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Song Z, Zhu Z, Zhang H, Wang S, Zou L. Extraction of brain function pattern with visual-capture-task fMRI using dynamic time-window method in ADHD children. Behav Brain Res 2024; 460:114828. [PMID: 38135189 DOI: 10.1016/j.bbr.2023.114828] [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: 06/24/2023] [Revised: 12/04/2023] [Accepted: 12/18/2023] [Indexed: 12/24/2023]
Abstract
Attention deficit/Hyperactivity disorder (ADHD) has a great impact on children's development. This paper uses a novel adaptive brain state extraction algorithm to construct a dynamic time-window brain network, which captures the brain function pattern characteristics of ADHD children with higher temporal resolution. The test data were acquired by functional magnetic resonance imaging (fMRI) obtained from 23 children with ADHD during the visual-capture-task [age: (8.27 ± 2.77)]. A spatial standard deviation method is used after the initial data processing, to extract the brain activity pattern state; An improved clustering algorithm is constructed to verify the changes made to the dynamic time-window brain network model. There can be seen clear differences between each state within 0.05 s after the test. The results show that our improved new framework can effectively obtain the characteristics of dynamic brain functional connection strength changes during the task. In addition, the new algorithm is able to capture the dynamic changes of the brain network, with an 80 % improvement compared to traditional methods for the average modularity value Q. This work demonstrates a novel approach to find out the pattern changes between dynamic brain function connections, which can be of great significance for the adjuvant treatment of children with ADHD.
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Affiliation(s)
- Zhiwei Song
- The School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, China; The School of Mechanical and Electrical, Changzhou Vocational Institute of Textile and Garment, Changzhou, Jiangsu 213164, China
| | - Zhihao Zhu
- The School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, China
| | - Han Zhang
- The School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, China
| | - Suhong Wang
- Clinical Psychology, the Third Affiliated Hospital of Soochow University Juqian Road No. 185, Changzhou, Jiangsu 213164, China
| | - Ling Zou
- The School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, China; The Key Laboratory of Brain Machine Collaborative Intelligence Foundation of Zhejiang Province, Hangzhou, Zhejiang 310018, China.
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2
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Cota VR, Cançado SAV, Moraes MFD. On temporal scale-free non-periodic stimulation and its mechanisms as an infinite improbability drive of the brain's functional connectogram. Front Neuroinform 2023; 17:1173597. [PMID: 37293579 PMCID: PMC10244597 DOI: 10.3389/fninf.2023.1173597] [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: 02/24/2023] [Accepted: 05/02/2023] [Indexed: 06/10/2023] Open
Abstract
Rationalized development of electrical stimulation (ES) therapy is of paramount importance. Not only it will foster new techniques and technologies with increased levels of safety, efficacy, and efficiency, but it will also facilitate the translation from basic research to clinical practice. For such endeavor, design of new technologies must dialogue with state-of-the-art neuroscientific knowledge. By its turn, neuroscience is transitioning-a movement started a couple of decades earlier-into adopting a new conceptual framework for brain architecture, in which time and thus temporal patterns plays a central role in the neuronal representation of sampled data from the world. This article discusses how neuroscience has evolved to understand the importance of brain rhythms in the overall functional architecture of the nervous system and, consequently, that neuromodulation research should embrace this new conceptual framework. Based on such support, we revisit the literature on standard (fixed-frequency pulsatile stimuli) and mostly non-standard patterns of ES to put forward our own rationale on how temporally complex stimulation schemes may impact neuromodulation strategies. We then proceed to present a low frequency, on average (thus low energy), scale-free temporally randomized ES pattern for the treatment of experimental epilepsy, devised by our group and termed NPS (Non-periodic Stimulation). The approach has been shown to have robust anticonvulsant effects in different animal models of acute and chronic seizures (displaying dysfunctional hyperexcitable tissue), while also preserving neural function. In our understanding, accumulated mechanistic evidence suggests such a beneficial mechanism of action may be due to the natural-like characteristic of a scale-free temporal pattern that may robustly compete with aberrant epileptiform activity for the recruitment of neural circuits. Delivering temporally patterned or random stimuli within specific phases of the underlying oscillations (i.e., those involved in the communication within and across brain regions) could both potentiate and disrupt the formation of neuronal assemblies with random probability. The usage of infinite improbability drive here is obviously a reference to the "The Hitchhiker's Guide to the Galaxy" comedy science fiction classic, written by Douglas Adams. The parallel is that dynamically driving brain functional connectogram, through neuromodulation, in a manner that would not favor any specific neuronal assembly and/or circuit, could re-stabilize a system that is transitioning to fall under the control of a single attractor. We conclude by discussing future avenues of investigation and their potentially disruptive impact on neurotechnology, with a particular interest in NPS implications in neural plasticity, motor rehabilitation, and its potential for clinical translation.
