09/19/2022] [Indexed: 12/29/2022] Open
Abstract
Two structurally connected brain regions are more likely to interact, with the lengths of the structural bundles, their widths, myelination, and the topology of the structural connectome influencing the timing of the interactions. We introduce an in vivo approach for measuring functional delays across the whole brain in humans (of either sex) using magneto/electroencephalography (MEG/EEG) and integrating them with the structural bundles. The resulting topochronic map of the functional delays/velocities shows that larger bundles have faster velocities. We estimated the topochronic map in multiple sclerosis patients, who have damaged myelin sheaths, and controls, demonstrating greater delays in patients across the network and that structurally lesioned tracts were slowed down more than unaffected ones. We provide a novel framework for estimating functional transmission delays in vivo at the single-subject and single-tract level.SIGNIFICANCE STATEMENT This article provides a straightforward way to estimate patient-specific delays and conduction velocities in the CNS, at the individual level, in healthy and diseased subjects. To do so, it uses a principled way to merge magnetoencephalography (MEG)/electroencephalography (EEG) and tractography.
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Affiliation(s)
- P Sorrentino
- Institut de Neurosciences des Systèmes, Aix-Marseille Université, 13005 Marseille, France
- Institute of Applied Sciences and Intelligent Systems, National Research Council, 80078 Pozzuoli, Italy
| | - S Petkoski
- Institut de Neurosciences des Systèmes, Aix-Marseille Université, 13005 Marseille, France
| | - M Sparaco
- Department of Advanced Medical and Surgical Sciences, University of Campania Luigi Vanvitelli, 81100 Caserta, Italy
| | - E Troisi Lopez
- Department of Motor Sciences and Wellness, Parthenope University of Naples, 80133 Naples, Italy
| | - E Signoriello
- Department of Advanced Medical and Surgical Sciences, University of Campania Luigi Vanvitelli, 81100 Caserta, Italy
| | - F Baselice
- Department of Engineering, Parthenope University of Naples, 80143 Naples, Italy
| | - S Bonavita
- Department of Advanced Medical and Surgical Sciences, University of Campania Luigi Vanvitelli, 81100 Caserta, Italy
| | - M A Pirozzi
- Biostructure and Bioimaging Institute, National Research Council, 80145 Naples, Italy
| | - M Quarantelli
- Biostructure and Bioimaging Institute, National Research Council, 80145 Naples, Italy
| | - G Sorrentino
- Department of Motor Sciences and Wellness, Parthenope University of Naples, 80133 Naples, Italy
- Institute for Diagnosis and Cure Hermitage Capodimonte, 80131 Naples, Italy
| | - V Jirsa
- Institut de Neurosciences des Systèmes, Aix-Marseille Université, 13005 Marseille, France
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91
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EEG Network Analysis in Epilepsy with Generalized Tonic–Clonic Seizures Alone. Brain Sci 2022; 12:brainsci12111574. [DOI: 10.3390/brainsci12111574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 11/14/2022] [Accepted: 11/15/2022] [Indexed: 11/22/2022] Open
Abstract
Many contradictory theories regarding epileptogenesis in idiopathic generalized epilepsy have been proposed. This study aims to define the network that takes part in the formation of the spike-wave discharges in patients with generalized tonic–clonic seizures alone (GTCSa) and elucidate the network characteristics. Furthermore, we intend to define the most influential brain areas and clarify the connectivity pattern among them. The data were collected from 23 patients with GTCSa utilizing low-density electroencephalogram (EEG). The source localization of generalized spike-wave discharges (GSWDs) was conducted using the Standardized low-resolution brain electromagnetic tomography (sLORETA) methodology. Cortical connectivity was calculated utilizing the imaginary part of coherence. The network characteristics were investigated through small-world propensity and the integrated value of influence (IVI). Source localization analysis estimated that most sources of GSWDs were in the superior frontal gyrus and anterior cingulate. Graph theory analysis revealed that epileptic sources created a network that tended to be regularized during generalized spike-wave activity. The IVI analysis concluded that the most influential nodes were the left insular gyrus and the left inferior parietal gyrus at 3 and 4 Hz, respectively. In conclusion, some nodes acted mainly as generators of GSWDs and others as influential ones across the whole network.
