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Fleury M, Figueiredo P, Vourvopoulos A, Lécuyer A. Two is better? combining EEG and fMRI for BCI and neurofeedback: a systematic review. J Neural Eng 2023; 20:051003. [PMID: 37879343 DOI: 10.1088/1741-2552/ad06e1] [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: 03/22/2023] [Accepted: 10/25/2023] [Indexed: 10/27/2023]
Abstract
Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two commonly used non-invasive techniques for measuring brain activity in neuroscience and brain-computer interfaces (BCI).Objective. In this review, we focus on the use of EEG and fMRI in neurofeedback (NF) and discuss the challenges of combining the two modalities to improve understanding of brain activity and achieve more effective clinical outcomes. Advanced technologies have been developed to simultaneously record EEG and fMRI signals to provide a better understanding of the relationship between the two modalities. However, the complexity of brain processes and the heterogeneous nature of EEG and fMRI present challenges in extracting useful information from the combined data.Approach. We will survey existing EEG-fMRI combinations and recent studies that exploit EEG-fMRI in NF, highlighting the experimental and technical challenges.Main results. We made a classification of the different combination of EEG-fMRI for NF, we provide a review of multimodal analysis methods for EEG-fMRI features. We also survey the current state of research on EEG-fMRI in the different existing NF paradigms. Finally, we also identify some of the remaining challenges in this field.Significance. By exploring EEG-fMRI combinations in NF, we are advancing our knowledge of brain function and its applications in clinical settings. As such, this review serves as a valuable resource for researchers, clinicians, and engineers working in the field of neural engineering and rehabilitation, highlighting the promising future of EEG-fMRI-based NF.
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Affiliation(s)
- Mathis Fleury
- Univ Rennes, Inria, CNRS, Inserm, Empenn ERL U1228 Rennes, France
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Patrícia Figueiredo
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Athanasios Vourvopoulos
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Anatole Lécuyer
- Univ Rennes, Inria, CNRS, Inserm, Empenn ERL U1228 Rennes, France
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2
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Liu Q, Yang L, Zhang Z, Yang H, Zhang Y, Wu J. The Feature, Performance, and Prospect of Advanced Electrodes for Electroencephalogram. BIOSENSORS 2023; 13:bios13010101. [PMID: 36671936 PMCID: PMC9855417 DOI: 10.3390/bios13010101] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 12/22/2022] [Accepted: 01/03/2023] [Indexed: 05/12/2023]
Abstract
Recently, advanced electrodes have been developed, such as semi-dry, dry contact, dry non-contact, and microneedle array electrodes. They can overcome the issues of wet electrodes and maintain high signal quality. However, the variations in these electrodes are still unclear and not explained, and there is still confusion regarding the feasibility of electrodes for different application scenarios. In this review, the physical features and electroencephalogram (EEG) signal performances of these advanced EEG electrodes are introduced in view of the differences in contact between the skin and electrodes. Specifically, contact features, biofeatures, impedance, signal quality, and artifacts are discussed. The application scenarios and prospects of different types of EEG electrodes are also elucidated.
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3
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Ikemoto S, von Ellenrieder N, Gotman J. EEG-fMRI of epileptiform discharges: non-invasive investigation of the whole brain. Epilepsia 2022; 63:2725-2744. [PMID: 35822919 DOI: 10.1111/epi.17364] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 07/08/2022] [Accepted: 07/11/2022] [Indexed: 02/01/2023]
Abstract
Simultaneous EEG-fMRI is a unique and non-invasive method for investigating epileptic activity. Interictal epileptiform discharge-related EEG-fMRI provides cortical and subcortical blood oxygen level-dependent (BOLD) signal changes specific to epileptic discharges. As a result, EEG-fMRI has revealed insights into generators and networks involved in epileptic activity in different types of epilepsy, demonstrating-for instance-the implication of the thalamus in human generalized spike and wave discharges and the role of the Default Mode Network (DMN) in absences and focal epilepsy, and proposed a mechanism for the cortico-subcortical interactions in Lennox-Gastaut syndrome discharges. EEG-fMRI can find deep sources of epileptic activity not available to scalp EEG or MEG and provides critical new information to delineate the epileptic focus when considering surgical treatment or electrode implantation. In recent years, methodological advances, such as artifact removal and automatic detection of events have rendered this method easier to implement, and its clinical potential has since been established by evidence of the impact of BOLD response on clinical decision-making and of the relationship between concordance of BOLD responses with extent of resection and surgical outcome. This review presents the recent developments in EEG-fMRI methodology and EEG-fMRI studies in different types of epileptic disorders as follows: EEG-fMRI acquisition, gradient and pulse artifact removal, statistical analysis, clinical applications, pre-surgical evaluation, altered physiological state in generalized genetic epilepsy, and pediatric EEG-fMRI studies.
