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Bonetti L, Brattico E, Carlomagno F, Cabral J, Stevner A, Deco G, Whybrow PC, Pearce M, Pantazis D, Vuust P, Kringelbach ML. Spatiotemporal whole-brain activity and functional connectivity of melodies recognition. Cereb Cortex 2024; 34:bhae320. [PMID: 39110413 PMCID: PMC11304985 DOI: 10.1093/cercor/bhae320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 07/12/2024] [Accepted: 07/26/2024] [Indexed: 08/10/2024] Open
Abstract
Music is a non-verbal human language, built on logical, hierarchical structures, that offers excellent opportunities to explore how the brain processes complex spatiotemporal auditory sequences. Using the high temporal resolution of magnetoencephalography, we investigated the unfolding brain dynamics of 70 participants during the recognition of previously memorized musical sequences compared to novel sequences matched in terms of entropy and information content. Measures of both whole-brain activity and functional connectivity revealed a widespread brain network underlying the recognition of the memorized auditory sequences, which comprised primary auditory cortex, superior temporal gyrus, insula, frontal operculum, cingulate gyrus, orbitofrontal cortex, basal ganglia, thalamus, and hippocampus. Furthermore, while the auditory cortex responded mainly to the first tones of the sequences, the activity of higher-order brain areas such as the cingulate gyrus, frontal operculum, hippocampus, and orbitofrontal cortex largely increased over time during the recognition of the memorized versus novel musical sequences. In conclusion, using a wide range of analytical techniques spanning from decoding to functional connectivity and building on previous works, our study provided new insights into the spatiotemporal whole-brain mechanisms for conscious recognition of auditory sequences.
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Affiliation(s)
- Leonardo Bonetti
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, 8000 Aarhus/Aalborg, Denmark
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, OX39BX Oxford, United Kingdom
- Department of Psychiatry, University of Oxford, OX37JX Oxford, United Kingdom
| | - Elvira Brattico
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, 8000 Aarhus/Aalborg, Denmark
- Department of Education, Psychology, Communication, University of Bari Aldo Moro, 70121 Bari, Italy
| | - Francesco Carlomagno
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, 8000 Aarhus/Aalborg, Denmark
| | - Joana Cabral
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, 8000 Aarhus/Aalborg, Denmark
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, OX39BX Oxford, United Kingdom
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057 Braga, Portugal
| | - Angus Stevner
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, 8000 Aarhus/Aalborg, Denmark
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, OX39BX Oxford, United Kingdom
| | - Gustavo Deco
- Computational and Theoretical Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, 08018 Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, Spain
| | - Peter C Whybrow
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, 90095 Los Angeles, CA, United States
| | - Marcus Pearce
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, 8000 Aarhus/Aalborg, Denmark
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), 02139 Cambridge, MA, United States
| | - Peter Vuust
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, 8000 Aarhus/Aalborg, Denmark
| | - Morten L Kringelbach
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, 8000 Aarhus/Aalborg, Denmark
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, OX39BX Oxford, United Kingdom
- Department of Psychiatry, University of Oxford, OX37JX Oxford, United Kingdom
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2
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Pigorini A, Avanzini P, Barborica A, Bénar CG, David O, Farisco M, Keller CJ, Manfridi A, Mikulan E, Paulk AC, Roehri N, Subramanian A, Vulliémoz S, Zelmann R. Simultaneous invasive and non-invasive recordings in humans: A novel Rosetta stone for deciphering brain activity. J Neurosci Methods 2024; 408:110160. [PMID: 38734149 DOI: 10.1016/j.jneumeth.2024.110160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 04/10/2024] [Accepted: 05/01/2024] [Indexed: 05/13/2024]
Abstract
Simultaneous noninvasive and invasive electrophysiological recordings provide a unique opportunity to achieve a comprehensive understanding of human brain activity, much like a Rosetta stone for human neuroscience. In this review we focus on the increasingly-used powerful combination of intracranial electroencephalography (iEEG) with scalp electroencephalography (EEG) or magnetoencephalography (MEG). We first provide practical insight on how to achieve these technically challenging recordings. We then provide examples from clinical research on how simultaneous recordings are advancing our understanding of epilepsy. This is followed by the illustration of how human neuroscience and methodological advances could benefit from these simultaneous recordings. We conclude with a call for open data sharing and collaboration, while ensuring neuroethical approaches and argue that only with a true collaborative approach the promises of simultaneous recordings will be fulfilled.
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Affiliation(s)
- Andrea Pigorini
- Department of Biomedical, Surgical and Dental Sciences, Università degli Studi di Milano, Milan, Italy; UOC Maxillo-facial Surgery and dentistry, Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico, Milan, Italy.
| | - Pietro Avanzini
- Institute of Neuroscience, Consiglio Nazionale delle Ricerche, Parma, Italy
| | | | - Christian-G Bénar
- Aix Marseille Univ, Inserm, U1106, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Olivier David
- Aix Marseille Univ, Inserm, U1106, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Michele Farisco
- Centre for Research Ethics and Bioethics, Department of Public Health and Caring Sciences, Uppsala University, P.O. Box 256, Uppsala, SE 751 05, Sweden; Science and Society Unit Biogem, Biology and Molecular Genetics Institute, Via Camporeale snc, Ariano Irpino, AV 83031, Italy
| | - Corey J Keller
- Department of Psychiatry & Behavioral Sciences, Stanford University Medical Center, Stanford, CA 94305, USA; Wu Tsai Neurosciences Institute, Stanford University Medical Center, Stanford, CA 94305, USA; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA 94394, USA
| | - Alfredo Manfridi
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, Milan, Italy
| | - Ezequiel Mikulan
- Department of Health Sciences, Università degli Studi di Milano, Milan, Italy
| | - Angelique C Paulk
- Department of Neurology and Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Nicolas Roehri
- EEG and Epilepsy Unit, Dpt of Clinical Neurosciences, Geneva University Hospitals and University of Geneva, Switzerland
| | - Ajay Subramanian
- Department of Psychiatry & Behavioral Sciences, Stanford University Medical Center, Stanford, CA 94305, USA; Wu Tsai Neurosciences Institute, Stanford University Medical Center, Stanford, CA 94305, USA; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA 94394, USA
| | - Serge Vulliémoz
- EEG and Epilepsy Unit, Dpt of Clinical Neurosciences, Geneva University Hospitals and University of Geneva, Switzerland
| | - Rina Zelmann
- Department of Neurology and Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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3
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Liu Y, Su H, Li C. Effect of Inverse Solutions, Connectivity Measures, and Node Sizes on EEG Source Network: A Simultaneous EEG Study. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2644-2653. [PMID: 39024075 DOI: 10.1109/tnsre.2024.3430312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Brain network provides an essential perspective for studying normal and pathological brain activities. Reconstructing the brain network in the source space becomes more needed, for example, as a target in non-invasive neuromodulation. Precise estimating source activities from the scalp EEG is still challenging because it is an ill-posed question and because of the volume conduction effect. There is no consensus on how to reconstruct the EEG source network. This study uses simultaneous scalp EEG and stereo-EEG to investigate the effect of inverse solutions, connectivity measures, and node sizes on the reconstruction of the source network. We evaluated the performance of different methods on both source activity and network. Numerical simulation was also carried out for comparison. The weighted phase-lag index (wPLI) method achieved significantly better performance on the reconstructed networks in source space than five other connectivity measures (directed transfer function (DTF), partial directed coherence (PDC), efficient effective connectivity (EEC), Pearson correlation coefficient (PCC), and amplitude envelope correlation (AEC)). There is no significant difference between the inverse solutions (standardized low-resolution brain electromagnetic tomography (sLORETA), weighted minimum norm estimate (wMNE), and linearly constrained minimum variance (LCMV) beamforming) on the reconstructed source networks. The source network based on signal phases can fit intracranial activities better than signal waveform properties or causality. Our study provides a basis for reconstructing source space networks from scalp EEG, especially for future neuromodulation research.
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Afnan J, Cai Z, Lina JM, Abdallah C, Delaire E, Avigdor T, Ros V, Hedrich T, von Ellenrieder N, Kobayashi E, Frauscher B, Gotman J, Grova C. EEG/MEG source imaging of deep brain activity within the maximum entropy on the mean framework: Simulations and validation in epilepsy. Hum Brain Mapp 2024; 45:e26720. [PMID: 38994740 PMCID: PMC11240147 DOI: 10.1002/hbm.26720] [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: 01/15/2024] [Revised: 04/16/2024] [Accepted: 05/06/2024] [Indexed: 07/13/2024] Open
Abstract
Electro/Magneto-EncephaloGraphy (EEG/MEG) source imaging (EMSI) of epileptic activity from deep generators is often challenging due to the higher sensitivity of EEG/MEG to superficial regions and to the spatial configuration of subcortical structures. We previously demonstrated the ability of the coherent Maximum Entropy on the Mean (cMEM) method to accurately localize the superficial cortical generators and their spatial extent. Here, we propose a depth-weighted adaptation of cMEM to localize deep generators more accurately. These methods were evaluated using realistic MEG/high-density EEG (HD-EEG) simulations of epileptic activity and actual MEG/HD-EEG recordings from patients with focal epilepsy. We incorporated depth-weighting within the MEM framework to compensate for its preference for superficial generators. We also included a mesh of both hippocampi, as an additional deep structure in the source model. We generated 5400 realistic simulations of interictal epileptic discharges for MEG and HD-EEG involving a wide range of spatial extents and signal-to-noise ratio (SNR) levels, before investigating EMSI on clinical HD-EEG in 16 patients and MEG in 14 patients. Clinical interictal epileptic discharges were marked by visual inspection. We applied three EMSI methods: cMEM, depth-weighted cMEM and depth-weighted minimum norm estimate (MNE). The ground truth was defined as the true simulated generator or as a drawn region based on clinical information available for patients. For deep sources, depth-weighted cMEM improved the localization when compared to cMEM and depth-weighted MNE, whereas depth-weighted cMEM did not deteriorate localization accuracy for superficial regions. For patients' data, we observed improvement in localization for deep sources, especially for the patients with mesial temporal epilepsy, for which cMEM failed to reconstruct the initial generator in the hippocampus. Depth weighting was more crucial for MEG (gradiometers) than for HD-EEG. Similar findings were found when considering depth weighting for the wavelet extension of MEM. In conclusion, depth-weighted cMEM improved the localization of deep sources without or with minimal deterioration of the localization of the superficial sources. This was demonstrated using extensive simulations with MEG and HD-EEG and clinical MEG and HD-EEG for epilepsy patients.
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Affiliation(s)
- Jawata Afnan
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada
- Integrated Program in Neuroscience, McGill University, Montréal, Québec, Canada
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Zhengchen Cai
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Jean-Marc Lina
- Physnum Team, Centre De Recherches Mathématiques, Montréal, Québec, Canada
- Electrical Engineering Department, École De Technologie Supérieure, Montréal, Québec, Canada
- Center for Advanced Research in Sleep Medicine, Sacré-Coeur Hospital, Montréal, Québec, Canada
| | - Chifaou Abdallah
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada
- Integrated Program in Neuroscience, McGill University, Montréal, Québec, Canada
- Analytical Neurophysiology Lab, Department of Neurology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Edouard Delaire
- Multimodal Functional Imaging Lab, Department of Physics and Concordia School of Health, Concordia University, Montréal, Québec, Canada
| | - Tamir Avigdor
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada
- Integrated Program in Neuroscience, McGill University, Montréal, Québec, Canada
- Analytical Neurophysiology Lab, Department of Neurology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Victoria Ros
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Tanguy Hedrich
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada
| | - Nicolas von Ellenrieder
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Eliane Kobayashi
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Analytical Neurophysiology Lab, Department of Neurology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Jean Gotman
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Christophe Grova
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Physnum Team, Centre De Recherches Mathématiques, Montréal, Québec, Canada
- Multimodal Functional Imaging Lab, Department of Physics and Concordia School of Health, Concordia University, Montréal, Québec, Canada
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5
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Herff SA, Bonetti L, Cecchetti G, Vuust P, Kringelbach ML, Rohrmeier MA. Hierarchical syntax model of music predicts theta power during music listening. Neuropsychologia 2024; 199:108905. [PMID: 38740179 DOI: 10.1016/j.neuropsychologia.2024.108905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 03/07/2024] [Accepted: 05/06/2024] [Indexed: 05/16/2024]
Abstract
Linguistic research showed that the depth of syntactic embedding is reflected in brain theta power. Here, we test whether this also extends to non-linguistic stimuli, specifically music. We used a hierarchical model of musical syntax to continuously quantify two types of expert-annotated harmonic dependencies throughout a piece of Western classical music: prolongation and preparation. Prolongations can roughly be understood as a musical analogue to linguistic coordination between constituents that share the same function (e.g., 'pizza' and 'pasta' in 'I ate pizza and pasta'). Preparation refers to the dependency between two harmonies whereby the first implies a resolution towards the second (e.g., dominant towards tonic; similar to how the adjective implies the presence of a noun in 'I like spicy … '). Source reconstructed MEG data of sixty-five participants listening to the musical piece was then analysed. We used Bayesian Mixed Effects models to predict theta envelope in the brain, using the number of open prolongation and preparation dependencies as predictors whilst controlling for audio envelope. We observed that prolongation and preparation both carry independent and distinguishable predictive value for theta band fluctuation in key linguistic areas such as the Angular, Superior Temporal, and Heschl's Gyri, or their right-lateralised homologues, with preparation showing additional predictive value for areas associated with the reward system and prediction. Musical expertise further mediated these effects in language-related brain areas. Results show that predictions of precisely formalised music-theoretical models are reflected in the brain activity of listeners which furthers our understanding of the perception and cognition of musical structure.
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Affiliation(s)
- Steffen A Herff
- Sydney Conservatorium of Music, University of Sydney, Sydney, Australia; The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, Australia; Digital and Cognitive Musicology Lab, College of Humanities, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
| | - Leonardo Bonetti
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, Aarhus/Aalborg, Denmark; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, United Kingdom; Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Gabriele Cecchetti
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, Australia; Digital and Cognitive Musicology Lab, College of Humanities, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Peter Vuust
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, Aarhus/Aalborg, Denmark
| | - Morten L Kringelbach
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, Aarhus/Aalborg, Denmark; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, United Kingdom; Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Martin A Rohrmeier
- Digital and Cognitive Musicology Lab, College of Humanities, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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Jacobs J, Klotz KA, Pizzo F, Federico P. Beyond Stereo-EEG: Is It Worth Combining Stereo-EEG With Other Diagnostic Methods? J Clin Neurophysiol 2024; 41:444-449. [PMID: 38935658 DOI: 10.1097/wnp.0000000000001086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
SUMMARY Stereo-EEG is a widely used method to improve the diagnostic precision of presurgical workup in patients with refractory epilepsy. Its ability to detect epileptic activity and identify epileptic networks largely depends on the chosen implantation strategy. Even in an ideal situation, electrodes record activity generated in <10% of the brain and contacts only record from brain tissue in their immediate proximity. In this article, the authors discuss how recording stereo-EEG simultaneously with other diagnostic methods can improve its diagnostic value in clinical and research settings. It can help overcome the limited spatial coverage of intracranial recording and better understand the sources of epileptic activity. Simultaneous scalp EEG is the most widely available method, often used to understand large epileptic networks, seizure propagation, and EEG activity occurring on the contralateral hemisphere. Simultaneous magnetoencephalography allows for more precise source localization and identification of deep sources outside the stereo-EEG coverage. Finally, simultaneous functional MRI can highlight metabolic changes following epileptic activity and help understand the widespread network changes associated with interictal activity. This overview highlights advantages and methodological challenges for all these methods. Clinical use and research applications are presented for each approach.