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92
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Droby A, Nosatzki S, Edry Y, Thaler A, Giladi N, Mirelman A, Maidan I. The interplay between structural and functional connectivity in early stage Parkinson's disease patients. J Neurol Sci 2022; 442:120452. [PMID: 36265263 DOI: 10.1016/j.jns.2022.120452] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 08/21/2022] [Accepted: 10/04/2022] [Indexed: 10/31/2022]
Abstract
The mechanisms underlying cognitive disturbances in Parkinson's disease (PD) are poorly understood but likely to depend on the ongoing degenerative processes affecting structural and functional connectivity (FC). This pilot study examined patterns of FC alterations during a cognitive task using EEG and structural characteristics of white matter (WM) pathways connecting these activated regions in early-stage PD. Eleven PD patients and nine healthy controls (HCs) underwent EEG recording during an auditory oddball task and MRI scans. Source localization was performed and Gaussian mixture model was fitted to identify brain regions with high power during task performance. These areas served as seed regions for connectivity analysis. FC among these regions was assessed by measures of magnitude squared coherence (MSC), and phase-locking value (PLV), while structural connectivity was evaluated using fiber tracking based on diffusion tensor imaging (DTI). The paracentral lobule (PL), superior parietal lobule (SPL), superior and middle frontal gyrus (SMFG), parahippocampal gyrus, superior and middle temporal gyri (STG, MTG) demonstrated increased activation during task performance. Compared to HCs, PD showed lower FC between SMFG and PL and between SMFG and SPL in MSC (p = 0.012 and p = 0.036 respectively). No significant differences between the groups were observed in PLV and the measured DTI metrics along WM tracts. These findings demonstrate that in early PD, cognitive performance changes might be attributed to FC alterations, suggesting that FC is affected early on in the degenerative process, whereas structural damage is more prominent in advanced stages as a result of the disease burden accumulation.
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Affiliation(s)
- Amgad Droby
- Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Department of Neurology, Sackler School of Medicine, Tel Aviv University, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
| | - Shai Nosatzki
- Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Israel
| | - Yariv Edry
- Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Israel
| | - Avner Thaler
- Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Department of Neurology, Sackler School of Medicine, Tel Aviv University, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Nir Giladi
- Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Department of Neurology, Sackler School of Medicine, Tel Aviv University, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Anat Mirelman
- Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Department of Neurology, Sackler School of Medicine, Tel Aviv University, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Inbal Maidan
- Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Department of Neurology, Sackler School of Medicine, Tel Aviv University, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
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93
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Ignatiadis K, Barumerli R, Tóth B, Baumgartner R. Effects of individualized brain anatomies and EEG electrode positions on inferred activity of the primary auditory cortex. Front Neuroinform 2022; 16:970372. [DOI: 10.3389/fninf.2022.970372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 09/05/2022] [Indexed: 11/13/2022] Open
Abstract
Due to its high temporal resolution and non-invasive nature, electroencephalography (EEG) is considered a method of great value for the field of auditory cognitive neuroscience. In performing source space analyses, localization accuracy poses a bottleneck, which precise forward models based on individualized attributes such as subject anatomy or electrode locations aim to overcome. Yet acquiring anatomical images or localizing EEG electrodes requires significant additional funds and processing time, making it an oftentimes inaccessible asset. Neuroscientific software offers template solutions, on which analyses can be based. For localizing the source of auditory evoked responses, we here compared the results of employing such template anatomies and electrode positions versus the subject-specific ones, as well as combinations of the two. All considered cases represented approaches commonly used in electrophysiological studies. We considered differences between two commonly used inverse solutions (dSPM, sLORETA) and targeted the primary auditory cortex; a notoriously small cortical region that is located within the lateral sulcus, thus particularly prone to errors in localization. Through systematical comparison of early evoked component metrics and spatial leakage, we assessed how the individualization steps impacted the analyses outcomes. Both electrode locations as well as subject anatomies were found to have an effect, which though varied based on the configuration considered. When comparing the inverse solutions, we moreover found that dSPM more consistently benefited from individualization of subject morphologies compared to sLORETA, suggesting it to be the better choice for auditory cortex localization.