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Affiliation(s)
- Vinícius Rosa Cota
- Rehab Technologies - INAIL Lab, Istituto Italiano di Tecnologia, Genoa, Italy
- Laboratory of Neuroengineering and Neuroscience, Department of Electrical Engineering, Federal University of São João del-Rei, São João del Rei, Brazil
| | - Sérgio Augusto Vieira Cançado
- Núcleo Avançado de Tratamento das Epilepsias (NATE), Felício Rocho Hospital, Fundação Felice Rosso, Belo Horizonte, Brazil
| | - Márcio Flávio Dutra Moraes
- Department of Physiology and Biophysics, Núcleo de Neurociências, Federal University of Minas Gerais, Belo Horizonte, Brazil
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3
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Lee YJ, Bae H, Byun JC, Kwon S, Oh SS, Kim S. Clinical Usefulness of Simultaneous Electroencephalography and Functional Magnetic Resonance Imaging in Children With Focal Epilepsy. J Clin Neurol 2022; 18:535-546. [PMID: 36062771 PMCID: PMC9444567 DOI: 10.3988/jcn.2022.18.5.535] [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: 01/19/2022] [Revised: 04/04/2022] [Accepted: 04/04/2022] [Indexed: 11/17/2022] Open
Abstract
Background and Purpose The current study analyzed the interictal epileptiform discharge (IED)-related hemodynamic response and aimed to determine the clinical usefulness of simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) in defining the epileptogenic zone (EZ) in children with focal epilepsy. Methods Patients with focal epilepsy showing IEDs on conventional EEG were evaluated using EEG-fMRI. Statistical analyses were performed using the times of spike as events modeled with multiple hemodynamic response functions. The area showing the most significant t-value for blood-oxygen-level-dependent (BOLD) changes was compared with the presumed EZ. Moreover, BOLD responses between -9 and +9 s around the spike times were analyzed to track the hemodynamic response patterns over time. Results Half (n=13) of 26 EEG-fMRI investigations of 19 patients were successful. Two patients showed 2 different types of spikes, resulting in 15 analyses. The maximum BOLD response was concordant with the EZ in 11 (73.3%) of the 15 analyses. In 10 (66.7%) analyses, the BOLD response localized the EZs more specifically. Focal BOLD responses in the EZs occurred before IEDs in 11 analyses and were often widespread after IEDs. Hemodynamic response patterns were consistent in the same epilepsy syndrome or when repeating the investigation in the same patients. Conclusions EEG-fMRI can provide additional information for localizing the EZ in children with focal epilepsy, and also reveal the pathogenesis of pediatric epilepsy by evaluating the patterns in the hemodynamic response across time windows of IEDs.
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Affiliation(s)
- Yun Jeong Lee
- Department of Pediatrics, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, Korea
| | - Hyunwoo Bae
- Department of Pediatrics, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, Korea
| | - Jun Chul Byun
- Department of Pediatrics, Keimyung University Dongsan Hospital, Keimyung University School of Medicine, Daegu, Korea
| | - Soonhak Kwon
- Department of Pediatrics, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, Korea
| | - Sung Suk Oh
- Medical Device Development Center, Daegu-Gyeongbuk Medical Innovation Foundation (K-MEDI hub), Daegu, Korea.
| | - Saeyoon Kim
- Department of Pediatrics, Yeungnam University Medical Center, Yeungnam University College of Medicine, Daegu, Korea.
