1
|
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.
Collapse
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
| |
Collapse
|
2
|
Nuttall R, El Mir A, Jäger C, Letz S, Wohlschläger A, Schneider G. Broadly applicable methods for the detection of artefacts in electroencephalography acquired simultaneously with hemodynamic recordings. MethodsX 2023; 11:102376. [PMID: 37767154 PMCID: PMC10520509 DOI: 10.1016/j.mex.2023.102376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Electroencephalography (EEG) data, acquired simultaneously with magnetic resonance imaging (MRI), must be corrected for artefacts related to MR gradient switches (GS) and the cardioballistic (CB) effect. Canonical approaches require additional signal acquisition for artefact detection (e.g., MR volume onsets, ECG), without which the EEG data would be rendered uncleanable from these artefacts.•We present two broadly applicable methods for artefact detection based on peak detection combined with temporal constraints with respect to periodicity directly from the EEG data itself; no additional signals are required. We validated the performance of our methods versus the two canonical approaches for detection of GS/CB artefact, respectively, on 26 healthy human EEG-functional MRI resting-state datasets. Utilising various performance metrics, we found our methods to perform as well as - and sometimes better than - the canonical standard approaches. With as little as one EEG channel recording, our methods can be applied to detect GS/CB artefacts in EEG data acquired simultaneously with MRI in the absence of MR volume onsets and/or an ECG recording. The detected artefact onsets can then be fed into the standard artefact correction software.
Collapse
Affiliation(s)
- Rachel Nuttall
- Department of Anesthesiology and Intensive Care, School of Medicine, Technical University of Munich, Klinikum rechts der Isar, Munich 81675, Germany
| | - Aya El Mir
- Department of Anesthesiology and Intensive Care, School of Medicine, Technical University of Munich, Klinikum rechts der Isar, Munich 81675, Germany
- New York University Abu Dhabi, Engineering Division, Saadiyat Marina District, Abu Dhabi, United Arab Emirates
| | - Cilia Jäger
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Klinikum rechts der Isar, Munich 81675, Germany
| | - Svenja Letz
- Department of Anesthesiology and Intensive Care, School of Medicine, Technical University of Munich, Klinikum rechts der Isar, Munich 81675, Germany
| | - Afra Wohlschläger
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Klinikum rechts der Isar, Munich 81675, Germany
| | - Gerhard Schneider
- Department of Anesthesiology and Intensive Care, School of Medicine, Technical University of Munich, Klinikum rechts der Isar, Munich 81675, Germany
| |
Collapse
|
3
|
Guedj C, Tyrand R, Badier E, Planchamp L, Stringer M, Zimmermann MO, Férat V, Ha-Vinh Leuchter R, Grouiller F. Self-Regulation of Attention in Children in a Virtual Classroom Environment: A Feasibility Study. Bioengineering (Basel) 2023; 10:1352. [PMID: 38135943 PMCID: PMC10741222 DOI: 10.3390/bioengineering10121352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 11/17/2023] [Accepted: 11/23/2023] [Indexed: 12/24/2023] Open
Abstract
Attention is a crucial cognitive function that enables us to selectively focus on relevant information from the surrounding world to achieve our goals. Impairments in sustained attention pose challenges, particularly in children with attention deficit hyperactivity disorder, a neurodevelopmental disorder characterized by impulsive and inattentive behavior. While psychostimulant medications are the most effective ADHD treatment, they often yield unwanted side effects, making it crucial to explore non-pharmacological treatments. We propose a groundbreaking protocol that combines electroencephalography-based neurofeedback with virtual reality (VR) as an innovative approach to address attention deficits. By integrating a virtual classroom environment, we aim to enhance the transferability of attentional control skills while simultaneously increasing motivation and interest among children. The present study demonstrates the feasibility of this approach through an initial assessment involving a small group of healthy children, showcasing its potential for future evaluation in ADHD children. Preliminary results indicate high engagement and positive feedback. Pre- and post-protocol assessments via EEG and fMRI recordings suggest changes in attentional function. Further validation is required, but this protocol is a significant advancement in neurofeedback therapy for ADHD. The integration of EEG-NFB and VR presents a novel avenue for enhancing attentional control and addressing behavioral challenges in children with ADHD.
Collapse
Affiliation(s)
- Carole Guedj
- Swiss Center for Affective Sciences, University of Geneva, 1202 Geneva, Switzerland; (R.T.); (E.B.); (F.G.)
- Laboratory of Behavioral Neurology and Imaging of Cognition, Department of Basic Neurosciences, University of Geneva, 1202 Geneva, Switzerland; (L.P.); (M.S.); (M.O.Z.)
| | - Rémi Tyrand
- Swiss Center for Affective Sciences, University of Geneva, 1202 Geneva, Switzerland; (R.T.); (E.B.); (F.G.)
| | - Emmanuel Badier
- Swiss Center for Affective Sciences, University of Geneva, 1202 Geneva, Switzerland; (R.T.); (E.B.); (F.G.)
| | - Lou Planchamp
- Laboratory of Behavioral Neurology and Imaging of Cognition, Department of Basic Neurosciences, University of Geneva, 1202 Geneva, Switzerland; (L.P.); (M.S.); (M.O.Z.)
| | - Madison Stringer
- Laboratory of Behavioral Neurology and Imaging of Cognition, Department of Basic Neurosciences, University of Geneva, 1202 Geneva, Switzerland; (L.P.); (M.S.); (M.O.Z.)
| | - Myriam Ophelia Zimmermann
- Laboratory of Behavioral Neurology and Imaging of Cognition, Department of Basic Neurosciences, University of Geneva, 1202 Geneva, Switzerland; (L.P.); (M.S.); (M.O.Z.)
| | - Victor Férat
- Functional Brain Mapping Laboratory, Department of Basic Neurosciences, University of Geneva, 1202 Geneva, Switzerland;
| | - Russia Ha-Vinh Leuchter
- Division of Development and Growth, Department of Paediatrics, Gynaecology and Obstetrics, Geneva University Hospitals, 1205 Geneva, Switzerland;
| | - Frédéric Grouiller
- Swiss Center for Affective Sciences, University of Geneva, 1202 Geneva, Switzerland; (R.T.); (E.B.); (F.G.)
- Laboratory of Behavioral Neurology and Imaging of Cognition, Department of Basic Neurosciences, University of Geneva, 1202 Geneva, Switzerland; (L.P.); (M.S.); (M.O.Z.)