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Affiliation(s)
- Satoru Ikemoto
- Montreal Neurological Institute and Hospital, 3801 Rue University, Montreal, QC, Canada.,The Jikei University School of Medicine, Department of Pediatrics, 3-25-8 Nishi-Shimbashi, Minato-ku, Tokyo, Japan
| | | | - Jean Gotman
- Montreal Neurological Institute and Hospital, 3801 Rue University, Montreal, QC, Canada
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4
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Cruttenden CE, Taylor JM, Ahmadi M, Zhang Y, Zhu XH, Chen W, Rajamani R. Reference-Free Adaptive Filtering of Extracellular Neural Signals Recording in Ultra-High Field Magnetic Resonance Imaging Scanners: Removal of Periodic Interferences. Biomed Signal Process Control 2022; 71:102758. [PMID: 35069775 PMCID: PMC8782249 DOI: 10.1016/j.bspc.2021.102758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
This paper focuses on the removal of periodic artifacts from neural signals recorded in rats in ultra-high field (UHF) MRI scanners, using a reference free adaptive feedforward method. Recording extracellular neural signals in the UHF environment is motivated by the desire to combine neural recording and UHF functional magnetic resonance imaging (fMRI) to better understand brain function. However, the neural signals are found to have extremely high noise artifacts of a periodic nature due to electromagnetic interference and due to small oscillatory motions. In particular, noise at 60 Hz and several harmonics of 60 Hz, sinusoidal noise from a pump, and low frequency breathing motion artifacts are observed. Due to significant overlap between the noise frequencies and the neural frequency region of interest, band pass filters cannot be effectively utilized in this application. Hence, this paper develops adaptive least squares feedforward cancellation filters to remove the periodic artifacts. The interference fundamental frequency is identified precisely using an implementation of k-means in an iterative approach. The paper includes significant animal data from rats recorded in an IACUC-approved procedure in 9.4T and 16.4T MRI machines. For breathing artifacts filtered from 4 rats, the mean signal cancellation values at the harmonic interference frequencies are 5.18, 12.97, and 20.87 dB/Hz for a sliding template subtraction, a single-stage impulse reference method, and the cascaded adaptive filtering approach respectively. For pump artifacts filtered from 2 chronically implanted rats, mean signal cancellation values are 2.85, 9.52 and 12.06 dB/Hz respectively. The experimental results show that periodic noise is very effectively removed by the developed cascaded adaptive least squares feedforward algorithm.
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Affiliation(s)
- Corey E. Cruttenden
- University of Minnesota Department of Mechanical Engineering, 111 Church St. SE, Minneapolis, MN 55455.,University of Minnesota Center for Magnetic Resonance Research, 2021 6th St. SE, Minneapolis, MN 55455
| | - Jennifer M. Taylor
- University of Minnesota Center for Magnetic Resonance Research, 2021 6th St. SE, Minneapolis, MN 55455.,University of Minnesota Department of Biomedical Engineering, 312 Church St. SE, Minneapolis MN 55455
| | - Mahdi Ahmadi
- University of Minnesota Department of Mechanical Engineering, 111 Church St. SE, Minneapolis, MN 55455
| | - Yi Zhang
- University of Minnesota Center for Magnetic Resonance Research, 2021 6th St. SE, Minneapolis, MN 55455
| | - Xiao-Hong Zhu
- University of Minnesota Center for Magnetic Resonance Research, 2021 6th St. SE, Minneapolis, MN 55455
| | - Wei Chen
- University of Minnesota Center for Magnetic Resonance Research, 2021 6th St. SE, Minneapolis, MN 55455
| | - Rajesh Rajamani
- University of Minnesota Department of Mechanical Engineering, 111 Church St. SE, Minneapolis, MN 55455.,Correspondence
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5
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Loushy I, Gurevitch G, Gazit T, Medvedovsky M, Khoo HM, Gotman J, Fahoum F. Bilateral epileptic networks in congenital and acquired corpus callosum defects: EEG-fMRI study. Epilepsy Behav 2021; 120:107986. [PMID: 33965723 DOI: 10.1016/j.yebeh.2021.107986] [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: 01/05/2021] [Revised: 04/03/2021] [Accepted: 04/08/2021] [Indexed: 11/30/2022]
Abstract
