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Pigorini A, Avanzini P, Barborica A, Bénar CG, David O, Farisco M, Keller CJ, Manfridi A, Mikulan E, Paulk AC, Roehri N, Subramanian A, Vulliémoz S, Zelmann R. Simultaneous invasive and non-invasive recordings in humans: A novel Rosetta stone for deciphering brain activity. J Neurosci Methods 2024; 408:110160. [PMID: 38734149 DOI: 10.1016/j.jneumeth.2024.110160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 04/10/2024] [Accepted: 05/01/2024] [Indexed: 05/13/2024]
Abstract
Simultaneous noninvasive and invasive electrophysiological recordings provide a unique opportunity to achieve a comprehensive understanding of human brain activity, much like a Rosetta stone for human neuroscience. In this review we focus on the increasingly-used powerful combination of intracranial electroencephalography (iEEG) with scalp electroencephalography (EEG) or magnetoencephalography (MEG). We first provide practical insight on how to achieve these technically challenging recordings. We then provide examples from clinical research on how simultaneous recordings are advancing our understanding of epilepsy. This is followed by the illustration of how human neuroscience and methodological advances could benefit from these simultaneous recordings. We conclude with a call for open data sharing and collaboration, while ensuring neuroethical approaches and argue that only with a true collaborative approach the promises of simultaneous recordings will be fulfilled.
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Affiliation(s)
- Andrea Pigorini
- Department of Biomedical, Surgical and Dental Sciences, Università degli Studi di Milano, Milan, Italy; UOC Maxillo-facial Surgery and dentistry, Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico, Milan, Italy.
| | - Pietro Avanzini
- Institute of Neuroscience, Consiglio Nazionale delle Ricerche, Parma, Italy
| | | | - Christian-G Bénar
- Aix Marseille Univ, Inserm, U1106, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Olivier David
- Aix Marseille Univ, Inserm, U1106, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Michele Farisco
- Centre for Research Ethics and Bioethics, Department of Public Health and Caring Sciences, Uppsala University, P.O. Box 256, Uppsala, SE 751 05, Sweden; Science and Society Unit Biogem, Biology and Molecular Genetics Institute, Via Camporeale snc, Ariano Irpino, AV 83031, Italy
| | - Corey J Keller
- Department of Psychiatry & Behavioral Sciences, Stanford University Medical Center, Stanford, CA 94305, USA; Wu Tsai Neurosciences Institute, Stanford University Medical Center, Stanford, CA 94305, USA; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA 94394, USA
| | - Alfredo Manfridi
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, Milan, Italy
| | - Ezequiel Mikulan
- Department of Health Sciences, Università degli Studi di Milano, Milan, Italy
| | - Angelique C Paulk
- Department of Neurology and Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Nicolas Roehri
- EEG and Epilepsy Unit, Dpt of Clinical Neurosciences, Geneva University Hospitals and University of Geneva, Switzerland
| | - Ajay Subramanian
- Department of Psychiatry & Behavioral Sciences, Stanford University Medical Center, Stanford, CA 94305, USA; Wu Tsai Neurosciences Institute, Stanford University Medical Center, Stanford, CA 94305, USA; Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA 94394, USA
| | - Serge Vulliémoz
- EEG and Epilepsy Unit, Dpt of Clinical Neurosciences, Geneva University Hospitals and University of Geneva, Switzerland
| | - Rina Zelmann
- Department of Neurology and Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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Zhang W, Jiang M, Teo KAC, Bhuvanakantham R, Fong L, Sim WKJ, Guo Z, Foo CHV, Chua RHJ, Padmanabhan P, Leong V, Lu J, Gulyás B, Guan C. Revealing the spatiotemporal brain dynamics of covert speech compared with overt speech: A simultaneous EEG-fMRI study. Neuroimage 2024; 293:120629. [PMID: 38697588 DOI: 10.1016/j.neuroimage.2024.120629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 04/17/2024] [Accepted: 04/29/2024] [Indexed: 05/05/2024] Open
Abstract
Covert speech (CS) refers to speaking internally to oneself without producing any sound or movement. CS is involved in multiple cognitive functions and disorders. Reconstructing CS content by brain-computer interface (BCI) is also an emerging technique. However, it is still controversial whether CS is a truncated neural process of overt speech (OS) or involves independent patterns. Here, we performed a word-speaking experiment with simultaneous EEG-fMRI. It involved 32 participants, who generated words both overtly and covertly. By integrating spatial constraints from fMRI into EEG source localization, we precisely estimated the spatiotemporal dynamics of neural activity. During CS, EEG source activity was localized in three regions: the left precentral gyrus, the left supplementary motor area, and the left putamen. Although OS involved more brain regions with stronger activations, CS was characterized by an earlier event-locked activation in the left putamen (peak at 262 ms versus 1170 ms). The left putamen was also identified as the only hub node within the functional connectivity (FC) networks of both OS and CS, while showing weaker FC strength towards speech-related regions in the dominant hemisphere during CS. Path analysis revealed significant multivariate associations, indicating an indirect association between the earlier activation in the left putamen and CS, which was mediated by reduced FC towards speech-related regions. These findings revealed the specific spatiotemporal dynamics of CS, offering insights into CS mechanisms that are potentially relevant for future treatment of self-regulation deficits, speech disorders, and development of BCI speech applications.
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Affiliation(s)
- Wei Zhang
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Muyun Jiang
- School of Computer Science and Engineering, Nanyang Technological University, Singapore
| | - Kok Ann Colin Teo
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; IGP-Neuroscience, Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore; Division of Neurosurgery, National University Health System, Singapore
| | - Raghavan Bhuvanakantham
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - LaiGuan Fong
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore
| | - Wei Khang Jeremy Sim
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore; IGP-Neuroscience, Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore
| | - Zhiwei Guo
- School of Computer Science and Engineering, Nanyang Technological University, Singapore
| | | | | | - Parasuraman Padmanabhan
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Victoria Leong
- Division of Psychology, Nanyang Technological University, Singapore; Department of Pediatrics, University of Cambridge, United Kingdom
| | - Jia Lu
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore; DSO National Laboratories, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Balázs Gulyás
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
| | - Cuntai Guan
- School of Computer Science and Engineering, Nanyang Technological University, Singapore.
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Tanaka H, Jiang P. P1, N170, and N250 Event-related Potential Components Reflect Temporal Perception Processing in Face and Body Personal Identification. J Cogn Neurosci 2024; 36:1265-1281. [PMID: 38652104 DOI: 10.1162/jocn_a_02167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
Human faces and bodies represent various socially important signals. Although adults encounter numerous new people in daily life, they can recognize hundreds to thousands of different individuals. However, the neural mechanisms that differentiate one person from another person are unclear. This study aimed to clarify the temporal dynamics of the cognitive processes of face and body personal identification using face-sensitive ERP components (P1, N170, and N250). The present study performed three blocks (face-face, face-body, and body-body) of different ERP adaptation paradigms. Furthermore, in the above three blocks, ERP components were used to compare brain biomarkers under three conditions (same person, different person of the same sex, and different person of the opposite sex). The results showed that the P1 amplitude for the face-face block was significantly greater than that for the body-body block, that the N170 amplitude for a different person of the same sex condition was greater than that for the same person condition in the right hemisphere only, and that the N250 amplitude gradually increased as the degree of face and body sex-social categorization grew closer (i.e., same person condition > different person of the same sex condition > different person of the opposite sex condition). These results suggest that early processing of the face and body processes the face and body separately and that structural encoding and personal identification of the face and body process the face and body collaboratively.
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Affiliation(s)
| | - Peilun Jiang
- Kanazawa University Graduate School, Kanazawa City, Japan
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4
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Meyer P, Baeuchl C, Hoppstädter M. Insights from simultaneous EEG-fMRI and patient data illuminate the role of the anterior medial temporal lobe in N400 generation. Neuropsychologia 2024; 193:108762. [PMID: 38142959 DOI: 10.1016/j.neuropsychologia.2023.108762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 11/17/2023] [Accepted: 12/16/2023] [Indexed: 12/26/2023]
Abstract
The N400, a negative event-related potential (ERP) peaking approximately 400 ms after stimulus onset, is known to reflect the processing of semantic information. While scalp recordings have contributed to understanding the psychological processes underlying the N400, they have been limited in identifying its neural basis. However, recent intracranial ERP recordings and fMRI studies have shed light on the crucial role of the anterior medial temporal lobe (AMTL) in semantic information processing. These findings suggest that the N400 partially represents activity in the AMTL structures. To investigate the neural underpinnings of the N400 effect, we simultaneously recorded ERPs and event-related fMRI during a semantic priming paradigm in a sample of 12 young, healthy subjects. Additionally, we collected ERPs and structural brain data from older healthy adults and patients with amnestic mild cognitive impairment (aMCI), a population characterized by neurodegenerative changes in the AMTL. In our fMRI results, we identified bilateral loci in the AMTL as the global maxima. Employing an EEG-informed fMRI analysis, we explored trial-to-trial fluctuations in semantic processing by linking single-trial N400 amplitudes to the Blood Oxygen Level Dependent (BOLD) signal. This approach provided the first direct evidence linking the N400 recorded at the scalp level to the corresponding BOLD signal in the AMTL. Consistent with these findings, patients with aMCI exhibited a diminished N400 effect compared to healthy older adults. Furthermore, voxel-based morphometry analysis revealed a correlation between the magnitude of the N400 effect and the integrity of the AMTL. By integrating data from simultaneous EEG-fMRI, and patient studies, our research advances our understanding of the neural substrate of the N400 and highlights the critical involvement of the AMTL in semantic processing.
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Affiliation(s)
- Patric Meyer
- SRH University Heidelberg, Heidelberg, Germany; Department for General and Applied Linguistics, Heidelberg University, Heidelberg, Germany; Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
| | - Christian Baeuchl
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
| | - Michael Hoppstädter
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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Algermissen J, Swart JC, Scheeringa R, Cools R, den Ouden HEM. Prefrontal signals precede striatal signals for biased credit assignment in motivational learning biases. Nat Commun 2024; 15:19. [PMID: 38168089 PMCID: PMC10762147 DOI: 10.1038/s41467-023-44632-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 12/22/2023] [Indexed: 01/05/2024] Open
Abstract
Actions are biased by the outcomes they can produce: Humans are more likely to show action under reward prospect, but hold back under punishment prospect. Such motivational biases derive not only from biased response selection, but also from biased learning: humans tend to attribute rewards to their own actions, but are reluctant to attribute punishments to having held back. The neural origin of these biases is unclear. Specifically, it remains open whether motivational biases arise primarily from the architecture of subcortical regions or also reflect cortical influences, the latter being typically associated with increased behavioral flexibility and control beyond stereotyped behaviors. Simultaneous EEG-fMRI allowed us to track which regions encoded biased prediction errors in which order. Biased prediction errors occurred in cortical regions (dorsal anterior and posterior cingulate cortices) before subcortical regions (striatum). These results highlight that biased learning is not a mere feature of the basal ganglia, but arises through prefrontal cortical contributions, revealing motivational biases to be a potentially flexible, sophisticated mechanism.