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4
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Parastesh F, Mehrabbeik M, Rajagopal K, Jafari S, Perc M. Synchronization in Hindmarsh-Rose neurons subject to higher-order interactions. CHAOS (WOODBURY, N.Y.) 2022; 32:013125. [PMID: 35105127 DOI: 10.1063/5.0079834] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 01/03/2022] [Indexed: 06/14/2023]
Abstract
Higher-order interactions might play a significant role in the collective dynamics of the brain. With this motivation, we here consider a simplicial complex of neurons, in particular, studying the effects of pairwise and three-body interactions on the emergence of synchronization. We assume pairwise interactions to be mediated through electrical synapses, while for second-order interactions, we separately study diffusive coupling and nonlinear chemical coupling. For all the considered cases, we derive the necessary conditions for synchronization by means of linear stability analysis, and we compute the synchronization errors numerically. Our research shows that the second-order interactions, even if of weak strength, can lead to synchronization under significantly lower first-order coupling strengths. Moreover, the overall synchronization cost is reduced due to the introduction of three-body interactions if compared to pairwise interactions.
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Affiliation(s)
- Fatemeh Parastesh
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran polytechnic), Tehran 159163-4311, Iran
| | - Mahtab Mehrabbeik
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran polytechnic), Tehran 159163-4311, Iran
| | - Karthikeyan Rajagopal
- Centre for Nonlinear Systems, Chennai Institute of Technology, Tamil Nadu 600069, India
| | - Sajad Jafari
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran polytechnic), Tehran 159163-4311, Iran
| | - Matjaž Perc
- Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, 2000 Maribor, Slovenia
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Ebrahimzadeh E, Shams M, Seraji M, Sadjadi SM, Rajabion L, Soltanian-Zadeh H. Localizing Epileptic Foci Using Simultaneous EEG-fMRI Recording: Template Component Cross-Correlation. Front Neurol 2021; 12:695997. [PMID: 34867704 PMCID: PMC8634837 DOI: 10.3389/fneur.2021.695997] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 09/29/2021] [Indexed: 02/01/2023] Open
Abstract
Conventional EEG-fMRI methods have been proven to be of limited use in the sense that they cannot reveal the information existing in between the spikes. To resolve this issue, the current study obtains the epileptic components time series detected on EEG and uses them to fit the Generalized Linear Model (GLM), as a substitution for classical regressors. This approach allows for a more precise localization, and equally importantly, the prediction of the future behavior of the epileptic generators. The proposed method approaches the localization process in the component domain, rather than the electrode domain (EEG), and localizes the generators through investigating the spatial correlation between the candidate components and the spike template, as well as the medical records of the patient. To evaluate the contribution of EEG-fMRI and concordance between fMRI and EEG, this method was applied on the data of 30 patients with refractory epilepsy. The results demonstrated the significant numbers of 29 and 24 for concordance and contribution, respectively, which mark improvement as compared to the existing literature. This study also shows that while conventional methods often fail to properly localize the epileptogenic zones in deep brain structures, the proposed method can be of particular use. For further evaluation, the concordance level between IED-related BOLD clusters and Seizure Onset Zone (SOZ) has been quantitatively investigated by measuring the distance between IED/SOZ locations and the BOLD clusters in all patients. The results showed the superiority of the proposed method in delineating the spike-generating network compared to conventional EEG-fMRI approaches. In all, the proposed method goes beyond the conventional methods by breaking the dependency on spikes and using the outside-the-scanner spike templates and the selected components, achieving an accuracy of 97%. Doing so, this method contributes to improving the yield of EEG-fMRI and creates a more realistic perception of the neural behavior of epileptic generators which is almost without precedent in the literature.