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94
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A ventral stream-prefrontal cortex processing cascade enables working memory gating dynamics. Commun Biol 2022; 5:1086. [PMID: 36224253 PMCID: PMC9556714 DOI: 10.1038/s42003-022-04048-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 09/29/2022] [Indexed: 11/09/2022] Open
Abstract
The representation of incoming information, goals and the flexible processing of these are required for cognitive control. Efficient mechanisms are needed to decide when it is important that novel information enters working memory (WM) and when these WM 'gates' have to be closed. Compared to neural foundations of maintaining information in WM, considerably less is known about what neural mechanisms underlie the representational dynamics during WM gating. Using different EEG analysis methods, we trace the path of mental representations along the human cortex during WM gate opening and closing. We show temporally nested representational dynamics during WM gate opening and closing depending on multiple independent neural activity profiles. These activity profiles are attributable to a ventral stream-prefrontal cortex processing cascade. The representational dynamics start in the ventral stream during WM gate opening and WM gate closing before prefrontal cortical regions are modulated. A regional specific activity profile is shown within the prefrontal cortex depending on whether WM gates are opened or closed, matching overarching concepts of prefrontal cortex functions. The study closes an essential conceptual gap detailing the neural dynamics underlying how mental representations drive the WM gate to open or close to enable WM functions such as updating and maintenance.
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95
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Wang H, Zhang Y, Cheng H, Yan F, Song D, Wang Q, Cai S, Wang Y, Huang L. Selective corticocortical connectivity suppression during propofol-induced anesthesia in healthy volunteers. Cogn Neurodyn 2022; 16:1029-1043. [PMID: 36237410 PMCID: PMC9508318 DOI: 10.1007/s11571-021-09775-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 11/17/2021] [Accepted: 12/13/2021] [Indexed: 11/03/2022] Open
Abstract
We comprehensively studied directional feedback and feedforward connectivity to explore potential connectivity changes that underlie propofol-induced deep sedation. We further investigated the corticocortical connectivity patterns within and between hemispheres. Sixty-channel electroencephalographic data were collected from 19 healthy volunteers in a resting wakefulness state and propofol-induced deep unconsciousness state defined by a bispectral index value of 40. A source analysis was employed to locate cortical activity. The Desikan-Killiany atlas was used to partition cortices, and directional functional connectivity was assessed by normalized symbolic transfer entropy between higher-order (prefrontal and frontal) and lower-order (auditory, sensorimotor and visual) cortices and between hot-spot frontal and parietal cortices. We found that propofol significantly suppressed feedforward connectivity from the left parietal to right frontal cortex and bidirectional connectivity between the left frontal and left parietal cortex, between the frontal and auditory cortex, and between the frontal and sensorimotor cortex. However, there were no significant changes in either feedforward or feedback connectivity between the prefrontal and all the lower-order cortices and between the frontal and visual cortices or in feedback connectivity from the frontal to parietal cortex. Propofol anesthetic selectively decreased the unidirectional interaction between higher-order frontoparietal cortices and bidirectional interactions between the higher-order frontal cortex and lower-order auditory and sensorimotor cortices, which indicated that both feedback and feedforward connectivity were suppressed under propofol-induced deep sedation. Our findings provide critical insights into the connectivity changes underlying the top-down mechanism of propofol anesthesia at deep sedation. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-021-09775-x.