- CIBM MRI Cognitive and Affective Neuroimaging Section, Center for Biomedical Imaging, University of Geneva, 1202 Geneva, Switzerland
| |
Collapse
|
4
|
Gallego-Rudolf J, Corsi-Cabrera M, Concha L, Ricardo-Garcell J, Pasaye-Alcaraz E. Preservation of EEG spectral power features during simultaneous EEG-fMRI. Front Neurosci 2022; 16:951321. [PMID: 36620439 PMCID: PMC9816433 DOI: 10.3389/fnins.2022.951321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 12/08/2022] [Indexed: 12/24/2022] Open
Abstract
Introduction Electroencephalographic (EEG) data quality is severely compromised when recorded inside the magnetic resonance (MR) environment. Here we characterized the impact of the ballistocardiographic (BCG) artifact on resting-state EEG spectral properties and compared the effectiveness of seven common BCG correction methods to preserve EEG spectral features. We also assessed if these methods retained posterior alpha power reactivity to an eyes closure-opening (EC-EO) task and compared the results from EEG-informed fMRI analysis using different BCG correction approaches. Method Electroencephalographic data from 20 healthy young adults were recorded outside the MR environment and during simultaneous fMRI acquisition. The gradient artifact was effectively removed from EEG-fMRI acquisitions using Average Artifact Subtraction (AAS). The BCG artifact was corrected with seven methods: AAS, Optimal Basis Set (OBS), Independent Component Analysis (ICA), OBS followed by ICA, AAS followed by ICA, PROJIC-AAS and PROJIC-OBS. EEG signal preservation was assessed by comparing the spectral power of traditional frequency bands from the corrected rs-EEG-fMRI data with the data recorded outside the scanner. We then assessed the preservation of posterior alpha functional reactivity by computing the ratio between the EC and EO conditions during the EC-EO task. EEG-informed fMRI analysis of the EC-EO task was performed using alpha power-derived BOLD signal predictors obtained from the EEG signals corrected with different methods. Results The BCG artifact caused significant distortions (increased absolute power, altered relative power) across all frequency bands. Artifact residuals/signal losses were present after applying all correction methods. The EEG reactivity to the EC-EO task was better preserved with ICA-based correction approaches, particularly when using ICA feature extraction to isolate alpha power fluctuations, which allowed to accurately predict hemodynamic signal fluctuations during the EEG-informed fMRI analysis. Discussion Current software solutions for the BCG artifact problem offer limited efficiency to preserve the EEG spectral power properties using this particular EEG setup. The state-of-the-art approaches tested here can be further refined and should be combined with hardware implementations to better preserve EEG signal properties during simultaneous EEG-fMRI. Existing and novel BCG artifact correction methods should be validated by evaluating signal preservation of both ERPs and spontaneous EEG spectral power.
Collapse
Affiliation(s)
- Jonathan Gallego-Rudolf
- Unidad de Resonancia Magnética, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, Mexico
| | - María Corsi-Cabrera
- Laboratorio de Sueño, Facultad de Psicología, Universidad Nacional Autónoma de México, Mexico City, Mexico,Unidad de Neurodesarrollo, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
| | - Luis Concha
- Laboratorio de Conectividad Cerebral, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, Mexico
| | - Josefina Ricardo-Garcell
- Unidad de Neurodesarrollo, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
| | - Erick Pasaye-Alcaraz
- Unidad de Resonancia Magnética, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, Mexico,*Correspondence: Erick Pasaye-Alcaraz,
| |
Collapse
|
5
|
Uji M, Cross N, Pomares FB, Perrault AA, Jegou A, Nguyen A, Aydin U, Lina JM, Dang-Vu TT, Grova C. Data-driven beamforming technique to attenuate ballistocardiogram artefacts in electroencephalography-functional magnetic resonance imaging without detecting cardiac pulses in electrocardiography recordings. Hum Brain Mapp 2021; 42:3993-4021. [PMID: 34101939 PMCID: PMC8288107 DOI: 10.1002/hbm.25535] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 05/03/2021] [Accepted: 05/06/2021] [Indexed: 11/21/2022] Open
Abstract
Simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a very promising non‐invasive neuroimaging technique. However, EEG data obtained from the simultaneous EEG–fMRI are strongly influenced by MRI‐related artefacts, namely gradient artefacts (GA) and ballistocardiogram (BCG) artefacts. When compared to the GA correction, the BCG correction is more challenging to remove due to its inherent variabilities and dynamic changes over time. The standard BCG correction (i.e., average artefact subtraction [AAS]), require detecting cardiac pulses from simultaneous electrocardiography (ECG) recording. However, ECG signals are also distorted and will become problematic for detecting reliable cardiac peaks. In this study, we focused on a beamforming spatial filtering technique to attenuate all unwanted source activities outside of the brain. Specifically, we applied the beamforming technique to attenuate the BCG artefact in EEG–fMRI, and also to recover meaningful task‐based neural signals during an attentional network task (ANT) which required participants to identify visual cues and respond accurately. We analysed EEG–fMRI data in 20 healthy participants during the ANT, and compared four different BCG corrections (non‐BCG corrected, AAS BCG corrected, beamforming + AAS BCG corrected, beamforming BCG corrected). We demonstrated that the beamforming approach did not only significantly reduce the BCG artefacts, but also significantly recovered the expected task‐based brain activity when compared to the standard AAS correction. This data‐driven beamforming technique appears promising especially for longer data acquisition of sleep and resting EEG–fMRI. Our findings extend previous work regarding the recovery of meaningful EEG signals by an optimized suppression of MRI‐related artefacts.