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Affiliation(s)
- Julia Jacobs
- University of Calgary, Calgary, Alberta, Canada
- University Medical Center Freiburg, University of Freiburg, Freiburg, Germany; and
| | | | - Francesca Pizzo
- Epileptology Department, INSERM, Aix Marseille Universite; Marseille, France
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7
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Wirsich J, Iannotti GR, Ridley B, Shamshiri EA, Sheybani L, Grouiller F, Bartolomei F, Seeck M, Lazeyras F, Ranjeva JP, Guye M, Vulliemoz S. Altered correlation of concurrently recorded EEG-fMRI connectomes in temporal lobe epilepsy. Netw Neurosci 2024; 8:466-485. [PMID: 38952816 PMCID: PMC11142634 DOI: 10.1162/netn_a_00362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 01/17/2024] [Indexed: 07/03/2024] Open
Abstract
Whole-brain functional connectivity networks (connectomes) have been characterized at different scales in humans using EEG and fMRI. Multimodal epileptic networks have also been investigated, but the relationship between EEG and fMRI defined networks on a whole-brain scale is unclear. A unified multimodal connectome description, mapping healthy and pathological networks would close this knowledge gap. Here, we characterize the spatial correlation between the EEG and fMRI connectomes in right and left temporal lobe epilepsy (rTLE/lTLE). From two centers, we acquired resting-state concurrent EEG-fMRI of 35 healthy controls and 34 TLE patients. EEG-fMRI data was projected into the Desikan brain atlas, and functional connectomes from both modalities were correlated. EEG and fMRI connectomes were moderately correlated. This correlation was increased in rTLE when compared to controls for EEG-delta/theta/alpha/beta. Conversely, multimodal correlation in lTLE was decreased in respect to controls for EEG-beta. While the alteration was global in rTLE, in lTLE it was locally linked to the default mode network. The increased multimodal correlation in rTLE and decreased correlation in lTLE suggests a modality-specific lateralized differential reorganization in TLE, which needs to be considered when comparing results from different modalities. Each modality provides distinct information, highlighting the benefit of multimodal assessment in epilepsy.
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Affiliation(s)
- Jonathan Wirsich
- EEG and Epilepsy Unit, Division of Neurology, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Giannina Rita Iannotti
- EEG and Epilepsy Unit, Division of Neurology, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Ben Ridley
- Aix-Marseille Univ, CNRS, CRMBM 7339, Marseille, France
- AP-HM CHU Timone, CEMEREM, Marseille, France
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Elhum A. Shamshiri
- EEG and Epilepsy Unit, Division of Neurology, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Laurent Sheybani
- EEG and Epilepsy Unit, Division of Neurology, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
- UCL Queen Square Institute of Neurology, Queen Square, London, UK
| | - Frédéric Grouiller
- Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland
| | - Fabrice Bartolomei
- Aix-Marseille Univ, INS, INSERM, UMR 1106, Marseille, France
- AP-HM CHU Timone, Service d’épileptologie, Marseille, France
| | - Margitta Seeck
- EEG and Epilepsy Unit, Division of Neurology, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - François Lazeyras
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Jean-Philippe Ranjeva
- Aix-Marseille Univ, CNRS, CRMBM 7339, Marseille, France
- AP-HM CHU Timone, CEMEREM, Marseille, France
| | - Maxime Guye
- Aix-Marseille Univ, CNRS, CRMBM 7339, Marseille, France
- AP-HM CHU Timone, CEMEREM, Marseille, France
| | - Serge Vulliemoz
- EEG and Epilepsy Unit, Division of Neurology, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
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8
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Satish A, Keller VG, Raza S, Fitzpatrick S, Horner AJ. Theta and alpha oscillations in human hippocampus and medial parietal cortex support the formation of location-based representations. Hippocampus 2024; 34:284-301. [PMID: 38520305 DOI: 10.1002/hipo.23605] [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: 10/18/2023] [Revised: 02/13/2024] [Accepted: 03/06/2024] [Indexed: 03/25/2024]
Abstract
Our ability to navigate in a new environment depends on learning new locations. Mental representations of locations are quickly accessible during navigation and allow us to know where we are regardless of our current viewpoint. Recent functional magnetic resonance imaging (fMRI) research using pattern classification has shown that these location-based representations emerge in the retrosplenial cortex and parahippocampal gyrus, regions theorized to be critically involved in spatial navigation. However, little is currently known about the oscillatory dynamics that support the formation of location-based representations. We used magnetoencephalogram (MEG) recordings to investigate region-specific oscillatory activity in a task where participants could form location-based representations. Participants viewed videos showing that two perceptually distinct scenes (180° apart) belonged to the same location. This "overlap" video allowed participants to bind the two distinct scenes together into a more coherent location-based representation. Participants also viewed control "non-overlap" videos where two distinct scenes from two different locations were shown, where no location-based representation could be formed. In a post-video behavioral task, participants successfully matched the two viewpoints shown in the overlap videos, but not the non-overlap videos, indicating they successfully learned the locations in the overlap condition. Comparing oscillatory activity between the overlap and non-overlap videos, we found greater theta and alpha/beta power during the overlap relative to non-overlap videos, specifically at time-points when we expected scene integration to occur. These oscillations localized to regions in the medial parietal cortex (precuneus and retrosplenial cortex) and the medial temporal lobe, including the hippocampus. Therefore, we find that theta and alpha/beta oscillations in the hippocampus and medial parietal cortex are likely involved in the formation of location-based representations.
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Affiliation(s)
- Akul Satish
- Department of Psychology, University of York, York, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | | | - Sumaiyah Raza
- Department of Psychology, University of York, York, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | | | - Aidan J Horner
- Department of Psychology, University of York, York, UK
- York Biomedical Research Institute, University of York, York, UK
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9
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Cai Z, Jiang X, Bagić A, Worrell GA, Richardson M, He B. Spontaneous HFO Sequences Reveal Propagation Pathways for Precise Delineation of Epileptogenic Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.02.592202. [PMID: 38746136 PMCID: PMC11092614 DOI: 10.1101/2024.05.02.592202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Epilepsy, a neurological disorder affecting millions worldwide, poses great challenges in precisely delineating the epileptogenic zone - the brain region generating seizures - for effective treatment. High-frequency oscillations (HFOs) are emerging as promising biomarkers; however, the clinical utility is hindered by the difficulties in distinguishing pathological HFOs from non- epileptiform activities at single electrode and single patient resolution and understanding their dynamic role in epileptic networks. Here, we introduce an HFO-sequencing approach to analyze spontaneous HFOs traversing cortical regions in 40 drug-resistant epilepsy patients. This data- driven method automatically detected over 8.9 million HFOs, pinpointing pathological HFO- networks, and unveiled intricate millisecond-scale spatiotemporal dynamics, stability, and functional connectivity of HFOs in prolonged intracranial EEG recordings. These HFO sequences demonstrated a significant improvement in localization of epileptic tissue, with an 818.47% increase in concordance with seizure-onset zone (mean error: 2.92 mm), compared to conventional benchmarks. They also accurately predicted seizure outcomes for 90% AUC based on pre-surgical information using generalized linear models. Importantly, this mapping remained reliable even with short recordings (mean standard deviation: 3.23 mm for 30-minute segments). Furthermore, HFO sequences exhibited distinct yet highly repetitive spatiotemporal patterns, characterized by pronounced synchrony and predominant inward information flow from periphery towards areas involved in propagation, suggesting a crucial role for excitation-inhibition balance in HFO initiation and progression. Together, these findings shed light on the intricate organization of epileptic network and highlight the potential of HFO-sequencing as a translational tool for improved diagnosis, surgical targeting, and ultimately, better outcomes for vulnerable patients with drug-resistant epilepsy. One Sentence Summary Pathological fast brain oscillations travel like traffic along varied routes, outlining recurrently visited neural sites emerging as critical hotspots in epilepsy network.
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10
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Guan X, Yang HJ, Zhang X, Wang N, Han H, Tang R, Hu Z, Youssef K, Vora K, Krishnam MS, Christodoulou AG, Li D, Sharif B, Dharmakumar R. Non-electrocardiogram-gated, free-breathing, off-resonance reduced, high-resolution, whole-heart myocardial T 2 * mapping at 3 T within 5 min. Magn Reson Med 2024; 91:1936-1950. [PMID: 38174593 DOI: 10.1002/mrm.29968] [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: 04/12/2023] [Revised: 11/21/2023] [Accepted: 11/26/2023] [Indexed: 01/05/2024]
Abstract
PURPOSE Widely used conventional 2D T2 * approaches that are based on breath-held, electrocardiogram (ECG)-gated, multi-gradient-echo sequences are prone to motion artifacts in the presence of incomplete breath holding or arrhythmias, which is common in cardiac patients. To address these limitations, a 3D, non-ECG-gated, free-breathing T2 * technique that enables rapid whole-heart coverage was developed and validated. METHODS A continuous random Gaussian 3D k-space sampling was implemented using a low-rank tensor framework for motion-resolved 3D T2 * imaging. This approach was tested in healthy human volunteers and in swine before and after intravenous administration of ferumoxytol. RESULTS Spatial-resolution matched T2 * images were acquired with 2-3-fold reduction in scan time using the proposed T2 * mapping approach relative to conventional T2 * mapping. Compared with the conventional approach, T2 * images acquired with the proposed method demonstrated reduced off-resonance and flow artifacts, leading to higher image quality and lower coefficient of variation in T2 *-weighted images of the myocardium of swine and humans. Mean myocardial T2 * values determined using the proposed and conventional approaches were highly correlated and showed minimal bias. CONCLUSION The proposed non-ECG-gated, free-breathing, 3D T2 * imaging approach can be performed within 5 min or less. It can overcome critical image artifacts from undesirable cardiac and respiratory motion and bulk off-resonance shifts at the heart-lung interface. The proposed approach is expected to facilitate faster and improved cardiac T2 * mapping in those with limited breath-holding capacity or arrhythmias.
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Affiliation(s)
- Xingmin Guan
- Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Hsin-Jung Yang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Xinheng Zhang
- Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, California, USA
| | - Nan Wang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Hui Han
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Richard Tang
- Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Zhehao Hu
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Khalid Youssef
- Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Keyur Vora
- Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Mayil S Krishnam
- Department of Radiology, Stanford University, Palo Alto, California, USA
| | - Anthony G Christodoulou
- Department of Bioengineering, University of California Los Angeles, Los Angeles, California, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, California, USA
| | - Behzad Sharif
- Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Rohan Dharmakumar
- Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA
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11
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Okamura A, Hashizume A, Kagawa K, Seyama G, Yoshino A, Yamawaki S, Horie N, Iida K. Magnetoencephalographic detection of synchronized epileptic activity between the hippocampus and insular cortex. Epilepsy Behav Rep 2024; 26:100669. [PMID: 38699062 PMCID: PMC11063376 DOI: 10.1016/j.ebr.2024.100669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Revised: 04/05/2024] [Accepted: 04/17/2024] [Indexed: 05/05/2024] Open
Abstract
Most magnetoencephalographic signals are derived from synchronized activity in the brain surface cortex. By contrast, the contribution of synchronized activity in the deep brain to magnetoencephalography (MEG) has remained unclear. We compared stereotactic electroencephalography (sEEG) with simultaneous MEG findings in a patient with temporal lobe epilepsy to determine the conditions under which MEG could also detect sEEG findings. The synchrony and similarity of the waves were evaluated using visual inspection and wavelet coherence. A 45-year-old woman with intractable temporal lobe epilepsy underwent sEEG and MEG simultaneously to determine the laterality and precise location of the epileptic focus. When spike-and-waves were seen in the right hippocampal head alone, no distinct spike-and-waves were observed visually in the right temporal MEG. The seizure then spread to the right insula on sEEG with a rhythmic theta frequency while synchronous activity was observed in the right temporal MEG channels. When polyspikes appeared in the right hippocampus, the right temporal MEG showed electrical activity with relatively high similarity to that of the right hippocampal head and insular cortex but less similarity to that of the right lateral temporal lobe cortex. MEG might detect epileptic activity synchronized between the hippocampus and insular cortex.
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Affiliation(s)
- Akitake Okamura
- Epilepsy Center, Hiroshima University Hospital, Hiroshima, Japan
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Akira Hashizume
- Epilepsy Center, Hiroshima University Hospital, Hiroshima, Japan
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Kota Kagawa
- Epilepsy Center, Hiroshima University Hospital, Hiroshima, Japan
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Go Seyama
- Epilepsy Center, Hiroshima University Hospital, Hiroshima, Japan
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Atsuo Yoshino
- Center for Brain, Mind and KANSEI Science Research, Hiroshima University, Hiroshima, Japan
| | - Shigeto Yamawaki
- Center for Brain, Mind and KANSEI Science Research, Hiroshima University, Hiroshima, Japan
| | - Nobutaka Horie
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Koji Iida
- Epilepsy Center, Hiroshima University Hospital, Hiroshima, Japan
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12
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López-Madrona VJ, Trébuchon A, Mindruta I, Barbeau EJ, Barborica A, Pistol C, Oane I, Alario FX, Bénar CG. Identification of Early Hippocampal Dynamics during Recognition Memory with Independent Component Analysis. eNeuro 2024; 11:ENEURO.0183-23.2023. [PMID: 38514193 PMCID: PMC10993203 DOI: 10.1523/eneuro.0183-23.2023] [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: 05/30/2023] [Revised: 11/24/2023] [Accepted: 12/11/2023] [Indexed: 03/23/2024] Open
Abstract
The hippocampus is generally considered to have relatively late involvement in recognition memory, its main electrophysiological signature being between 400 and 800 ms after stimulus onset. However, most electrophysiological studies have analyzed the hippocampus as a single responsive area, selecting only a single-site signal exhibiting the strongest effect in terms of amplitude. These classical approaches may not capture all the dynamics of this structure, hindering the contribution of other hippocampal sources that are not located in the vicinity of the selected site. We combined intracerebral electroencephalogram recordings from epileptic patients with independent component analysis during a recognition memory task involving the recognition of old and new images. We identified two sources with different responses emerging from the hippocampus: a fast one (maximal amplitude at ∼250 ms) that could not be directly identified from raw recordings and a latter one, peaking at ∼400 ms. The former component presented different amplitudes between old and new items in 6 out of 10 patients. The latter component had different delays for each condition, with a faster activation (∼290 ms after stimulus onset) for recognized items. We hypothesize that both sources represent two steps of hippocampal recognition memory, the faster reflecting the input from other structures and the latter the hippocampal internal processing. Recognized images evoking early activations would facilitate neural computation in the hippocampus, accelerating memory retrieval of complementary information. Overall, our results suggest that the hippocampal activity is composed of several sources with an early activation related to recognition memory.