OBJECTIVES Electroencephalography-correlated functional magnetic resonance imaging (EEG-fMRI) allows imaging of brain-wide epileptic networks, and demonstrates that focal interictal epileptic activity is sometimes accompanied by bilateral functional activations. The corpus callosum (CC) facilitates bilateral spread of epileptic activity and at times targeted surgically for drug-resistant epilepsy (DRE). We hypothesized that focal epileptic networks are more unilateral in patients lacking intact CC. METHODS We included focal DRE patients who underwent pre-surgical EEG-fMRI and had CC agenesis (group A, n = 5), patients who previously underwent anterior callosotomy as treatment for drop attacks and continued having seizures (group B, n = 6), and control group of patients with focal epilepsy and intact CC (group C, n = 9). Blood-oxygenation-level-dependent (BOLD) signal maps were generated for interictal epileptic discharges. To quantify bi-hemispheric distribution of epileptic networks, laterality indices were compared between groups. Anatomical and diffusion-weighted imaging demonstrated white matter pathways. RESULTS 96% of studies demonstrated bilateral activations. Laterality indices were similar in groups A and C, whereas group B demonstrated a more bilateral network than group C (p = 0.028). Diffusion-weighted and anatomical imaging showed aberrant white matter pathways and larger anterior commissure in groups A and B. 68% of studies showed maximal activation cluster concordant with the presumed epileptic focus, 28% showed non-maximal activation at presumed focus. SIGNIFICANCE Focal epileptic activity is associated with bilateral functional activations despite lack of intact CC, and is associated with stronger contralateral activation in patients after anterior callosotomy compared to controls. These findings disprove our initial hypothesis, and combined with white matter structural imaging, may indicate that the CC is not a sole route of propagation of epileptic activity, which might spread via anterior commissure. Our study demonstrates the utility of EEG-fMRI in assessing epileptic networks and potentially aiding in tailoring surgical treatments in DRE patients with callosal anomalies, and in callosal surgeries.
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Affiliation(s)
- Itai Loushy
- Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Guy Gurevitch
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Tomer Gazit
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Mordekhay Medvedovsky
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Agnes Ginges Center of Neurogenetics, Department of Neurology, Hadassah - Hebrew University Medical Center, Jerusalem, Israel
| | - Hui Ming Khoo
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada; Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Japan
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Firas Fahoum
- Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
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6
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Cohen N, Ebrahimi Y, Medvedovsky M, Gurevitch G, Aizenstein O, Hendler T, Fahoum F, Gazit T. Interictal Epileptiform Discharge Dynamics in Peri-sylvian Polymicrogyria Using EEG-fMRI. Front Neurol 2021; 12:658239. [PMID: 34149595 PMCID: PMC8212705 DOI: 10.3389/fneur.2021.658239] [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: 01/25/2021] [Accepted: 05/05/2021] [Indexed: 11/13/2022] Open
Abstract
Polymicrogyria (PMG) is a common malformation of cortical development associated with a higher susceptibility to epileptic seizures. Seizures secondary to PMG are characterized by difficult-to-localize cerebral sources due to the complex and widespread lesion structure. Tracing the dynamics of interictal epileptiform discharges (IEDs) in patients with epilepsy has been shown to reveal the location of epileptic activity sources, crucial for successful treatment in cases of focal drug-resistant epilepsy. In this case series IED dynamics were evaluated with simultaneous EEG-fMRI recordings in four patients with unilateral peri-sylvian polymicrogyria (PSPMG) by tracking BOLD activations over time: before, during and following IED appearance on scalp EEG. In all cases, focal BOLD activations within the lesion itself preceded the activity associated with the time of IED appearance on EEG, which showed stronger and more widespread activations. We therefore propose that early hemodynamic activity corresponding to IEDs may hold important localizing information potentially leading to the cerebral sources of epileptic activity. IEDs are suggested to develop within a small area in the PSPMG lesion with structural properties obscuring the appearance of their electric field on the scalp and only later engage widespread structures which allow the production of large currents which are recognized as IEDs on EEG.