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Affiliation(s)
- Elias Ebrahimzadeh
- CIPCE, 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
| | - Mohammad Shams
- Neural Engineering Laboratory, Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, United States
| | - Masoud Seraji
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, United States.,Behavioral and Neural Sciences Graduate Program, Rutgers University, Newark, NJ, United States
| | - Seyyed Mostafa Sadjadi
- CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Lila Rajabion
- School of Graduate Studies, SUNY Empire State College, Manhattan, NY, United States
| | - Hamid Soltanian-Zadeh
- CIPCE, 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.,Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, MI, United States
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6
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Chaudhary UJ, Centeno M, Carmichael DW, Diehl B, Walker MC, Duncan JS, Lemieux L. Mapping Epileptic Networks Using Simultaneous Intracranial EEG-fMRI. Front Neurol 2021; 12:693504. [PMID: 34621233 PMCID: PMC8490636 DOI: 10.3389/fneur.2021.693504] [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: 04/11/2021] [Accepted: 07/20/2021] [Indexed: 11/29/2022] Open
Abstract
Background: Potentially curative epilepsy surgery can be offered if a single, discrete epileptogenic zone (EZ) can be identified. For individuals in whom there is no clear concordance between clinical localization, scalp EEG, and imaging data, intracranial EEG (icEEG) may be needed to confirm a predefined hypothesis regarding irritative zone (IZ), seizure onset zone (SOZ), and EZ prior to surgery. However, icEEG has limited spatial sampling and may fail to reveal the full extent of epileptogenic network if predefined hypothesis is not correct. Simultaneous icEEG-fMRI has been safely acquired in humans and allows exploration of neuronal activity at the whole-brain level related to interictal epileptiform discharges (IED) captured intracranially. Methods: We report icEEG-fMRI in eight patients with refractory focal epilepsy who had resective surgery and good postsurgical outcome. Surgical resection volume in seizure-free patients post-surgically reflects confirmed identification of the EZ. IEDs on icEEG were classified according to their topographic distribution and localization (Focal, Regional, Widespread, and Non-contiguous). We also divided IEDs by their location within the surgical resection volume [primary IZ (IZ1) IED] or outside [secondary IZ (IZ2) IED]. The distribution of fMRI blood oxygen level-dependent (BOLD) changes associated with individual IED classes were assessed over the whole brain using a general linear model. The concordance of resulting BOLD map was evaluated by comparing localization of BOLD clusters with surgical resection volume. Additionally, we compared the concordance of BOLD maps and presence of BOLD clusters in remote brain areas: precuneus, cuneus, cingulate, medial frontal, and thalamus for different IED classes. Results: A total of 38 different topographic IED classes were identified across the 8 patients: Focal (22) and non-focal (16, Regional = 9, Widespread = 2, Non-contiguous = 5). Twenty-nine IEDs originated from IZ1 and 9 from IZ2. All IED classes were associated with BOLD changes. BOLD maps were concordant with the surgical resection volume for 27/38 (71%) IED classes, showing statistical global maximum BOLD cluster or another cluster in the surgical resection volume. The concordance of BOLD maps with surgical resection volume was greater (p < 0.05) for non-focal (87.5%, 14/16) as compared to Focal (59%, 13/22) IED classes. Additionally, BOLD clusters in remote cortical and deep brain areas were present in 84% (32/38) of BOLD maps, more commonly (15/16; 93%) for non-focal IED-related BOLD maps. Conclusions: Simultaneous icEEG-fMRI can reveal BOLD changes at the whole-brain level for a wide range of IEDs on icEEG. BOLD clusters within surgical resection volume and remote brain areas were more commonly seen for non-focal IED classes, suggesting that a wider hemodynamic network is at play.