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Affiliation(s)
- Johannes Algermissen
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.
| | - Jennifer C Swart
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - René Scheeringa
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen, Essen, Germany
| | - Roshan Cools
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Hanneke E M den Ouden
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.
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Zhao G, Yu L, Chen P, Zhu K, Yang L, Lin W, Luo Y, Dou Z, Xu H, Zhang P, Zhu T, Yu S. Neural mechanisms of attentional bias to emotional faces in patients with chronic insomnia disorder. J Psychiatr Res 2024; 169:49-57. [PMID: 38000184 DOI: 10.1016/j.jpsychires.2023.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 10/27/2023] [Accepted: 11/15/2023] [Indexed: 11/26/2023]
Abstract
OBJECTIVE This study used event-related potential (ERP) and resting-state functional connectivity (rs-FC) approaches to investigate the neural mechanisms underlying the emotional attention bias in patients with chronic insomnia disorder (CID). METHODS Twenty-five patients with CID and thirty-three demographically matched healthy controls (HCs) completed clinical questionnaires and underwent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) scans. EEG analysis examined the group differences in terms of reaction times, P3 amplitudes, event-related spectral perturbations, and inter-trial phase synchrony. Subsequently, seed-based rs-FC analysis of the amygdala nuclei (including the central-medial amygdala [CMA] and basolateral amygdala [BLA]) was performed. The relationship between P3 amplitude, rs-FC and clinical symptom severity in patients with CID was further investigated by correlation analysis. RESULTS CID patients exhibited shorter reaction times than HCs in both standard and deviant stimuli, with the abnormalities becoming more pronounced as attention allocation increased. Compared to HCs, ERP analysis revealed increased P3 amplitude, theta wave power, and inter-trial synchrony in CID patients. The rs-FC analysis showed increased connectivity of the BLA-occipital pole, CMA-precuneus, and CMA-angular gyrus and decreased connectivity of the CMA-thalamus in CID patients. Notably, correlation analysis of the EEG and fMRI measurements showed a significant positive correlation between the P3 amplitude and the rs-FC of the CMA-PCU. CONCLUSION This study confirms an emotional attention bias in CID, specifically in the neural mechanisms of attention processing that vary depending on the allocation of attentional resources. Abnormal connectivity in the emotion-cognition networks may constitute the neural basis of the abnormal scalp activation pattern.
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Affiliation(s)
- Guangli Zhao
- School of Rehabilitation and Health Preservation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Liyong Yu
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Peixin Chen
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Keli Zhu
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Lu Yang
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Wenting Lin
- School of Rehabilitation and Health Preservation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yucai Luo
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Zeyang Dou
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Hao Xu
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China; Center of Interventional Medicine, Affiliated Hospital of North Sichuan Medical College, North Sichuan Medical College, Nanchong, China
| | - Pan Zhang
- Nervous System Disease Treatment Center, Traditional Chinese Medicine Hospital of Meishan, Meishan, China.
| | - Tianmin Zhu
- School of Rehabilitation and Health Preservation, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
| | - Siyi Yu
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China; Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
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7
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Miletínová E, Piorecký M, Koudelka V, Jiříček S, Tomeček D, Brunovský M, Horáček J, Bušková J. Alterations of sleep initiation in NREM parasomnia after sleep deprivation - A multimodal pilot study. Sleep Med X 2023; 6:100086. [PMID: 37745863 PMCID: PMC10511487 DOI: 10.1016/j.sleepx.2023.100086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 08/24/2023] [Accepted: 09/11/2023] [Indexed: 09/26/2023] Open
Abstract
Objectives NREM parasomnias also known as disorders of arousal (DOA) are characterised by abnormal motor and autonomic activation during arousals primarily from slow wave sleep. Dissociative state between sleep and wake is likely responsible for clinical symptoms of DOA. We therefore investigated potential dissociation outside of parasomnic events by using simultaneous 256-channel EEG (hdEEG) and functional magnetic resonance imaging (fMRI). Methods Eight DOA patients (3 women, mean age = 27.8; SD = 4.2) and 8 gender and age matched healthy volunteers (3 women, mean age = 26,5; SD = 4.0) were included into the study. They underwent 30-32 h of sleep deprivation followed by hdEEG and fMRI recording. We determined 2 conditions: falling asleep (FA) and arousal (A), that occurred outside of deep sleep and/or parasomnic event. We used multimodal approach using data obtained from EEG, fMRI and EEG-fMRI integration approach. Results DOA patients showed increase in delta and beta activity over postcentral gyrus and cuneus during awakening period. This group expressed increased connectivity between motor cortex and cingulate during arousals unrelated to parasomnic events in the beta frequency band. They also showed lower connectivity between different portions of cingulum. In contrast, the greater connectivity was found between thalamus and some cortical areas, such as occipital cortex. Conclusion Our findings suggest a complex alteration in falling asleep and arousal mechanisms at both subcortical and cortical levels in response to sleep deprivation. As this alteration is present also outside of slow wave sleep and/or parasomnic episodes we believe this could be a trait factor of DOA.
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Affiliation(s)
- E. Miletínová
- National Institute of Mental Health, Topolova 748, Klecany, Czech Republic
- Third Faculty of Medicine, Charles University in Prague, Ruská 87, Prague, Czech Republic
| | - M. Piorecký
- National Institute of Mental Health, Topolova 748, Klecany, Czech Republic
- Department of Biomedical Technology, Faculty of Biomedical Engineering, CTU in Prague, Czech Republic
| | - V. Koudelka
- National Institute of Mental Health, Topolova 748, Klecany, Czech Republic
- Department of Biomedical Technology, Faculty of Biomedical Engineering, CTU in Prague, Czech Republic
| | - S. Jiříček
- National Institute of Mental Health, Topolova 748, Klecany, Czech Republic
- Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
- Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - D. Tomeček
- National Institute of Mental Health, Topolova 748, Klecany, Czech Republic
- Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
- Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - M. Brunovský
- National Institute of Mental Health, Topolova 748, Klecany, Czech Republic
- Third Faculty of Medicine, Charles University in Prague, Ruská 87, Prague, Czech Republic
| | - J. Horáček
- National Institute of Mental Health, Topolova 748, Klecany, Czech Republic
- Third Faculty of Medicine, Charles University in Prague, Ruská 87, Prague, Czech Republic
| | - J. Bušková
- National Institute of Mental Health, Topolova 748, Klecany, Czech Republic
- Third Faculty of Medicine, Charles University in Prague, Ruská 87, Prague, Czech Republic
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Ciapponi C, Li Y, Osorio Becerra DA, Rodarie D, Casellato C, Mapelli L, D’Angelo E. Variations on the theme: focus on cerebellum and emotional processing. Front Syst Neurosci 2023; 17:1185752. [PMID: 37234065 PMCID: PMC10206087 DOI: 10.3389/fnsys.2023.1185752] [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: 03/13/2023] [Accepted: 04/18/2023] [Indexed: 05/27/2023] Open
Abstract
The cerebellum operates exploiting a complex modular organization and a unified computational algorithm adapted to different behavioral contexts. Recent observations suggest that the cerebellum is involved not just in motor but also in emotional and cognitive processing. It is therefore critical to identify the specific regional connectivity and microcircuit properties of the emotional cerebellum. Recent studies are highlighting the differential regional localization of genes, molecules, and synaptic mechanisms and microcircuit wiring. However, the impact of these regional differences is not fully understood and will require experimental investigation and computational modeling. This review focuses on the cellular and circuit underpinnings of the cerebellar role in emotion. And since emotion involves an integration of cognitive, somatomotor, and autonomic activity, we elaborate on the tradeoff between segregation and distribution of these three main functions in the cerebellum.
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Affiliation(s)
- Camilla Ciapponi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Yuhe Li
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | | | - Dimitri Rodarie
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Centro Ricerche Enrico Fermi, Rome, Italy
| | - Claudia Casellato
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Lisa Mapelli
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Egidio D’Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
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9
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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] [Grants] [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.
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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
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10
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Agrawal S, Chinnadurai V, Sharma R. Hemodynamic functional connectivity optimization of frequency EEG microstates enables attention LSTM framework to classify distinct temporal cortical communications of different cognitive tasks. Brain Inform 2022; 9:25. [PMID: 36219346 PMCID: PMC9554110 DOI: 10.1186/s40708-022-00173-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 09/28/2022] [Indexed: 11/24/2022] Open
Abstract
Temporal analysis of global cortical communication of cognitive tasks in coarse EEG information is still challenging due to the underlying complex neural mechanisms. This study proposes an attention-based time-series deep learning framework that processes fMRI functional connectivity optimized quasi-stable frequency microstates for classifying distinct temporal cortical communications of the cognitive task. Seventy volunteers were subjected to visual target detection tasks, and their electroencephalogram (EEG) and functional MRI (fMRI) were acquired simultaneously. At first, the acquired EEG information was preprocessed and bandpass to delta, theta, alpha, beta, and gamma bands and then subjected to quasi-stable frequency-microstate estimation. Subsequently, time-series elicitation of each frequency microstates is optimized with graph theory measures of simultaneously eliciting fMRI functional connectivity between frontal, parietal, and temporal cortices. The distinct neural mechanisms associated with each optimized frequency-microstate were analyzed using microstate-informed fMRI. Finally, these optimized, quasi-stable frequency microstates were employed to train and validate the attention-based Long Short-Term Memory (LSTM) time-series architecture for classifying distinct temporal cortical communications of the target from other cognitive tasks. The temporal, sliding input sampling windows were chosen between 180 to 750 ms/segment based on the stability of transition probabilities of the optimized microstates. The results revealed 12 distinct frequency microstates capable of deciphering target detections' temporal cortical communications from other task engagements. Particularly, fMRI functional connectivity measures of target engagement were observed significantly correlated with the right-diagonal delta (r = 0.31), anterior-posterior theta (r = 0.35), left-right theta (r = - 0.32), alpha (r = - 0.31) microstates. Further, neuro-vascular information of microstate-informed fMRI analysis revealed the association of delta/theta and alpha/beta microstates with cortical communications and local neural processing, respectively. The classification accuracies of the attention-based LSTM were higher than the traditional LSTM architectures, particularly the frameworks that sampled the EEG data with a temporal width of 300 ms/segment. In conclusion, the study demonstrates reliable temporal classifications of global cortical communication of distinct tasks using an attention-based LSTM utilizing fMRI functional connectivity optimized quasi-stable frequency microstates.
