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Verdonk C, Teed AR, White EJ, Ren X, Stewart JL, Paulus MP, Khalsa SS. Heartbeat-evoked neural response abnormalities in generalized anxiety disorder during peripheral adrenergic stimulation. Neuropsychopharmacology 2024:10.1038/s41386-024-01806-5. [PMID: 38291167 DOI: 10.1038/s41386-024-01806-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 12/22/2023] [Accepted: 01/16/2024] [Indexed: 02/01/2024]
Abstract
Hyperarousal symptoms in generalized anxiety disorder (GAD) are often incongruent with the observed physiological state, suggesting that abnormal processing of interoceptive signals is a characteristic feature of the disorder. To examine the neural mechanisms underlying interoceptive dysfunction in GAD, we evaluated whether adrenergic modulation of cardiovascular signaling differentially affects the heartbeat-evoked potential (HEP), an electrophysiological marker of cardiac interoception, during concurrent electroencephalogram and functional magnetic resonance imaging (EEG-fMRI) scanning. Intravenous infusions of the peripheral adrenergic agonist isoproterenol (0.5 and 2.0 micrograms, μg) were administered in a randomized, double-blinded and placebo-controlled fashion to dynamically perturb the cardiovascular system while recording the associated EEG-fMRI responses. During the 0.5 μg isoproterenol infusion, the GAD group (n = 24) exhibited significantly larger changes in HEP amplitude in an opposite direction than the healthy comparison (HC) group (n = 24). In addition, the GAD group showed significantly larger absolute HEP amplitudes than the HC group during saline infusions, when cardiovascular tone did not increase. No significant group differences in HEP amplitude were identified during the 2.0 μg isoproterenol infusion. Using analyzable blood oxygenation level-dependent fMRI data from participants with concurrent EEG-fMRI data (21 GAD and 21 HC), we found that the aforementioned HEP effects were uncorrelated with fMRI signals in the insula, ventromedial prefrontal cortex, dorsal anterior cingulate cortex, amygdala, and somatosensory cortex, brain regions implicated in cardiac signal processing in prior fMRI studies. These findings provide additional evidence of dysfunctional cardiac interoception in GAD and identify neural processes at the electrophysiological level that may be independent from blood oxygen level-dependent responses during peripheral adrenergic stimulation.
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Affiliation(s)
- Charles Verdonk
- Laureate Institute for Brain Research, Tulsa, OK, USA
- VIFASOM (EA 7330 Vigilance Fatigue, Sommeil et Santé Publique), Université Paris Cité, Paris, France
- French Armed Forces Biomedical Research Institute, Brétigny-sur-Orge, France
| | - Adam R Teed
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Evan J White
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Xi Ren
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Jennifer L Stewart
- Laureate Institute for Brain Research, Tulsa, OK, USA
- Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA
| | - Martin P Paulus
- Laureate Institute for Brain Research, Tulsa, OK, USA
- Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA
| | - Sahib S Khalsa
- Laureate Institute for Brain Research, Tulsa, OK, USA.
- Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA.
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2
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Verdonk C, Teed AR, White EJ, Ren X, Stewart JL, Paulus MP, Khalsa SS. Heartbeat-evoked neural response abnormalities in generalized anxiety disorder during peripheral adrenergic stimulation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.09.23291166. [PMID: 37398268 PMCID: PMC10312828 DOI: 10.1101/2023.06.09.23291166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Hyperarousal symptoms in generalized anxiety disorder (GAD) are often incongruent with the observed physiological state, suggesting that abnormal processing of interoceptive signals is a characteristic feature of the disorder. To examine the neural mechanisms underlying interoceptive dysfunction in GAD, we evaluated whether adrenergic modulation of cardiovascular signaling differentially affects the heartbeat evoked potential (HEP), an electrophysiological marker of cardiac interoception, during concurrent electroencephalogram and functional magnetic resonance imaging (EEG-fMRI) scanning. Intravenous infusions of the peripheral adrenergic agonist isoproterenol (0.5 and 2.0 micrograms, μg) were administered in a randomized, double-blinded and placebo-controlled fashion to dynamically perturb the cardiovascular system while recording the associated EEG-fMRI responses. During the 0.5 μg isoproterenol infusion, the GAD group (n=24) exhibited significantly larger changes in HEP amplitude in an opposite direction than the HC group (n=24). In addition, the GAD group showed significantly larger absolute HEP amplitudes than HC during saline infusions, when cardiovascular tone did not increase. No significant group differences in HEP amplitude were identified during the 2.0 μg isoproterenol infusion. Using analyzable blood oxygenation level dependent fMRI data from participants with concurrent EEG-fMRI data (21 GAD and 21 HC), we found that the aforementioned HEP effects were uncorrelated with fMRI signals in the insula, ventromedial prefrontal cortex, dorsal anterior cingulate cortex, amygdala, and somatosensory cortex, brain regions implicated in cardiac signal processing according to prior fMRI studies. These findings provide additional evidence of dysfunctional cardiac interoception in GAD and identify neural processes at the electrophysiological level that may be independent from blood oxygen level-dependent responses during peripheral adrenergic stimulation.