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Affiliation(s)
| | - Agnès Trébuchon
- Epileptology and Cerebral Rhythmology, APHM, Timone Hospital, Marseille 13005, France
- Functional and Stereotactic Neurosurgery, APHM, Timone Hospital, Marseille 13005, France
| | - Ioana Mindruta
- Physics Department, University of Bucharest, Bucharest, Romania
| | - Emmanuel J Barbeau
- Centre de Recherche Cerveau et Cognition, Université de Toulouse, Université Paul Sabatier Toulouse, Toulouse 31052, France
- Centre National de la Recherche Scientifique, CerCo (UMR5549), Toulouse 31052, France
| | | | - Costi Pistol
- Physics Department, University of Bucharest, Bucharest, Romania
| | - Irina Oane
- Physics Department, University of Bucharest, Bucharest, Romania
| | | | - Christian G Bénar
- Inst Neurosci Syst, INS, INSERM, Aix Marseille Univ, Marseille 13005, France
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13
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Fernández-Martín R, Feys O, Juvené E, Aeby A, Urbain C, De Tiège X, Wens V. Towards the automated detection of interictal epileptiform discharges with magnetoencephalography. J Neurosci Methods 2024; 403:110052. [PMID: 38151188 DOI: 10.1016/j.jneumeth.2023.110052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/08/2023] [Accepted: 12/21/2023] [Indexed: 12/29/2023]
Abstract
BACKGROUND The analysis of clinical magnetoencephalography (MEG) in patients with epilepsy traditionally relies on visual identification of interictal epileptiform discharges (IEDs), which is time consuming and dependent on subjective criteria. NEW METHOD Here, we explore the ability of Independent Components Analysis (ICA) and Hidden Markov Modeling (HMM) to automatically detect and localize IEDs. We tested our pipelines on resting-state MEG recordings from 10 school-aged children with (multi)focal epilepsy. RESULTS In focal epilepsy patients, both pipelines successfully detected visually identified IEDs, but also revealed unidentified low-amplitude IEDs. Success was more mitigated in patients with multifocal epilepsy, as our automated pipeline missed IED activity associated with some foci-an issue that could be alleviated by post-hoc manual selection of epileptiform ICs or HMM states. COMPARISON WITH EXISTING METHODS We compared our results with visual IED detection by an experienced clinical magnetoencephalographer, getting heightened sensitivity and requiring minimal input from clinical practitioners. CONCLUSIONS IED detection based on ICA or HMM represents an efficient way to identify IED localization and timing. The development of these automatic IED detection algorithms provide a step forward in clinical MEG practice by decreasing the duration of MEG analysis and enhancing its sensitivity.
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Affiliation(s)
- Raquel Fernández-Martín
- Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et de Neuroimagerie translationnelles (LNbT), Brussels, Belgium.
| | - Odile Feys
- Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et de Neuroimagerie translationnelles (LNbT), Brussels, Belgium; Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B.), Hôpital Erasme, Department of Neurology, Brussels, Belgium
| | - Elodie Juvené
- Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B.), Department of Pediatric Neurology, Brussels, Belgium
| | - Alec Aeby
- Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B.), Department of Pediatric Neurology, Brussels, Belgium
| | - Charline Urbain
- Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et de Neuroimagerie translationnelles (LNbT), Brussels, Belgium; Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Centre for Research in Cognition and Neurosciences (CRCN), Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), Brussels, Belgium
| | - Xavier De Tiège
- Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et de Neuroimagerie translationnelles (LNbT), Brussels, Belgium; Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B.), Hôpital Erasme, Service of translational Neuroimaging, Brussels, Belgium
| | - Vincent Wens
- Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et de Neuroimagerie translationnelles (LNbT), Brussels, Belgium; Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B.), Hôpital Erasme, Service of translational Neuroimaging, Brussels, Belgium
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14
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Feys O, De Tiège X. From cryogenic to on-scalp magnetoencephalography for the evaluation of paediatric epilepsy. Dev Med Child Neurol 2024; 66:298-306. [PMID: 37421175 DOI: 10.1111/dmcn.15689] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 05/28/2023] [Accepted: 06/02/2023] [Indexed: 07/09/2023]
Abstract
Magnetoencephalography (MEG) is a neurophysiological technique based on the detection of brain magnetic fields. Whole-head MEG systems typically house a few hundred sensors requiring cryogenic cooling in a rigid one-size-fits-all (commonly adult-sized) helmet to keep a thermal insulation space. This leads to an increased brain-to-sensor distance in children, because of their smaller head circumference, and decreased signal-to-noise ratio. MEG allows detection and localization of interictal and ictal epileptiform discharges, and pathological high frequency oscillations, as a part of the presurgical assessment of children with refractory focal epilepsy, where electroencephalography is not contributive. MEG can also map the eloquent cortex before surgical resection. MEG also provides insights into the physiopathology of both generalized and focal epilepsy. On-scalp recordings based on cryogenic-free sensors have demonstrated their use in the field of childhood focal epilepsy and should become a reference technique for diagnosing epilepsy in the paediatric population. WHAT THIS PAPER ADDS: Magnetoencephalography (MEG) contributes to the diagnosis and understanding of paediatric epilepsy. On-scalp MEG recordings demonstrate some advantages over cryogenic MEG.
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Affiliation(s)
- Odile Feys
- Department of Neurology, Université libre de Bruxelles, Hôpital Universitaire de Bruxelles, Hôpital Erasme, Bruxelles, Belgium
- Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles, Université libre de Bruxelles, ULB Neuroscience Institute, Bruxelles, Belgium
| | - Xavier De Tiège
- Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles, Université libre de Bruxelles, ULB Neuroscience Institute, Bruxelles, Belgium
- Department of Translational Neuroimaging, Université libre de Bruxelles, Hôpital Universitaire de Bruxelles, Hôpital Erasme, Bruxelles, Belgium
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15
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Feys O, Ferez M, Corvilain P, Schuind S, Rikir E, Legros B, Gaspard N, Holmes N, Brookes M, Wens V, De Tiège X. On-Scalp Magnetoencephalography Based On Optically Pumped Magnetometers Can Detect Mesial Temporal Lobe Epileptiform Discharges. Ann Neurol 2024; 95:620-622. [PMID: 38050959 DOI: 10.1002/ana.26844] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 11/16/2023] [Accepted: 11/30/2023] [Indexed: 12/07/2023]
Affiliation(s)
- Odile Feys
- Department of Neurology, Hôpital Universitaire de Bruxelles (HUB), Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
- Laboratoire de Neuroimagerie et Neuroanatomie translationnelles (LN2T), Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Brussels, Belgium
| | - Maxime Ferez
- Laboratoire de Neuroimagerie et Neuroanatomie translationnelles (LN2T), Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Brussels, Belgium
| | - Pierre Corvilain
- Laboratoire de Neuroimagerie et Neuroanatomie translationnelles (LN2T), Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Brussels, Belgium
| | - Sophie Schuind
- Department of Neurosurgery, Hôpital Universitaire de Bruxelles (HUB), Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Estelle Rikir
- Department of Neurology, Hôpital Universitaire de Bruxelles (HUB), Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Benjamin Legros
- Department of Neurology, Hôpital Universitaire de Bruxelles (HUB), Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Nicolas Gaspard
- Department of Neurology, Hôpital Universitaire de Bruxelles (HUB), Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
- Department of Neurology, Yale University, New Haven, Connecticut, USA
| | - Niall Holmes
- School of Physics and Astronomy, Sir Peter Mansfield Imaging Center, University of Nottingham, Nottingham, UK
| | - Matthew Brookes
- School of Physics and Astronomy, Sir Peter Mansfield Imaging Center, University of Nottingham, Nottingham, UK
| | - Vincent Wens
- Laboratoire de Neuroimagerie et Neuroanatomie translationnelles (LN2T), Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Brussels, Belgium
- Department of Translational Neuroimaging, Hôpital Universitaire de Bruxelles (HUB), Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Xavier De Tiège
- Laboratoire de Neuroimagerie et Neuroanatomie translationnelles (LN2T), Université libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Brussels, Belgium
- Department of Translational Neuroimaging, Hôpital Universitaire de Bruxelles (HUB), Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
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16
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Medina Villalon S, Makhalova J, López-Madrona VJ, Garnier E, Badier JM, Bartolomei F, Bénar CG. Combining independent component analysis and source localization for improving spatial sampling of stereoelectroencephalography in epilepsy. Sci Rep 2024; 14:4071. [PMID: 38374380 PMCID: PMC10876572 DOI: 10.1038/s41598-024-54359-4] [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: 09/14/2023] [Accepted: 02/12/2024] [Indexed: 02/21/2024] Open
Abstract
Stereoelectroencephalography is a powerful intracerebral EEG recording method for the presurgical evaluation of epilepsy. It consists in implanting depth electrodes in the patient's brain to record electrical activity and map the epileptogenic zone, which should be resected to render the patient seizure-free. Stereoelectroencephalography has high spatial accuracy and signal-to-noise ratio but remains limited in the coverage of the explored brain regions. Thus, the implantation might provide a suboptimal sampling of epileptogenic regions. We investigate the potential of improving a suboptimal stereoelectroencephalography recording by performing source localization on stereoelectroencephalography signals. We propose combining independent component analysis, connectivity measures to identify components of interest, and distributed source modelling. This approach was tested on two patients with two implantations each, the first failing to characterize the epileptogenic zone and the second giving a better diagnosis. We demonstrate that ictal and interictal source localization performed on the first stereoelectroencephalography recordings matches the findings of the second stereo-EEG exploration. Our findings suggest that independent component analysis followed by source localization on the topographies of interest is a promising method for retrieving the epileptogenic zone in case of suboptimal implantation.
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Affiliation(s)
- Samuel Medina Villalon
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Julia Makhalova
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | | | - Elodie Garnier
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | | | - Fabrice Bartolomei
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Christian G Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.
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17
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Zhou DJ, Gumenyuk V, Taraschenko O, Grobelny BT, Stufflebeam SM, Peled N. Visualization of the Spatiotemporal Propagation of Interictal Spikes in Temporal Lobe Epilepsy: A MEG Pilot Study. Brain Topogr 2024; 37:116-125. [PMID: 37966675 DOI: 10.1007/s10548-023-01017-z] [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: 03/08/2023] [Accepted: 10/25/2023] [Indexed: 11/16/2023]
Abstract
Magnetoencephalography (MEG) is clinically used to localize interictal spikes in discrete brain areas of epilepsy patients through the equivalent current dipole (ECD) method, but does not account for the temporal dynamics of spike activity. Recent studies found that interictal spike propagation beyond the temporal lobe may be associated with worse postsurgical outcomes, but studies using whole-brain data such as in MEG remain limited. In this pilot study, we developed a tool that visualizes the spatiotemporal dynamics of interictal MEG spikes normalized to spike-free sleep activity to assess their onset and propagation patterns in patients with temporal lobe epilepsy (TLE). We extracted interictal source data containing focal epileptiform activity in awake and asleep states from seven patients whose MEG ECD clusters localized to the temporal lobe and normalized the data against spike-free sleep recordings. We calculated the normalized activity over time per cortical label, confirmed maximal activity at onset, and mapped the activity over a 10 ms interval onto each patient's brain using a custom-built Multi-Modal Visualization Tool. The onset of activity in all patients appeared near the clinically determined epileptogenic zone. By 10 ms, four of the patients had propagated source activity restricted to within the temporal lobe, and three had propagated source activity spread to extratemporal regions. Using this tool, we show that noninvasively identifying the onset and propagation of interictal spike activity in MEG can be achieved, which may help provide further insight into epileptic networks and guide surgical planning and interventions in patients with TLE.
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Affiliation(s)
- Daniel J Zhou
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA
| | - Valentina Gumenyuk
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA
| | - Olga Taraschenko
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA
| | - Bartosz T Grobelny
- Department of Neurosurgery, Saint Luke's Health System of Kansas City, Kansas City, MO, USA
| | - Steven M Stufflebeam
- MGH/HST Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Harvard Medical School, Cambridge, MA, USA
| | - Noam Peled
- MGH/HST Martinos Center for Biomedical Imaging, Charlestown, MA, USA.
- Harvard Medical School, Cambridge, MA, USA.
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18
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Ye S, Bagić A, He B. Disentanglement of Resting State Brain Networks for Localizing Epileptogenic Zone in Focal Epilepsy. Brain Topogr 2024; 37:152-168. [PMID: 38112884 PMCID: PMC10771380 DOI: 10.1007/s10548-023-01025-z] [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: 05/15/2023] [Accepted: 11/20/2023] [Indexed: 12/21/2023]
Abstract
The objective of this study is to extract pathological brain networks from interictal period of E/MEG recordings to localize epileptic foci for presurgical evaluation. We proposed here a resting state E/MEG analysis framework, to disentangle brain functional networks represented by neural oscillations. By using an Embedded Hidden Markov Model, we constructed a state space for resting state recordings consisting of brain states with different spatiotemporal patterns. Functional connectivity analysis along with graph theory was applied on the extracted brain states to quantify the network features of the extracted brain states, based on which the source location of pathological states is determined. The method is evaluated by computer simulations and our simulation results revealed the proposed framework can extract brain states with high accuracy regarding both spatial and temporal profiles. We further evaluated the framework as compared with intracranial EEG defined seizure onset zone in 10 patients with drug-resistant focal epilepsy who underwent MEG recordings and were seizure free after surgical resection. The real patient data analysis showed very good localization results using the extracted pathological brain states in 6/10 patients, with localization error of about 15 mm as compared to the seizure onset zone. We show that the pathological brain networks can be disentangled from the resting-state electromagnetic recording and could be identified based on the connectivity features. The framework can serve as a useful tool in extracting brain functional networks from noninvasive resting state electromagnetic recordings, and promises to offer an alternative to aid presurgical evaluation guiding intracranial EEG electrodes implantation.
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Affiliation(s)
- Shuai Ye
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA
| | - Anto Bagić
- Department of Neurology, University of Pittsburgh Comprehensive Epilepsy Center (UPCEC), University of Pittsburgh Medical School, Pittsburgh, PA, USA
| | - Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA.