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Affiliation(s)
- Umair J Chaudhary
- Department of Clinical and Experimental Epilepsy, University College London (UCL) Institute of Neurology, National Hospital for Neurology and Neurosurgery, London, United Kingdom.,Magnetic Resonance Imaging (MRI) Unit, Epilepsy Society, Chalfont St. Peter, United Kingdom.,Neurology Department, University Hospital Coventry and Warwickshire, Coventry, United Kingdom
| | - Maria Centeno
- Department of Clinical and Experimental Epilepsy, University College London (UCL) Institute of Neurology, National Hospital for Neurology and Neurosurgery, London, United Kingdom.,Magnetic Resonance Imaging (MRI) Unit, Epilepsy Society, Chalfont St. Peter, United Kingdom.,Epilepsy Unit, Neurology Department, Hospital Clinic Barcelona, Barcelona, Spain
| | - David W Carmichael
- Imaging and Biophysics Unit, University College London (UCL) Institute of Child Health, London, United Kingdom
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, University College London (UCL) Institute of Neurology, National Hospital for Neurology and Neurosurgery, London, United Kingdom.,Magnetic Resonance Imaging (MRI) Unit, Epilepsy Society, Chalfont St. Peter, United Kingdom.,Clinical Neurophysiology, National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Matthew C Walker
- Department of Clinical and Experimental Epilepsy, University College London (UCL) Institute of Neurology, National Hospital for Neurology and Neurosurgery, London, United Kingdom.,Magnetic Resonance Imaging (MRI) Unit, Epilepsy Society, Chalfont St. Peter, United Kingdom
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, University College London (UCL) Institute of Neurology, National Hospital for Neurology and Neurosurgery, London, United Kingdom.,Magnetic Resonance Imaging (MRI) Unit, Epilepsy Society, Chalfont St. Peter, United Kingdom
| | - Louis Lemieux
- Department of Clinical and Experimental Epilepsy, University College London (UCL) Institute of Neurology, National Hospital for Neurology and Neurosurgery, London, United Kingdom.,Magnetic Resonance Imaging (MRI) Unit, Epilepsy Society, Chalfont St. Peter, United Kingdom
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7
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Nonperiodic stimulation for the treatment of refractory epilepsy: Applications, mechanisms, and novel insights. Epilepsy Behav 2021; 121:106609. [PMID: 31704250 DOI: 10.1016/j.yebeh.2019.106609] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2019] [Revised: 10/14/2019] [Accepted: 10/14/2019] [Indexed: 11/21/2022]
Abstract
Electrical stimulation of the central nervous system is a promising alternative for the treatment of pharmacoresistant epilepsy. Successful clinical and experimental stimulation is most usually carried out as continuous trains of current or voltage pulses fired at rates of 100 Hz or above, since lower frequencies yield controversial results. On the other hand, stimulation frequency should be as low as possible, in order to maximize implant safety and battery efficiency. Moreover, the development of stimulation approaches has been largely empirical in general, while they should be engineered with the neurobiology of epilepsy in mind if a more robust, efficient, efficacious, and safe application is intended. In an attempt to reconcile evidence of therapeutic effect with the understanding of the underpinnings of epilepsy, our group has developed a nonstandard form of low-frequency stimulation with randomized interpulse intervals termed nonperiodic stimulation (NPS). The rationale was that an irregular temporal pattern would impair neural hypersynchronization, which is a hallmark of epilepsy. In this review, we start by briefly revisiting the literature on the molecular, cellular, and network level mechanisms of epileptic phenomena in order to highlight this often-overlooked emergent property of cardinal importance in the pathophysiology of the disease. We then review our own studies on the efficacy of NPS against acute and chronic experimental seizures and also on the anatomical and physiological mechanism of the method, paying special attention to the hypothesis that the lack of temporal regularity induces desynchronization. We also put forward a novel insight regarding the temporal structure of NPS that may better encompass the set of findings published by the group: the fact that intervals between stimulation pulses have a distribution that follows a power law and thus may induce natural-like activity that would compete with epileptiform discharge for the recruitment of networks. We end our discussion by mentioning ongoing research and future projects of our lab.