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Affiliation(s)
- Charles Verdonk
- Laureate Institute for Brain Research, Tulsa, Oklahoma, United States
- VIFASOM (EA 7330 Vigilance Fatigue, Sommeil et Santé Publique), Université Paris Cité, Paris, France
- French Armed Forces Biomedical Research Institute, Brétigny-sur-Orge, France
| | - Adam R. Teed
- Laureate Institute for Brain Research, Tulsa, Oklahoma, United States
| | - Evan J. White
- Laureate Institute for Brain Research, Tulsa, Oklahoma, United States
| | - Xi Ren
- Laureate Institute for Brain Research, Tulsa, Oklahoma, United States
| | - Jennifer L. Stewart
- Laureate Institute for Brain Research, Tulsa, Oklahoma, United States
- Oxley College of Health Sciences, University of Tulsa, Tulsa, Oklahoma, United States
| | - Martin P. Paulus
- Laureate Institute for Brain Research, Tulsa, Oklahoma, United States
- Oxley College of Health Sciences, University of Tulsa, Tulsa, Oklahoma, United States
| | - Sahib S. Khalsa
- Laureate Institute for Brain Research, Tulsa, Oklahoma, United States
- Oxley College of Health Sciences, University of Tulsa, Tulsa, Oklahoma, United States
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3
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Hossain MS, Mahmud S, Khandakar A, Al-Emadi N, Chowdhury FA, Mahbub ZB, Reaz MBI, Chowdhury MEH. MultiResUNet3+: A Full-Scale Connected Multi-Residual UNet Model to Denoise Electrooculogram and Electromyogram Artifacts from Corrupted Electroencephalogram Signals. Bioengineering (Basel) 2023; 10:bioengineering10050579. [PMID: 37237649 DOI: 10.3390/bioengineering10050579] [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: 04/05/2023] [Revised: 04/26/2023] [Accepted: 05/04/2023] [Indexed: 05/28/2023] Open
Abstract
Electroencephalogram (EEG) signals immensely suffer from several physiological artifacts, including electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG) artifacts, which must be removed to ensure EEG's usability. This paper proposes a novel one-dimensional convolutional neural network (1D-CNN), i.e., MultiResUNet3+, to denoise physiological artifacts from corrupted EEG. A publicly available dataset containing clean EEG, EOG, and EMG segments is used to generate semi-synthetic noisy EEG to train, validate and test the proposed MultiResUNet3+, along with four other 1D-CNN models (FPN, UNet, MCGUNet, LinkNet). Adopting a five-fold cross-validation technique, all five models' performance is measured by estimating temporal and spectral percentage reduction in artifacts, temporal and spectral relative root mean squared error, and average power ratio of each of the five EEG bands to whole spectra. The proposed MultiResUNet3+ achieved the highest temporal and spectral percentage reduction of 94.82% and 92.84%, respectively, in EOG artifacts removal from EOG-contaminated EEG. Moreover, compared to the other four 1D-segmentation models, the proposed MultiResUNet3+ eliminated 83.21% of the spectral artifacts from the EMG-corrupted EEG, which is also the highest. In most situations, our proposed model performed better than the other four 1D-CNN models, evident by the computed performance evaluation metrics.