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Affiliation(s)
- Noa Cohen
- Sagol Brain Institute, Wohl Institute for Advanced Imaging, Sourasky Medical Center, Tel Aviv, Israel.,Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Yoram Ebrahimi
- Sagol Brain Institute, Wohl Institute for Advanced Imaging, Sourasky Medical Center, Tel Aviv, Israel
| | - Mordekhay Medvedovsky
- Department of Neurology, Agnes Ginges Center of Neurogenetics, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
| | - Guy Gurevitch
- Sagol Brain Institute, Wohl Institute for Advanced Imaging, Sourasky Medical Center, Tel Aviv, Israel.,Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Orna Aizenstein
- Sagol Brain Institute, Wohl Institute for Advanced Imaging, Sourasky Medical Center, Tel Aviv, Israel.,Department of Diagnostic Imaging, Sourasky Medical Center, Tel Aviv, Israel
| | - Talma Hendler
- Sagol Brain Institute, Wohl Institute for Advanced Imaging, Sourasky Medical Center, Tel Aviv, Israel.,Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.,School of Psychological Science, Tel Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Firas Fahoum
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.,Electroencephalography and Epilepsy Unit, Sourasky Medical Center, Tel Aviv, Israel
| | - Tomer Gazit
- Sagol Brain Institute, Wohl Institute for Advanced Imaging, Sourasky Medical Center, Tel Aviv, Israel.,Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
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7
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Sadjadi SM, Ebrahimzadeh E, Shams M, Seraji M, Soltanian-Zadeh H. Localization of Epileptic Foci Based on Simultaneous EEG-fMRI Data. Front Neurol 2021; 12:645594. [PMID: 33986718 PMCID: PMC8110922 DOI: 10.3389/fneur.2021.645594] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 03/11/2021] [Indexed: 02/01/2023] Open
Abstract
Combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) enables a non-invasive investigation of the human brain function and evaluation of the correlation of these two important modalities of brain activity. This paper explores recent reports on using advanced simultaneous EEG–fMRI methods proposed to map the regions and networks involved in focal epileptic seizure generation. One of the applications of EEG and fMRI combination as a valuable clinical approach is the pre-surgical evaluation of patients with epilepsy to map and localize the precise brain regions associated with epileptiform activity. In the process of conventional analysis using EEG–fMRI data, the interictal epileptiform discharges (IEDs) are visually extracted from the EEG data to be convolved as binary events with a predefined hemodynamic response function (HRF) to provide a model of epileptiform BOLD activity and use as a regressor for general linear model (GLM) analysis of the fMRI data. This review examines the methodologies involved in performing such studies, including techniques used for the recording of EEG inside the scanner, artifact removal, and statistical analysis of the fMRI signal. It then discusses the results reported for patients with primary generalized epilepsy and patients with different types of focal epileptic disorders. An important matter that these results have brought to light is that the brain regions affected by interictal epileptic discharges might not be limited to the ones where they have been generated. The developed methods can help reveal the regions involved in or affected by a seizure onset zone (SOZ). As confirmed by the reviewed literature, EEG–fMRI provides information that comes particularly useful when evaluating patients with refractory epilepsy for surgery.
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Affiliation(s)
- Seyyed Mostafa Sadjadi
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Elias Ebrahimzadeh
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.,Neuroimage Signal and Image Analysis Group, School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Mohammad Shams
- Neural Engineering Laboratory, Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, United States
| | - Masoud Seraji
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, United States.,Behavioral and Neural Sciences Graduate Program, Rutgers University, Newark, NJ, United States
| | - Hamid Soltanian-Zadeh
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.,Neuroimage Signal and Image Analysis Group, School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.,Medical Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, MI, United States
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8
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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.