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Affiliation(s)
- Swati Agrawal
- Institute of Nuclear Medicine and Allied Sciences, Lucknow Road, Timarpur, Delhi, 110054, India
- Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi, 110042, India
| | - Vijayakumar Chinnadurai
- Institute of Nuclear Medicine and Allied Sciences, Lucknow Road, Timarpur, Delhi, 110054, India.
| | - Rinku Sharma
- Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi, 110042, India
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11
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Faramarzi A, Sharini H, Shanbehzadeh M, Pour MY, Fooladi M, Jalalvandi M, Amiri S, Kazemi-Arpanahi H. Anhedonia symptoms: The assessment of brain functional mechanism following music stimuli using functional magnetic resonance imaging. Psychiatry Res Neuroimaging 2022; 326:111532. [PMID: 36095991 DOI: 10.1016/j.pscychresns.2022.111532] [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: 05/21/2022] [Revised: 08/13/2022] [Accepted: 08/19/2022] [Indexed: 11/16/2022]
Abstract
PURPOSE This study aimed to investigate the effect of music stimulation on the brain functional mechanism of depressed patients with anhedonia symptoms using functional magnetic resonance imaging (fMRI). METHODS Participants in this study included 20 healthy subjects as the control group, 25 subjects with depression and no anhedonia as the intervention group A, and 24 subjects with depression and anhedonia as the intervention group B. The safely emotional stimulation was done by Iranian music. To investigate the effect of music therapy on the brain, a task including 50 tracks of 12 s Iranian music (traditional and pop) was used. Finally, the data were analyzed using SPM Toolbox in MATLAB software. RESULTS The results showed that brain patterns in depressed patients with and without anhedonia could be distinguished based on positive and negative musical stimuli (p < 0.05). Important fMRI biomarker such as effective connectivity strength related to the fronto-limbic network, including the supragenual ACC, subgenual ACC, AMYG, and FFG were evaluated in depressed patients with anhedonia. CONCLUSION This was the first study to investigate the neural circuits involved in music-related emotional processing in patients with anhedonia symptoms. These findings could help advance neurological understandings of anhedonia and suggest new treatments.
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Affiliation(s)
- A Faramarzi
- Department of Biomedical Engineering, Faculty of Medicine, Kermanshah University of Medical Sciences (KUMS), Kermanshah, Iran
| | - H Sharini
- Department of Biomedical Engineering, Faculty of Medicine, Kermanshah University of Medical Sciences (KUMS), Kermanshah, Iran
| | - M Shanbehzadeh
- Department of Health Information Technology, Faculty of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - M Yousef Pour
- Faculty of Medicine, Aja University of Medical Science, Tehran, Iran
| | - M Fooladi
- Department of Medical Physics and Biomedical Engineering, Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - M Jalalvandi
- Department of Neuroscience and Addiction Science, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Sh Amiri
- Department of Clinical Psychology, Faculty of Medicine, Kermanshah University of Medical Sciences (KUMS), Kermanshah, Iran
| | - H Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran; Student Research Committeh, Abadan University of Medical Sciences, Abadan, Iran.
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12
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Zhang Q, Luo C, Ngetich R, Zhang J, Jin Z, Li L. Visual Selective Attention P300 Source in Frontal-Parietal Lobe: ERP and fMRI Study. Brain Topogr 2022; 35:636-650. [PMID: 36178537 DOI: 10.1007/s10548-022-00916-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 09/03/2022] [Indexed: 11/28/2022]
Abstract
Visual selective attention can be achieved into bottom-up and top-down attention. Different selective attention tasks involve different attention control ways. The pop-out task requires more bottom-up attention, whereas the search task involves more top-down attention. P300, which is the positive potential generated by the brain in the latency of 300 ~ 600 ms after stimulus, reflects the processing of attention. There is no consensus on the P300 source. The aim of present study is to study the source of P300 elicited by different visual selective attention. We collected thirteen participants' P300 elicited by pop-out and search tasks with event-related potentials (ERP). We collected twenty-six participants' activation brain regions in pop-out and search tasks with functional magnetic resonance imaging (fMRI). And we analyzed the sources of P300 using the ERP and fMRI integration with high temporal resolution and high spatial resolution. ERP results indicated that the pop-out task induced larger P300 than the search task. P300 induced by the two tasks distributed at frontal and parietal lobes, with P300 induced by the pop-out task mainly at the parietal lobe and that induced by the search task mainly at the frontal lobe. Further ERP and fMRI integration analysis showed that neural difference sources of P300 were the right precentral gyrus, left superior frontal gyrus (medial orbital), left middle temporal gyrus, left rolandic operculum, right postcentral gyrus, and left angular gyrus. Our study suggests that the frontal and parietal lobes contribute to the P300 component of visual selective attention.
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Affiliation(s)
- Qiuzhu Zhang
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Cimei Luo
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Ronald Ngetich
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Junjun Zhang
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Zhenlan Jin
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Ling Li
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
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13
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Patil AU, Madathil D, Fan YT, Tzeng OJL, Huang CM, Huang HW. Neurofeedback for the Education of Children with ADHD and Specific Learning Disorders: A Review. Brain Sci 2022; 12:brainsci12091238. [PMID: 36138974 PMCID: PMC9497239 DOI: 10.3390/brainsci12091238] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 09/03/2022] [Accepted: 09/07/2022] [Indexed: 12/02/2022] Open
Abstract
Neurofeedback (NF) is a type of biofeedback in which an individual’s brain activity is measured and presented to them to support self-regulation of ongoing brain oscillations and achieve specific behavioral and neurophysiological outcomes. NF training induces changes in neurophysiological circuits that are associated with behavioral changes. Recent evidence suggests that the NF technique can be used to train electrical brain activity and facilitate learning among children with learning disorders. Toward this aim, this review first presents a generalized model for NF systems, and then studies involving NF training for children with disorders such as dyslexia, attention-deficit/hyperactivity disorder (ADHD), and other specific learning disorders such as dyscalculia and dysgraphia are reviewed. The discussion elaborates on the potential for translational applications of NF in educational and learning settings with details. This review also addresses some issues concerning the role of NF in education, and it concludes with some solutions and future directions. In order to provide the best learning environment for children with ADHD and other learning disorders, it is critical to better understand the role of NF in educational settings. The review provides the potential challenges of the current systems to aid in highlighting the issues undermining the efficacy of current systems and identifying solutions to address them. The review focuses on the use of NF technology in education for the development of adaptive teaching methods and the best learning environment for children with learning disabilities.
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Affiliation(s)
- Abhishek Uday Patil
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
| | - Deepa Madathil
- Jindal Institute of Behavioural Sciences, O.P. Jindal Global University, Haryana 131001, India
| | - Yang-Tang Fan
- Graduate Institute of Medicine, Yuan Ze University, Taoyuan 320315, Taiwan
| | - Ovid J. L. Tzeng
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
- Centre for Intelligent Drug Systems and Smart Bio-Devices (IDSB), National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
- College of Humanities and Social Sciences, Taipei Medical University, Taipei 106339, Taiwan
- Department of Educational Psychology and Counseling, National Taiwan Normal University, Taipei 106308, Taiwan
- Hong Kong Institute for Advanced Studies, City University of Hong Kong, Hong Kong
| | - Chih-Mao Huang
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
- Centre for Intelligent Drug Systems and Smart Bio-Devices (IDSB), National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
| | - Hsu-Wen Huang
- Department of Linguistics and Translation, City University of Hong Kong, Hong Kong
- Correspondence: ; Tel.: +852-3442-2579
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Ebrahimzadeh E, Saharkhiz S, Rajabion L, Oskouei HB, Seraji M, Fayaz F, Saliminia S, Sadjadi SM, Soltanian-Zadeh H. Simultaneous electroencephalography-functional magnetic resonance imaging for assessment of human brain function. Front Syst Neurosci 2022; 16:934266. [PMID: 35966000 PMCID: PMC9371554 DOI: 10.3389/fnsys.2022.934266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 07/08/2022] [Indexed: 02/01/2023] Open
Abstract
Electroencephalography (EEG) and functional Magnetic Resonance Imaging (MRI) have long been used as tools to examine brain activity. Since both methods are very sensitive to changes of synaptic activity, simultaneous recording of EEG and fMRI can provide both high temporal and spatial resolution. Therefore, the two modalities are now integrated into a hybrid tool, EEG-fMRI, which encapsulates the useful properties of the two. Among other benefits, EEG-fMRI can contribute to a better understanding of brain connectivity and networks. This review lays its focus on the methodologies applied in performing EEG-fMRI studies, namely techniques used for the recording of EEG inside the scanner, artifact removal, and statistical analysis of the fMRI signal. We will investigate simultaneous resting-state and task-based EEG-fMRI studies and discuss their clinical and technological perspectives. Moreover, it is established that the brain regions affected by a task-based neural activity might not be limited to the regions in which they have been initiated. Advanced methods can help reveal the regions responsible for or affected by a developed neural network. Therefore, we have also looked into studies related to characterization of structure and dynamics of brain networks. The reviewed literature suggests that EEG-fMRI can provide valuable complementary information about brain neural networks and functions.
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Affiliation(s)
- Elias Ebrahimzadeh
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- *Correspondence: Elias Ebrahimzadeh, ,
| | - Saber Saharkhiz
- Department of Pharmacology-Physiology, Faculty of Medicine, University of Sherbrooke, Sherbrooke, Canada
| | - Lila Rajabion
- School of Graduate Studies, State University of New York Empire State College, Manhattan, NY, United States
| | | | - Masoud Seraji
- Department of Psychology, University of Texas at Austin, Austin, TX, United States
| | - Farahnaz Fayaz
- Department of Biomedical Engineering, School of Electrical Engineering, Payame Noor University of North Tehran, Tehran, Iran
| | - Sarah Saliminia
- Department of Biomedical Engineering, School of Electrical Engineering, Payame Noor University of North Tehran, Tehran, Iran
| | - Seyyed Mostafa Sadjadi
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Hamid Soltanian-Zadeh
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
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15
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A Cumulants-Based Human Brain Decoding. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6474515. [PMID: 35860640 PMCID: PMC9293498 DOI: 10.1155/2022/6474515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 06/12/2022] [Accepted: 06/23/2022] [Indexed: 11/17/2022]
Abstract
Human cognition is influenced by the way the nervous system processes information and is linked to this mechanical explanation of the human body’s cognitive function. Accuracy is the key emphasis in neuroscience which may be enhanced by utilising new hardware, mathematical, statistical, and computational methodologies. Feature extraction and feature selection also play a crucial function in gaining improved accuracy since the proper characteristics can identify brain states efficiently. However, both feature extraction and selection procedures are dependent on mathematical and statistical techniques which implies that mathematical and statistical techniques have a direct or indirect influence on prediction accuracy. The forthcoming challenges of the brain-computer interface necessitate a thorough critical understanding of the complicated structure and uncertain behavior of the brain. It is impossible to upgrade hardware periodically, and thus, an option is necessary to collect maximum information from the brain against varied actions. The mathematical and statistical combination could be the ideal answer for neuroscientists which can be utilised for feature extraction, feature selection, and classification. That is why in this research a statistical technique is offered together with specialised feature extraction and selection methods to increase the accuracy. A score fusion function is changed utilising an enhanced cumulants-driven likelihood ratio test employing multivariate pattern analysis. Functional MRI data were acquired from 12 patients versus a visual test that comprises of pictures from five distinct categories. After cleaning the data, feature extraction and selection were done using mathematical approaches, and lastly, the best match of the projected class was established using the likelihood ratio test. To validate the suggested approach, it is compared with the current methods reported in recent research.