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8
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Sadjadi SM, Ebrahimzadeh E, Shams M, Seraji M, Soltanian-Zadeh H. Localization of Epileptic Foci Based on Simultaneous EEG-fMRI Data. Front Neurol 2021; 12:645594. [PMID: 33986718 PMCID: PMC8110922 DOI: 10.3389/fneur.2021.645594] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 03/11/2021] [Indexed: 02/01/2023] Open
Abstract
Combining functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) enables a non-invasive investigation of the human brain function and evaluation of the correlation of these two important modalities of brain activity. This paper explores recent reports on using advanced simultaneous EEG–fMRI methods proposed to map the regions and networks involved in focal epileptic seizure generation. One of the applications of EEG and fMRI combination as a valuable clinical approach is the pre-surgical evaluation of patients with epilepsy to map and localize the precise brain regions associated with epileptiform activity. In the process of conventional analysis using EEG–fMRI data, the interictal epileptiform discharges (IEDs) are visually extracted from the EEG data to be convolved as binary events with a predefined hemodynamic response function (HRF) to provide a model of epileptiform BOLD activity and use as a regressor for general linear model (GLM) analysis of the fMRI data. This review examines the methodologies involved in performing such studies, including techniques used for the recording of EEG inside the scanner, artifact removal, and statistical analysis of the fMRI signal. It then discusses the results reported for patients with primary generalized epilepsy and patients with different types of focal epileptic disorders. An important matter that these results have brought to light is that the brain regions affected by interictal epileptic discharges might not be limited to the ones where they have been generated. The developed methods can help reveal the regions involved in or affected by a seizure onset zone (SOZ). As confirmed by the reviewed literature, EEG–fMRI provides information that comes particularly useful when evaluating patients with refractory epilepsy for surgery.
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Affiliation(s)
- Seyyed Mostafa Sadjadi
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Elias Ebrahimzadeh
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.,Neuroimage Signal and Image Analysis Group, School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Mohammad Shams
- Neural Engineering Laboratory, Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, United States
| | - Masoud Seraji
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, United States.,Behavioral and Neural Sciences Graduate Program, Rutgers University, Newark, NJ, United States
| | - Hamid Soltanian-Zadeh
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.,Neuroimage Signal and Image Analysis Group, School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.,Medical Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, MI, United States
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9
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Bullock M, Jackson GD, Abbott DF. Artifact Reduction in Simultaneous EEG-fMRI: A Systematic Review of Methods and Contemporary Usage. Front Neurol 2021; 12:622719. [PMID: 33776886 PMCID: PMC7991907 DOI: 10.3389/fneur.2021.622719] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 01/29/2021] [Indexed: 11/13/2022] Open
Abstract
Simultaneous electroencephalography-functional MRI (EEG-fMRI) is a technique that combines temporal (largely from EEG) and spatial (largely from fMRI) indicators of brain dynamics. It is useful for understanding neuronal activity during many different event types, including spontaneous epileptic discharges, the activity of sleep stages, and activity evoked by external stimuli and decision-making tasks. However, EEG recorded during fMRI is subject to imaging, pulse, environment and motion artifact, causing noise many times greater than the neuronal signals of interest. Therefore, artifact removal methods are essential to ensure that artifacts are accurately removed, and EEG of interest is retained. This paper presents a systematic review of methods for artifact reduction in simultaneous EEG-fMRI from literature published since 1998, and an additional systematic review of EEG-fMRI studies published since 2016. The aim of the first review is to distill the literature into clear guidelines for use of simultaneous EEG-fMRI artifact reduction methods, and the aim of the second review is to determine the prevalence of artifact reduction method use in contemporary studies. We find that there are many published artifact reduction techniques available, including hardware, model based, and data-driven methods, but there are few studies published that adequately compare these methods. In contrast, recent EEG-fMRI studies show overwhelming use of just one or two artifact reduction methods based on literature published 15–20 years ago, with newer methods rarely gaining use outside the group that developed them. Surprisingly, almost 15% of EEG-fMRI studies published since 2016 fail to adequately describe the methods of artifact reduction utilized. We recommend minimum standards for reporting artifact reduction techniques in simultaneous EEG-fMRI studies and suggest that more needs to be done to make new artifact reduction techniques more accessible for the researchers and clinicians using simultaneous EEG-fMRI.