Collapse
Affiliation(s)
- Makoto Uji
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montréal, Québec, Canada
| | - Nathan Cross
- PERFORM Centre, Center for Studies in Behavioral Neurobiology, Department of Health, Kinesiology and Applied Physiology, Concordia University, Montréal, Québec, Canada.,Institut Universitaire de Gériatrie de Montréal and CRIUGM, CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montréal, Québec, Canada
| | - Florence B Pomares
- PERFORM Centre, Center for Studies in Behavioral Neurobiology, Department of Health, Kinesiology and Applied Physiology, Concordia University, Montréal, Québec, Canada.,Institut Universitaire de Gériatrie de Montréal and CRIUGM, CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montréal, Québec, Canada
| | - Aurore A Perrault
- PERFORM Centre, Center for Studies in Behavioral Neurobiology, Department of Health, Kinesiology and Applied Physiology, Concordia University, Montréal, Québec, Canada.,Institut Universitaire de Gériatrie de Montréal and CRIUGM, CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montréal, Québec, Canada
| | - Aude Jegou
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montréal, Québec, Canada.,Aix-Marseille University, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Alex Nguyen
- PERFORM Centre, Center for Studies in Behavioral Neurobiology, Department of Health, Kinesiology and Applied Physiology, Concordia University, Montréal, Québec, Canada.,Institut Universitaire de Gériatrie de Montréal and CRIUGM, CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montréal, Québec, Canada
| | - Umit Aydin
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montréal, Québec, Canada.,Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Jean-Marc Lina
- Departement de Genie Electrique, Ecole de Technologie Superieure, Montreal, Quebec, Canada.,Centre de Recherches Mathematiques, Montréal, Québec, Canada
| | - Thien Thanh Dang-Vu
- PERFORM Centre, Center for Studies in Behavioral Neurobiology, Department of Health, Kinesiology and Applied Physiology, Concordia University, Montréal, Québec, Canada.,Institut Universitaire de Gériatrie de Montréal and CRIUGM, CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montréal, Québec, Canada
| | - Christophe Grova
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montréal, Québec, Canada.,Centre de Recherches Mathematiques, Montréal, Québec, Canada.,Multimodal Functional Imaging Lab, Biomedical Engineering Department, Neurology and Neurosurgery Department, McGill University, Montréal, Québec, Canada
| |
Collapse
|
6
|
Bullock M, Jackson GD, Abbott DF. Artifact Reduction in Simultaneous EEG-fMRI: A Systematic Review of Methods and Contemporary Usage. Front Neurol 2021; 12:622719. [PMID: 33776886 PMCID: PMC7991907 DOI: 10.3389/fneur.2021.622719] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 01/29/2021] [Indexed: 11/13/2022] Open
Abstract
Simultaneous electroencephalography-functional MRI (EEG-fMRI) is a technique that combines temporal (largely from EEG) and spatial (largely from fMRI) indicators of brain dynamics. It is useful for understanding neuronal activity during many different event types, including spontaneous epileptic discharges, the activity of sleep stages, and activity evoked by external stimuli and decision-making tasks. However, EEG recorded during fMRI is subject to imaging, pulse, environment and motion artifact, causing noise many times greater than the neuronal signals of interest. Therefore, artifact removal methods are essential to ensure that artifacts are accurately removed, and EEG of interest is retained. This paper presents a systematic review of methods for artifact reduction in simultaneous EEG-fMRI from literature published since 1998, and an additional systematic review of EEG-fMRI studies published since 2016. The aim of the first review is to distill the literature into clear guidelines for use of simultaneous EEG-fMRI artifact reduction methods, and the aim of the second review is to determine the prevalence of artifact reduction method use in contemporary studies. We find that there are many published artifact reduction techniques available, including hardware, model based, and data-driven methods, but there are few studies published that adequately compare these methods. In contrast, recent EEG-fMRI studies show overwhelming use of just one or two artifact reduction methods based on literature published 15–20 years ago, with newer methods rarely gaining use outside the group that developed them. Surprisingly, almost 15% of EEG-fMRI studies published since 2016 fail to adequately describe the methods of artifact reduction utilized. We recommend minimum standards for reporting artifact reduction techniques in simultaneous EEG-fMRI studies and suggest that more needs to be done to make new artifact reduction techniques more accessible for the researchers and clinicians using simultaneous EEG-fMRI.
Collapse
Affiliation(s)
- Madeleine Bullock
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia.,Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia
| | - Graeme D Jackson
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia.,Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia.,Department of Medicine (Austin Health), The University of Melbourne, Melbourne, VIC, Australia
| | - David F Abbott
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia.,Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia.,Department of Medicine (Austin Health), The University of Melbourne, Melbourne, VIC, Australia
| |
Collapse
|
7
|
Wirsich J, Jorge J, Iannotti GR, Shamshiri EA, Grouiller F, Abreu R, Lazeyras F, Giraud AL, Gruetter R, Sadaghiani S, Vulliémoz S. The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T. Neuroimage 2021; 231:117864. [PMID: 33592241 DOI: 10.1016/j.neuroimage.2021.117864] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 01/21/2021] [Accepted: 02/09/2021] [Indexed: 01/01/2023] Open
Abstract
Both electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are non-invasive methods that show complementary aspects of human brain activity. Despite measuring different proxies of brain activity, both the measured blood-oxygenation (fMRI) and neurophysiological recordings (EEG) are indirectly coupled. The electrophysiological and BOLD signal can map the underlying functional connectivity structure at the whole brain scale at different timescales. Previous work demonstrated a moderate but significant correlation between resting-state functional connectivity of both modalities, however there is a wide range of technical setups to measure simultaneous EEG-fMRI and the reliability of those measures between different setups remains unknown. This is true notably with respect to different magnetic field strengths (low and high field) and different spatial sampling of EEG (medium to high-density electrode coverage). Here, we investigated the reproducibility of the bimodal EEG-fMRI functional connectome in the most comprehensive resting-state simultaneous EEG-fMRI dataset compiled to date including a total of 72 subjects from four different imaging centers. Data was acquired from 1.5T, 3T and 7T scanners with simultaneously recorded EEG using 64 or 256 electrodes. We demonstrate that the whole-brain monomodal connectivity reproducibly correlates across different datasets and that a moderate crossmodal correlation between EEG and fMRI connectivity of r ≈ 0.3 can be reproducibly extracted in low- and high-field scanners. The crossmodal correlation was strongest in the EEG-β frequency band but exists across all frequency bands. Both homotopic and within intrinsic connectivity network (ICN) connections contributed the most to the crossmodal relationship. This study confirms, using a considerably diverse range of recording setups, that simultaneous EEG-fMRI offers a consistent estimate of multimodal functional connectomes in healthy subjects that are dominantly linked through a functional core of ICNs across spanning across the different timescales measured by EEG and fMRI. This opens new avenues for estimating the dynamics of brain function and provides a better understanding of interactions between EEG and fMRI measures. This observed level of reproducibility also defines a baseline for the study of alterations of this coupling in pathological conditions and their role as potential clinical markers.