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16
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17
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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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18
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Gutiérrez-Tobal GC, Kheirandish-Gozal L, Gozal D, Hornero R. Editorial: Unraveling Sleep and Its Disorders Using Novel Analytical Approaches. Front Neurosci 2022; 16:924359. [PMID: 35663551 PMCID: PMC9159350 DOI: 10.3389/fnins.2022.924359] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 05/04/2022] [Indexed: 11/23/2022] Open
Affiliation(s)
- Gonzalo C. Gutiérrez-Tobal
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Centro de Investigacion Biomedica en Red en Bioingenieria, Biomateriales y Nanomedicina (CIBER-BBN), Valladolid, Spain
- *Correspondence: Gonzalo C. Gutiérrez-Tobal
| | - Leila Kheirandish-Gozal
- Department of Child Health, Child Health Research Institute, The University of Missouri School of Medicine, Columbia, MO, United States
| | - David Gozal
- Department of Child Health, Child Health Research Institute, The University of Missouri School of Medicine, Columbia, MO, United States
| | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Centro de Investigacion Biomedica en Red en Bioingenieria, Biomateriales y Nanomedicina (CIBER-BBN), Valladolid, Spain
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Abstract
There are various obstacles in the way of use of EEG. Among these, the major obstacles are the artifacts. While some artifacts are avoidable, due to the nature of the EEG techniques there are inevitable artifacts as well. Artifacts can be categorized as internal/physiological or external/non-physiological. The most common internal artifacts are ocular or muscular origins. Internal artifacts are difficult to detect and remove, because they contain signal information as well. For both resting state EEG and ERP studies, artifact handling needs to be carefully carried out in order to retain the maximal signal. Therefore, an effective management of these inevitable artifacts is critical for the EEG based researches. Many researchers from various fields studied this challenging phenomenon and came up with some solutions. However, the developed methods are not well known by the real practitioners of EEG as a tool because of their limited knowledge about these engineering approaches. They still use the traditional visual inspection of the EEG. This work aims to inform the researchers working in the field of EEG about the artifacts and artifact management options available in order to increase the awareness of the available tools such as EEG preprocessing pipelines.
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20
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Basedau H, Peng KP, May A, Mehnert J. High-Density Electroencephalography-Informed Multiband Functional Magnetic Resonance Imaging Reveals Rhythm-Specific Activations Within the Trigeminal Nociceptive Network. Front Neurosci 2022; 16:802239. [PMID: 35651631 PMCID: PMC9149083 DOI: 10.3389/fnins.2022.802239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 03/30/2022] [Indexed: 11/17/2022] Open
Abstract
The interest in exploring trigeminal pain processing has grown in recent years, mainly due to various pathologies (such as migraine) related to this system. However, research efforts have mainly focused on understanding molecular mechanisms or studying pathological states. On the contrary, non-invasive imaging studies are limited by either spatial or temporal resolution depending on the modality used. This can be overcome by using multimodal imaging techniques such as simultaneous functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). Although this technique has already been applied to neuroscientific research areas and consequently gained insights into diverse sensory systems and pathologies, only a few studies have applied EEG-fMRI in the field of pain processing and none in the trigeminal system. Focusing on trigeminal nociception, we used a trigeminal pain paradigm, which has been well-studied in either modality. For validation, we first acquired stand-alone measures with each imaging modality before fusing them in a simultaneous session. Furthermore, we introduced a new, yet simple, non-parametric correlation technique, which exploits trial-to-trial variance of both measurement techniques with Spearman’s correlations, to consolidate the results gained by the two modalities. This new technique does not presume a linear relationship and needs a few repetitions per subject. We also showed cross-validation by analyzing visual stimulations. Using these techniques, we showed that EEG power changes in the theta-band induced by trigeminal pain correlate with fMRI activation within the brainstem, whereas those of gamma-band oscillations correlate with BOLD signals in higher cortical areas.
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Yeung MK, Chu VW. Viewing neurovascular coupling through the lens of combined EEG-fNIRS: A systematic review of current methods. Psychophysiology 2022; 59:e14054. [PMID: 35357703 DOI: 10.1111/psyp.14054] [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: 11/15/2021] [Revised: 02/01/2022] [Accepted: 03/08/2022] [Indexed: 12/25/2022]
Abstract
Neurovascular coupling is a key physiological mechanism that occurs in the healthy human brain, and understanding this process has implications for understanding the aging and neuropsychiatric populations. Combined electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) has emerged as a promising, noninvasive tool for probing neurovascular interactions in humans. However, the utility of this approach critically depends on the methodological quality used for multimodal integration. Despite a growing number of combined EEG-fNIRS applications reported in recent years, the methodological rigor of past studies remains unclear, limiting the accurate interpretation of reported findings and hindering the translational application of this multimodal approach. To fill this knowledge gap, we critically evaluated various methodological aspects of previous combined EEG-fNIRS studies performed in healthy individuals. A literature search was conducted using PubMed and PsycINFO on June 28, 2021. Studies involving concurrent EEG and fNIRS measurements in awake and healthy individuals were selected. After screening and eligibility assessment, 96 studies were included in the methodological evaluation. Specifically, we critically reviewed various aspects of participant sampling, experimental design, signal acquisition, data preprocessing, outcome selection, data analysis, and results presentation reported in these studies. Altogether, we identified several notable strengths and limitations of the existing EEG-fNIRS literature. In light of these limitations and the features of combined EEG-fNIRS, recommendations are made to improve and standardize research practices to facilitate the use of combined EEG-fNIRS when studying healthy neurovascular coupling processes and alterations in neurovascular coupling among various populations.
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Affiliation(s)
- Michael K Yeung
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China
| | - Vivian W Chu
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China
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22
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Warbrick T. Simultaneous EEG-fMRI: What Have We Learned and What Does the Future Hold? SENSORS (BASEL, SWITZERLAND) 2022; 22:2262. [PMID: 35336434 PMCID: PMC8952790 DOI: 10.3390/s22062262] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [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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23
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Moradi N, LeVan P, Akin B, Goodyear BG, Sotero RC. Holo-Hilbert spectral-based noise removal method for EEG high-frequency bands. J Neurosci Methods 2021; 368:109470. [PMID: 34973273 DOI: 10.1016/j.jneumeth.2021.109470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 12/23/2021] [Accepted: 12/26/2021] [Indexed: 11/16/2022]
Abstract
Simultaneous EEG-fMRI is a growing and promising field, as it has great potential to further our understanding of the spatiotemporal dynamics of brain function in health and disease. In particular, there is much interest in understanding the fMRI correlates of brain activity in the gamma band (> 30 Hz), as these frequencies are thought to be associated with cognitive processes involving perception, attention, and memory, as well as with disorders such as schizophrenia and autism. However, progress in this area has been limited due to issues such as MR-induced artifacts in EEG recordings, which seem to be more problematic for gamma frequencies. This paper presents a noise removal method for the gamma band of EEG that is based on the Holo-Hilbert spectral analysis (HHSA), but with a new implementation strategy. HHSA uses a nested empirical mode decomposition (EMD) to identify amplitude and frequency modulations (AM and FM, respectively) by averaging over frequencies with high and significant powers. Our method examines gamma band by applying two layers of EMD to the FM and AM components, removing components with very low power based on the power-instantaneous frequency spectrum, and subsequently reconstructs the denoised gamma-band signal from the remaining components. Simulations demonstrate that our proposed method efficiently reduces artifacts while preserving the original gamma signal which is especially critical for simultaneous EEG/fMRI studies.
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Affiliation(s)
- Narges Moradi
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada; Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
| | - Pierre LeVan
- Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute and Departments of Paediatrics, University of Calgary, Calgary, Canada; Department of Radiology, Medical Physics, Medical Center, University of Freiburg, Faculty of Medicine, Germany
| | - Burak Akin
- Department of Radiology, Medical Physics, Medical Center, University of Freiburg, Faculty of Medicine, Germany; Section on Functional Imaging Methods, NIMH, NIH, Bethesda, MD, USA
| | - Bradley G Goodyear
- Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Roberto C Sotero
- Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
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Chatzichristos C, Kofidis E, Van Paesschen W, De Lathauwer L, Theodoridis S, Van Huffel S. Early soft and flexible fusion of electroencephalography and functional magnetic resonance imaging via double coupled matrix tensor factorization for multisubject group analysis. Hum Brain Mapp 2021; 43:1231-1255. [PMID: 34806255 PMCID: PMC8837580 DOI: 10.1002/hbm.25717] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 09/29/2021] [Accepted: 10/18/2021] [Indexed: 11/12/2022] Open
Abstract
Data fusion refers to the joint analysis of multiple datasets that provide different (e.g., complementary) views of the same task. In general, it can extract more information than separate analyses can. Jointly analyzing electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) measurements has been proved to be highly beneficial to the study of the brain function, mainly because these neuroimaging modalities have complementary spatiotemporal resolution: EEG offers good temporal resolution while fMRI is better in its spatial resolution. The EEG–fMRI fusion methods that have been reported so far ignore the underlying multiway nature of the data in at least one of the modalities and/or rely on very strong assumptions concerning the relation of the respective datasets. For example, in multisubject analysis, it is commonly assumed that the hemodynamic response function is a priori known for all subjects and/or the coupling across corresponding modes is assumed to be exact (hard). In this article, these two limitations are overcome by adopting tensor models for both modalities and by following soft and flexible coupling approaches to implement the multimodal fusion. The obtained results are compared against those of parallel independent component analysis and hard coupling alternatives, with both synthetic and real data (epilepsy and visual oddball paradigm). Our results demonstrate the clear advantage of using soft and flexible coupled tensor decompositions in scenarios that do not conform with the hard coupling assumption.