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Affiliation(s)
- Haidong Wang
- School of Life Science and Technology, Xidian University, No. 2 South Taibai Road, Xi’an, 710071 China
| | - Yun Zhang
- School of Life Science and Technology, Xidian University, No. 2 South Taibai Road, Xi’an, 710071 China
| | - Huanhuan Cheng
- School of Life Science and Technology, Xidian University, No. 2 South Taibai Road, Xi’an, 710071 China
| | - Fei Yan
- Department of Anesthesiology & Center for Brain Science, The First Affiliated Hospital of Xi’an Jiaotong University, No. 277 West Yanta Road, Xi’an, 710061 China
| | - Dawei Song
- Department of Anesthesiology & Center for Brain Science, The First Affiliated Hospital of Xi’an Jiaotong University, No. 277 West Yanta Road, Xi’an, 710061 China
| | - Qiang Wang
- Department of Anesthesiology & Center for Brain Science, The First Affiliated Hospital of Xi’an Jiaotong University, No. 277 West Yanta Road, Xi’an, 710061 China
| | - Suping Cai
- School of Life Science and Technology, Xidian University, No. 2 South Taibai Road, Xi’an, 710071 China
| | - Yubo Wang
- School of Life Science and Technology, Xidian University, No. 2 South Taibai Road, Xi’an, 710071 China
| | - Liyu Huang
- School of Life Science and Technology, Xidian University, No. 2 South Taibai Road, Xi’an, 710071 China
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96
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Singh J, Ebersole JS, Brinkmann BH. From theory to practical fundamentals of electroencephalographic source imaging in localizing the epileptogenic zone. Epilepsia 2022; 63:2476-2490. [PMID: 35811476 PMCID: PMC9796417 DOI: 10.1111/epi.17361] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 07/07/2022] [Accepted: 07/07/2022] [Indexed: 01/01/2023]
Abstract
With continued advancement in computational technologies, the analysis of electroencephalography (EEG) has shifted from pure visual analysis to a noninvasive computational technique called EEG source imaging (ESI), which involves mathematical modeling of dipolar and distributed sources of a given scalp EEG pattern. ESI is a noninvasive phase I test for presurgical localization of the seizure onset zone in focal epilepsy. It is a relatively inexpensive modality, as it leverages scalp EEG and magnetic resonance imaging (MRI) data already collected typically during presurgical evaluation. With an adequate number of electrodes and combined with patient-specific MRI-based head models, ESI has proven to be a valuable and accurate clinical diagnostic tool for localizing the epileptogenic zone. Despite its advantages, however, ESI is routinely used at only a minority of epilepsy centers. This paper reviews the current evidence and practical fundamentals for using ESI of interictal and ictal epileptic activity during the presurgical evaluation of drug-resistant patients. We identify common errors in processing and interpreting ESI studies, describe the differences in approach needed for localizing interictal and ictal EEG discharges through practical examples, and describe best practices for optimizing the diagnostic information available from these studies.
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Affiliation(s)
- Jaysingh Singh
- Department of NeurologyThe Ohio State University Wexner Medical CenterColumbusOhioUSA
| | - John S. Ebersole
- Northeast Regional Epilepsy GroupAtlantic Health Neuroscience InstituteSummitNew JerseyUSA
| | - Benjamin H. Brinkmann
- Department of NeurologyMayo ClinicRochesterMinnesotaUSA,Department of Biomedical EngineeringMayo ClinicRochesterMinnesotaUSA
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97
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Patient-specific solution of the electrocorticography forward problem in deforming brain. Neuroimage 2022; 263:119649. [PMID: 36167268 DOI: 10.1016/j.neuroimage.2022.119649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 08/25/2022] [Accepted: 09/23/2022] [Indexed: 11/22/2022] Open
Abstract
Invasive intracranial electroencephalography (iEEG), or electrocorticography (ECoG), measures electric potential directly on the surface of the brain and can be used to inform treatment planning for epilepsy surgery. Combined with numerical modeling it can further improve accuracy of epilepsy surgery planning. Accurate solution of the iEEG forward problem, which is a crucial prerequisite for solving the iEEG inverse problemin epilepsy seizure onset zone localization, requires accurate representation of the patient's brain geometry and tissue electrical conductivity after implantation of electrodes. However, implantation of subdural grid electrodes causes the brain to deform, which invalidates preoperatively acquired image data. Moreover, postoperative magnetic resonance imaging (MRI) is incompatible with implanted electrodes and computed tomography (CT) has insufficient range of soft tissue contrast, which precludes both MRI and CT from being used to obtain the deformed postoperative geometry. In this paper, we present a biomechanics-based image warping procedure using preoperative MRI for tissue classification and postoperative CT for locating implanted electrodes to perform non-rigid registration of the preoperative image data to the postoperative configuration. We solve the iEEG forward problem on the predicted postoperative geometry using the finite element method (FEM) which accounts for patient-specific inhomogeneity and anisotropy of tissue conductivity. Results for the simulation of a current source in the brain show large differences in electric potential predicted by the models based on the original images and the deformed images corresponding to the brain geometry deformed by placement of invasive electrodes. Computation of the lead field matrix (useful for solution of the iEEG inverse problem) also showed significant differences between the different models. The results suggest that rapid and accurate solution of the forward problem in a deformed brain for a given patient is achievable.