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Affiliation(s)
- Madeleine Bullock
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia.,Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia
| | - Graeme D Jackson
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia.,Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia.,Department of Medicine (Austin Health), The University of Melbourne, Melbourne, VIC, Australia
| | - David F Abbott
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia.,Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia.,Department of Medicine (Austin Health), The University of Melbourne, Melbourne, VIC, Australia
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10
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Shin A, Byun JC, Hwang SK, Kwon S, Lee YJ. Clinical Characteristics of Epilepsy and Its Risk Factors in Neurofibromatosis Type 1: A Single-Center Study. ANNALS OF CHILD NEUROLOGY 2021. [DOI: 10.26815/acn.2020.00283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
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11
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Van Eyndhoven S, Dupont P, Tousseyn S, Vervliet N, Van Paesschen W, Van Huffel S, Hunyadi B. Augmenting interictal mapping with neurovascular coupling biomarkers by structured factorization of epileptic EEG and fMRI data. Neuroimage 2020; 228:117652. [PMID: 33359347 PMCID: PMC7903163 DOI: 10.1016/j.neuroimage.2020.117652] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 11/28/2020] [Accepted: 12/04/2020] [Indexed: 12/20/2022] Open
Abstract
EEG-correlated fMRI analysis is widely used to detect regional BOLD fluctuations that are synchronized to interictal epileptic discharges, which can provide evidence for localizing the ictal onset zone. However, the typical, asymmetrical and mass-univariate approach cannot capture the inherent, higher order structure in the EEG data, nor multivariate relations in the fMRI data, and it is nontrivial to accurately handle varying neurovascular coupling over patients and brain regions. We aim to overcome these drawbacks in a data-driven manner by means of a novel structured matrix-tensor factorization: the single-subject EEG data (represented as a third-order spectrogram tensor) and fMRI data (represented as a spatiotemporal BOLD signal matrix) are jointly decomposed into a superposition of several sources, characterized by space-time-frequency profiles. In the shared temporal mode, Toeplitz-structured factors account for a spatially specific, neurovascular 'bridge' between the EEG and fMRI temporal fluctuations, capturing the hemodynamic response's variability over brain regions. By analyzing interictal data from twelve patients, we show that the extracted source signatures provide a sensitive localization of the ictal onset zone (10/12). Moreover, complementary parts of the IOZ can be uncovered by inspecting those regions with the most deviant neurovascular coupling, as quantified by two entropy-like metrics of the hemodynamic response function waveforms (9/12). Hence, this multivariate, multimodal factorization provides two useful sets of EEG-fMRI biomarkers, which can assist the presurgical evaluation of epilepsy. We make all code required to perform the computations available at https://github.com/svaneynd/structured-cmtf.
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Affiliation(s)
- Simon Van Eyndhoven
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Belgium.
| | - Patrick Dupont
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium; Leuven Brain Institute, Leuven, Belgium
| | - Simon Tousseyn
- Academic Center for Epileptology, Kempenhaeghe and Maastricht UMC+, Heeze, the Netherlands
| | - Nico Vervliet
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Belgium
| | - Wim Van Paesschen
- Laboratory for Epilepsy Research, KU Leuven, Leuven, Belgium; Department of Neurology, University Hospitals Leuven, Leuven, Belgium
| | - Sabine Van Huffel
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Belgium
| | - Borbála Hunyadi
- Circuits and Systems Group (CAS), Department of Microelectronics, Delft University of Technology, Delft, the Netherlands
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12
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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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13
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Iannotti GR, Preti MG, Grouiller F, Carboni M, De Stefano P, Pittau F, Momjian S, Carmichael D, Centeno M, Seeck M, Korff CM, Schaller K, De Ville DV, Vulliemoz S. Modulation of epileptic networks by transient interictal epileptic activity: A dynamic approach to simultaneous EEG-fMRI. NEUROIMAGE-CLINICAL 2020; 28:102467. [PMID: 33395963 PMCID: PMC7645285 DOI: 10.1016/j.nicl.2020.102467] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 09/15/2020] [Accepted: 10/09/2020] [Indexed: 12/27/2022]
Abstract
EEG-fMRI has been instrumental in characterizing brain networks in epilepsy. Its value is documented in the pre-surgical assessment of drug-resistant epilepsy. The delineation of brain areas to resect is fundamental for the post-surgical outcome. Standard EEG-fMRI in epilepsy assesses static functional connectivity of the network. EEG-fMRI dynamic connectivity identifies transitory features of specific connections. We integrate dynamic fMRI connectivity and dynamic patterns of simultaneous scalp EEG. This allows to better characterize the spatiotemporal aspects of epileptic networks. This may help in more efficiently target the surgical intervention.