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Affiliation(s)
- Christos Chatzichristos
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Eleftherios Kofidis
- Department of Statistics and Insurance Science, University of Piraeus, Piraeus, Greece.,Computer Technology Institute and Press "Diophantus" (CTI), Patras, Greece
| | | | - Lieven De Lathauwer
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium.,Engineering, Science and Technology, KU Leuven Kulak, Kortrijk, Belgium
| | - Sergios Theodoridis
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece.,Department of Electronic Systems, University of Aalborg, Aalborg, Denmark
| | - Sabine Van Huffel
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
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25
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Watanabe N, Takeda M. Neurophysiological dynamics for psychological resilience: A view from the temporal axis. Neurosci Res 2021; 175:53-61. [PMID: 34801599 DOI: 10.1016/j.neures.2021.11.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 11/16/2021] [Indexed: 10/19/2022]
Abstract
When an individual is faced with adversity, the brain and body work cooperatively to adapt to it. This adaptive process is termed psychological resilience, and recent studies have identified several neurophysiological factors ("neurophysiological resilience"), such as monoamines, oscillatory brain activity, hemodynamics, autonomic activity, stress hormones, and immune systems. Each factor is activated in an interactive manner during specific time windows after exposure to stress. Thus, the differences in psychological resilience levels among individuals can be characterized by differences in the temporal dynamics of neurophysiological resilience. In this review, after briefly introducing the frequently used approaches in this research field and the well-known factors of neurophysiological resilience, we summarize the temporal dynamics of neurophysiological resilience. This viewpoint clarifies an important time window, the more-than-one-hour scale, but the neurophysiological dynamics during this window remain elusive. To address this issue, we propose exploring brain-wide oscillatory activities using concurrent functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) techniques.
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Affiliation(s)
- Noriya Watanabe
- Research Center for Brain Communication, Research Institute, Kochi University of Technology, Kochi, Japan; Center for Information and Neural Networks, National Institute of Information and Communications Technology, Osaka, Japan.
| | - Masaki Takeda
- Research Center for Brain Communication, Research Institute, Kochi University of Technology, Kochi, Japan
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26
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Kline A, Forkert ND, Felfeliyan B, Pittman D, Goodyear B, Ronsky J. fMRI-Informed EEG for brain mapping of imagined lower limb movement: Feasibility of a brain computer interface. J Neurosci Methods 2021; 363:109339. [PMID: 34454954 DOI: 10.1016/j.jneumeth.2021.109339] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 08/17/2021] [Accepted: 08/21/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND EEG and fMRI have contributed greatly to our understanding of brain activity and its link to behaviors by helping to identify both when and where the activity occurs. This is particularly important in the development of brain-computer interfaces (BCIs), where feed forward systems gather data from imagined brain activity and then send that information to an effector. The purpose of this study was to develop and evaluate a computational approach that enables an accurate mapping of spatial brain activity (fMRI) in relation to the temporal receptors (EEG electrodes) associated with imagined lower limb movement. NEW METHOD EEG and fMRI data from 16 healthy, male participants while imagining lower limb movement were used for this purpose. A combined analysis of fMRI data and EEG electrode locations was developed to identify EEG electrodes with a high likelihood of capturing imagined lower limb movement originating from various clusters of brain activity. This novel feature selection tool was used to develop an artificial neural network model to classify right and left lower limb movement. RESULTS Results showed that left versus right lower limb imagined movement could be classified with 66.5% accuracy using this approach. Comparison with existing methods: Adopting a purely data-driven approach for feature selection to use in the right/left classification task resulted in the same accuracy (66.6%) but with reduced interpretability. CONCLUSIONS The developed fMRI-informed EEG approach could pave the way towards improved brain computer interfaces for lower limb movement while also being applicable to other systems where fMRI could be helpful to inform EEG acquisition and processing.
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Affiliation(s)
- Adrienne Kline
- Department of Biomedical Engineering, University of Calgary, Calgary, Alberta, Canada.
| | - Nils D Forkert
- Department of Biomedical Engineering, University of Calgary, Calgary, Alberta, Canada
| | - Banafshe Felfeliyan
- Department of Biomedical Engineering, University of Calgary, Calgary, Alberta, Canada
| | - Daniel Pittman
- Department of Radiology, University of Calgary, Calgary, Alberta, Canada
| | - Bradley Goodyear
- Department of Biomedical Engineering, University of Calgary, Calgary, Alberta, Canada; Department of Radiology, University of Calgary, Calgary, Alberta, Canada
| | - Janet Ronsky
- Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, Alberta, Canada
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27
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Oscillatory Correlates of Intentional Forgetting: The Role of Theta and Alpha Power in Item-Method Directed Forgetting. eNeuro 2021; 8:ENEURO.0022-21.2021. [PMID: 34583932 PMCID: PMC8503960 DOI: 10.1523/eneuro.0022-21.2021] [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: 12/10/2020] [Revised: 08/04/2021] [Accepted: 08/06/2021] [Indexed: 11/28/2022] Open
Abstract
Results from item-method directed forgetting suggest that individuals are able to intentionally forget processed information. Most research suggests that either selective rehearsal of to-be-remembered or inhibitory control of to-be-forgotten information is accountable for the effects of intentional forgetting. Some research, however, hypothesized that the time to process information mediates the underlying mechanism. To test this hypothesis, the current study investigated associations between oscillatory power in theta (3–7.5 Hz) and alpha frequencies (8–13 Hz) and intentional forgetting in human participants and explored whether or not these mechanisms depended on processing time. Previously, theta power was shown to be associated with the creation of episodic memory traces and alpha power with inhibition. We therefore expected to find associations between these neural signatures and behavioral effects. Consistent with our hypotheses, we revealed increased theta power for to-be-remembered and increased alpha power for to-be-forgotten information and that the effects of activity in both frequency bands were influenced by the time individuals were given for processing the memory cue. These results suggest that not one but two mechanisms, rehearsal and inhibitory control, are accountable for item-method directed forgetting, both with different temporal profiles.
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28
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Martinek R, Ladrova M, Sidikova M, Jaros R, Behbehani K, Kahankova R, Kawala-Sterniuk A. Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part II: Brain Signals. SENSORS (BASEL, SWITZERLAND) 2021; 21:6343. [PMID: 34640663 PMCID: PMC8512967 DOI: 10.3390/s21196343] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 09/12/2021] [Accepted: 09/14/2021] [Indexed: 12/14/2022]
Abstract
As it was mentioned in the previous part of this work (Part I)-the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work-various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.
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Affiliation(s)
- Radek Martinek
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Martina Ladrova
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Michaela Sidikova
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Rene Jaros
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Khosrow Behbehani
- College of Engineering, The University of Texas in Arlington, Arlington, TX 76019, USA;
| | - Radana Kahankova
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
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29
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Wearable, Integrated EEG-fNIRS Technologies: A Review. SENSORS 2021; 21:s21186106. [PMID: 34577313 PMCID: PMC8469799 DOI: 10.3390/s21186106] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 09/01/2021] [Accepted: 09/04/2021] [Indexed: 02/04/2023]
Abstract
There has been considerable interest in applying electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) simultaneously for multimodal assessment of brain function. EEG–fNIRS can provide a comprehensive picture of brain electrical and hemodynamic function and has been applied across various fields of brain science. The development of wearable, mechanically and electrically integrated EEG–fNIRS technology is a critical next step in the evolution of this field. A suitable system design could significantly increase the data/image quality, the wearability, patient/subject comfort, and capability for long-term monitoring. Here, we present a concise, yet comprehensive, review of the progress that has been made toward achieving a wearable, integrated EEG–fNIRS system. Significant marks of progress include the development of both discrete component-based and microchip-based EEG–fNIRS technologies; modular systems; miniaturized, lightweight form factors; wireless capabilities; and shared analogue-to-digital converter (ADC) architecture between fNIRS and EEG data acquisitions. In describing the attributes, advantages, and disadvantages of current technologies, this review aims to provide a roadmap toward the next generation of wearable, integrated EEG–fNIRS systems.
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30
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Mandal R, Budde R, Lawlor GL, Irazoqui P. Utilizing multimodal imaging to visualize potential mechanism for sudden death in epilepsy. Epilepsy Behav 2021; 122:108124. [PMID: 34237676 PMCID: PMC8429091 DOI: 10.1016/j.yebeh.2021.108124] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 05/20/2021] [Accepted: 05/31/2021] [Indexed: 12/28/2022]
Abstract
Sudden death in epilepsy or SUDEP is a fatal condition that accounts for more than 4000 deaths each year. Limited clinical and preclinical data on sudden death suggest critical contributions from autonomic, cardiac, and respiratory pathways. A potential mechanism for such sudden and severe cardiorespiratory dysregulation may be linked to acid reflux-induced laryngospasm. Here, we expand on our previous investigations and utilize a novel multimodal approach to provide visual evidence of acid reflux-initiated cardiorespiratory distress and subsequent sudden death in seizing rats. We used systemic kainic acid to acutely induce seizure activity in Long Evans rats, under urethane anesthesia. We recorded electroencephalography (EEG), electrocardiography (ECG), chest plethysmography, and esophageal pH signals through a multimodal recording platform, during simultaneous fast MRI scans of the rat stomach and esophagus. MRI images, in correlation with electrophysiology data were used to identify seizure progression, stomach acid movement up the esophagus, cardiorespiratory changes, and sudden death. In all cases of sudden death, esophageal pH recordings alongside MRI images visualized stomach acid movement up the esophagus. Severe cardiac (ST segment elevation), respiratory (intermittent apnea) and brain activity (EEG narrowing due to hypoxia) changes were observed only after acid reached larynx, which strongly suggested onset of laryngospasm following acid reflux. The complementary information coming from electrophysiology and fast MRI scans provided insight into the mechanism of esophageal reflux, laryngospasm, obstructive apnea, and subsequent sudden death in seizing animals. The results carry clinical significance as it outlines a potential mechanism that may be relevant to SUDEP in humans.
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Affiliation(s)
| | - Ryan Budde
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
| | - Georgia L. Lawlor
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
| | - Pedro Irazoqui
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA,School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
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31
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Daly I. Removal of physiological artifacts from simultaneous EEG and fMRI recordings. Clin Neurophysiol 2021; 132:2371-2383. [PMID: 34454264 DOI: 10.1016/j.clinph.2021.05.036] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 03/31/2021] [Accepted: 05/29/2021] [Indexed: 11/29/2022]
Abstract
OBJECTIVE Simultaneous recording of the electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) allows a combination of eletrophysiological and haemodynamic information to be used to form a more complete picture of cerebral dynamics. However, EEG recorded within the MRI scanner is contaminated by both imaging artifacts and physiological artifacts. The majority of the techniques used to pre-process such EEG focus on removal of the imaging and balistocardiogram artifacts, with some success, but don't remove all other physiological artifacts. METHODS We propose a new offline EEG artifact removal method based upon a combination of independent component analysis and fMRI-based head movement estimation to aid the removal of physiological artifacts from EEG recorded during EEG-fMRI recordings. Our method makes novel use of head movement trajectories estimated from the fMRI recording in order to assist with identifying physiological artifacts in the EEG and is designed to be used after removal of the fMRI imaging artifact from the EEG. RESULTS We evaluate our method on EEG recorded during a joint EEG-fMRI session from healthy adult participants. Our method significantly reduces the influence of all types of physiological artifacts on the EEG. We also compare our method with a state-of-the-art physiological artifact removal method and demonstrate superior performance removing physiological artifacts. CONCLUSIONS Our proposed method is able to remove significantly more physiological artifact components from the EEG, recorded during a joint EEG-fMRI session, than other state-of-the-art methods. SIGNIFICANCE Our proposed method represents a marked improvement over current processing pipelines for removing physiological noise from EEG recorded during a joint EEG-fMRI session.