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Affiliation(s)
- Md Shafayet Hossain
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Sakib Mahmud
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Nasser Al-Emadi
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Farhana Ahmed Chowdhury
- Department of Electronics and Telecommunication Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, Bangladesh
| | - Zaid Bin Mahbub
- Department of Mathematics and Physics, North South University, Dhaka 1229, Bangladesh
| | - Mamun Bin Ibne Reaz
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
- Department of Electrical and Electronic Engineering, Independent University, Bashundhara, Dhaka 1229, Bangladesh
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4
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Lee H, Graham SJ, Kuo W, Lin F. Ballistocardiogram suppression in concurrent EEG-MRI by dynamic modeling of heartbeats. Hum Brain Mapp 2022; 43:4444-4457. [PMID: 35695703 PMCID: PMC9435020 DOI: 10.1002/hbm.25965] [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: 01/04/2022] [Revised: 04/19/2022] [Accepted: 04/21/2022] [Indexed: 12/30/2022] Open
Abstract
The ballistocardiogram (BCG), the induced electric potentials by the head motion originating from heartbeats, is a prominent source of noise in electroencephalography (EEG) data during magnetic resonance imaging (MRI). Although methods have been proposed to suppress the BCG artifact, more work considering the variability of cardiac cycles and head motion across time and subjects is needed to provide highly robust correction. Here, a method called "dynamic modeling of heartbeats" (DMH) is proposed to reduce BCG artifacts in EEG data recorded inside an MRI system. The DMH method models BCG artifacts by combining EEG points at time instants with similar dynamics. The modeled BCG artifact is then subtracted from the EEG recording to suppress the BCG artifact. Performance of DMH was tested and specifically compared with the Optimal Basis Set (OBS) method on EEG data recorded inside a 3T MRI system with either no MRI acquisition (Inside-MRI), echo-planar imaging (EPI-EEG), or fast MRI acquisition using simultaneous multi-slice and inverse imaging methods (SMS-InI-EEG). In a steady-state visual evoked response (SSVEP) paradigm, the 15-Hz oscillatory neuronal activity at the visual cortex after DMH processing was about 130% of that achieved by OBS processing for Inside-MRI, SMS-InI-EEG, and EPI-EEG conditions. The DMH method is computationally efficient for suppressing BCG artifacts and in the future may help to improve the quality of EEG data recorded in high-field MRI systems for neuroscientific and clinical applications.
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Affiliation(s)
- Hsin‐Ju Lee
- Physical Sciences PlatformSunnybrook Research InstituteTorontoOntarioCanada,Department of Medical BiophysicsUniversity of TorontoTorontoOntarioCanada
| | - Simon J. Graham
- Physical Sciences PlatformSunnybrook Research InstituteTorontoOntarioCanada,Department of Medical BiophysicsUniversity of TorontoTorontoOntarioCanada
| | - Wen‐Jui Kuo
- Institute of NeuroscienceNational Yang Ming Chiao‐Tung UniversityTaipeiTaiwan,Brain Research CenterNational Yang‐Ming Chiao‐Tung UniversityTaipeiTaiwan
| | - Fa‐Hsuan Lin
- Physical Sciences PlatformSunnybrook Research InstituteTorontoOntarioCanada,Department of Medical BiophysicsUniversity of TorontoTorontoOntarioCanada
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5
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Yu J, Li C, Lou K, Wei C, Liu Q. Embedding decomposition for artifacts removal in EEG signals. J Neural Eng 2022; 19. [PMID: 35378524 DOI: 10.1088/1741-2552/ac63eb] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 04/04/2022] [Indexed: 11/12/2022]
Abstract
Electroencephalogram (EEG) recordings are often contaminated with artifacts. Various methods have been developed to eliminate or weaken the influence of artifacts. However, most of them rely on prior experience for analysis. Here, we propose an deep learning framework to separate neural signal and artifacts in the embedding space and reconstruct the denoised signal, which is called DeepSeparator. DeepSeparator employs an encoder to extract and amplify the features in the raw EEG, a module called decomposer to extract the trend, detect and suppress artifact and a decoder to reconstruct the denoised signal. Besides, DeepSeparator can extract the artifact, which largely increases the model interpretability. The proposed method is tested with a semi-synthetic EEG dataset and a real task-related EEG dataset, suggesting that DeepSeparator outperforms the conventional models in both EOG and EMG artifact removal. DeepSeparator can be extended to multi-channel EEG and data with any arbitrary length. It may motivate future developments and application of deep learning-based EEG denoising. The code for DeepSeparator is available at https://github.com/ncclabsustech/DeepSeparator.