Collapse
Affiliation(s)
- Jonathan Wirsich
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva, Geneva, Switzerland.
| | - João Jorge
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Systems Division, Swiss Center for Electronics and Microtechnology (CSEM), Neuchâtel, Switzerland
| | - Giannina Rita Iannotti
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva, Geneva, Switzerland
| | - Elhum A Shamshiri
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva, Geneva, Switzerland
| | - Frédéric Grouiller
- Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland
| | - Rodolfo Abreu
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico - Universidade de Lisboa, Lisbon, Portugal; Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - François Lazeyras
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Anne-Lise Giraud
- Department of Neuroscience, University of Geneva, Geneva, Switzerland
| | - Rolf Gruetter
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland; Department of Radiology, University of Lausanne, Lausanne, Switzerland
| | - Sepideh Sadaghiani
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, United States; Psychology Department, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Serge Vulliémoz
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva, Geneva, Switzerland
| |
Collapse
|
8
|
Iannotti GR, Preti MG, Grouiller F, Carboni M, De Stefano P, Pittau F, Momjian S, Carmichael D, Centeno M, Seeck M, Korff CM, Schaller K, De Ville DV, Vulliemoz S. Modulation of epileptic networks by transient interictal epileptic activity: A dynamic approach to simultaneous EEG-fMRI. NEUROIMAGE-CLINICAL 2020; 28:102467. [PMID: 33395963 PMCID: PMC7645285 DOI: 10.1016/j.nicl.2020.102467] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 09/15/2020] [Accepted: 10/09/2020] [Indexed: 12/27/2022]
Abstract
EEG-fMRI has been instrumental in characterizing brain networks in epilepsy. Its value is documented in the pre-surgical assessment of drug-resistant epilepsy. The delineation of brain areas to resect is fundamental for the post-surgical outcome. Standard EEG-fMRI in epilepsy assesses static functional connectivity of the network. EEG-fMRI dynamic connectivity identifies transitory features of specific connections. We integrate dynamic fMRI connectivity and dynamic patterns of simultaneous scalp EEG. This allows to better characterize the spatiotemporal aspects of epileptic networks. This may help in more efficiently target the surgical intervention.
Epileptic networks, defined as brain regions involved in epileptic brain activity, have been mapped by functional connectivity in simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) recordings. This technique allows to define brain hemodynamic changes, measured by the Blood Oxygen Level Dependent (BOLD) signal, associated to the interictal epileptic discharges (IED), which together with ictal events constitute a signature of epileptic disease. Given the highly time-varying nature of epileptic activity, a dynamic functional connectivity (dFC) analysis of EEG-fMRI data appears particularly suitable, having the potential to identify transitory features of specific connections in epileptic networks. In the present study, we propose a novel method, defined dFC-EEG, that integrates dFC assessed by fMRI with the information recorded by simultaneous scalp EEG, in order to identify the connections characterised by a dynamic profile correlated with the occurrence of IED, forming the dynamic epileptic subnetwork. Ten patients with drug-resistant focal epilepsy were included, with different aetiology and showing a widespread (or multilobar) BOLD activation, defined as involving at least two distinct clusters, located in two different lobes and/or extended to the hemisphere contralateral to the epileptic focus. The epileptic focus was defined from the IED-related BOLD map. Regions involved in the occurrence of interictal epileptic activity; i.e., forming the epileptic network, were identified by a general linear model considering the timecourse of the fMRI-defined focus as main regressor. dFC between these regions was assessed with a sliding-window approach. dFC timecourses were then correlated with the sliding-window variance of the IED signal (VarIED), to identify connections whose dynamics related to the epileptic activity; i.e., the dynamic epileptic subnetwork. As expected, given the very different clinical picture of each individual, the extent of this subnetwork was highly variable across patients, but was but was reduced of at least 30% with respect to the initially identified epileptic network in 9/10 patients. The connections of the dynamic subnetwork were most commonly close to the epileptic focus, as reflected by the laterality index of the subnetwork connections, reported higher than the one within the original epileptic network. Moreover, the correlation between dFC timecourses and VarIED was predominantly positive, suggesting a strengthening of the dynamic subnetwork associated to the occurrence of IED. The integration of dFC and scalp IED offers a more specific description of the epileptic network, identifying connections strongly influenced by IED. These findings could be relevant in the pre-surgical evaluation for the resection or disconnection of the epileptogenic zone and help in reaching a better post-surgical outcome. This would be particularly important for patients characterised by a widespread pathological brain activity which challenges the surgical intervention.
Collapse
Affiliation(s)
- G R Iannotti
- EEG and Epilepsy, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland; Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Switzerland; Neurosurgery, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland.
| | - M G Preti
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - F Grouiller
- Swiss Center for Affective Sciences, University of Geneva, Switzerland; Laboratory of Behavioral Neurology and Imaging of Cognition, Department of Fundamental Neurosciences, University of Geneva, Switzerland
| | - M Carboni
- EEG and Epilepsy, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland; Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Switzerland
| | - P De Stefano
- EEG and Epilepsy, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland
| | - F Pittau
- EEG and Epilepsy, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland; Epilepsy Unit, Institution de Lavigny, Switzerland
| | - S Momjian
- Neurosurgery, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland
| | - D Carmichael
- Biomedical Engineering Department, Kings College London, United Kingdom; Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London, United Kingdom
| | - M Centeno
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London, United Kingdom; Epilepsy Unit, Neurology Department, Clinica Universidad de Pamplona, Navarra, Spain
| | - M Seeck
- EEG and Epilepsy, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland
| | - C M Korff
- Pediatric Neurology Unit, University Hospitals of Geneva, Geneva, Switzerland
| | - K Schaller
- Neurosurgery, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland
| | - D Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - S Vulliemoz
- EEG and Epilepsy, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland
| |
Collapse
|
9
|
Wirsich J, Giraud AL, Sadaghiani S. Concurrent EEG- and fMRI-derived functional connectomes exhibit linked dynamics. Neuroimage 2020; 219:116998. [PMID: 32480035 DOI: 10.1016/j.neuroimage.2020.116998] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 05/07/2020] [Accepted: 05/26/2020] [Indexed: 12/18/2022] Open
Abstract
Long-range connectivity has become the most studied feature of human functional Magnetic Resonance Imaging (fMRI), yet the spatial and temporal relationship between its whole-brain dynamics and electrophysiological connectivity remains largely unknown. FMRI-derived functional connectivity exhibits spatial reconfigurations or time-varying dynamics at infraslow (<0.1Hz) speeds. Conversely, electrophysiological connectivity is based on cross-region coupling of fast oscillations (~1-100Hz). It is unclear whether such fast oscillation-based coupling varies at infraslow speeds, temporally coinciding with infraslow dynamics across the fMRI-based connectome. If so, does the association of fMRI-derived and electrophysiological dynamics spatially vary over the connectome across the functionally distinct electrophysiological oscillation bands? In two concurrent electroencephalography (EEG)-fMRI resting-state datasets, oscillation-based coherence in all canonical bands (delta through gamma) indeed reconfigured at infraslow speeds in tandem with fMRI-derived connectivity changes in corresponding region-pairs. Interestingly, irrespective of EEG frequency-band the cross-modal tie of connectivity dynamics comprised a large proportion of connections distributed across the entire connectome. However, there were frequency-specific differences in the relative strength of the cross-modal association. This association was strongest in visual to somatomotor connections for slower EEG-bands, and in connections involving the Default Mode Network for faster EEG-bands. Methodologically, the findings imply that neural connectivity dynamics can be reliably measured by fMRI despite heavy susceptibility to noise, and by EEG despite shortcomings of source reconstruction. Biologically, the findings provide evidence that contrast with known territories of oscillation power, oscillation coupling in all bands slowly reconfigures in a highly distributed manner across the whole-brain connectome.