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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
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9
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Berger A, Cohen N, Fahoum F, Medvedovsky M, Meller A, Ekstein D, Benifla M, Aizenstein O, Fried I, Gazit T, Strauss I. Preoperative localization of seizure onset zones by magnetic source imaging, EEG-correlated functional MRI, and their combination. J Neurosurg 2021; 134:1037-1043. [PMID: 32413858 DOI: 10.3171/2020.3.jns192794] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2019] [Accepted: 03/06/2020] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Preoperative localization of seizure onset zones (SOZs) is an evolving field in the treatment of refractory epilepsy. Both magnetic source imaging (MSI), and the more recent EEG-correlated functional MRI (EEG-fMRI), have shown applicability in assisting surgical planning. The purpose of this study was to evaluate the capability of each method and their combination in localizing the seizure onset lobe (SL). METHODS The study included 14 patients who underwent both MSI and EEG-fMRI before undergoing implantation of intracranial EEG (icEEG) as part of the presurgical planning of the resection of an epileptogenic zone (EZ) during the years 2012-2018. The estimated location of the SL by each method was compared with the location determined by icEEG. Identification rates of the SL were compared between the different methods. RESULTS MSI and EEG-fMRI showed similar identification rates of SL locations in relation to icEEG results (88% ± 31% and 73% ± 42%, respectively; p = 0.281). The additive use of the coverage lobes of both methods correctly identified 100% of the SL, significantly higher than EEG-fMRI alone (p = 0.039) and nonsignificantly higher than MSI (p = 0.180). False-identification rates of the additive coverage lobes were significantly higher than MSI (p = 0.026) and EEG-fMRI (p = 0.027). The intersecting lobes of both methods showed the lowest false identification rate (13% ± 6%, p = 0.01). CONCLUSIONS Both MSI and EEG-fMRI can assist in the presurgical evaluation of patients with refractory epilepsy. The additive use of both tests confers a high identification rate in finding the SL. This combination can help in focusing implantation of icEEG electrodes targeting the SOZ.
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Affiliation(s)
- Assaf Berger
- 1Department of Neurosurgery
- 7Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv
| | - Noa Cohen
- 2Sagol Brain Institute
- 7Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv
| | - Firas Fahoum
- 3Department of Neurology, and
- 7Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv
| | - Mordekhay Medvedovsky
- 4Department of Neurology, Hadassah Medical Center, Jerusalem
- 8Hebrew University Hadassah Medical School, Jerusalem; and
| | - Aaron Meller
- 2Sagol Brain Institute
- 7Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv
| | - Dana Ekstein
- 4Department of Neurology, Hadassah Medical Center, Jerusalem
- 8Hebrew University Hadassah Medical School, Jerusalem; and
| | - Mony Benifla
- 5Department of Neurosurgery, Rambam Health Care Campus, Haifa
- 9Rappaport Faculty of Medicine-Technion, Haifa, Israel
| | - Orna Aizenstein
- 6Department of Radiology, Tel Aviv Medical Center, Tel Aviv
- 7Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv
| | - Itzhak Fried
- 1Department of Neurosurgery
- 7Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv
| | - Tomer Gazit
- 2Sagol Brain Institute
- 7Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv
| | - Ido Strauss
- 1Department of Neurosurgery
- 7Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv
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10
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Cohen N, Tsizin E, Fried I, Fahoum F, Hendler T, Gazit T, Medvedovsky M. Conductive gel bridge sensor for motion tracking in simultaneous EEG-fMRI recordings. Epilepsy Res 2018; 149:117-122. [PMID: 30623776 DOI: 10.1016/j.eplepsyres.2018.12.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 11/27/2018] [Accepted: 12/17/2018] [Indexed: 10/27/2022]
Abstract
EEG-fMRI allows the localization of the hemodynamic correlates of neural activity and has been shown to be useful as a diagnostic tool in pre-surgical evaluation of refractory epilepsy. However, EEG recordings may be highly contaminated by artifacts induced by movements inside the magnetic field thus rendering the scan difficult for interpretation. Existing methods for motion correction require additional equipment or hardware modification. We introduce a simple method for motion artifact detection, the conductive gel bridge sensor (CGBS), easily applicable using the standard setup. We report examples of CGBS use in two patients with epilepsy and demonstrate the method's ability to successfully differentiate between epochs of brain activity and those of movement.