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19
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Reisinger L, Demarchi G, Weisz N. Eavesdropping on Tinnitus Using MEG: Lessons Learned and Future Perspectives. J Assoc Res Otolaryngol 2023; 24:531-547. [PMID: 38015287 PMCID: PMC10752863 DOI: 10.1007/s10162-023-00916-z] [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: 07/26/2023] [Accepted: 11/06/2023] [Indexed: 11/29/2023] Open
Abstract
Tinnitus has been widely investigated in order to draw conclusions about the underlying causes and altered neural activity in various brain regions. Existing studies have based their work on different tinnitus frameworks, ranging from a more local perspective on the auditory cortex to the inclusion of broader networks and various approaches towards tinnitus perception and distress. Magnetoencephalography (MEG) provides a powerful tool for efficiently investigating tinnitus and aberrant neural activity both spatially and temporally. However, results are inconclusive, and studies are rarely mapped to theoretical frameworks. The purpose of this review was to firstly introduce MEG to interested researchers and secondly provide a synopsis of the current state. We divided recent tinnitus research in MEG into study designs using resting state measurements and studies implementing tone stimulation paradigms. The studies were categorized based on their theoretical foundation, and we outlined shortcomings as well as inconsistencies within the different approaches. Finally, we provided future perspectives on how to benefit more efficiently from the enormous potential of MEG. We suggested novel approaches from a theoretical, conceptual, and methodological point of view to allow future research to obtain a more comprehensive understanding of tinnitus and its underlying processes.
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Affiliation(s)
- Lisa Reisinger
- Centre for Cognitive Neuroscience and Department of Psychology, Paris-Lodron-University Salzburg, Salzburg, Austria.
| | - Gianpaolo Demarchi
- Centre for Cognitive Neuroscience and Department of Psychology, Paris-Lodron-University Salzburg, Salzburg, Austria
| | - Nathan Weisz
- Centre for Cognitive Neuroscience and Department of Psychology, Paris-Lodron-University Salzburg, Salzburg, Austria
- Neuroscience Institute, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
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20
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Badier JM, Schwartz D, Bénar CG, Kanzari K, Daligault S, Romain R, Mitryukovskiy S, Fourcault W, Josselin V, Le Prado M, Jung J, Palacios-Laloy A, Romain C, Bartolomei F, Labyt E, Bonini F. Helium Optically Pumped Magnetometers Can Detect Epileptic Abnormalities as Well as SQUIDs as Shown by Intracerebral Recordings. eNeuro 2023; 10:ENEURO.0222-23.2023. [PMID: 37932045 PMCID: PMC10748329 DOI: 10.1523/eneuro.0222-23.2023] [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: 06/27/2023] [Revised: 09/22/2023] [Accepted: 09/26/2023] [Indexed: 11/08/2023] Open
Abstract
Magnetoencephalography based on superconducting quantum interference devices (SQUIDs) has been shown to improve the diagnosis and surgical treatment decision for presurgical evaluation of drug-resistant epilepsy. Still, its use remains limited because of several constraints such as cost, fixed helmet size, and the obligation of immobility. A new generation of sensors, optically pumped magnetometers (OPMs), could overcome these limitations. In this study, we validate the ability of helium-based OPM (4He-OPM) sensors to record epileptic brain activity thanks to simultaneous recordings with intracerebral EEG [stereotactic EEG (SEEG)]. We recorded simultaneous SQUIDs-SEEG and 4He-OPM-SEEG signals in one patient during two sessions. We show that epileptic activities on intracerebral EEG can be recorded by OPMs with a better signal-to noise ratio than classical SQUIDs. The OPM sensors open new venues for the widespread application of magnetoencephalography in the management of epilepsy and other neurologic diseases and fundamental neuroscience.
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Affiliation(s)
- Jean-Michel Badier
- Institut de Neurosciences des Systèmes, Institut National de la Santé et de la Recherche Médicale, Aix Marseille Université, Marseille 13005, France
| | - Denis Schwartz
- MEG Departement, CERMEP-Imagerie du Vivant, Lyon 69003, France
| | - Christian-George Bénar
- Institut de Neurosciences des Systèmes, Institut National de la Santé et de la Recherche Médicale, Aix Marseille Université, Marseille 13005, France
| | - Khoubeib Kanzari
- Institut de Neurosciences des Systèmes, Institut National de la Santé et de la Recherche Médicale, Aix Marseille Université, Marseille 13005, France
| | | | - Rudy Romain
- CEA-LETI, MINATEC, Université Grenoble Alpes, Grenoble 38054, France
- MAG4Health, Grenoble 38000, France
| | - Sergey Mitryukovskiy
- CEA-LETI, MINATEC, Université Grenoble Alpes, Grenoble 38054, France
- MAG4Health, Grenoble 38000, France
| | - William Fourcault
- CEA-LETI, MINATEC, Université Grenoble Alpes, Grenoble 38054, France
| | - Vincent Josselin
- CEA-LETI, MINATEC, Université Grenoble Alpes, Grenoble 38054, France
| | - Matthieu Le Prado
- CEA-LETI, MINATEC, Université Grenoble Alpes, Grenoble 38054, France
- MAG4Health, Grenoble 38000, France
| | - Julien Jung
- Centre de Recherche en Neurosciences de Lyon, Unité Mixte de Recherche S1028, Centre National de la Recherche Scientifique, Hospices Civils de Lyon, Institut National de la Santé et de la Recherche Médicale, Université Lyon 1, Lyon 69002, France
| | - Augustin Palacios-Laloy
- CEA-LETI, MINATEC, Université Grenoble Alpes, Grenoble 38054, France
- MAG4Health, Grenoble 38000, France
| | - Carron Romain
- Institut de Neurosciences des Systèmes, Institut National de la Santé et de la Recherche Médicale, Aix Marseille Université, Marseille 13005, France
- Department of Functional and Stereotactic Neurosurgery, Hôpital de la Timone, Assistance Publique-Hôpitaux de Marseille, Marseille 3005, France
| | - Fabrice Bartolomei
- Institut de Neurosciences des Systèmes, Institut National de la Santé et de la Recherche Médicale, Aix Marseille Université, Marseille 13005, France
- Department of Epileptology and Cerebral Rythmology, Hôpital de la Timone, Assistance Publique-Hôpitaux de Marseille, Marseille 3005, France
| | - Etienne Labyt
- CEA-LETI, MINATEC, Université Grenoble Alpes, Grenoble 38054, France
- MAG4Health, Grenoble 38000, France
| | - Francesca Bonini
- Institut de Neurosciences des Systèmes, Institut National de la Santé et de la Recherche Médicale, Aix Marseille Université, Marseille 13005, France
- MEG Departement, CERMEP-Imagerie du Vivant, Lyon 69003, France
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21
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Ishizaki T, Maesawa S, Nakatsubo D, Yamamoto H, Torii J, Mutoh M, Natsume J, Hoshiyama M, Saito R. Connectivity alteration in thalamic nuclei and default mode network-related area in memory processes in mesial temporal lobe epilepsy using magnetoencephalography. Sci Rep 2023; 13:10632. [PMID: 37391474 PMCID: PMC10313774 DOI: 10.1038/s41598-023-37834-2] [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: 05/11/2023] [Accepted: 06/28/2023] [Indexed: 07/02/2023] Open
Abstract
This work aimed to investigate the involvement of the thalamic nuclei in mesial temporal lobe epilepsy (MTLE) and identify the influence of interictal epileptic discharges on the neural basis of memory processing by evaluating the functional connectivity (FC) between the thalamic nuclei and default mode network-related area (DMNRA) using magnetoencephalography. Preoperative datasets of nine patients with MTLE with seizure-free status after surgery and those of nine healthy controls were analyzed. The FC between the thalamic nuclei (anterior nucleus [ANT], mediodorsal nucleus [MD], intralaminar nuclei [IL]), hippocampus, and DMNRA was examined for each of the resting, pre-spike, spike, and post-spike periods in the delta to ripple bands using magnetoencephalography. The FC between the ANT, MD, hippocampus, and medial prefrontal cortex increased in the gamma to ripple bands, whereas the FC between the ANT, IL, and DMNRA decreased in the delta to beta bands, compared with that of the healthy controls at rest. Compared with the rest period, the pre-spike period had significantly decreased FC between the ANT, MD, and DMNRA in the ripple band. Different FC changes between the thalamic nuclei, hippocampus, and DMNRA of specific connections in a particular band may reflect impairment or compensation in the memory processes.
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Affiliation(s)
- Tomotaka Ishizaki
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, 65 Tsurumai, Showa, Nagoya, Aichi, 466-8550, Japan
| | - Satoshi Maesawa
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, 65 Tsurumai, Showa, Nagoya, Aichi, 466-8550, Japan.
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan.
| | - Daisuke Nakatsubo
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, 65 Tsurumai, Showa, Nagoya, Aichi, 466-8550, Japan
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
| | - Hiroyuki Yamamoto
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
- Department of Pediatrics, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Jun Torii
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, 65 Tsurumai, Showa, Nagoya, Aichi, 466-8550, Japan
| | - Manabu Mutoh
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, 65 Tsurumai, Showa, Nagoya, Aichi, 466-8550, Japan
| | - Jun Natsume
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
- Department of Pediatrics, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Minoru Hoshiyama
- Brain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan
| | - Ryuta Saito
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, 65 Tsurumai, Showa, Nagoya, Aichi, 466-8550, Japan
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22
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Bani A, Ha SM, Xiao P, Earnest T, Lee J, Sotiras A. Scalable Orthonormal Projective NMF via Diversified Stochastic Optimization. INFORMATION PROCESSING IN MEDICAL IMAGING : PROCEEDINGS OF THE ... CONFERENCE 2023; 13939:497-508. [PMID: 37969113 PMCID: PMC10642358 DOI: 10.1007/978-3-031-34048-2_38] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2023]
Abstract
The increasing availability of large-scale neuroimaging initiatives opens exciting opportunities for discovery science of human brain structure and function. Data-driven techniques, such as Orthonormal Projective Non-negative Matrix Factorization (opNMF), are well positioned to explore multivariate relationships in big data towards uncovering brain organization. opNMF enjoys advantageous interpretability and reproducibility compared to commonly used matrix factorization methods like Principal Component Analysis (PCA) and Independent Component Analysis (ICA), which led to its wide adoption in clinical computational neuroscience. However, applying opNMF in large-scale cohort studies is hindered by its limited scalability caused by its accompanying computational complexity. In this work, we address the computational challenges of opNMF using a stochastic optimization approach that learns over mini-batches of the data. Additionally, we diversify the stochastic batches via repulsive point processes, which reduce redundancy in the mini-batches and in turn lead to lower variance in the updates. We validated our framework on gray matter tissue density maps estimated from 1000 subjects part of the Open Access Series of Imaging (OASIS) dataset. We demonstrated that operations over mini-batches of data yield significant reduction in computational cost. Importantly, we showed that our novel optimization does not compromise the accuracy or interpretability of factors when compared to standard opNMF. The proposed model enables new investigations of brain structure using big neuroimaging data that could improve our understanding of brain structure in health and disease.
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Affiliation(s)
- Abdalla Bani
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
| | - Sung Min Ha
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
| | - Pan Xiao
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
| | - Thomas Earnest
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
| | - John Lee
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
- Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, MO 63108, USA
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23
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Zhou W, Sun J, Han B, Ren J, Li Y. Test and Analysis of High-Permeability Material's Microstructure in Magnetic Shielding Device. MATERIALS (BASEL, SWITZERLAND) 2023; 16:ma16113956. [PMID: 37297090 DOI: 10.3390/ma16113956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/19/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023]
Abstract
The magnetic shielding device is used to provide an extreme weak magnetic field, which plays a key role in variety of fields. Since the high-permeability material constituting the magnetic shielding device determines the magnetic shielding performance, it is important to evaluate the property of the high-permeability material. In this paper, the relationship between the microstructure and the magnetic properties of the high-permeability material is analyzed using minimum free energy principle based on magnetic domain theory, and the test method of the material's microstructure including the material composition, the texture and the grain structure to reflect the magnetic properties is put forward. The test result shows that the grain structure is closely related to the initial permeability and the coercivity, which is highly consistent with the theory. As a result, it provides a more efficient way to evaluate the property of the high-permeability material. The test method proposed in the paper has important significance in the high efficiency sampling inspection of the high-permeability material.
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Affiliation(s)
- Weiyong Zhou
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
- Hangzhou Innovation Institute, Beihang University, Hangzhou 310051, China
- Zhejiang Provincial Key Laboratory of Ultra-Weak Magnetic-Field Space and Applied Technology, Hangzhou 310051, China
| | - Jinji Sun
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
- Hangzhou Innovation Institute, Beihang University, Hangzhou 310051, China
- Zhejiang Provincial Key Laboratory of Ultra-Weak Magnetic-Field Space and Applied Technology, Hangzhou 310051, China
- Hefei National Laboratory, Hefei 230088, China
| | - Bangcheng Han
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
- Hangzhou Innovation Institute, Beihang University, Hangzhou 310051, China
- Zhejiang Provincial Key Laboratory of Ultra-Weak Magnetic-Field Space and Applied Technology, Hangzhou 310051, China
- Hefei National Laboratory, Hefei 230088, China
| | - Jianyi Ren
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
| | - Yifei Li
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
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24
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Boon LI, Hillebrand A, Schoonheim MM, Twisk JW, Stam CJ, Berendse HW. Cortical and Subcortical Changes in MEG Activity Reflect Parkinson's Progression over a Period of 7 Years. Brain Topogr 2023:10.1007/s10548-023-00965-w. [PMID: 37154884 DOI: 10.1007/s10548-023-00965-w] [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: 04/15/2023] [Indexed: 05/10/2023]
Abstract
In this study of early functional changes in Parkinson's disease (PD), we aimed to provide a comprehensive assessment of the development of changes in both cortical and subcortical neurophysiological brain activity, including their association with clinical measures of disease severity. Repeated resting-state MEG recordings and clinical assessments were obtained in the context of a unique longitudinal cohort study over a seven-year period using a multiple longitudinal design. We used linear mixed-models to analyze the relationship between neurophysiological (spectral power and functional connectivity) and clinical data. At baseline, early-stage (drug-naïve) PD patients demonstrated spectral slowing compared to healthy controls in both subcortical and cortical brain regions, most outspoken in the latter. Over time, spectral slowing progressed in strong association with clinical measures of disease progression (cognitive and motor). Global functional connectivity was not different between groups at baseline and hardly changed over time. Therefore, investigation of associations with clinical measures of disease progression were not deemed useful. An analysis of individual connections demonstrated differences between groups at baseline (higher frontal theta, lower parieto-occipital alpha2 band functional connectivity) and over time in PD patients (increase in frontal delta and theta band functional connectivity). Our results suggest that spectral measures are promising candidates in the search for non-invasive markers of both early-stage PD and of the ongoing disease process.