Collapse
Affiliation(s)
- Jonathan Wirsich
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA; Department of Psychology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
| | - Anne-Lise Giraud
- Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland
| | - Sepideh Sadaghiani
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA; Department of Psychology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| |
Collapse
|
10
|
Sadaghiani S, Wirsich J. Intrinsic connectome organization across temporal scales: New insights from cross-modal approaches. Netw Neurosci 2020; 4:1-29. [PMID: 32043042 PMCID: PMC7006873 DOI: 10.1162/netn_a_00114] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 11/11/2019] [Indexed: 12/17/2022] Open
Abstract
The discovery of a stable, whole-brain functional connectivity organization that is largely independent of external events has drastically extended our view of human brain function. However, this discovery has been primarily based on functional magnetic resonance imaging (fMRI). The role of this whole-brain organization in fast oscillation-based connectivity as measured, for example, by electroencephalography (EEG) and magnetoencephalography (MEG) is only beginning to emerge. Here, we review studies of intrinsic connectivity and its whole-brain organization in EEG, MEG, and intracranial electrophysiology with a particular focus on direct comparisons to connectome studies in fMRI. Synthesizing this literature, we conclude that irrespective of temporal scale over four orders of magnitude, intrinsic neurophysiological connectivity shows spatial similarity to the connectivity organization commonly observed in fMRI. A shared structural connectivity basis and cross-frequency coupling are possible mechanisms contributing to this similarity. Acknowledging that a stable whole-brain organization governs long-range coupling across all timescales of neural processing motivates researchers to take "baseline" intrinsic connectivity into account when investigating brain-behavior associations, and further encourages more widespread exploration of functional connectomics approaches beyond fMRI by using EEG and MEG modalities.
Collapse
Affiliation(s)
- Sepideh Sadaghiani
- Psychology Department, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Jonathan Wirsich
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| |
Collapse
|
11
|
Investigating the variability of cardiac pulse artifacts across heartbeats in simultaneous EEG-fMRI recordings: A 7T study. Neuroimage 2019; 191:21-35. [PMID: 30742980 DOI: 10.1016/j.neuroimage.2019.02.021] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 01/04/2019] [Accepted: 02/07/2019] [Indexed: 11/24/2022] Open
Abstract
Electroencephalography (EEG) recordings performed in magnetic resonance imaging (MRI) scanners are affected by complex artifacts caused by heart function, often termed pulse artifacts (PAs). PAs can strongly compromise EEG data quality, and remain an open problem for EEG-fMRI. This study investigated the properties and mechanisms of PA variability across heartbeats, which has remained largely unaddressed to date, and evaluated its impact on PA correction approaches. Simultaneous EEG-fMRI was performed at 7T on healthy participants at rest or under visual stimulation, with concurrent recordings of breathing and cardiac activity. PA variability was found to contribute to EEG variance with more than 500 μV2 at 7T, which extrapolates to 92 μV2 at 3T. Clustering analyses revealed that PA variability not only is linked to variations in head position/orientation, as previously hypothesized, but also, and more importantly, to the respiratory cycle and to heart rate fluctuations. The latter mechanisms are associated to short-timescale variability (even across consecutive heartbeats), and their importance varied across EEG channels. In light of this PA variability, three PA correction techniques were compared: average artifact subtraction (AAS), optimal basis sets (OBS), and an approach based on K-means clustering. All methods allowed the recovery of visual evoked potentials from the EEG data; nonetheless, OBS and K-means tended to outperform AAS, likely due to the inability of the latter in modeling short-timescale variability. Altogether, these results offer novel insights into the dynamics and underlying mechanisms of the pulse artifact, with important consequences for its correction, relevant to most EEG-fMRI applications.
Collapse
|
12
|
Daniel AJ, Smith JA, Spencer GS, Jorge J, Bowtell R, Mullinger KJ. Exploring the relative efficacy of motion artefact correction techniques for EEG data acquired during simultaneous fMRI. Hum Brain Mapp 2018; 40:578-596. [PMID: 30339731 PMCID: PMC6492138 DOI: 10.1002/hbm.24396] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Revised: 08/30/2018] [Accepted: 08/31/2018] [Indexed: 11/25/2022] Open
Abstract
Simultaneous EEG‐fMRI allows multiparametric characterisation of brain function, in principle enabling a more complete understanding of brain responses; unfortunately the hostile MRI environment severely reduces EEG data quality. Simply eliminating data segments containing gross motion artefacts [MAs] (generated by movement of the EEG system and head in the MRI scanner's static magnetic field) was previously believed sufficient. However recently the importance of removal of all MAs has been highlighted and new methods developed. A systematic comparison of the ability to remove MAs and retain underlying neuronal activity using different methods of MA detection and post‐processing algorithms is needed to guide the neuroscience community. Using a head phantom, we recorded MAs while simultaneously monitoring the motion using three different approaches: Reference Layer Artefact Subtraction (RLAS), Moiré Phase Tracker (MPT) markers and Wire Loop Motion Sensors (WLMS). These EEG recordings were combined with EEG responses to simple visual tasks acquired on a subject outside the MRI environment. MAs were then corrected using the motion information collected with each of the methods combined with different analysis pipelines. All tested methods retained the neuronal signal. However, often the MA was not removed sufficiently to allow accurate detection of the underlying neuronal signal. We show that the MA is best corrected using the RLAS combined with post‐processing using a multichannel, recursive least squares (M‐RLS) algorithm. This method needs to be developed further to enable practical utility; thus, WLMS combined with M‐RLS currently provides the best compromise between EEG data quality and practicalities of motion detection.