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Affiliation(s)
- Junjie Yu
- Department of Biomedical Engineering, Southern University of Science and Technology, No. 1088, Xueyuan Rd., Xili, Nanshan District, Shenzhen, Guangdong, 518055, P. R. China, Shenzhen, Guangdong, 518055, CHINA
| | - Chenyi Li
- The Chinese University of Hong Kong - Shenzhen, Shenzhen, China, Shenzhen, Guangdong, 518172, CHINA
| | - Kexin Lou
- Department of Biomedical Engineering, Southern University of Science and Technology, No. 1088, Xueyuan Rd., Xili, Nanshan District, Shenzhen, Guangdong, 518055, P. R. China, Shenzhen, Guangdong, 518055, CHINA
| | - Chen Wei
- Department of Biomedical Engineering, Southern University of Science and Technology, No. 1088, Xueyuan Rd., Xili, Nanshan District, Shenzhen, Guangdong, 518055, P. R. China, Shenzhen, 518055, CHINA
| | - Quanying Liu
- Biomedical Engineering, Southern University of Science and Technology, No. 1088, Xueyuan Rd., Xili, Nanshan District, Shenzhen, Guangdong, 518055, P. R. China, Shenzhen, 518055, CHINA
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6
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Warbrick T. Simultaneous EEG-fMRI: What Have We Learned and What Does the Future Hold? SENSORS 2022; 22:s22062262. [PMID: 35336434 PMCID: PMC8952790 DOI: 10.3390/s22062262] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 03/11/2022] [Accepted: 03/13/2022] [Indexed: 02/01/2023]
Abstract
Simultaneous EEG-fMRI has developed into a mature measurement technique in the past 25 years. During this time considerable technical and analytical advances have been made, enabling valuable scientific contributions to a range of research fields. This review will begin with an introduction to the measurement principles involved in EEG and fMRI and the advantages of combining these methods. The challenges faced when combining the two techniques will then be considered. An overview of the leading application fields where EEG-fMRI has made a significant contribution to the scientific literature and emerging applications in EEG-fMRI research trends is then presented.
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Affiliation(s)
- Tracy Warbrick
- Brain Products GmbH, Zeppelinstrasse 7, 82205 Gilching, Germany
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7
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Mayeli A, Al Zoubi O, Henry K, Wong CK, White EJ, Luo Q, Zotev V, Refai H, Bodurka J. Automated pipeline for EEG artifact reduction (APPEAR) recorded during fMRI. J Neural Eng 2021; 18:10.1088/1741-2552/ac1037. [PMID: 34192674 PMCID: PMC10696919 DOI: 10.1088/1741-2552/ac1037] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 06/30/2021] [Indexed: 11/11/2022]
Abstract
Objective.Simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) recordings offer a high spatiotemporal resolution approach to study human brain and understand the underlying mechanisms mediating cognitive and behavioral processes. However, the high susceptibility of EEG to MRI-induced artifacts hinders a broad adaptation of this approach. More specifically, EEG data collected during fMRI acquisition are contaminated with MRI gradients and ballistocardiogram artifacts, in addition to artifacts of physiological origin. There have been several attempts for reducing these artifacts with manual and time-consuming pre-processing, which may result in biasing EEG data due to variations in selecting steps order, parameters, and classification of artifactual independent components. Thus, there is a strong urge to develop a fully automatic and comprehensive pipeline for reducing all major EEG artifacts. In this work, we introduced an open-access toolbox with a fully automatic pipeline for reducing artifacts from EEG data collected simultaneously with fMRI (refer to APPEAR).Approach.The pipeline integrates average template subtraction and independent component analysis to suppress both MRI-related and physiological artifacts. To validate our results, we tested APPEAR on EEG data recorded from healthy control subjects during resting-state (n= 48) and task-based (i.e. event-related-potentials (ERPs);n= 8) paradigms. The chosen gold standard is an expert manual review of the EEG database.Main results.We compared manually and automated corrected EEG data during resting-state using frequency analysis and continuous wavelet transformation and found no significant differences between the two corrections. A comparison between ERP data recorded during a so-called stop-signal task (e.g. amplitude measures and signal-to-noise ratio) also showed no differences between the manually and fully automatic fMRI-EEG-corrected data.Significance.APPEAR offers the first comprehensive open-source toolbox that can speed up advancement of EEG analysis and enhance replication by avoiding experimenters' preferences while allowing for processing large EEG-fMRI cohorts composed of hundreds of subjects with manageable researcher time and effort.