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98
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Cross-Modal Reorganization From Both Visual and Somatosensory Modalities in Cochlear Implanted Children and Its Relationship to Speech Perception. Otol Neurotol 2022; 43:e872-e879. [PMID: 35970165 DOI: 10.1097/mao.0000000000003619] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
HYPOTHESIS We hypothesized that children with cochlear implants (CIs) who demonstrate cross-modal reorganization by vision also demonstrate cross-modal reorganization by somatosensation and that these processes are interrelated and impact speech perception. BACKGROUND Cross-modal reorganization, which occurs when a deprived sensory modality's cortical resources are recruited by other intact modalities, has been proposed as a source of variability underlying speech perception in deaf children with CIs. Visual and somatosensory cross-modal reorganization of auditory cortex have been documented separately in CI children, but reorganization in these modalities has not been documented within the same subjects. Our goal was to examine the relationship between cross-modal reorganization from both visual and somatosensory modalities within a single group of CI children. METHODS We analyzed high-density electroencephalogram responses to visual and somatosensory stimuli and current density reconstruction of brain activity sources. Speech perception in noise testing was performed. Current density reconstruction patterns were analyzed within the entire subject group and across groups of CI children exhibiting good versus poor speech perception. RESULTS Positive correlations between visual and somatosensory cross-modal reorganization suggested that neuroplasticity in different sensory systems may be interrelated. Furthermore, CI children with good speech perception did not show recruitment of frontal or auditory cortices during visual processing, unlike CI children with poor speech perception. CONCLUSION Our results reflect changes in cortical resource allocation in pediatric CI users. Cross-modal recruitment of auditory and frontal cortices by vision, and cross-modal reorganization of auditory cortex by somatosensation, may underlie variability in speech and language outcomes in CI children.
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99
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Herrera B, Westerberg JA, Schall MS, Maier A, Woodman GF, Schall JD, Riera JJ. Resolving the mesoscopic missing link: Biophysical modeling of EEG from cortical columns in primates. Neuroimage 2022; 263:119593. [PMID: 36031184 PMCID: PMC9968827 DOI: 10.1016/j.neuroimage.2022.119593] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 08/16/2022] [Accepted: 08/24/2022] [Indexed: 10/31/2022] Open
Abstract
Event-related potentials (ERP) are among the most widely measured indices for studying human cognition. While their timing and magnitude provide valuable insights, their usefulness is limited by our understanding of their neural generators at the circuit level. Inverse source localization offers insights into such generators, but their solutions are not unique. To address this problem, scientists have assumed the source space generating such signals comprises a set of discrete equivalent current dipoles, representing the activity of small cortical regions. Based on this notion, theoretical studies have employed forward modeling of scalp potentials to understand how changes in circuit-level dynamics translate into macroscopic ERPs. However, experimental validation is lacking because it requires in vivo measurements of intracranial brain sources. Laminar local field potentials (LFP) offer a mechanism for estimating intracranial current sources. Yet, a theoretical link between LFPs and intracranial brain sources is missing. Here, we present a forward modeling approach for estimating mesoscopic intracranial brain sources from LFPs and predict their contribution to macroscopic ERPs. We evaluate the accuracy of this LFP-based representation of brain sources utilizing synthetic laminar neurophysiological measurements and then demonstrate the power of the approach in vivo to clarify the source of a representative cognitive ERP component. To that end, LFP was measured across the cortical layers of visual area V4 in macaque monkeys performing an attention demanding task. We show that area V4 generates dipoles through layer-specific transsynaptic currents that biophysically recapitulate the ERP component through the detailed forward modeling. The constraints imposed on EEG production by this method also revealed an important dissociation between computational and biophysical contributors. As such, this approach represents an important bridge between laminar microcircuitry, through the mesoscopic activity of cortical columns to the patterns of EEG we measure at the scalp.