Collapse
Affiliation(s)
- Alexander J Daniel
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - James A Smith
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Glyn S Spencer
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom.,Department of Physics, Loughborough University, Leicestershire, United Kingdom
| | - João Jorge
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Richard Bowtell
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Karen J Mullinger
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom.,Birmingham University Imaging Centre, School of Psychology, University of Birmingham, Birmingham, United Kingdom
| |
Collapse
|
13
|
Wong CK, Luo Q, Zotev V, Phillips R, Chan KWC, Bodurka J. Automatic cardiac cycle determination directly from EEG-fMRI data by multi-scale peak detection method. J Neurosci Methods 2018; 304:168-184. [DOI: 10.1016/j.jneumeth.2018.03.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Revised: 03/21/2018] [Accepted: 03/27/2018] [Indexed: 11/30/2022]
|
14
|
Adaptive optimal basis set for BCG artifact removal in simultaneous EEG-fMRI. Sci Rep 2018; 8:8902. [PMID: 29891929 PMCID: PMC5995808 DOI: 10.1038/s41598-018-27187-6] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Accepted: 05/30/2018] [Indexed: 11/13/2022] Open
Abstract
Electroencephalography (EEG) signals recorded during simultaneous functional magnetic resonance imaging (fMRI) are contaminated by strong artifacts. Among these, the ballistocardiographic (BCG) artifact is the most challenging, due to its complex spatio-temporal dynamics associated with ongoing cardiac activity. The presence of BCG residuals in EEG data may hide true, or generate spurious correlations between EEG and fMRI time-courses. Here, we propose an adaptive Optimal Basis Set (aOBS) method for BCG artifact removal. Our method is adaptive, as it can estimate the delay between cardiac activity and BCG occurrence on a beat-to-beat basis. The effective creation of an optimal basis set by principal component analysis (PCA) is therefore ensured by a more accurate alignment of BCG occurrences. Furthermore, aOBS can automatically estimate which components produced by PCA are likely to be BCG artifact-related and therefore need to be removed. The aOBS performance was evaluated on high-density EEG data acquired with simultaneous fMRI in healthy subjects during visual stimulation. As aOBS enables effective reduction of BCG residuals while preserving brain signals, we suggest it may find wide application in simultaneous EEG-fMRI studies.
Collapse
|
15
|
Marino M, Liu Q, Del Castello M, Corsi C, Wenderoth N, Mantini D. Heart-Brain Interactions in the MR Environment: Characterization of the Ballistocardiogram in EEG Signals Collected During Simultaneous fMRI. Brain Topogr 2018; 31:337-345. [PMID: 29427251 DOI: 10.1007/s10548-018-0631-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Accepted: 02/06/2018] [Indexed: 01/02/2023]
Abstract
The ballistocardiographic (BCG) artifact is linked to cardiac activity and occurs in electroencephalographic (EEG) recordings acquired inside the magnetic resonance (MR) environment. Its variability in terms of amplitude, waveform shape and spatial distribution over subject's scalp makes its attenuation a challenging task. In this study, we aimed to provide a detailed characterization of the BCG properties, including its temporal dependency on cardiac events and its spatio-temporal dynamics. To this end, we used high-density EEG data acquired during simultaneous functional MR imaging in six healthy volunteers. First, we investigated the relationship between cardiac activity and BCG occurrences in the EEG recordings. We observed large variability in the delay between ECG and subsequent BCG events (ECG-BCG delay) across subjects and non-negligible epoch-by-epoch variations at the single subject level. The inspection of spatial-temporal variations revealed a prominent non-stationarity of the BCG signal. We identified five main BCG waves, which were common across subjects. Principal component analysis revealed two spatially distinct patterns to explain most of the variance (85% in total). These components are possibly related to head rotation and pulse-driven scalp expansion, respectively. Our results may inspire the development of novel, more effective methods for the removal of the BCG, capable of isolating and attenuating artifact occurrences while preserving true neuronal activity.
Collapse
Affiliation(s)
- Marco Marino
- Neural Control of Movement Laboratory, ETH Zurich, 8057, Zurich, Switzerland
- Department of Experimental Psychology, University of Oxford, Oxford, OX1 3UD, UK
- Laboratory of Movement Control and Neuroplasticity, KU Leuven, 3001, Louvain, Belgium
| | - Quanying Liu
- Neural Control of Movement Laboratory, ETH Zurich, 8057, Zurich, Switzerland
- Laboratory of Movement Control and Neuroplasticity, KU Leuven, 3001, Louvain, Belgium
| | - Mariangela Del Castello
- Department of Electrical, Electronic, and Information Engineering "Gugliemo Marconi", University of Bologna, 40136, Bologna, Italy
| | - Cristiana Corsi
- Department of Electrical, Electronic, and Information Engineering "Gugliemo Marconi", University of Bologna, 40136, Bologna, Italy
| | - Nicole Wenderoth
- Neural Control of Movement Laboratory, ETH Zurich, 8057, Zurich, Switzerland
- Laboratory of Movement Control and Neuroplasticity, KU Leuven, 3001, Louvain, Belgium
| | - Dante Mantini
- Neural Control of Movement Laboratory, ETH Zurich, 8057, Zurich, Switzerland.
- Department of Experimental Psychology, University of Oxford, Oxford, OX1 3UD, UK.
- Laboratory of Movement Control and Neuroplasticity, KU Leuven, 3001, Louvain, Belgium.
| |
Collapse
|
16
|
Sahib AK, Erb M, Marquetand J, Martin P, Elshahabi A, Klamer S, Vulliemoz S, Scheffler K, Ethofer T, Focke NK. Evaluating the impact of fast-fMRI on dynamic functional connectivity in an event-based paradigm. PLoS One 2018; 13:e0190480. [PMID: 29357371 PMCID: PMC5777653 DOI: 10.1371/journal.pone.0190480] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2017] [Accepted: 12/15/2017] [Indexed: 01/08/2023] Open
Abstract
The human brain is known to contain several functional networks that interact dynamically. Therefore, it is desirable to analyze the temporal features of these networks by dynamic functional connectivity (dFC). A sliding window approach was used in an event-related fMRI (visual stimulation using checkerboards) to assess the impact of repetition time (TR) and window size on the temporal features of BOLD dFC. In addition, we also examined the spatial distribution of dFC and tested the feasibility of this approach for the analysis of interictal epileptiforme discharges. 15 healthy controls (visual stimulation paradigm) and three patients with epilepsy (EEG-fMRI) were measured with EPI-fMRI. We calculated the functional connectivity degree (FCD) by determining the total number of connections of a given voxel above a predefined threshold based on Pearson correlation. FCD could capture hemodynamic changes relative to stimulus onset in controls. A significant effect of TR and window size was observed on FCD estimates. At a conventional TR of 2.6 s, FCD values were marginal compared to FCD values using sub-seconds TRs achievable with multiband (MB) fMRI. Concerning window sizes, a specific maximum of FCD values (inverted u-shape behavior) was found for each TR, indicating a limit to the possible gain in FCD for increasing window size. In patients, a dynamic FCD change was found relative to the onset of epileptiform EEG patterns, which was compatible with their clinical semiology. Our findings indicate that dynamic FCD transients are better detectable with sub-second TR than conventional TR. This approach was capable of capturing neuronal connectivity across various regions of the brain, indicating a potential to study the temporal characteristics of interictal epileptiform discharges and seizures in epilepsy patients or other brain diseases with brief events.