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Affiliation(s)
- Lennard I Boon
- Department of Neurology, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
- Department of Clinical Neurophysiology and Magnetoencephalography Center, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
| | - Arjan Hillebrand
- Department of Neurology, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
- Department of Clinical Neurophysiology and Magnetoencephalography Center, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Jos W Twisk
- Department of Epidemiology and Biostatistics, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Department of Neurology, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
- Department of Clinical Neurophysiology and Magnetoencephalography Center, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Henk W Berendse
- Department of Neurology, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
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25
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Jawata A, Nicolás von E, Jean-Marc L, Giovanni P, Giorgio A, Zhengchen C, Tanguy H, Chifaou A, Hassan K, Birgit F, Jean G, Christophe G. Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas. Neuroimage 2023; 274:120158. [PMID: 37149236 DOI: 10.1016/j.neuroimage.2023.120158] [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: 10/03/2022] [Revised: 03/27/2023] [Accepted: 05/04/2023] [Indexed: 05/08/2023] Open
Abstract
BACKGROUND Magnetoencephalography (MEG) is a widely used non-invasive tool to estimate brain activity with high temporal resolution. However, due to the ill-posed nature of the MEG source imaging (MSI) problem, the ability of MSI to identify accurately underlying brain sources along the cortical surface is still uncertain and requires validation. METHOD We validated the ability of MSI to estimate the background resting state activity of 45 healthy participants by comparing it to the intracranial EEG (iEEG) atlas (https://mni-open-ieegatlas. RESEARCH mcgill.ca/). First, we applied wavelet-based Maximum Entropy on the Mean (wMEM) as an MSI technique. Next, we converted MEG source maps into intracranial space by applying a forward model to the MEG-reconstructed source maps, and estimated virtual iEEG (ViEEG) potentials on each iEEG channel location; we finally quantitatively compared those with actual iEEG signals from the atlas for 38 regions of interest in the canonical frequency bands. RESULTS The MEG spectra were more accurately estimated in the lateral regions compared to the medial regions. The regions with higher amplitude in the ViEEG than in the iEEG were more accurately recovered. In the deep regions, MEG-estimated amplitudes were largely underestimated and the spectra were poorly recovered. Overall, our wMEM results were similar to those obtained with minimum norm or beamformer source localization. Moreover, the MEG largely overestimated oscillatory peaks in the alpha band, especially in the anterior and deep regions. This is possibly due to higher phase synchronization of alpha oscillations over extended regions, exceeding the spatial sensitivity of iEEG but detected by MEG. Importantly, we found that MEG-estimated spectra were more comparable to spectra from the iEEG atlas after the aperiodic components were removed. CONCLUSION This study identifies brain regions and frequencies for which MEG source analysis is likely to be reliable, a promising step towards resolving the uncertainty in recovering intracerebral activity from non-invasive MEG studies.
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Affiliation(s)
- Afnan Jawata
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, H3A 2B4, Canada; Integrated Program in Neuroscience, McGill University, Montréal, Québec H3A 1A1, Canada; Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec H3A 2B4, Canada.
| | - Ellenrieder Nicolás von
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec H3A 2B4, Canada
| | - Lina Jean-Marc
- Centre De Recherches En Mathématiques, Montréal, Québec H3C 3J7, Canada; Electrical Engineering Department, École De Technologie Supérieure, Montréal, Québec H3C 1K3, Canada
| | - Pellegrino Giovanni
- Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - Arcara Giorgio
- Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - Cai Zhengchen
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec H3A 2B4, Canada
| | - Hedrich Tanguy
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, H3A 2B4, Canada
| | - Abdallah Chifaou
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, H3A 2B4, Canada
| | - Khajehpour Hassan
- Physics Department and PERFORM Centre, Concordia University, Montréal, Québec H4B 1R6, Canada
| | - Frauscher Birgit
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec H3A 2B4, Canada
| | - Gotman Jean
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec H3A 2B4, Canada
| | - Grova Christophe
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, H3A 2B4, Canada; Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec H3A 2B4, Canada; Centre De Recherches En Mathématiques, Montréal, Québec H3C 3J7, Canada; Physics Department and PERFORM Centre, Concordia University, Montréal, Québec H4B 1R6, Canada.
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26
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Frauscher B, Bénar CG, Engel JJ, Grova C, Jacobs J, Kahane P, Wiebe S, Zjilmans M, Dubeau F. Neurophysiology, Neuropsychology, and Epilepsy, in 2022: Hills We Have Climbed and Hills Ahead. Neurophysiology in epilepsy. Epilepsy Behav 2023; 143:109221. [PMID: 37119580 DOI: 10.1016/j.yebeh.2023.109221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 05/01/2023]
Abstract
Since the discovery of the human electroencephalogram (EEG), neurophysiology techniques have become indispensable tools in our armamentarium to localize epileptic seizures. New signal analysis techniques and the prospects of artificial intelligence and big data will offer unprecedented opportunities to further advance the field in the near future, ultimately resulting in improved quality of life for many patients with drug-resistant epilepsy. This article summarizes selected presentations from Day 1 of the two-day symposium "Neurophysiology, Neuropsychology, Epilepsy, 2022: Hills We Have Climbed and the Hills Ahead". Day 1 was dedicated to highlighting and honoring the work of Dr. Jean Gotman, a pioneer in EEG, intracranial EEG, simultaneous EEG/ functional magnetic resonance imaging, and signal analysis of epilepsy. The program focused on two main research directions of Dr. Gotman, and was dedicated to "High-frequency oscillations, a new biomarker of epilepsy" and "Probing the epileptic focus from inside and outside". All talks were presented by colleagues and former trainees of Dr. Gotman. The extended summaries provide an overview of historical and current work in the neurophysiology of epilepsy with emphasis on novel EEG biomarkers of epilepsy and source imaging and concluded with an outlook on the future of epilepsy research, and what is needed to bring the field to the next level.
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Affiliation(s)
- B Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
| | - C G Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - J Jr Engel
- David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - C Grova
- Multimodal Functional Imaging Lab, PERFORM Centre, Department of Physics, Concordia University, Montreal, QC, Canada; Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, QC, Canada; Montreal Neurological Institute and Hospital, Neurology and Neurosurgery Department, McGill University, Montreal, QC, Canada
| | - J Jacobs
- Department of Pediatric and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - P Kahane
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institute Neurosciences, Department of Neurology, 38000 Grenoble, France
| | - S Wiebe
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - M Zjilmans
- Stichting Epilepsie Instellingen Nederland, The Netherlands; Brain Center, University Medical Center Utrecht, The Netherlands
| | - F Dubeau
- Montreal Neurological Institute and Hospital, Neurology and Neurosurgery Department, McGill University, Montreal, QC, Canada
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27
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Roth BJ. Biomagnetism: The First Sixty Years. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094218. [PMID: 37177427 PMCID: PMC10181075 DOI: 10.3390/s23094218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 04/21/2023] [Accepted: 04/22/2023] [Indexed: 05/15/2023]
Abstract
Biomagnetism is the measurement of the weak magnetic fields produced by nerves and muscle. The magnetic field of the heart-the magnetocardiogram (MCG)-is the largest biomagnetic signal generated by the body and was the first measured. Magnetic fields have been detected from isolated tissue, such as a peripheral nerve or cardiac muscle, and these studies have provided insights into the fundamental properties of biomagnetism. The magnetic field of the brain-the magnetoencephalogram (MEG)-has generated much interest and has potential clinical applications to epilepsy, migraine, and psychiatric disorders. The biomagnetic inverse problem, calculating the electrical sources inside the brain from magnetic field recordings made outside the head, is difficult, but several techniques have been introduced to solve it. Traditionally, biomagnetic fields are recorded using superconducting quantum interference device (SQUID) magnetometers, but recently, new sensors have been developed that allow magnetic measurements without the cryogenic technology required for SQUIDs.
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Affiliation(s)
- Bradley J Roth
- Department of Physics, Oakland University, Rochester, MI 48309, USA
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28
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Sun J, Ren J, Li J, Huang Y. Measurement and Analysis of Magnetic Properties of Permalloy for Magnetic Shielding Devices under Different Temperature Environments. MATERIALS (BASEL, SWITZERLAND) 2023; 16:3253. [PMID: 37110086 PMCID: PMC10145743 DOI: 10.3390/ma16083253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 04/15/2023] [Accepted: 04/19/2023] [Indexed: 06/19/2023]
Abstract
The relative permeability, coercivity, and remanence of permalloy are closely related to the performance of magnetic shielding devices. In this paper, the relationship between the magnetic properties of permalloy and the working temperature of magnetic shielding devices is measured. Firstly, the measurement method of permalloy properties based on the simulated impact method is analyzed. What is more, a magnetic property test system consisting of a soft magnetic material tester and a high-low temperature chamber for permalloy ring samples at different temperatures was established to measure DC and AC (0.01 Hz to 1 kHz) magnetic properties at different temperatures (-60 °C to 140 °C). Finally, the results show that compared with room temperature (25 °C), the initial permeability (μi) decreases by 69.64% at -60 °C and increases by 38.23% at 140 °C, and the coercivity (hc) decreases by 34.81% at -60 °C and increases by 8.93% at 140 °C, which are the key parameters in the magnetic shielding device. It can be concluded that the relative permeability and remanence of permalloy are positively correlated with temperature, while the saturation magnetic flux density and coercivity are negatively correlated with temperature. This paper is of great significance to the magnetic analysis and design of magnetic shielding devices.
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Affiliation(s)
- Jinji Sun
- Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China; (J.S.); (J.R.); (Y.H.)
- Zhejiang Provincial Key Laboratory of Ultra-Weak Magnetic-Field Space and Applied Technology, Hangzhou Innovation Institute, Beihang University, Hangzhou 310052, China
| | - Jianyi Ren
- Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China; (J.S.); (J.R.); (Y.H.)
- Zhejiang Provincial Key Laboratory of Ultra-Weak Magnetic-Field Space and Applied Technology, Hangzhou Innovation Institute, Beihang University, Hangzhou 310052, China
| | - Jin Li
- Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China; (J.S.); (J.R.); (Y.H.)
- Zhejiang Provincial Key Laboratory of Ultra-Weak Magnetic-Field Space and Applied Technology, Hangzhou Innovation Institute, Beihang University, Hangzhou 310052, China
| | - Yuejing Huang
- Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China; (J.S.); (J.R.); (Y.H.)
- Zhejiang Provincial Key Laboratory of Ultra-Weak Magnetic-Field Space and Applied Technology, Hangzhou Innovation Institute, Beihang University, Hangzhou 310052, China
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29
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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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30
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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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31
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Colenbier N, Sareen E, Del-Aguila Puntas T, Griffa A, Pellegrino G, Mantini D, Marinazzo D, Arcara G, Amico E. Task matters: Individual MEG signatures from naturalistic and neurophysiological brain states. Neuroimage 2023; 271:120021. [PMID: 36918139 DOI: 10.1016/j.neuroimage.2023.120021] [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: 11/17/2022] [Revised: 02/21/2023] [Accepted: 03/10/2023] [Indexed: 03/14/2023] Open
Abstract
The discovery that human brain connectivity data can be used as a "fingerprint" to identify a given individual from a population, has become a burgeoning research area in the neuroscience field. Recent studies have identified the possibility to extract these brain signatures from the temporal rich dynamics of resting-state magneto encephalography (MEG) recordings. Nevertheless, it is still uncertain to what extent MEG signatures can serve as an indicator of human identifiability during task-related conduct. Here, using MEG data from naturalistic and neurophysiological tasks, we show that identification improves in tasks relative to resting-state, providing compelling evidence for a task dependent axis of MEG signatures. Notably, improvements in identifiability were more prominent in strictly controlled tasks. Lastly, the brain regions contributing most towards individual identification were also modified when engaged in task activities. We hope that this investigation advances our understanding of the driving factors behind brain identification from MEG signals.
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Affiliation(s)
| | - Ekansh Sareen
- Medical Image Processing Laboratory, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Tamara Del-Aguila Puntas
- Laboratorio de Psicobiologia, Departmento de Psicología Experimental, Facultad de Psicología, Universidad de Sevilla, Spain
| | - Alessandra Griffa
- Medical Image Processing Laboratory, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Switzerland; Leenaards Memory Center, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | | | - Dante Mantini
- Movement Control and Neuroplasticity Research Group, KU Leuven, Belgium
| | - Daniele Marinazzo
- Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Ghent, Belgium
| | | | - Enrico Amico
- Medical Image Processing Laboratory, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Switzerland.
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32
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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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33
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Krishnan B, Tousseyn S, Wang ZI, Murakami H, Wu G, Burgess R, Iasemidis L, Najm I, Alexopoulos AV. Novel noninvasive identification of patient-specific epileptic networks in focal epilepsies: Linking single-photon emission computed tomography perfusion during seizures with resting-state magnetoencephalography dynamics. Hum Brain Mapp 2023; 44:1695-1710. [PMID: 36480260 PMCID: PMC9921232 DOI: 10.1002/hbm.26168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 08/31/2022] [Accepted: 11/18/2022] [Indexed: 12/13/2022] Open
Abstract
Single-photon emission computed tomography (SPECT) during seizures and magnetoencephalography (MEG) during the interictal state are noninvasive modalities employed in the localization of the epileptogenic zone in patients with drug-resistant focal epilepsy (DRFE). The present study aims to investigate whether there exists a preferentially high MEG functional connectivity (FC) among those regions of the brain that exhibit hyperperfusion or hypoperfusion during seizures. We studied MEG and SPECT data in 30 consecutive DRFE patients who had resective epilepsy surgery. We parcellated each ictal perfusion map into 200 regions of interest (ROIs) and generated ROI time series using source modeling of MEG data. FC between ROIs was quantified using coherence and phase-locking value. We defined a generalized linear model to relate the connectivity of each ROI, ictal perfusion z score, and distance between ROIs. We compared the coefficients relating perfusion z score to FC of each ROI and estimated the connectivity within and between resected and unresected ROIs. We found that perfusion z scores were strongly correlated with the FC of hyper-, and separately, hypoperfused ROIs across patients. High interictal connectivity was observed between hyperperfused brain regions inside and outside the resected area. High connectivity was also observed between regions of ictal hypoperfusion. Importantly, the ictally hypoperfused regions had a low interictal connectivity to regions that became hyperperfused during seizures. We conclude that brain regions exhibiting hyperperfusion during seizures highlight a preferentially connected interictal network, whereas regions of ictal hypoperfusion highlight a separate, discrete and interconnected, interictal network.