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Affiliation(s)
- Ian Daly
- Brain-computer interfaces and Neural Engineering laboratory, School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, Essex CO4 3SQ, United Kingdom.
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32
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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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33
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Scrivener CL. When Is Simultaneous Recording Necessary? A Guide for Researchers Considering Combined EEG-fMRI. Front Neurosci 2021; 15:636424. [PMID: 34267620 PMCID: PMC8276697 DOI: 10.3389/fnins.2021.636424] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 06/01/2021] [Indexed: 11/19/2022] Open
Abstract
Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide non-invasive measures of brain activity at varying spatial and temporal scales, offering different views on brain function for both clinical and experimental applications. Simultaneous recording of these measures attempts to maximize the respective strengths of each method, while compensating for their weaknesses. However, combined recording is not necessary to address all research questions of interest, and experiments may have greater statistical power to detect effects by maximizing the signal-to-noise ratio in separate recording sessions. While several existing papers discuss the reasons for or against combined recording, this article aims to synthesize these arguments into a flow chart of questions that researchers can consider when deciding whether to record EEG and fMRI separately or simultaneously. Given the potential advantages of simultaneous EEG-fMRI, the aim is to provide an initial overview of the most important concepts and to direct readers to relevant literature that will aid them in this decision.
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Affiliation(s)
- Catriona L. Scrivener
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
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34
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Neural Correlates of Motor Recovery after Robot-Assisted Training in Chronic Stroke: A Multimodal Neuroimaging Study. Neural Plast 2021; 2021:8866613. [PMID: 34211549 PMCID: PMC8208881 DOI: 10.1155/2021/8866613] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 04/19/2021] [Accepted: 05/29/2021] [Indexed: 11/17/2022] Open
Abstract
Stroke is a leading cause of motor disability worldwide, and robot-assisted therapies have been increasingly applied to facilitate the recovery process. However, the underlying mechanism and induced neuroplasticity change remain partially understood, and few studies have investigated this from a multimodality neuroimaging perspective. The current study adopted BCI-guided robot hand therapy as the training intervention and combined multiple neuroimaging modalities to comprehensively understand the potential association between motor function alteration and various neural correlates. We adopted EEG-informed fMRI technique to understand the functional regions sensitive to training intervention. Additionally, correlation analysis among training effects, nonlinear property change quantified by fractal dimension (FD), and integrity of M1-M1 (M1: primary motor cortex) anatomical connection were performed. EEG-informed fMRI analysis indicated that for iM1 (iM1: ipsilesional M1) regressors, regions with significantly increased partial correlation were mainly located in contralesional parietal, prefrontal, and sensorimotor areas and regions with significantly decreased partial correlation were mainly observed in the ipsilesional supramarginal gyrus and superior temporal gyrus. Pearson's correlations revealed that the interhemispheric asymmetry change significantly correlated with the training effect as well as the integrity of M1-M1 anatomical connection. In summary, our study suggested that multiple functional brain regions not limited to motor areas were involved during the recovery process from multimodality perspective. The correlation analyses suggested the essential role of interhemispheric interaction in motor rehabilitation. Besides, the underlying structural substrate of the bilateral M1-M1 connection might relate to the interhemispheric change. This study might give some insights in understanding the neuroplasticity induced by the integrated BCI-guided robot hand training intervention and further facilitate the design of therapies for chronic stroke patients.
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35
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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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36
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Criss CR, Melton MS, Ulloa SA, Simon JE, Clark BC, France CR, Grooms DR. Rupture, reconstruction, and rehabilitation: A multi-disciplinary review of mechanisms for central nervous system adaptations following anterior cruciate ligament injury. Knee 2021; 30:78-89. [PMID: 33873089 DOI: 10.1016/j.knee.2021.03.009] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 02/18/2021] [Accepted: 03/18/2021] [Indexed: 02/02/2023]
Abstract
BACKGROUND Despite surgical reconstruction and extensive rehabilitation, persistent quadriceps inhibition, gait asymmetry, and functional impairment remain prevalent in patients after anterior cruciate ligament (ACL) injury. A combination of reports have suggested underlying central nervous system adaptations in those after injury govern long-term neuromuscular impairments. The classic assumption has been to attribute neurophysiologic deficits to components of injury, but other factors across the continuum of care (e.g. surgery, perioperative analgesia, and rehabilitative strategies) have been largely overlooked. OBJECTIVE This review provides a multidisciplinary perspective to 1) provide a narrative review of studies reporting neuroplasticity following ACL injury in order to inform clinicians of the current state of literature and 2) provide a mechanistic framework of neurophysiologic deficits with potential clinical implications across all phases of injury and recovery (injury, surgery, and rehabilitation) RESULTS: Studies using a variety of neurophysiologic modalities have demonstrated peripheral and central nervous system adaptations in those with prior ACL injury. Longitudinal investigations suggest neurophysiologic changes at spinal-reflexive and corticospinal pathways follow a unique timecourse across injury, surgery, and rehabilitation. CONCLUSION Clinicians should consider the unique injury, surgery, anesthesia, and rehabilitation on central nervous system adaptations. Therapeutic strategies across the continuum of care may be beneficial to mitigate maladaptive neuroplasticity in those after ACL injury.
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Affiliation(s)
- Cody R Criss
- Ohio Musculoskeletal and Neurological Institute, Ohio University, Athens, OH, USA.
| | - M Stephen Melton
- Department of Anesthesiology, Duke University Medical Center, Durham, NC, USA
| | - Sergio A Ulloa
- OhioHealth Physician Group Heritage College: Orthopedic and Sports Medicine, OhioHealth O'Bleness Memorial Hospital, Athens, OH, USA
| | - Janet E Simon
- Ohio Musculoskeletal and Neurological Institute, Ohio University, Athens, OH, USA; Division of Athletic Training, School of Applied Health Sciences and Wellness, College of Health Sciences and Professions, Ohio University, Athens, OH, USA
| | - Brian C Clark
- Ohio Musculoskeletal and Neurological Institute, Ohio University, Athens, OH, USA; Department of Biomedical Sciences, Ohio University, Athens, OH, USA
| | - Christopher R France
- Ohio Musculoskeletal and Neurological Institute, Ohio University, Athens, OH, USA; Department of Psychology, College of Arts and Sciences, Ohio University, Athens, OH, USA
| | - Dustin R Grooms
- Ohio Musculoskeletal and Neurological Institute, Ohio University, Athens, OH, USA; Division of Athletic Training, School of Applied Health Sciences and Wellness, College of Health Sciences and Professions, Ohio University, Athens, OH, USA; Division of Physical Therapy, School of Rehabilitation and Communication Sciences, College of Health Sciences and Professions, Ohio University, Athens, OH, USA
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37
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Luo C, Brüers S, Berry I, VanRullen R, Reddy L. Tentative fMRI signatures of perceptual echoes in early visual cortex. Neuroimage 2021; 237:118053. [PMID: 33930536 DOI: 10.1016/j.neuroimage.2021.118053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 03/04/2021] [Accepted: 03/27/2021] [Indexed: 10/21/2022] Open
Abstract
The visual Impulse Response Function (IRF) can be estimated by cross-correlating random luminance sequences with concurrently recorded EEG. It typically contains a strong 10 Hz oscillatory component, suggesting that visual information reverberates in the human brain as a "perceptual echo". The neural origin of these echoes remains unknown. To address this question, we recorded EEG and fMRI in two separate sessions. In both sessions, a disk whose luminance followed a random (white noise) sequence was presented in the upper left quadrant. Individual IRFs were derived from the EEG session. These IRFs were then used as "response templates" to reconstruct an estimate of the EEG during the fMRI session, by convolution with the corresponding random luminance sequences. The 7-14 Hz (alpha, the main frequency component of the IRF) envelope of the reconstructed EEG was finally used as an fMRI regressor, to determine which brain voxels co-varied with the oscillations elicited by the luminance sequence, i.e., the "perceptual echoes". The reconstructed envelope of EEG alpha was significantly correlated with BOLD responses in V1 and V2. Surprisingly, this correlation was visible outside, but not within the directly (retinotopically) stimulated region. We tentatively interpret this lack of alpha modulation as a BOLD saturation effect, since the overall stimulus-induced BOLD response was inversely related, across voxels, to the signal variability over time. In conclusion, our results suggest that perceptual echoes originate in early visual cortex, driven by widespread activity in V1 and V2, not retinotopically restricted, but possibly reflecting the propagation of a travelling alpha wave.
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Affiliation(s)
- Canhuang Luo
- Université de Toulouse, Centre de Recherche Cerveau et Cognition, Université Paul Sabatier, 31062, Toulouse, France; Centre National de la Recherche Scientifique, Unité Mixte de Recherche 5549, Faculté de Médecine de Purpan, 31052, Toulouse Cedex, France
| | - Sasskia Brüers
- Université de Toulouse, Centre de Recherche Cerveau et Cognition, Université Paul Sabatier, 31062, Toulouse, France; Centre National de la Recherche Scientifique, Unité Mixte de Recherche 5549, Faculté de Médecine de Purpan, 31052, Toulouse Cedex, France
| | - Isabelle Berry
- Université de Toulouse, Centre de Recherche Cerveau et Cognition, Université Paul Sabatier, 31062, Toulouse, France; Centre National de la Recherche Scientifique, Unité Mixte de Recherche 5549, Faculté de Médecine de Purpan, 31052, Toulouse Cedex, France; Toulouse NeuroImaging Center, INSERM, U825, Toulouse, France
| | - Rufin VanRullen
- Université de Toulouse, Centre de Recherche Cerveau et Cognition, Université Paul Sabatier, 31062, Toulouse, France; Centre National de la Recherche Scientifique, Unité Mixte de Recherche 5549, Faculté de Médecine de Purpan, 31052, Toulouse Cedex, France
| | - Leila Reddy
- Université de Toulouse, Centre de Recherche Cerveau et Cognition, Université Paul Sabatier, 31062, Toulouse, France; Centre National de la Recherche Scientifique, Unité Mixte de Recherche 5549, Faculté de Médecine de Purpan, 31052, Toulouse Cedex, France.
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38
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Philiastides MG, Tu T, Sajda P. Inferring Macroscale Brain Dynamics via Fusion of Simultaneous EEG-fMRI. Annu Rev Neurosci 2021; 44:315-334. [PMID: 33761268 DOI: 10.1146/annurev-neuro-100220-093239] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Advances in the instrumentation and signal processing for simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) have enabled new ways to observe the spatiotemporal neural dynamics of the human brain. Central to the utility of EEG-fMRI neuroimaging systems are the methods for fusing the two data streams, with machine learning playing a key role. These methods can be dichotomized into those that are symmetric and asymmetric in terms of how the two modalities inform the fusion. Studies using these methods have shown that fusion yields new insights into brain function that are not possible when each modality is acquired separately. As technology improves and methods for fusion become more sophisticated, the future of EEG-fMRI for noninvasive measurement of brain dynamics includes mesoscale mapping at ultrahigh magnetic resonance fields, targeted perturbation-based neuroimaging, and using deep learning to uncover nonlinear representations that link the electrophysiological and hemodynamic measurements.