Collapse
Affiliation(s)
- Ashish Kaul Sahib
- Werner Reichardt Centre for Integrative Neuroscience, Tuebingen, Germany
- Department of Biomedical Magnetic Resonance, University Hospital Tuebingen, Tuebingen, Germany
- Department of Neurology/Epileptology, University Hospital Tuebingen and Hertie Institute of Clinical Brain Research, Tuebingen, Germany
- Graduate School of Neural and Behavioural Sciences/International Max Planck Research School, University of Tuebingen, Tuebingen, Germany
| | - Michael Erb
- Department of Biomedical Magnetic Resonance, University Hospital Tuebingen, Tuebingen, Germany
| | - Justus Marquetand
- Department of Neurology/Epileptology, University Hospital Tuebingen and Hertie Institute of Clinical Brain Research, Tuebingen, Germany
| | - Pascal Martin
- Department of Neurology/Epileptology, University Hospital Tuebingen and Hertie Institute of Clinical Brain Research, Tuebingen, Germany
| | - Adham Elshahabi
- Department of Neurology/Epileptology, University Hospital Tuebingen and Hertie Institute of Clinical Brain Research, Tuebingen, Germany
- MEG-Center, University of Tuebingen, Tuebingen, Germany
| | - Silke Klamer
- Department of Neurology/Epileptology, University Hospital Tuebingen and Hertie Institute of Clinical Brain Research, Tuebingen, Germany
| | - Serge Vulliemoz
- Department of Neurology, University Hospital of Geneva, Geneva, Switzerland
| | - Klaus Scheffler
- Department of Biomedical Magnetic Resonance, University Hospital Tuebingen, Tuebingen, Germany
- Max-Planck-Institute for Biological Cybernetics, Tuebingen, Germany
| | - Thomas Ethofer
- Werner Reichardt Centre for Integrative Neuroscience, Tuebingen, Germany
- Department of Biomedical Magnetic Resonance, University Hospital Tuebingen, Tuebingen, Germany
| | - Niels K. Focke
- Werner Reichardt Centre for Integrative Neuroscience, Tuebingen, Germany
- Department of Neurology/Epileptology, University Hospital Tuebingen and Hertie Institute of Clinical Brain Research, Tuebingen, Germany
- Clinical Neurophysiology, University Medicine, Goettingen, Germany
- * E-mail:
| |
Collapse
|
17
|
Hermans K, de Munck JC, Verdaasdonk R, Boon P, Krausz G, Prueckl R, Ossenblok P. Effectiveness of Reference Signal-Based Methods for Removal of EEG Artifacts Due to Subtle Movements During fMRI Scanning. IEEE Trans Biomed Eng 2016; 63:2638-2646. [PMID: 27576236 DOI: 10.1109/tbme.2016.2602038] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Subtle motion of an epileptic patient examined with co-registered EEG and functional MRI (EEG-fMRI) may often lead to spurious fMRI activation patterns when true epileptic spikes are contaminated with motion artefacts. In recent years, methods relying on reference signals for correcting these subtle movements in the EEG have emerged. In this study, the performance of two reference-based devices are compared to the template-based method with regard to their ability to remove movement-related artifacts in EEG measured during scanning. METHODS Measurements were performed with a novel double layer cap consisting of 29 EEG and 29 reference electrodes, and with a current loop cap consisting of 60 electrodes and three current loop wires attached to the cap. EEG was acquired inside the scanner during resting state, as well as when the subject was performing a cued movement task. For the double layer cap recordings, newly developed artifact removal algorithms are introduced and both reference signal-based methods are compared to a template-based correction method. RESULTS The BCG artifacts occurring at resting state could be removed successfully by both the reference signal-based methods as well as by the template-based method. However, the reference signal-based methods were also capable of removing EEG artifacts induced by subtle movements, whereas the template-based method failed to remove these artifacts. CONCLUSION Reference signal-based methods enable to correct for artifacts due to subtle movements, which are not removed by commonly used template-based removal algorithms. SIGNIFICANCE Sensitivity of EEG-fMRI analysis in patients with focal epilepsy is improved by avoiding erroneous detections of subtle movements as epileptic spikes in the EEG.
Collapse
|
18
|
Iannotti GR, Grouiller F, Centeno M, Carmichael DW, Abela E, Wiest R, Korff C, Seeck M, Michel C, Pittau F, Vulliemoz S. Epileptic networks are strongly connected with and without the effects of interictal discharges. Epilepsia 2016; 57:1086-96. [PMID: 27153929 DOI: 10.1111/epi.13400] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2016] [Indexed: 02/04/2023]
Abstract
OBJECTIVE Epilepsy is increasingly considered as the dysfunction of a pathologic neuronal network (epileptic network) rather than a single focal source. We aimed to assess the interactions between the regions that comprise the epileptic network and to investigate their dependence on the occurrence of interictal epileptiform discharges (IEDs). METHODS We analyzed resting state simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) recordings in 10 patients with drug-resistant focal epilepsy with multifocal IED-related blood oxygen level-dependent (BOLD) responses and a maximum t-value in the IED field. We computed functional connectivity (FC) maps of the epileptic network using two types of seed: (1) a 10-mm diameter sphere centered in the global maximum of IED-related BOLD map, and (2) the independent component with highest correlation to the IED-related BOLD map, named epileptic component. For both approaches, we compared FC maps before and after regressing out the effect of IEDs in terms of maximum and mean t-values and percentage of map overlap. RESULTS Maximum and mean FC maps t-values were significantly lower after regressing out IEDs at the group level (p < 0.01). Overlap extent was 85% ± 12% and 87% ± 12% when the seed was the 10-mm diameter sphere and the epileptic component, respectively. SIGNIFICANCE Regions involved in a specific epileptic network show coherent BOLD fluctuations independent of scalp EEG IEDs. FC topography and strength is largely preserved by removing the IED effect. This could represent a signature of a sustained pathologic network with contribution from epileptic activity invisible to the scalp EEG.