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Affiliation(s)
- Balu Krishnan
- Neurological InstituteEpilepsy Center, Cleveland ClinicClevelandOhioUSA
| | - Simon Tousseyn
- Academic Center for EpileptologyKempenhaeghe and Maastricht UMC+HeezeThe Netherlands
| | - Zhong Irene Wang
- Neurological InstituteEpilepsy Center, Cleveland ClinicClevelandOhioUSA
| | - Hiroatsu Murakami
- Neurological InstituteEpilepsy Center, Cleveland ClinicClevelandOhioUSA
| | - Guiyun Wu
- Neurological InstituteEpilepsy Center, Cleveland ClinicClevelandOhioUSA
| | - Richard Burgess
- Neurological InstituteEpilepsy Center, Cleveland ClinicClevelandOhioUSA
| | - Leonidas Iasemidis
- Department of Translational NeuroscienceBarrow Neurological InstituteScottsdaleArizonaUSA
- Department of NeurologyBarrow Neurological InstituteScottsdaleArizonaUSA
| | - Imad Najm
- Neurological InstituteEpilepsy Center, Cleveland ClinicClevelandOhioUSA
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34
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Lee S, Wu S, Tao JX, Rose S, Warnke PC, Issa NP, van Drongelen W. Manifestation of Hippocampal Interictal Discharges on Clinical Scalp EEG Recordings. J Clin Neurophysiol 2023; 40:144-150. [PMID: 34010227 PMCID: PMC8590709 DOI: 10.1097/wnp.0000000000000867] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
PURPOSE Epileptiform activity limited to deep sources such as the hippocampus currently lacks reliable scalp correlates. Recent studies, however, have found that a subset of hippocampal interictal discharges may be associated with visible scalp signals, suggesting that some types of hippocampal activity may be monitored noninvasively. The purpose of this study is to characterize the relationship between these scalp waveforms and the underlying intracranial activity. METHODS Paired intracranial and scalp EEG recordings obtained from 16 patients were used to identify hippocampal interictal discharges. Discharges were grouped by waveform shape, and spike-triggered averages of the intracranial and scalp signals were calculated for each group. Cross-correlation of intracranial and scalp spike-triggered averages was used to determine their temporal relationship, and topographic maps of the scalp were generated for each group. RESULTS Cross-correlation of intracranial and scalp correlates resulted in two classes of scalp waveforms-those with and without time delays from the associated hippocampal discharges. Scalp signals with no delay showed topographies with a broad field with higher amplitudes on the side ipsilateral to the discharges and a left-right flip in polarity-observations consistent with the volume conduction of a single unilateral deep source. In contrast, scalp correlates with time lags showed rotational dynamics, suggesting synaptic propagation mechanisms. CONCLUSIONS The temporal relationship between the intracranial and scalp signals suggests that both volume conduction and synaptic propagation contribute to these scalp manifestations. Furthermore, the topographic evolution of these scalp waveforms may be used to distinguish spikes that are limited to the hippocampus from those that travel to or engage other brain areas.
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Affiliation(s)
- Somin Lee
- Department of Pediatrics, The University of Chicago, Chicago, IL, 60607, USA
- Committee on Neurobiology, The University of Chicago, Chicago, IL, 60607, USA
| | - Shasha Wu
- Department of Neurology, The University of Chicago, Chicago, IL, 60607, USA
| | - James X. Tao
- Department of Neurology, The University of Chicago, Chicago, IL, 60607, USA
| | - Sandra Rose
- Department of Neurology, The University of Chicago, Chicago, IL, 60607, USA
| | - Peter C. Warnke
- Department of Surgery, The University of Chicago, Chicago, IL, 60607, USA
| | - Naoum P. Issa
- Department of Neurology, The University of Chicago, Chicago, IL, 60607, USA
| | - Wim van Drongelen
- Department of Pediatrics, The University of Chicago, Chicago, IL, 60607, USA
- Committee on Neurobiology, The University of Chicago, Chicago, IL, 60607, USA
- Department of Neurology, The University of Chicago, Chicago, IL, 60607, USA
- Committee on Computational Neuroscience, The University of Chicago, Chicago, IL, 60607, USA
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35
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Dai Z, Zhang S, Wang H, Chen Z, Zhang W, Hu X, Yao Z, Lu Q. Hampered gamma oscillations induced by sad emotion underlying suicide attempt in major depressive disorder. Psychiatry Clin Neurosci 2023; 77:20-29. [PMID: 36207792 DOI: 10.1111/pcn.13487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 09/18/2022] [Accepted: 09/30/2022] [Indexed: 01/06/2023]
Abstract
AIM Major depressive disorder (MDD) is associated with high suicidality, especially for those with suicide attempt (SA). Although impaired oscillatory activity has been previously reported in patients with SA, little is known about precise temporal-spatial variability of its neural dynamics. To solve this, the current study probed the spectral power and network interactions underlying SA in MDD. METHODS The present study recruited 104 subjects including 56 subjects with MDD (30 with SA and 26 without SA) and 48 healthy controls, who performed sad expressions recognition task during magnetoencephalography (MEG) recording. By investigating source-reconstructed MEG-data, brain states representing different task stages were estimated from a Hidden Markov model. Spectrum power and network connectivity were compared via Gaussian Mixture Models, and fractional occupancy (FO) of states were compared via an independent F-test. RESULTS Brain states were corresponding to various frequencies (theta/beta/low gamma/ high gamma). In low gamma band (35-45 Hz), the early visual state exhibited increased activation and hyper inter-network connectivity between visual regions and the limbic system, while the middle fronto-parietal state exhibited attenuated activation and decreased intra-network connectivity within fronto-parietal regions in SA group. Crucially, FO values of these two states were significantly correlated with the suicide risks. CONCLUSIONS Suicide behavior of patients with MDD was significantly associated with aberrant oscillations in low gamma band. Elevated oscillations in occipital cortices and attenuated oscillations in fronto-parietal cortices were significantly associated with SA. Manifesting sadness indulging and reckless decision-making, the hampered temporal characteristics could help explain the neural-electric basis of SA.
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Affiliation(s)
- Zhongpeng Dai
- School of Biological Sciences & Medical Engineering, Child Development and Learning Science, Key Laboratory of Child Development and Learning Science, Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, China.,Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
| | - Siqi Zhang
- School of Biological Sciences & Medical Engineering, Child Development and Learning Science, Key Laboratory of Child Development and Learning Science, Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, China
| | - Huan Wang
- School of Biological Sciences & Medical Engineering, Child Development and Learning Science, Key Laboratory of Child Development and Learning Science, Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, China
| | - Zhilu Chen
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, China
| | - Wei Zhang
- School of Biological Sciences & Medical Engineering, Child Development and Learning Science, Key Laboratory of Child Development and Learning Science, Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, China
| | - Xiaowen Hu
- School of Biological Sciences & Medical Engineering, Child Development and Learning Science, Key Laboratory of Child Development and Learning Science, Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, China
| | - Zhijian Yao
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, China.,Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Child Development and Learning Science, Key Laboratory of Child Development and Learning Science, Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, China
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36
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Coelli S, Medina Villalon S, Bonini F, Velmurugan J, López-Madrona VJ, Carron R, Bartolomei F, Badier JM, Bénar CG. Comparison of beamformer and ICA for dynamic connectivity analysis: A simultaneous MEG-SEEG study. Neuroimage 2023; 265:119806. [PMID: 36513288 DOI: 10.1016/j.neuroimage.2022.119806] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 10/25/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022] Open
Abstract
Magnetoencephalography (MEG) is a powerful tool for estimating brain connectivity with both good spatial and temporal resolution. It is particularly helpful in epilepsy to characterize non-invasively the epileptic networks. However, using MEG to map brain networks requires solving a difficult inverse problem that introduces uncertainty in the activity localization and connectivity measures. Our goal here was to compare independent component analysis (ICA) followed by dipole source localization and the linearly constrained minimum-variance beamformer (LCMV-BF) for characterizing regions with interictal epileptic activity and their dynamic connectivity. After a simulation study, we compared ICA and LCMV-BF results with intracerebral EEG (stereotaxic EEG, SEEG) recorded simultaneously in 8 epileptic patients, which provide a unique 'ground truth' to which non-invasive results can be confronted. We compared the signal time courses extracted applying ICA and LCMV-BF on MEG data to that of SEEG, both for the actual signals and the dynamic connectivity computed using cross-correlation (evolution of links in time). With our simulations, we illustrated the different effect of the temporal and spatial correlation among sources on the two methods. While ICA was more affected by the temporal correlation but robust against spatial configurations, LCMV-BF showed opposite behavior. Moreover, ICA seems more suited to retrieve the simulated networks. In case of real patient data, good MEG/SEEG correlation and good localization were obtained in 6 out of 8 patients. In 4 of them ICA had the best performance (higher correlation, lower localization distance). In terms of dynamic connectivity, the evolution in time of the cross-correlation links could be retrieved in 5 patients out of 6, however, with more variable results in terms of correlation and distance. In two patients LCMV-BF had better results than ICA. In one patient the two methods showed equally good outcomes, and in the remaining two patients ICA performed best. In conclusion, our results obtained by exploiting simultaneous MEG/SEEG recordings suggest that ICA and LCMV-BF have complementary qualities for retrieving the dynamics of interictal sources and their network interactions.
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Affiliation(s)
- Stefania Coelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Samuel Medina Villalon
- Aix Marseille University, INSERM, INS, Inst Neurosci Syst, Marseille, France; APHM, Timone Hospital, Epileptology and Cerebral Rythmology, Marseille, France
| | - Francesca Bonini
- Aix Marseille University, INSERM, INS, Inst Neurosci Syst, Marseille, France; APHM, Timone Hospital, Epileptology and Cerebral Rythmology, Marseille, France
| | - Jayabal Velmurugan
- Aix Marseille University, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | | | - Romain Carron
- Aix Marseille University, INSERM, INS, Inst Neurosci Syst, Marseille, France; APHM, Timone Hospital, Functional and Stereotactic Neurosurgery, Marseille, France
| | - Fabrice Bartolomei
- Aix Marseille University, INSERM, INS, Inst Neurosci Syst, Marseille, France; APHM, Timone Hospital, Epileptology and Cerebral Rythmology, Marseille, France
| | - Jean-Michel Badier
- Aix Marseille University, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Christian-G Bénar
- Aix Marseille University, INSERM, INS, Inst Neurosci Syst, Marseille, France.
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37
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Tan V, Dockstader C, Moxon-Emre I, Mendlowitz S, Schacter R, Colasanto M, Voineskos AN, Akingbade A, Nishat E, Mabbott DJ, Arnold PD, Ameis SH. Preliminary Observations of Resting-State Magnetoencephalography in Nonmedicated Children with Obsessive-Compulsive Disorder. J Child Adolesc Psychopharmacol 2022; 32:522-532. [PMID: 36548364 PMCID: PMC9917323 DOI: 10.1089/cap.2022.0036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Background: Cortico-striato-thalamo-cortical (CSTC) network alterations are hypothesized to contribute to symptoms of obsessive-compulsive disorder (OCD). To date, very few studies have examined whether CSTC network alterations are present in children with OCD, who are medication naive. Medication-naive pediatric imaging samples may be optimal to study neural correlates of illness and identify brain-based markers, given the proximity to illness onset. Methods: Magnetoencephalography (MEG) data were analyzed at rest, in 18 medication-naive children with OCD (M = 12.1 years ±2.0 standard deviation [SD]; 10 M/8 F) and 13 typically developing children (M = 12.3 years ±2.2 SD; 6 M/7 F). Whole-brain MEG-derived resting-state functional connectivity (rs-fc), for alpha- and gamma-band frequencies were compared between OCD and typically developing (control) groups. Results: Increased MEG-derived rs-fc across alpha- and gamma-band frequencies was found in the OCD group compared to the control group. Increased MEG-derived rs-fc at alpha-band frequencies was evident across a number of regions within the CSTC circuitry and beyond, including the cerebellum and limbic regions. Increased MEG-derived rs-fc at gamma-band frequencies was restricted to the frontal and temporal cortices. Conclusions: This MEG study provides preliminary evidence of altered alpha and gamma networks, at rest, in medication-naive children with OCD. These results support prior findings pointing to the relevance of CSTC circuitry in pediatric OCD and further support accumulating evidence of altered connectivity between regions that extend beyond this network, including the cerebellum and limbic regions. Given the substantial portion of children and youth whose OCD symptoms do not respond to conventional treatments, our findings have implications for future treatment innovation research aiming to target and track whether brain patterns associated with having OCD may change with treatment and/or predict treatment response.
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Affiliation(s)
- Vinh Tan
- Human Biology Program, Faculty of Arts and Science, University of Toronto, Toronto, Canada
- Kimel Family Translational Imaging Genetics Research Laboratory, Centre for Addiction and Mental Health, Toronto, Canada
| | - Colleen Dockstader
- Human Biology Program, Faculty of Arts and Science, University of Toronto, Toronto, Canada
| | - Iska Moxon-Emre
- Cundill Centre for Child and Youth Depression, Margaret and Wallace McCain Centre for Child, Youth and Family Mental Health, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
| | - Sandra Mendlowitz
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Reva Schacter
- Department of Psychiatry, The Hospital for Sick Children, Toronto, Canada
| | - Marlena Colasanto
- Department of Applied Psychology and Human Development, Ontario Institute for Studies in Education, University of Toronto, Toronto, Canada
| | - Aristotle N. Voineskos
- Cundill Centre for Child and Youth Depression, Margaret and Wallace McCain Centre for Child, Youth and Family Mental Health, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Aquila Akingbade
- Human Biology Program, Faculty of Arts and Science, University of Toronto, Toronto, Canada
| | - Eman Nishat
- Neuroscience and Mental Health, The Hospital for Sick Children, Toronto, Canada
- Department of Physiology, Temetry Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Donald J. Mabbott
- Department of Physiology, Temetry Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, Canada
| | - Paul D. Arnold
- Department of Psychiatry, Cumming School of Medicine, The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Stephanie H. Ameis
- Cundill Centre for Child and Youth Depression, Margaret and Wallace McCain Centre for Child, Youth and Family Mental Health, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Neuroscience and Mental Health, The Hospital for Sick Children, Toronto, Canada
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Velmurugan J, Badier JM, Pizzo F, Medina Villalon S, Papageorgakis C, López-Madrona V, Jegou A, Carron R, Bartolomei F, Bénar CG. Virtual MEG sensors based on beamformer and independent component analysis can reconstruct epileptic activity as measured on simultaneous intracerebral recordings. Neuroimage 2022; 264:119681. [PMID: 36270623 DOI: 10.1016/j.neuroimage.2022.119681] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 09/30/2022] [Accepted: 10/10/2022] [Indexed: 11/09/2022] Open
Abstract
The prevailing gold standard for presurgical determination of epileptogenic brain networks is intracerebral EEG, a potent yet invasive approach. Magnetoencephalography (MEG) is a state-of-the art non-invasive method for investigating epileptiform discharges. However, it is not clear at what level the precision offered by MEG can reach that of SEEG. Here, we present a strategy for non-invasively retrieving the constituents of the interictal network, with high spatial and temporal precision. Our method is based on MEG and a combination of spatial filtering and independent component analysis (ICA). We validated this approach in twelve patients with drug-resistant focal epilepsy, thanks to the unprecedented ground truth provided by simultaneous recordings of MEG and SEEG. A minimum variance adaptive beamformer estimated the source time series and ICA was used to further decompose these time series into network constituents (MEG-ICs), each having a time series (virtual electrode) and a topography (spatial distribution of amplitudes in the brain). We show that MEG has a considerable sensitivity of 0.80 and 0.84 and a specificity of 0.93 and 0.91 for reconstructing deep and superficial sources, respectively, when compared to the ground truth (SEEG). For each epileptic MEG-IC (n = 131), we found at least one significantly correlating SEEG contact close to zero lag after correcting for multiple comparisons. All the patients except one had at least one epileptic component that was highly correlated (Spearman rho>0.3) with that of SEEG traces. MEG-ICs correlated well with SEEG traces. The strength of correlation coefficients did not depend on the depth of the SEEG contacts or the clinical outcome of the patient. A significant proportion of the MEG-ICs (n = 83/131) were localized in proximity with their maximally correlating SEEG, within a mean distance of 20±12.18mm. Our research is the first to validate the MEG-retrieved beamformer IC sources against SEEG-derived ground truth in a simultaneous MEG-SEEG framework. Observations from the present study suggest that non-invasive MEG source components may potentially provide additional information, comparable to SEEG in a number of instances.