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Affiliation(s)
- Marios G Philiastides
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow G12 8AD, Scotland;
| | - Tao Tu
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Paul Sajda
- Departments of Biomedical Engineering, Electrical Engineering, and Radiology and the Data Science Institute, Columbia University, New York, NY 10027, USA;
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Soh C, Wessel JR. Unexpected Sounds Nonselectively Inhibit Active Visual Stimulus Representations. Cereb Cortex 2021; 31:1632-1646. [PMID: 33140100 DOI: 10.1093/cercor/bhaa315] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 08/31/2020] [Accepted: 09/25/2020] [Indexed: 11/13/2022] Open
Abstract
The brain's capacity to process unexpected events is key to cognitive flexibility. The most well-known effect of unexpected events is the interruption of attentional engagement (distraction). We tested whether unexpected events interrupt attentional representations by activating a neural mechanism for inhibitory control. This mechanism is most well characterized within the motor system. However, recent work showed that it is automatically activated by unexpected events and can explain some of their nonmotor effects (e.g., on working memory representations). Here, human participants attended to lateralized flickering visual stimuli, producing steady-state visual evoked potentials (SSVEPs) in the scalp electroencephalogram. After unexpected sounds, the SSVEP was rapidly suppressed. Using a functional localizer (stop-signal) task and independent component analysis, we then identified a fronto-central EEG source whose activity indexes inhibitory motor control. Unexpected sounds in the SSVEP task also activated this source. Using single-trial analyses, we found that subcomponents of this source differentially relate to sound-induced SSVEP changes: While its N2 component predicted the subsequent suppression of the attended-stimulus SSVEP, the P3 component predicted the suppression of the SSVEP to the unattended stimulus. These results shed new light on the processes underlying fronto-central control signals and have implications for phenomena such as distraction and the attentional blink.
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Affiliation(s)
- Cheol Soh
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA 52245, USA
| | - Jan R Wessel
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA 52245, USA.,Department of Neurology, University of Iowa Hospitals and Clinics, Iowa City, IA 52242, USA
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40
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Abstract
Neurophysiological signals are crucial intermediaries, through which brain activity can be quantitatively measured and brain mechanisms are able to be revealed. In particular, non‐invasive neurophysiological signals, such as electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), are welcomed and frequently utilised in various studies since these signals can be non‐invasively recorded without harming the human brain while they convey abundant information pertaining to brain activity. The recorded neurophysiological signals are analysed to mine meaningful information for the understanding of brain mechanisms or are classified to distinguish different patterns (e.g., different cognitive states, brain diseases versus healthy controls). To date, remarkable progress has been made in both the analysis and classification of neurophysiological signals, but scholars are not feeling complacent. Consistent effort ought to be paid to advance the research of analysis and classification based on neurophysiological signals. In this paper, I express my thoughts regarding promising future directions in neurophysiological signal analysis and classification based on current developments and accomplishments. I will elucidate the thoughts after brief summaries of relevant backgrounds, accomplishments, and tendencies. According to my personal selection and preference, I mainly focus on brain connectivity, multidimensional array (tensor), multi‐modality, multiple task classification, deep learning, big data, and naturalistic experiment. Hopefully, my thoughts could give a little help to inspire new ideas and contribute to the research of the analysis and classification of neurophysiological signals in some way.
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Affiliation(s)
- Junhua Li
- Laboratory for Brain–Bionic Intelligence and Computational Neuroscience, Wuyi University, Jiangmen 529020, Guangdong, China
- Centre for Multidisciplinary Convergence Computing, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, Shaanxi, China
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ, UK
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41
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Bhutada AS, Sepúlveda P, Torres R, Ossandón T, Ruiz S, Sitaram R. Semi-Automated and Direct Localization and Labeling of EEG Electrodes Using MR Structural Images for Simultaneous fMRI-EEG. Front Neurosci 2021; 14:558981. [PMID: 33414699 PMCID: PMC7783406 DOI: 10.3389/fnins.2020.558981] [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: 05/04/2020] [Accepted: 10/08/2020] [Indexed: 11/13/2022] Open
Abstract
Electroencephalography (EEG) source reconstruction estimates spatial information from the brain’s electrical activity acquired using EEG. This method requires accurate identification of the EEG electrodes in a three-dimensional (3D) space and involves spatial localization and labeling of EEG electrodes. Here, we propose a new approach to tackle this two-step problem based on the simultaneous acquisition of EEG and magnetic resonance imaging (MRI). For the step of spatial localization of electrodes, we extract the electrode coordinates from the curvature of the protrusions formed in the high-resolution T1-weighted brain scans. In the next step, we assign labels to each electrode based on the distinguishing feature of the electrode’s distance profile in relation to other electrodes. We then compare the subject’s electrode data with template-based models of prelabeled distance profiles of correctly labeled subjects. Based on this approach, we could localize EEG electrodes in 26 head models with over 90% accuracy in the 3D localization of electrodes. Next, we performed electrode labeling of the subjects’ data with progressive improvements in accuracy: with ∼58% accuracy based on a single EEG-template, with ∼71% accuracy based on 3 EEG-templates, and with ∼76% accuracy using 5 EEG-templates. The proposed semi-automated method provides a simple alternative for the rapid localization and labeling of electrodes without the requirement of any additional equipment than what is already used in an EEG-fMRI setup.
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Affiliation(s)
- Abhishek S Bhutada
- Department of Molecular and Cellular Biology, University of California, Berkeley, Berkeley, CA, United States
| | - Pradyumna Sepúlveda
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Rafael Torres
- Department of Psychiatry, Faculty of Medicine, Interdisciplinary Center for Neuroscience, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Tomás Ossandón
- Department of Psychiatry, Faculty of Medicine, Interdisciplinary Center for Neuroscience, Pontificia Universidad Católica de Chile, Santiago, Chile.,Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Sergio Ruiz
- Department of Psychiatry, Faculty of Medicine, Interdisciplinary Center for Neuroscience, Pontificia Universidad Católica de Chile, Santiago, Chile.,Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Ranganatha Sitaram
- Department of Psychiatry, Faculty of Medicine, Interdisciplinary Center for Neuroscience, Pontificia Universidad Católica de Chile, Santiago, Chile.,Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile.,Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
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42
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McIntosh JR, Yao J, Hong L, Faller J, Sajda P. Ballistocardiogram Artifact Reduction in Simultaneous EEG-fMRI Using Deep Learning. IEEE Trans Biomed Eng 2020; 68:78-89. [PMID: 32746037 DOI: 10.1109/tbme.2020.3004548] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
OBJECTIVE The concurrent recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a technique that has received much attention due to its potential for combined high temporal and spatial resolution. However, the ballistocardiogram (BCG), a large-amplitude artifact caused by cardiac induced movement contaminates the EEG during EEG-fMRI recordings. Removal of BCG in software has generally made use of linear decompositions of the corrupted EEG. This is not ideal as the BCG signal propagates in a manner which is non-linearly dependent on the electrocardiogram (ECG). In this paper, we present a novel method for BCG artifact suppression using recurrent neural networks (RNNs). METHODS EEG signals were recovered by training RNNs on the nonlinear mappings between ECG and the BCG corrupted EEG. We evaluated our model's performance against the commonly used Optimal Basis Set (OBS) method at the level of individual subjects, and investigated generalization across subjects. RESULTS We show that our algorithm can generate larger average power reduction of the BCG at critical frequencies, while simultaneously improving task relevant EEG based classification. CONCLUSION The presented deep learning architecture can be used to reduce BCG related artifacts in EEG-fMRI recordings. SIGNIFICANCE We present a deep learning approach that can be used to suppress the BCG artifact in EEG-fMRI without the use of additional hardware. This method may have scope to be combined with current hardware methods, operate in real-time and be used for direct modeling of the BCG.
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43
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Chan MMY, Han YMY. The Effect of Transcranial Direct Current Stimulation in Changing Resting-State Functional Connectivity in Patients With Neurological Disorders: A Systematic Review. J Cent Nerv Syst Dis 2020; 12:1179573520976832. [PMID: 33402860 PMCID: PMC7745554 DOI: 10.1177/1179573520976832] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 11/03/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND People with neurological disorders are found to have abnormal resting-state functional connectivity (rsFC), which is associated with the persistent functional impairment found in these patients. Recently, transcranial direct current stimulation (tDCS) has been shown to improve rsFC, although the results are inconsistent. OBJECTIVE We hope to explore whether tDCS induces rsFC changes among patients with neurological disorders, whether rsFC is clinically relevant and how different tDCS parameters affect rsFC outcome among these individuals. METHODS A systematic review was conducted according to PRISMA guidelines (systematic review registration number: CRD42020168654). Randomized controlled trials that studied the tDCS effects on rsFC between the experimental and sham-controlled groups using either electrophysiological or neuroimaging methods were included. RESULTS Active tDCS can induce changes in both localized (ie, brain regions under the transcranial electrodes) and diffused (ie, brain regions not directly influenced by the transcranial electrodes) rsFC. Interestingly, fMRI studies showed that the default mode network was enhanced regardless of patients' diagnoses, the stimulation paradigms used or the rsFC analytical methods employed. Second, stimulation intensity, but not total stimulation time, appeared to positively influence the effect of tDCS on rsFC. LIMITATIONS AND CONCLUSION Due to the inherent heterogeneity in rsFC analytical methods and tDCS protocols, meta-analysis was not conducted. We recommend that future studies may investigate the effect of tDCS on rsFC for repeated cathodal stimulation. For clinicians, we suggest anodal stimulation at a higher stimulation intensity within the safety limit may maximize tDCS effects in modulating aberrant functional connectivity of patients with neurological disorders.