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Affiliation(s)
- Ahmad Mayeli
- Laureate Institute for Brain Research, Tulsa, OK, United States of America
- Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, United States of America
| | - Obada Al Zoubi
- Laureate Institute for Brain Research, Tulsa, OK, United States of America
- Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, United States of America
- Department of Psychiatry, Harvard Medical School
| | - Kaylee Henry
- Laureate Institute for Brain Research, Tulsa, OK, United States of America
- Department of Biomedical Engineering, University of Arkansas, Fayetteville, AR, United States
| | - Chung Ki Wong
- Laureate Institute for Brain Research, Tulsa, OK, United States of America
| | - Evan J White
- Laureate Institute for Brain Research, Tulsa, OK, United States of America
| | - Qingfei Luo
- Laureate Institute for Brain Research, Tulsa, OK, United States of America
| | - Vadim Zotev
- Laureate Institute for Brain Research, Tulsa, OK, United States of America
| | - Hazem Refai
- Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, United States of America
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, OK, United States of America
- Department of Biomedical Engineering, University of Arkansas, Fayetteville, AR, United States
| | - Tulsa 1000 Investigators
- Laureate Institute for Brain Research, Tulsa, OK, United States of America
- The Tulsa 1000 Investigators include the following contributors: Robin Aupperle, Ph.D., Jerzy Bodurka, Ph.D., Justin Feinstein, Ph.D., Sahib S Khalsa, M.D., Ph.D., Rayus Kuplicki, Ph.D., Martin P Paulus, M.D., Jonathan Savitz, Ph.D., Jennifer Stewart, Ph.D., Teresa A Victor, Ph.D
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8
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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.
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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
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9
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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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10
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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: 13] [Impact Index Per Article: 4.3] [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.
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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
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11
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Abreu R, Jorge J, Leal A, Koenig T, Figueiredo P. EEG Microstates Predict Concurrent fMRI Dynamic Functional Connectivity States. Brain Topogr 2021; 34:41-55. [PMID: 33161518 DOI: 10.1007/s10548-020-00805-1/figures/5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Accepted: 10/23/2020] [Indexed: 05/25/2023]
Abstract
Brain functional connectivity measured by resting-state fMRI varies over multiple time scales, and recurrent dynamic functional connectivity (dFC) states have been identified. These have been found to be associated with different cognitive and pathological states, with potential as disease biomarkers, but their neuronal underpinnings remain a matter of debate. A number of recurrent microstates have also been identified in resting-state EEG studies, which are thought to represent the quasi-simultaneous activity of large-scale functional networks reflecting time-varying brain states. Here, we hypothesized that fMRI-derived dFC states may be associated with these EEG microstates. To test this hypothesis, we quantitatively assessed the ability of EEG microstates to predict concurrent fMRI dFC states in simultaneous EEG-fMRI data collected from healthy subjects at rest. By training a random forests classifier, we found that the four canonical EEG microstates predicted fMRI dFC states with an accuracy of 90%, clearly outperforming alternative EEG features such as spectral power. Our results indicate that EEG microstates analysis yields robust signatures of fMRI dFC states, providing evidence of the electrophysiological underpinnings of dFC while also further supporting that EEG microstates reflect the dynamics of large-scale brain networks.