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Affiliation(s)
- Jayabal Velmurugan
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, F-13005, France
| | - Jean-Michel Badier
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, F-13005, France
| | - Francesca Pizzo
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, F-13005, France; APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, F-13005, France
| | - Samuel Medina Villalon
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, F-13005, France; APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, F-13005, France
| | | | | | - Aude Jegou
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, F-13005, France
| | - Romain Carron
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, F-13005, France; APHM, Timone Hospital, Functional and Stereotactic Neurosurgery, Marseille, F-13005, France
| | - Fabrice Bartolomei
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, F-13005, France; APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, F-13005, France
| | - Christian-G Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, F-13005, France.
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39
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Billig AJ, Lad M, Sedley W, Griffiths TD. The hearing hippocampus. Prog Neurobiol 2022; 218:102326. [PMID: 35870677 PMCID: PMC10510040 DOI: 10.1016/j.pneurobio.2022.102326] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 06/08/2022] [Accepted: 07/18/2022] [Indexed: 11/17/2022]
Abstract
The hippocampus has a well-established role in spatial and episodic memory but a broader function has been proposed including aspects of perception and relational processing. Neural bases of sound analysis have been described in the pathway to auditory cortex, but wider networks supporting auditory cognition are still being established. We review what is known about the role of the hippocampus in processing auditory information, and how the hippocampus itself is shaped by sound. In examining imaging, recording, and lesion studies in species from rodents to humans, we uncover a hierarchy of hippocampal responses to sound including during passive exposure, active listening, and the learning of associations between sounds and other stimuli. We describe how the hippocampus' connectivity and computational architecture allow it to track and manipulate auditory information - whether in the form of speech, music, or environmental, emotional, or phantom sounds. Functional and structural correlates of auditory experience are also identified. The extent of auditory-hippocampal interactions is consistent with the view that the hippocampus makes broad contributions to perception and cognition, beyond spatial and episodic memory. More deeply understanding these interactions may unlock applications including entraining hippocampal rhythms to support cognition, and intervening in links between hearing loss and dementia.
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Affiliation(s)
| | - Meher Lad
- Translational and Clinical Research Institute, Newcastle University Medical School, Newcastle upon Tyne, UK
| | - William Sedley
- Translational and Clinical Research Institute, Newcastle University Medical School, Newcastle upon Tyne, UK
| | - Timothy D Griffiths
- Biosciences Institute, Newcastle University Medical School, Newcastle upon Tyne, UK; Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK; Human Brain Research Laboratory, Department of Neurosurgery, University of Iowa Hospitals and Clinics, Iowa City, USA
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40
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Niso G, Krol LR, Combrisson E, Dubarry AS, Elliott MA, François C, Héjja-Brichard Y, Herbst SK, Jerbi K, Kovic V, Lehongre K, Luck SJ, Mercier M, Mosher JC, Pavlov YG, Puce A, Schettino A, Schön D, Sinnott-Armstrong W, Somon B, Šoškić A, Styles SJ, Tibon R, Vilas MG, van Vliet M, Chaumon M. Good scientific practice in EEG and MEG research: Progress and perspectives. Neuroimage 2022; 257:119056. [PMID: 35283287 PMCID: PMC11236277 DOI: 10.1016/j.neuroimage.2022.119056] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 02/25/2022] [Accepted: 03/01/2022] [Indexed: 11/22/2022] Open
Abstract
Good scientific practice (GSP) refers to both explicit and implicit rules, recommendations, and guidelines that help scientists to produce work that is of the highest quality at any given time, and to efficiently share that work with the community for further scrutiny or utilization. For experimental research using magneto- and electroencephalography (MEEG), GSP includes specific standards and guidelines for technical competence, which are periodically updated and adapted to new findings. However, GSP also needs to be regularly revisited in a broader light. At the LiveMEEG 2020 conference, a reflection on GSP was fostered that included explicitly documented guidelines and technical advances, but also emphasized intangible GSP: a general awareness of personal, organizational, and societal realities and how they can influence MEEG research. This article provides an extensive report on most of the LiveMEEG contributions and new literature, with the additional aim to synthesize ongoing cultural changes in GSP. It first covers GSP with respect to cognitive biases and logical fallacies, pre-registration as a tool to avoid those and other early pitfalls, and a number of resources to enable collaborative and reproducible research as a general approach to minimize misconceptions. Second, it covers GSP with respect to data acquisition, analysis, reporting, and sharing, including new tools and frameworks to support collaborative work. Finally, GSP is considered in light of ethical implications of MEEG research and the resulting responsibility that scientists have to engage with societal challenges. Considering among other things the benefits of peer review and open access at all stages, the need to coordinate larger international projects, the complexity of MEEG subject matter, and today's prioritization of fairness, privacy, and the environment, we find that current GSP tends to favor collective and cooperative work, for both scientific and for societal reasons.
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Affiliation(s)
- Guiomar Niso
- Psychological & Brain Sciences, Indiana University, Bloomington, IN, USA; Universidad Politecnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Laurens R Krol
- Neuroadaptive Human-Computer Interaction, Brandenburg University of Technology Cottbus-Senftenberg, Germany
| | - Etienne Combrisson
- Aix-Marseille University, Institut de Neurosciences de la Timone, France
| | | | | | | | - Yseult Héjja-Brichard
- Centre d'Ecologie Fonctionnelle et Evolutive, CNRS, EPHE, IRD, Université Montpellier, Montpellier, France
| | - Sophie K Herbst
- Cognitive Neuroimaging Unit, INSERM, CEA, CNRS, NeuroSpin center, Université Paris-Saclay, Gif/Yvette, France
| | - Karim Jerbi
- Cognitive and Computational Neuroscience Laboratory, Department of Psychology, University of Montreal, Montreal, QC, Canada; Mila - Quebec Artificial Intelligence Institute, Canada
| | - Vanja Kovic
- Faculty of Philosophy, Laboratory for neurocognition and applied cognition, University of Belgrade, Serbia
| | - Katia Lehongre
- Institut du Cerveau - Paris Brain Institute - ICM, Inserm U 1127, CNRS UMR 7225, APHP, Hôpital de la Pitié Salpêtrière, Sorbonne Université, Centre MEG-EEG, Centre de NeuroImagerie Recherche (CENIR), Paris, France
| | - Steven J Luck
- Center for Mind & Brain, University of California, Davis, CA, USA
| | - Manuel Mercier
- Aix Marseille Univ, Inserm, INS, Inst Neurosci Syst, Marseille, France
| | - John C Mosher
- McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yuri G Pavlov
- University of Tuebingen, Germany; Ural Federal University, Yekaterinburg, Russia
| | - Aina Puce
- Psychological & Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Antonio Schettino
- Erasmus University Rotterdam, Rotterdam, the Netherland; Institute for Globally Distributed Open Research and Education (IGDORE), Sweden
| | - Daniele Schön
- Aix Marseille Univ, Inserm, INS, Inst Neurosci Syst, Marseille, France
| | | | | | - Anđela Šoškić
- Faculty of Philosophy, Laboratory for neurocognition and applied cognition, University of Belgrade, Serbia; Teacher Education Faculty, University of Belgrade, Serbia
| | - Suzy J Styles
- Psychology, Nanyang Technological University, Singapore; Singapore Institute for Clinical Sciences, A*STAR, Singapore
| | - Roni Tibon
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK; School of Psychology, University of Nottingham, Nottingham, UK
| | - Martina G Vilas
- Ernst Strüngmann Institute for Neuroscience, Frankfurt am Main, Germany
| | | | - Maximilien Chaumon
- Institut du Cerveau - Paris Brain Institute - ICM, Inserm U 1127, CNRS UMR 7225, APHP, Hôpital de la Pitié Salpêtrière, Sorbonne Université, Centre MEG-EEG, Centre de NeuroImagerie Recherche (CENIR), Paris, France..
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41
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Sun R, Sohrabpour A, Worrell GA, He B. Deep neural networks constrained by neural mass models improve electrophysiological source imaging of spatiotemporal brain dynamics. Proc Natl Acad Sci U S A 2022; 119:e2201128119. [PMID: 35881787 PMCID: PMC9351497 DOI: 10.1073/pnas.2201128119] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 06/11/2022] [Indexed: 11/18/2022] Open
Abstract
Many efforts have been made to image the spatiotemporal electrical activity of the brain with the purpose of mapping its function and dysfunction as well as aiding the management of brain disorders. Here, we propose a non-conventional deep learning-based source imaging framework (DeepSIF) that provides robust and precise spatiotemporal estimates of underlying brain dynamics from noninvasive high-density electroencephalography (EEG) recordings. DeepSIF employs synthetic training data generated by biophysical models capable of modeling mesoscale brain dynamics. The rich characteristics of underlying brain sources are embedded in the realistic training data and implicitly learned by DeepSIF networks, avoiding complications associated with explicitly formulating and tuning priors in an optimization problem, as often is the case in conventional source imaging approaches. The performance of DeepSIF is evaluated by 1) a series of numerical experiments, 2) imaging sensory and cognitive brain responses in a total of 20 healthy subjects from three public datasets, and 3) rigorously validating DeepSIF's capability in identifying epileptogenic regions in a cohort of 20 drug-resistant epilepsy patients by comparing DeepSIF results with invasive measurements and surgical resection outcomes. DeepSIF demonstrates robust and excellent performance, producing results that are concordant with common neuroscience knowledge about sensory and cognitive information processing as well as clinical findings about the location and extent of the epileptogenic tissue and outperforming conventional source imaging methods. The DeepSIF method, as a data-driven imaging framework, enables efficient and effective high-resolution functional imaging of spatiotemporal brain dynamics, suggesting its wide applicability and value to neuroscience research and clinical applications.
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Affiliation(s)
- Rui Sun
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Abbas Sohrabpour
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
| | | | - Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
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42
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Mercier MR, Dubarry AS, Tadel F, Avanzini P, Axmacher N, Cellier D, Vecchio MD, Hamilton LS, Hermes D, Kahana MJ, Knight RT, Llorens A, Megevand P, Melloni L, Miller KJ, Piai V, Puce A, Ramsey NF, Schwiedrzik CM, Smith SE, Stolk A, Swann NC, Vansteensel MJ, Voytek B, Wang L, Lachaux JP, Oostenveld R. Advances in human intracranial electroencephalography research, guidelines and good practices. Neuroimage 2022; 260:119438. [PMID: 35792291 DOI: 10.1016/j.neuroimage.2022.119438] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/23/2022] [Accepted: 06/30/2022] [Indexed: 12/11/2022] Open
Abstract
Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.
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43
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López-Madrona VJ, Medina Villalon S, Badier JM, Trébuchon A, Jayabal V, Bartolomei F, Carron R, Barborica A, Vulliémoz S, Alario FX, Bénar CG. Magnetoencephalography can reveal deep brain network activities linked to memory processes. Hum Brain Mapp 2022; 43:4733-4749. [PMID: 35766240 PMCID: PMC9491290 DOI: 10.1002/hbm.25987] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 05/04/2022] [Accepted: 05/18/2022] [Indexed: 11/14/2022] Open
Abstract
Recording from deep neural structures such as hippocampus noninvasively and yet with high temporal resolution remains a major challenge for human neuroscience. Although it has been proposed that deep neuronal activity might be recordable during cognitive tasks using magnetoencephalography (MEG), this remains to be demonstrated as the contribution of deep structures to MEG recordings may be too small to be detected or might be eclipsed by the activity of large‐scale neocortical networks. In the present study, we disentangled mesial activity and large‐scale networks from the MEG signals thanks to blind source separation (BSS). We then validated the MEG BSS components using intracerebral EEG signals recorded simultaneously in patients during their presurgical evaluation of epilepsy. In the MEG signals obtained during a memory task involving the recognition of old and new images, we identified with BSS a putative mesial component, which was present in all patients and all control subjects. The time course of the component selectively correlated with stereo‐electroencephalography signals recorded from hippocampus and rhinal cortex, thus confirming its mesial origin. This finding complements previous studies with epileptic activity and opens new possibilities for using MEG to study deep brain structures in cognition and in brain disorders.
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Affiliation(s)
| | - Samuel Medina Villalon
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.,APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
| | | | - Agnès Trébuchon
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France.,APHM, Timone Hospital, Functional and Stereotactic Neurosurgery, Marseille, France
| | | | - Fabrice Bartolomei
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.,APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
| | - Romain Carron
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.,APHM, Timone Hospital, Functional and Stereotactic Neurosurgery, Marseille, France
| | | | - Serge Vulliémoz
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine Geneva, Geneva, Switzerland
| | | | - Christian G Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
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44
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Govaarts R, Beeldman E, Fraschini M, Griffa A, Engels MMA, van Es MA, Veldink JH, van den Berg LH, van der Kooi AJ, Pijnenburg YAL, de Visser M, Stam CJ, Raaphorst J, Hillebrand A. Cortical and subcortical changes in resting-state neuronal activity and connectivity in early symptomatic ALS and advanced frontotemporal dementia. Neuroimage Clin 2022; 34:102965. [PMID: 35217500 PMCID: PMC8867127 DOI: 10.1016/j.nicl.2022.102965] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 02/08/2022] [Accepted: 02/09/2022] [Indexed: 01/17/2023]
Abstract
The objective of this study was to examine if patterns of resting-state brain activity and functional connectivity in cortical and subcortical regions in patients with early symptomatic amyotrophic lateral sclerosis (ALS) resemble those of behavioural variant frontotemporal dementia (bvFTD). In a cross-sectional design, eyes-closed resting-state magnetoencephalography (MEG) data of 34 ALS patients, 18 bvFTD patients and 18 age- and gender-matched healthy controls (HCs) were projected to source-space using an atlas-based beamformer. Group differences in peak frequency, band-specific oscillatory activity and functional connectivity (corrected amplitude envelope correlation) in 78 cortical regions and 12 subcortical regions were determined. False discovery rate was used to correct for multiple comparisons. BvFTD patients, as compared to ALS and HCs, showed lower relative beta power in parietal, occipital, temporal and nearly all subcortical regions. Compared to HCs, patients with ALS and patients with bvFTD had a higher delta (0.5-4 Hz) and gamma (30-48 Hz) band resting-state functional connectivity in a high number of overlapping regions in the frontal lobe and in limbic and subcortical regions. Higher delta band connectivity was widespread in the bvFTD patients compared to HCs. ALS showed a more widespread higher gamma band functional connectivity compared to bvFTD. In conclusion, MEG in early symptomatic ALS patients shows resting-state functional connectivity changes in frontal, limbic and subcortical regions that overlap considerably with bvFTD. The findings show the potential of MEG to detect brain changes in early symptomatic phases of ALS and contribute to our understanding of the disease spectrum, with ALS and bvFTD at the two extreme ends.