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Affiliation(s)
- Noa Cohen
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Evgeny Tsizin
- School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Itzhak Fried
- Functional Neurosurgery Unit, Tel Aviv Medical Center, Tel Aviv, Israel; Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Firas Fahoum
- Epilepsy and EEG Unit, Department of Neurology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Talma Hendler
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Tomer Gazit
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
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11
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Hao Y, Khoo HM, von Ellenrieder N, Zazubovits N, Gotman J. DeepIED: An epileptic discharge detector for EEG-fMRI based on deep learning. NEUROIMAGE-CLINICAL 2017; 17:962-975. [PMID: 29321970 PMCID: PMC5752096 DOI: 10.1016/j.nicl.2017.12.005] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Revised: 11/02/2017] [Accepted: 12/02/2017] [Indexed: 11/28/2022]
Abstract
Presurgical evaluation that can precisely delineate the epileptogenic zone (EZ) is one important step for successful surgical resection treatment of refractory epilepsy patients. The noninvasive EEG-fMRI recording technique combined with general linear model (GLM) analysis is considered an important tool for estimating the EZ. However, the manual marking of interictal epileptic discharges (IEDs) needed in this analysis is challenging and time-consuming because the quality of the EEG recorded inside the scanner is greatly deteriorated compared to the usual EEG obtained outside the scanner. This is one of main impediments to the widespread use of EEG-fMRI in epilepsy. We propose a deep learning based semi-automatic IED detector that can find the candidate IEDs in the EEG recorded inside the scanner which resemble sample IEDs marked in the EEG recorded outside the scanner. The manual marking burden is greatly reduced as the expert need only edit candidate IEDs. The model is trained on data from 30 patients. Validation of IEDs detection accuracy on another 37 consecutive patients shows our method can improve the median sensitivity from 50.0% for the previously proposed template-based method to 84.2%, with false positive rate as 5 events/min. Reproducibility validation on 15 patients is applied to evaluate if our method can produce similar hemodynamic response maps compared with the manual marking ground truth results. We explore the concordance between the maximum hemodynamic response and the intracerebral EEG defined EZ and find that both methods produce similar percentage of concordance (76.9%, 10 out of 13 patients, electrode was absent in the maximum hemodynamic response in two patients). This tool will make EEG-fMRI analysis more practical for clinical usage. A deep learning based epileptic discharge detector for EEG-fMRI is proposed. The burden of manually marking epileptic discharges is greatly reduced. Our method can produce similar EEG-fMRI results compared with traditional method.
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Affiliation(s)
- Yongfu Hao
- Montreal Neurological Institute, McGill University, Montreal, Quebec H3A 2B4, Canada.
| | - Hui Ming Khoo
- Montreal Neurological Institute, McGill University, Montreal, Quebec H3A 2B4, Canada; Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Japan
| | | | - Natalja Zazubovits
- Montreal Neurological Institute, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Jean Gotman
- Montreal Neurological Institute, McGill University, Montreal, Quebec H3A 2B4, Canada
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Mano M, Lécuyer A, Bannier E, Perronnet L, Noorzadeh S, Barillot C. How to Build a Hybrid Neurofeedback Platform Combining EEG and fMRI. Front Neurosci 2017; 11:140. [PMID: 28377691 PMCID: PMC5359276 DOI: 10.3389/fnins.2017.00140] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Accepted: 03/07/2017] [Indexed: 01/18/2023] Open
Abstract
Multimodal neurofeedback estimates brain activity using information acquired with more than one neurosignal measurement technology. In this paper we describe how to set up and use a hybrid platform based on simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), then we illustrate how to use it for conducting bimodal neurofeedback experiments. The paper is intended for those willing to build a multimodal neurofeedback system, to guide them through the different steps of the design, setup, and experimental applications, and help them choose a suitable hardware and software configuration. Furthermore, it reports practical information from bimodal neurofeedback experiments conducted in our lab. The platform presented here has a modular parallel processing architecture that promotes real-time signal processing performance and simple future addition and/or replacement of processing modules. Various unimodal and bimodal neurofeedback experiments conducted in our lab showed high performance and accuracy. Currently, the platform is able to provide neurofeedback based on electroencephalography and functional magnetic resonance imaging, but the architecture and the working principles described here are valid for any other combination of two or more real-time brain activity measurement technologies.
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Affiliation(s)
- Marsel Mano
- Institut National de Recherche en Informatique et en Automatique (INRIA) Rennes, France
| | - Anatole Lécuyer
- Institut National de Recherche en Informatique et en Automatique (INRIA)Rennes, France; Institut de Recherche en Informatique et Systèmes Aléatoires (IIRISA)Rennes, France
| | - Elise Bannier
- Institut de Recherche en Informatique et Systèmes Aléatoires (IIRISA)Rennes, France; CHU PontchaillouRennes, France
| | - Lorraine Perronnet
- Institut National de Recherche en Informatique et en Automatique (INRIA) Rennes, France
| | - Saman Noorzadeh
- Institut National de Recherche en Informatique et en Automatique (INRIA) Rennes, France
| | - Christian Barillot
- Institut National de Recherche en Informatique et en Automatique (INRIA)Rennes, France; Institut de Recherche en Informatique et Systèmes Aléatoires (IIRISA)Rennes, France; Institut National de la Santé et de la Recherche MédicaleRennes, France
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