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Affiliation(s)
- 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), ICNAS, University of Coimbra, Coimbra, Portugal
| | - 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
| | - Alberto Leal
- Department of Neurophysiology, Centro Hospitalar Psiquiátrico de Lisboa, Lisbon, Portugal
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Patrícia Figueiredo
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
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12
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Abreu R, Jorge J, Leal A, Koenig T, Figueiredo P. EEG Microstates Predict Concurrent fMRI Dynamic Functional Connectivity States. Brain Topogr 2020; 34:41-55. [PMID: 33161518 DOI: 10.1007/s10548-020-00805-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Accepted: 10/23/2020] [Indexed: 12/14/2022]
Abstract
Brain functional connectivity measured by resting-state fMRI varies over multiple time scales, and recurrent dynamic functional connectivity (dFC) states have been identified. These have been found to be associated with different cognitive and pathological states, with potential as disease biomarkers, but their neuronal underpinnings remain a matter of debate. A number of recurrent microstates have also been identified in resting-state EEG studies, which are thought to represent the quasi-simultaneous activity of large-scale functional networks reflecting time-varying brain states. Here, we hypothesized that fMRI-derived dFC states may be associated with these EEG microstates. To test this hypothesis, we quantitatively assessed the ability of EEG microstates to predict concurrent fMRI dFC states in simultaneous EEG-fMRI data collected from healthy subjects at rest. By training a random forests classifier, we found that the four canonical EEG microstates predicted fMRI dFC states with an accuracy of 90%, clearly outperforming alternative EEG features such as spectral power. Our results indicate that EEG microstates analysis yields robust signatures of fMRI dFC states, providing evidence of the electrophysiological underpinnings of dFC while also further supporting that EEG microstates reflect the dynamics of large-scale brain networks.
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Affiliation(s)
- 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), ICNAS, University of Coimbra, Coimbra, Portugal
| | - 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
| | - Alberto Leal
- Department of Neurophysiology, Centro Hospitalar Psiquiátrico de Lisboa, Lisbon, Portugal
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Patrícia Figueiredo
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
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Chen JCC, Forsyth A, Dubowitz DJ, Muthukumaraswamy SD. On the Quality, Statistical Efficiency, and Safety of Simultaneously Recorded Multiband fMRI/EEG. Brain Topogr 2020; 33:303-316. [PMID: 32144628 DOI: 10.1007/s10548-020-00761-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 02/24/2020] [Indexed: 01/19/2023]
Abstract
The recent development of multiband functional magnetic resonance imaging (MB-fMRI) allows for the reduction of sampling period by simultaneously exciting multiple slices-the number of which is referred to as the multiband factor. Simultaneously recorded electroencephalography (EEG)/MB-fMRI has yet to be validated for data quality against conventional single band (SB)-fMRI. Pilot scans were conducted on phantoms twice and on a healthy volunteer to ensure no heating effects. In the main study, two thermometer probes were attached to 16 healthy individuals (ages 20-39, 9 females) whilst they completed two sets of 16-min resting-state and two sets of 9-min n-back task scans-each set consisting of one MB4 and one SB pulse sequence. No heating effects were reported and thermometer data showed mean increases of < 1.0 °C. Minimal differences between the two scan types were found in EEG channel variance and spectra. Expected decreases in MB4-fMRI tSNR were observed. In n-back task scans, little to no differences were detected in both EEG source analyses and fMRI local analyses for mixed effects. Resting-state posterior cingulate cortex seed-based analyses of the default mode network along with EEG-informed fMRI analysis of the occipital alpha anticorrelation effect showed improved statistical and spatial sensitivity at lower scan durations. Using EEG/MB4-fMRI for n-back tasks provided no statistical advantages nor disadvantages. However, for studying the resting-state, MB4-fMRI potentially allows for reduced scanning durations for equivalent statistical significance to be obtained or alternatively, larger effect sizes for the same scanning duration. As such, simultaneous EEG/MB4-fMRI is a viable alternative to EEG/SB-fMRI.
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Affiliation(s)
- Joseph C C Chen
- School of Pharmacy, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Anna Forsyth
- School of Pharmacy, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - David J Dubowitz
- Department of Anatomy and Medical Imaging, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.,Centre for Advanced MRI, Auckland UniServices Limited, Auckland, New Zealand
| | - Suresh D Muthukumaraswamy
- School of Pharmacy, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
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