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Affiliation(s)
- Melody MY Chan
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Yvonne MY Han
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China
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44
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Kaur A, Chaujar R, Chinnadurai V. Effects of Neural Mechanisms of Pretask Resting EEG Alpha Information on Situational Awareness: A Functional Connectivity Approach. HUMAN FACTORS 2020; 62:1150-1170. [PMID: 31461374 DOI: 10.1177/0018720819869129] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
OBJECTIVE In this study, the influence of pretask resting neural mechanisms on situational awareness (SA)-task is studied. BACKGROUND Pretask electroencephalography (EEG) information and Stroop effect are known to influence task engagement independently. However, neural mechanisms of pretask resting absolute alpha (PRAA) and pretask resting alpha frontal asymmetry (PRAFA) in influencing SA-task which is undergoing Stroop effect is still not understood. METHOD The study involved pretask resting EEG measurements from 18 healthy individuals followed by functional magnetic resonance imaging (fMRI) acquisition during SA-task. To understand the effect of pretask alpha information and Stroop effect on SA, a robust correlation between mean reaction time, SA Index, PRAA, and PRAFA were assessed. Furthermore, neural underpinnings of PRAA, PRAFA in SA-task, and functional connectivity were analyzed through the EEG-informed fMRI approach. RESULTS Significant robust correlation of reaction time was observed with SA Index (Pearson: r = .50, pcorr = .05) and PRAFA (Pearson: r = .63; pcorr = .01), respectively. Similarly, SA Index significantly correlated with PRAFA (Pearson: r = .56, pcorr = .01; Spearman: r = .61, pcorr = .007), and PRAA (Pearson: r = .59, pcorr = .005; Spearman: r = .59, pcorr = .002). Neural underpinnings of SA-task revealed regions involved in visual-processing and higher-order cognition. PRAA was primarily underpinned at frontal-temporal areas and functionally connected to SA-task regions pertaining to the emotional regulation. PRAFA has correlated with limbic and parietal regions, which are involved in integration of visual, emotion, and memory information of SA-task. CONCLUSION The results suggest a strong association of reaction time with SA-task and PRAFA and strongly support the hypothesis that PRAFA, PRAA, and associated neural mechanisms significantly influence the outcome of SA-task. APPLICATION It is beneficial to study the effect of pretask resting information on SA-task to improve SA.
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Affiliation(s)
- Ardaman Kaur
- Institute of Nuclear Medicine and Allied Sciences, Delhi, India
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45
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Owen LLW, Muntianu TA, Heusser AC, Daly PM, Scangos KW, Manning JR. A Gaussian Process Model of Human Electrocorticographic Data. Cereb Cortex 2020; 30:5333-5345. [PMID: 32495832 PMCID: PMC7472198 DOI: 10.1093/cercor/bhaa115] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 04/15/2020] [Accepted: 04/16/2020] [Indexed: 12/17/2022] Open
Abstract
We present a model-based method for inferring full-brain neural activity at millimeter-scale spatial resolutions and millisecond-scale temporal resolutions using standard human intracranial recordings. Our approach makes the simplifying assumptions that different people's brains exhibit similar correlational structure, and that activity and correlation patterns vary smoothly over space. One can then ask, for an arbitrary individual's brain: given recordings from a limited set of locations in that individual's brain, along with the observed spatial correlations learned from other people's recordings, how much can be inferred about ongoing activity at other locations throughout that individual's brain? We show that our approach generalizes across people and tasks, thereby providing a person- and task-general means of inferring high spatiotemporal resolution full-brain neural dynamics from standard low-density intracranial recordings.
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Affiliation(s)
- Lucy L W Owen
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA
| | - Tudor A Muntianu
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA
| | - Andrew C Heusser
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA
- Akili Interactive, Boston, MA 02110, USA
| | - Patrick M Daly
- Department of Psychiatry, University of California, San Francisco, CA 94143, USA
| | - Katherine W Scangos
- Department of Psychiatry, University of California, San Francisco, CA 94143, USA
| | - Jeremy R Manning
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA
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46
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Cichy RM, Oliva A. A M/EEG-fMRI Fusion Primer: Resolving Human Brain Responses in Space and Time. Neuron 2020; 107:772-781. [DOI: 10.1016/j.neuron.2020.07.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 06/25/2020] [Accepted: 06/30/2020] [Indexed: 10/23/2022]
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47
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Lee HJ, Huang SY, Kuo WJ, Graham SJ, Chu YH, Stenroos M, Lin FH. Concurrent electrophysiological and hemodynamic measurements of evoked neural oscillations in human visual cortex using sparsely interleaved fast fMRI and EEG. Neuroimage 2020; 217:116910. [PMID: 32389729 DOI: 10.1016/j.neuroimage.2020.116910] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 04/21/2020] [Accepted: 04/30/2020] [Indexed: 11/17/2022] Open
Abstract
Electroencephalography (EEG) concurrently collected with functional magnetic resonance imaging (fMRI) is heavily distorted by the repetitive gradient coil switching during the fMRI acquisition. The performance of the typical template-based gradient artifact suppression method can be suboptimal because the artifact changes over time. Gradient artifact residuals also impede the subsequent suppression of ballistocardiography artifacts. Here we propose recording continuous EEG with temporally sparse fast fMRI (fast fMRI-EEG) to minimize the EEG artifacts caused by MRI gradient coil switching without significantly compromising the field-of-view and spatiotemporal resolution of fMRI. Using simultaneous multi-slice inverse imaging to achieve whole-brain fMRI with isotropic 5-mm resolution in 0.1 s, and performing these acquisitions once every 2 s, we have 95% of the duty cycle available to record EEG with substantially less gradient artifact. We found that the standard deviation of EEG signals over the entire acquisition period in fast fMRI-EEG was reduced to 54% of that in conventional concurrent echo-planar imaging (EPI) and EEG recordings (EPI-EEG) across participants. When measuring 15-Hz steady-state visual evoked potentials (SSVEPs), the baseline-normalized oscillatory neural response in fast fMRI-EEG was 2.5-fold of that in EPI-EEG. The functional MRI responses associated with the SSVEP delineated by EPI and fast fMRI were similar in the spatial distribution, the elicited waveform, and detection power. Sparsely interleaved fast fMRI-EEG provides high-quality EEG without substantially compromising the quality of fMRI in evoked response measurements, and has the potential utility for applications where the onset of the target stimulus cannot be precisely determined, such as epilepsy.
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Affiliation(s)
- Hsin-Ju Lee
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Shu-Yu Huang
- Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan
| | - Wen-Jui Kuo
- Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan
| | - Simon J Graham
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Ying-Hua Chu
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Matti Stenroos
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Fa-Hsuan Lin
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada; Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.
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48
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Wang C, Kang M, Li Z, Li Y, Guan M, Zou Z, Wu M, Lou W, Xu J. Altered relation of resting-state alpha rhythm with blood oxygen level dependent signal in healthy aging: Evidence by EEG-fMRI fusion analysis. Clin Neurophysiol 2020; 131:2105-2114. [PMID: 32682238 DOI: 10.1016/j.clinph.2020.05.025] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 04/12/2020] [Accepted: 05/10/2020] [Indexed: 10/24/2022]
Abstract
OBJECTIVE The goal of this study is to explore the changes of spatial correlates of alpha rhythm in the aged adults. METHODS Electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) data were simultaneously recorded from 27 young and 19 elderly adults at resting state with their eyes closed. Alpha rhythm power fluctuation was extracted from EEG signal of parietal-occipital region and was fused with fMRI data by correlating alpha rhythm with blood oxygen level dependent (BOLD) signal using general linear models. RESULTS For both young adults and the elderly, the regions correlated with alpha rhythm power were widely distributed in cortical and subcortical regions. However, compared to young adults, correlations between alpha rhythm and the activity of thalamus and frontal regions were significantly reduced in the elderly. In addition, an increased correlation with alpha rhythm was found in frontal, insula and cingulate gyrus regions in the elderly. CONCLUSIONS Changes in the roles of the above brain regions may be present in the generation or modulation of alpha rhythm due to age advancing. SIGNIFICANCE This study provides novel insight into the alteration of the spatial correlates of alpha rhythm in the elderly by using simultaneous EEG-fMRI data fusion analysis.
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Affiliation(s)
- Chao Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China; National Engineering Research Center for Healthcare Devices, Guangzhou, China
| | - Mengfei Kang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China; National Engineering Research Center for Healthcare Devices, Guangzhou, China
| | - Zhonglin Li
- Department of Radiology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Yongli Li
- Department of Radiology, Henan Provincial People's Hospital, Zhengzhou, China; Department of Health Management, Henan Provincial People's Hospital, Zhengzhou, China
| | - Min Guan
- Department of Radiology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Zhi Zou
- Department of Radiology, Henan Provincial People's Hospital, Zhengzhou, China
| | - Min Wu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China; National Engineering Research Center for Healthcare Devices, Guangzhou, China
| | - Wutao Lou
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Jin Xu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China; National Engineering Research Center for Healthcare Devices, Guangzhou, China.
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Wei H, Jafarian A, Zeidman P, Litvak V, Razi A, Hu D, Friston KJ. Bayesian fusion and multimodal DCM for EEG and fMRI. Neuroimage 2020; 211:116595. [DOI: 10.1016/j.neuroimage.2020.116595] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Revised: 01/07/2020] [Accepted: 01/29/2020] [Indexed: 12/26/2022] Open
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50
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Kostandyan M, Park HRP, Bundt C, González-García C, Wisniewski D, Krebs RM, Boehler CN. Are all behavioral reward benefits created equally? An EEG-fMRI study. Neuroimage 2020; 215:116829. [PMID: 32283272 DOI: 10.1016/j.neuroimage.2020.116829] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 04/05/2020] [Accepted: 04/05/2020] [Indexed: 12/21/2022] Open
Abstract
Reward consistently boosts performance in cognitive tasks. Although many different reward manipulations exist, systematic comparisons are lacking. Reward effects on cognitive control are usually studied using monetary incentive delay (MID; cue-related reward information) or stimulus-reward association (SRA; target-related reward information) tasks. While for MID tasks, evidence clearly implicates reward-triggered global increases in proactive control, it is unclear how reward effects arise in SRA tasks, and in how far such mechanisms overlap during task preparation and target processing. Here, we address these questions with simultaneous EEG-fMRI using a Stroop task with four different block types. In addition to MID and SRA blocks, we used an SRA-task modification with reward-irrelevant cues (C-SRA) and regular reward-neutral Stroop-task blocks. Behaviorally, we observed superior performance for all reward conditions compared to Neutral, and more pronounced reward effects in the SRA and C-SRA blocks, compared to MID blocks. The fMRI data showed similar reward effects in value-related areas for events that signaled reward availability (MID cues and (C-)SRA targets), and comparable reward modulations in cognitive-control regions for all targets regardless of block type. This result pattern was echoed by the EEG data, showing clear markers of valuation and cognitive control, which only differed during task preparation, whereas reward-related modulations during target processing were again comparable across block types. Yet, considering only cue-related fMRI data, C-SRA cues triggered preparatory control processes beyond reward-unrelated MID cues, without simultaneous modulations in typical reward areas, implicating enhanced task preparation that is not directly driven by a concurrent neural reward-anticipation response.
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Affiliation(s)
| | - Haeme R P Park
- Department of Experimental Psychology, Ghent University, Belgium; Neuroscience Research Australia, Randwick, Australia; School of Psychology, University of New South Wales, Sydney, Australia
| | - Carsten Bundt
- Department of Experimental Psychology, Ghent University, Belgium
| | | | - David Wisniewski
- Department of Experimental Psychology, Ghent University, Belgium
| | - Ruth M Krebs
- Department of Experimental Psychology, Ghent University, Belgium
| | - C Nico Boehler
- Department of Experimental Psychology, Ghent University, Belgium
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