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Affiliation(s)
- Rosanne Govaarts
- Amsterdam University Medical Centers, University of Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam, the Netherlands.
| | - Emma Beeldman
- Amsterdam University Medical Centers, University of Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Matteo Fraschini
- University of Cagliari, Department of Electrical and Electronic Engineering, Cagliari, Italy
| | - Alessandra Griffa
- Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland; Institute of Bioengineering, Center of Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland
| | - Marjolein M A Engels
- Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology, Magnetoencephalography Centre, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Michael A van Es
- University Medical Centre Utrecht, Department of Neurology, Brain Centre Rudolf Magnus, Utrecht, the Netherlands
| | - Jan H Veldink
- University Medical Centre Utrecht, Department of Neurology, Brain Centre Rudolf Magnus, Utrecht, the Netherlands
| | - Leonard H van den Berg
- University Medical Centre Utrecht, Department of Neurology, Brain Centre Rudolf Magnus, Utrecht, the Netherlands
| | - Anneke J van der Kooi
- Amsterdam University Medical Centers, University of Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Yolande A L Pijnenburg
- Amsterdam University Medical Centers, Vrije Universiteit, Alzheimer Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Marianne de Visser
- Amsterdam University Medical Centers, University of Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Cornelis J Stam
- Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology, Magnetoencephalography Centre, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Joost Raaphorst
- Amsterdam University Medical Centers, University of Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Arjan Hillebrand
- Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology, Magnetoencephalography Centre, Amsterdam Neuroscience, Amsterdam, the Netherlands
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45
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Marhl U, Sander T, Jazbinšek V. Simulation Study of Different OPM-MEG Measurement Components. SENSORS 2022; 22:s22093184. [PMID: 35590874 PMCID: PMC9105726 DOI: 10.3390/s22093184] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 04/15/2022] [Accepted: 04/20/2022] [Indexed: 02/04/2023]
Abstract
Magnetoencephalography (MEG) is a neuroimaging technique that measures the magnetic fields of the brain outside of the head. In the past, the most suitable magnetometer for MEG was the superconducting quantum interference device (SQUID), but in recent years, a new type has also been used, the optically pumped magnetometer (OPM). OPMs can be configured to measure multiple directions of magnetic field simultaneously. This work explored whether combining multiple directions of the magnetic field lowers the source localization error of brain sources under various conditions of noise. We simulated dipolar-like sources for multiple configurations of both SQUID- and OPM-MEG systems. To test the performance of a given layout, we calculated the average signal-to-noise ratio and the root mean square of the simulated magnetic field; furthermore, we evaluated the performance of the dipole fit. The results showed that the field direction normal to the scalp yields a higher signal-to-noise ratio and that ambient noise has a much lower impact on its localization error; therefore, this is the optimal choice for source localization when only one direction of magnetic field can be measured. For a low number of OPMs, combining multiple field directions greatly improves the source localization results. Lastly, we showed that MEG sensors that can be placed closer to the brain are more suitable for localizing deeper sources.
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Affiliation(s)
- Urban Marhl
- Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, 2000 Maribor, Slovenia
- Department of Physics, Institute of Mathematics, Physics and Mechanics, Jadranska ulica 19, 1000 Ljubljana, Slovenia;
- Correspondence:
| | - Tilmann Sander
- Physikalisch-Technische Bundesanstalt, Abbestraße 2, 10587 Berlin, Germany;
| | - Vojko Jazbinšek
- Department of Physics, Institute of Mathematics, Physics and Mechanics, Jadranska ulica 19, 1000 Ljubljana, Slovenia;
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46
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Luppi JJ, Schoonhoven DN, van Nifterick AM, Gouw AA, Hillebrand A, Scheltens P, Stam CJ, de Haan W. Oscillatory Activity of the Hippocampus in Prodromal Alzheimer’s Disease: A Source-Space Magnetoencephalography Study. J Alzheimers Dis 2022; 87:317-333. [PMID: 35311705 PMCID: PMC9198749 DOI: 10.3233/jad-215464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: In Alzheimer’s disease (AD), oscillatory activity of the human brain slows down. However, oscillatory slowing varies between individuals, particularly in prodromal AD. Cortical oscillatory changes have shown suboptimal accuracy as diagnostic markers. We speculated that focusing on the hippocampus might prove more successful, particularly using magnetoencephalography (MEG) for capturing subcortical oscillatory activity. Objective: We explored MEG-based detection of hippocampal oscillatory abnormalities in prodromal AD patients. Methods: We acquired resting-state MEG data of 18 AD dementia patients, 18 amyloid-β-positive amnestic mild cognitive impairment (MCI, prodromal AD) patients, and 18 amyloid-β-negative persons with subjective cognitive decline (SCD). Oscillatory activity in 78 cortical regions and both hippocampi was reconstructed using beamforming. Between-group and hippocampal-cortical differences in spectral power were assessed. Classification accuracy was explored using ROC curves. Results: The MCI group showed intermediate power values between SCD and AD, except for the alpha range, where it was higher than both (p < 0.05 and p < 0.001). The largest differences between MCI and SCD were in the theta band, with higher power in MCI (p < 0.01). The hippocampi showed several unique group differences, such as higher power in the higher alpha band in MCI compared to SCD (p < 0.05). Classification accuracy (MCI versus SCD) was best for absolute theta band power in the right hippocampus (AUC = 0.87). Conclusion: In this MEG study, we detected oscillatory abnormalities of the hippocampi in prodromal AD patients. Moreover, hippocampus-based classification performed better than cortex-based classification. We conclude that a focus on hippocampal MEG may improve early detection of AD-related neuronal dysfunction.
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Affiliation(s)
- Janne J. Luppi
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Deborah N. Schoonhoven
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Anne M. van Nifterick
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Alida A. Gouw
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
- Department of Clinical Neurophysiology and MEG, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Philip Scheltens
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Cornelis J. Stam
- Department of Clinical Neurophysiology and MEG, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
| | - Willem de Haan
- Alzheimer Center and Department of Neurology, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
- Department of Clinical Neurophysiology and MEG, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC, The Netherlands
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Improving Detection of Hippocampal Epileptiform Activity Using Magnetoencephalography. J Clin Neurophysiol 2022; 39:240-246. [PMID: 35255008 DOI: 10.1097/wnp.0000000000000822] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
PURPOSE Magnetoencephalography (MEG) defines the spike-generating zone and provides targets for invasive monitoring with stereotactic electroencephalography. This retrospective, blinded, cross-sectional study determined whether MEG virtual sensors could identify hippocampal epileptiform activity. METHODS Using MEG beamformer analysis, virtual sensors were manually placed in bilateral hippocampi and corresponding virtual sensor waveforms were analyzed for the presence of epileptiform activity. These findings were compared with hippocampal stereotactic electroencephalography in the same patients. Concordance was determined using sensitivity and specificity. RESULTS Thirty patients (mean age 12.5 ± 5.9 years) and 35 hippocampi were included. Patients were also placed into subgroups based on conventional MEG analysis: temporal (n = 19), extratemporal (n = 10), and normal (n = 1). Overall, sensitivity and specificity were 57.9% and 50.0%, respectively (n = 35). Patients with temporal sources based on conventional MEG analysis had sensitivity and specificity of 80.0% and 36.4%, respectively (n = 21). Those with extratemporal sources based on conventional MEG had sensitivity and specificity of 42.9% and 80.0%, respectively (n = 12). CONCLUSIONS When grouped by conventional MEG analysis, virtual sensors can be useful to confirm mesial temporal dipoles seen with conventional analysis. SIGNIFICANCE This work may help support the use of MEG for the detection of epileptiform activity in the hippocampus and influence the planning of invasive electrode placement.
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Schoonhoven DN, Briels CT, Hillebrand A, Scheltens P, Stam CJ, Gouw AA. Sensitive and reproducible MEG resting-state metrics of functional connectivity in Alzheimer's disease. Alzheimers Res Ther 2022; 14:38. [PMID: 35219327 PMCID: PMC8881826 DOI: 10.1186/s13195-022-00970-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 01/30/2022] [Indexed: 01/08/2023]
Abstract
Background Analysis of functional brain networks in Alzheimer’s disease (AD) has been hampered by a lack of reproducible, yet valid metrics of functional connectivity (FC). This study aimed to assess both the sensitivity and reproducibility of the corrected amplitude envelope correlation (AEC-c) and phase lag index (PLI), two metrics of FC that are insensitive to the effects of volume conduction and field spread, in two separate cohorts of patients with dementia due to AD versus healthy elderly controls. Methods Subjects with a clinical diagnosis of AD dementia with biomarker proof, and a control group of subjective cognitive decline (SCD), underwent two 5-min resting-state MEG recordings. Data consisted of a test (AD = 28; SCD = 29) and validation (AD = 29; SCD = 27) cohort. Time-series were estimated for 90 regions of interest (ROIs) in the automated anatomical labelling (AAL) atlas. For each of five canonical frequency bands, the AEC-c and PLI were calculated between all 90 ROIs, and connections were averaged per ROI. General linear models were constructed to compare the global FC differences between the groups, assess the reproducibility, and evaluate the effects of age and relative power. Reproducibility of the regional FC differences was assessed using the Mann-Whitney U tests, with correction for multiple testing using the false discovery rate (FDR). Results The AEC-c showed significantly and reproducibly lower global FC for the AD group compared to SCD, in the alpha (8–13 Hz) and beta (13–30 Hz) bands, while the PLI revealed reproducibly lower FC for the AD group in the delta (0.5–4 Hz) band and higher FC for the theta (4–8 Hz) band. Regionally, the beta band AEC-c showed reproducibility for almost all ROIs (except for 13 ROIs in the frontal and temporal lobes). For the other bands, the AEC-c and PLI did not show regional reproducibility after FDR correction. The theta band PLI was susceptible to the effect of relative power. Conclusion For MEG, the AEC-c is a sensitive and reproducible metric, able to distinguish FC differences between patients with AD dementia and cognitively healthy controls. These two measures likely reflect different aspects of neural activity and show differential sensitivity to changes in neural dynamics. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-022-00970-4.
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Affiliation(s)
- Deborah N Schoonhoven
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands. .,Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
| | - Casper T Briels
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Alida A Gouw
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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Bosch-Bayard J, Biscay RJ, Fernandez T, Otero GA, Ricardo-Garcell J, Aubert-Vazquez E, Evans AC, Harmony T. EEG effective connectivity during the first year of life mirrors brain synaptogenesis, myelination, and early right hemisphere predominance. Neuroimage 2022; 252:119035. [PMID: 35218932 DOI: 10.1016/j.neuroimage.2022.119035] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 11/25/2021] [Accepted: 02/22/2022] [Indexed: 10/19/2022] Open
Abstract
INTRODUCTION The maturation of electroencephalogram (EEG) effective connectivity in healthy infants during the first year of life is described. METHODS Participants: A cross-sectional sample of 125 healthy at-term infants, from 0 to 12 months of age, underwent EEG in a state of quiet sleep. PROCEDURES The EEG primary currents at the source were described with the sLoreta method. An unmixing algorithm was applied to reduce the leakage, and the isolated effective coherence, a direct and directed measurement of information flow, was calculated. RESULTS AND DISCUSSION Initially, the highest indices of connectivity are at the subcortical nuclei, continuing to the parietal lobe, predominantly the right hemisphere, then expanding to temporal, occipital, and finally the frontal areas, which is consistent with the myelination process. Age-related connectivity changes were mostly long-range and bilateral. Connections increased with age, mainly in the right hemisphere, while they mainly decreased in the left hemisphere. Increased connectivity from 20 to 30 Hz, mostly at the right hemisphere. These findings were consistent with right hemisphere predominance during the first three years of life. Theta and alpha connections showed the greatest changes with age. Strong connectivity was found between the parietal, temporal, and occipital regions to the frontal lobes, responsible for executive functions and consistent with behavioral development during the first year. The thalamus exchanges information bidirectionally with all cortical regions and frequency bands. CONCLUSIONS The maturation of EEG connectivity during the first year in healthy infants is very consistent with synaptogenesis, reductions in synaptogenesis, myelination, and functional and behavioral development.
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Affiliation(s)
- Jorge Bosch-Bayard
- McGill Center for Integrative Neuroscience (MCIN), Ludmer Center for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal H3A2B4, Canada
| | - Rolando J Biscay
- Centro de Investigación en Matemáticas, Guanajuato 36023, Mexico
| | - Thalia Fernandez
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Boulevard Juriquilla 3001, Querétaro 76230, Mexico
| | - Gloria A Otero
- Facultad de Medicina, Universidad Autónoma del Estado de México, Toluca de Lerdo 50180, Mexico
| | - Josefina Ricardo-Garcell
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Boulevard Juriquilla 3001, Querétaro 76230, Mexico
| | | | - Alan C Evans
- McGill Center for Integrative Neuroscience (MCIN), Ludmer Center for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal H3A2B4, Canada
| | - Thalia Harmony
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Boulevard Juriquilla 3001, Querétaro 76230, Mexico.
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Royer J, Bernhardt BC, Larivière S, Gleichgerrcht E, Vorderwülbecke BJ, Vulliémoz S, Bonilha L. Epilepsy and brain network hubs. Epilepsia 2022; 63:537-550. [DOI: 10.1111/epi.17171] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 01/03/2022] [Accepted: 01/10/2022] [Indexed: 02/06/2023]
Affiliation(s)
- Jessica Royer
- Multimodal Imaging and Connectome Analysis Laboratory Montreal Neurological Institute and Hospital McGill University Montreal Quebec Canada
| | - Boris C. Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory Montreal Neurological Institute and Hospital McGill University Montreal Quebec Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory Montreal Neurological Institute and Hospital McGill University Montreal Quebec Canada
| | - Ezequiel Gleichgerrcht
- Department of Neurology Medical University of South Carolina Charleston South Carolina USA
| | - Bernd J. Vorderwülbecke
- EEG and Epilepsy Unit University Hospitals and Faculty of Medicine Geneva Geneva Switzerland
- Department of Neurology Epilepsy Center Berlin‐Brandenburg Charité–Universitätsmedizin Berlin Berlin Germany
| | - Serge Vulliémoz
- EEG and Epilepsy Unit University Hospitals and Faculty of Medicine Geneva Geneva Switzerland
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