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Affiliation(s)
- Beatriz Herrera
- Department of Biomedical Engineering, Florida International University, Miami, FL 33174, United States
| | - Jacob A. Westerberg
- Department of Psychology, Vanderbilt Brain Institute, Vanderbilt Vision Research Center, Vanderbilt University, 111 21st Avenue South, 301 Wilson Hall, Nashville, TN 37240, United States,Corresponding author. (J.A. Westerberg)
| | - Michelle S. Schall
- Department of Psychology, Vanderbilt Brain Institute, Vanderbilt Vision Research Center, Vanderbilt University, 111 21st Avenue South, 301 Wilson Hall, Nashville, TN 37240, United States
| | - Alexander Maier
- Department of Psychology, Vanderbilt Brain Institute, Vanderbilt Vision Research Center, Vanderbilt University, 111 21st Avenue South, 301 Wilson Hall, Nashville, TN 37240, United States
| | - Geoffrey F. Woodman
- Department of Psychology, Vanderbilt Brain Institute, Vanderbilt Vision Research Center, Vanderbilt University, 111 21st Avenue South, 301 Wilson Hall, Nashville, TN 37240, United States
| | - Jeffrey D. Schall
- Centre for Vision Research, Departments of Biology and Psychology, Vision: Science to Applications Program, York University, Toronto, ON M3J 1P3, Canada
| | - Jorge J. Riera
- Department of Biomedical Engineering, Florida International University, Miami, FL 33174, United States
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100
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Herreras O, Torres D, Martín-Vázquez G, Hernández-Recio S, López-Madrona VJ, Benito N, Makarov VA, Makarova J. Site-dependent shaping of field potential waveforms. Cereb Cortex 2022; 33:3636-3650. [PMID: 35972425 PMCID: PMC10068269 DOI: 10.1093/cercor/bhac297] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 07/07/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
The activity of neuron populations gives rise to field potentials (FPs) that extend beyond the sources. Their mixing in the volume dilutes the original temporal motifs in a site-dependent manner, a fact that has received little attention. And yet, it potentially rids of physiological significance the time-frequency parameters of individual waves (amplitude, phase, duration). This is most likely to happen when a single source or a local origin is erroneously assumed. Recent studies using spatial treatment of these signals and anatomically realistic modeling of neuron aggregates provide convincing evidence for the multisource origin and site-dependent blend of FPs. Thus, FPs generated in primary structures like the neocortex and hippocampus reach far and cross-contaminate each other but also, they add and even impose their temporal traits on distant regions. Furthermore, both structures house neurons that act as spatially distinct (but overlapped) FP sources whose activation is state, region, and time dependent, making the composition of so-called local FPs highly volatile and strongly site dependent. Since the spatial reach cannot be predicted without source geometry, it is important to assess whether waveforms and temporal motifs arise from a single source; otherwise, those from each of the co-active sources should be sought.
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Affiliation(s)
- Oscar Herreras
- Department of Translational Neuroscience, Cajal Institute, CSIC, Av. Doctor Arce 37, Madrid 28002, Spain
| | - Daniel Torres
- Department of Translational Neuroscience, Cajal Institute, CSIC, Av. Doctor Arce 37, Madrid 28002, Spain
| | - Gonzalo Martín-Vázquez
- Department of Translational Neuroscience, Cajal Institute, CSIC, Av. Doctor Arce 37, Madrid 28002, Spain
| | - Sara Hernández-Recio
- Department of Translational Neuroscience, Cajal Institute, CSIC, Av. Doctor Arce 37, Madrid 28002, Spain
| | - Víctor J López-Madrona
- Department of Translational Neuroscience, Cajal Institute, CSIC, Av. Doctor Arce 37, Madrid 28002, Spain
| | - Nuria Benito
- Department of Translational Neuroscience, Cajal Institute, CSIC, Av. Doctor Arce 37, Madrid 28002, Spain
| | - Valeri A Makarov
- Department of Applied Mathematics, Institute for Interdisciplinary Mathematics, Universidad Complutense of Madrid, Av. Paraninfo s/n, Madrid 28040, Spain
| | - Julia Makarova
- Department of Translational Neuroscience, Cajal Institute, CSIC, Av. Doctor Arce 37, Madrid 28002, Spain.,Department of Applied Mathematics, Institute for Interdisciplinary Mathematics, Universidad Complutense of Madrid, Av. Paraninfo s/n, Madrid 28040, Spain
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