Collapse
Affiliation(s)
- Giannina R Iannotti
- Functional Brain Mapping Lab, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Frédéric Grouiller
- Department of Radiology and Medical Informatics, University Hospital of Geneva, Geneva, Switzerland
| | - Maria Centeno
- Developmental Imaging and Biophysics Section, UCL Institute of Child Health, London, United Kingdom
| | - David W Carmichael
- Developmental Imaging and Biophysics Section, UCL Institute of Child Health, London, United Kingdom
| | - Eugenio Abela
- Support Center of Advanced Neuroimaging (SCAN), Institute for Diagnostic and Interventional Neuroradiology, University Hospital Inselspital, Bern, Switzerland
| | - Roland Wiest
- Support Center of Advanced Neuroimaging (SCAN), Institute for Diagnostic and Interventional Neuroradiology, University Hospital Inselspital, Bern, Switzerland
| | - Christian Korff
- Pediatric Neurology, Child and Adolescent Department, University Hospitals and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - Margitta Seeck
- Neurology Clinic, University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - Christoph Michel
- Functional Brain Mapping Lab, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Francesca Pittau
- Neurology Clinic, University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - Serge Vulliemoz
- Neurology Clinic, University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
| |
Collapse
|
19
|
Ballistocardiogram artifact correction taking into account physiological signal preservation in simultaneous EEG-fMRI. Neuroimage 2016; 135:45-63. [PMID: 27012501 DOI: 10.1016/j.neuroimage.2016.03.034] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2015] [Accepted: 03/14/2016] [Indexed: 11/21/2022] Open
Abstract
The ballistocardiogram (BCG) artifact is currently one of the most challenging in the EEG acquired concurrently with fMRI, with correction invariably yielding residual artifacts and/or deterioration of the physiological signals of interest. In this paper, we propose a family of methods whereby the EEG is decomposed using Independent Component Analysis (ICA) and a novel approach for the selection of BCG-related independent components (ICs) is used (PROJection onto Independent Components, PROJIC). Three ICA-based strategies for BCG artifact correction are then explored: 1) BCG-related ICs are removed from the back-reconstruction of the EEG (PROJIC); and 2-3) BCG-related ICs are corrected for the artifact occurrences using an Optimal Basis Set (OBS) or Average Artifact Subtraction (AAS) framework, before back-projecting all ICs onto EEG space (PROJIC-OBS and PROJIC-AAS, respectively). A novel evaluation pipeline is also proposed to assess the methods performance, which takes into account not only artifact but also physiological signal removal, allowing for a flexible weighting of the importance given to physiological signal preservation. This evaluation is used for the group-level parameter optimization of each algorithm on simultaneous EEG-fMRI data acquired using two different setups at 3T and 7T. Comparison with state-of-the-art BCG correction methods showed that PROJIC-OBS and PROJIC-AAS outperformed the others when priority was given to artifact removal or physiological signal preservation, respectively, while both PROJIC-AAS and AAS were in general the best choices for intermediate trade-offs. The impact of the BCG correction on the quality of event-related potentials (ERPs) of interest was assessed in terms of the relative reduction of the standard error (SE) across trials: 26/66%, 32/62% and 18/61% were achieved by, respectively, PROJIC, PROJIC-OBS and PROJIC-AAS, for data collected at 3T/7T. Although more significant improvements were achieved at 7T, the results were qualitatively comparable for both setups, which indicate the wide applicability of the proposed methodologies and recommendations.
Collapse
|
20
|
Grouiller F, Jorge J, Pittau F, van der Zwaag W, Iannotti GR, Michel CM, Vulliémoz S, Vargas MI, Lazeyras F. Presurgical brain mapping in epilepsy using simultaneous EEG and functional MRI at ultra-high field: feasibility and first results. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2016; 29:605-16. [PMID: 26946508 DOI: 10.1007/s10334-016-0536-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Revised: 02/11/2016] [Accepted: 02/12/2016] [Indexed: 11/29/2022]
Abstract
OBJECTIVES The aim of this study was to demonstrate that eloquent cortex and epileptic-related hemodynamic changes can be safely and reliably detected using simultaneous electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) recordings at ultra-high field (UHF) for clinical evaluation of patients with epilepsy. MATERIALS AND METHODS Simultaneous EEG-fMRI was acquired at 7 T using an optimized setup in nine patients with lesional epilepsy. According to the localization of the lesion, mapping of eloquent cortex (language and motor) was also performed in two patients. RESULTS Despite strong artifacts, efficient correction of intra-MRI EEG could be achieved with optimized artifact removal algorithms, allowing robust identification of interictal epileptiform discharges. Noise-sensitive topography-related analyses and electrical source localization were also performed successfully. Localization of epilepsy-related hemodynamic changes compatible with the lesion were detected in three patients and concordant with findings obtained at 3 T. Local loss of signal in specific regions, essentially due to B 1 inhomogeneities were found to depend on the geometric arrangement of EEG leads over the cap. CONCLUSION These results demonstrate that presurgical mapping of epileptic networks and eloquent cortex is both safe and feasible at UHF, with the benefits of greater spatial resolution and higher blood-oxygenation-level-dependent sensitivity compared with the more traditional field strength of 3 T.
Collapse
Affiliation(s)
- Frédéric Grouiller
- Department of Radiology and Medical Informatics, University of Geneva, Rue Gabrielle-Perret-Gentil 4, 1211, Geneva 14, Switzerland.
| | - João Jorge
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Francesca Pittau
- EEG and Epilepsy Unit, Department of Neurology, Geneva University Hospitals, Geneva, Switzerland
| | - Wietske van der Zwaag
- Biomedical Imaging Research Center, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Spinoza Centre for Neuroimaging, Amsterdam, The Netherlands
| | - Giannina Rita Iannotti
- Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland
| | - Christoph Martin Michel
- Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland
| | - Serge Vulliémoz
- EEG and Epilepsy Unit, Department of Neurology, Geneva University Hospitals, Geneva, Switzerland
| | - Maria Isabel Vargas
- Division of Neuroradiology, Geneva University Hospitals, Geneva, Switzerland
| | - François Lazeyras
- Department of Radiology and Medical Informatics, University of Geneva, Rue Gabrielle-Perret-Gentil 4, 1211, Geneva 14, Switzerland
| |
Collapse
|