Epileptic networks, defined as brain regions involved in epileptic brain activity, have been mapped by functional connectivity in simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) recordings. This technique allows to define brain hemodynamic changes, measured by the Blood Oxygen Level Dependent (BOLD) signal, associated to the interictal epileptic discharges (IED), which together with ictal events constitute a signature of epileptic disease. Given the highly time-varying nature of epileptic activity, a dynamic functional connectivity (dFC) analysis of EEG-fMRI data appears particularly suitable, having the potential to identify transitory features of specific connections in epileptic networks. In the present study, we propose a novel method, defined dFC-EEG, that integrates dFC assessed by fMRI with the information recorded by simultaneous scalp EEG, in order to identify the connections characterised by a dynamic profile correlated with the occurrence of IED, forming the dynamic epileptic subnetwork. Ten patients with drug-resistant focal epilepsy were included, with different aetiology and showing a widespread (or multilobar) BOLD activation, defined as involving at least two distinct clusters, located in two different lobes and/or extended to the hemisphere contralateral to the epileptic focus. The epileptic focus was defined from the IED-related BOLD map. Regions involved in the occurrence of interictal epileptic activity; i.e., forming the epileptic network, were identified by a general linear model considering the timecourse of the fMRI-defined focus as main regressor. dFC between these regions was assessed with a sliding-window approach. dFC timecourses were then correlated with the sliding-window variance of the IED signal (VarIED), to identify connections whose dynamics related to the epileptic activity; i.e., the dynamic epileptic subnetwork. As expected, given the very different clinical picture of each individual, the extent of this subnetwork was highly variable across patients, but was but was reduced of at least 30% with respect to the initially identified epileptic network in 9/10 patients. The connections of the dynamic subnetwork were most commonly close to the epileptic focus, as reflected by the laterality index of the subnetwork connections, reported higher than the one within the original epileptic network. Moreover, the correlation between dFC timecourses and VarIED was predominantly positive, suggesting a strengthening of the dynamic subnetwork associated to the occurrence of IED. The integration of dFC and scalp IED offers a more specific description of the epileptic network, identifying connections strongly influenced by IED. These findings could be relevant in the pre-surgical evaluation for the resection or disconnection of the epileptogenic zone and help in reaching a better post-surgical outcome. This would be particularly important for patients characterised by a widespread pathological brain activity which challenges the surgical intervention.
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Affiliation(s)
- G R Iannotti
- EEG and Epilepsy, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland; Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Switzerland; Neurosurgery, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland.
| | - M G Preti
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - F Grouiller
- Swiss Center for Affective Sciences, University of Geneva, Switzerland; Laboratory of Behavioral Neurology and Imaging of Cognition, Department of Fundamental Neurosciences, University of Geneva, Switzerland
| | - M Carboni
- EEG and Epilepsy, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland; Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Switzerland
| | - P De Stefano
- EEG and Epilepsy, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland
| | - F Pittau
- EEG and Epilepsy, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland; Epilepsy Unit, Institution de Lavigny, Switzerland
| | - S Momjian
- Neurosurgery, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland
| | - D Carmichael
- Biomedical Engineering Department, Kings College London, United Kingdom; Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London, United Kingdom
| | - M Centeno
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London, United Kingdom; Epilepsy Unit, Neurology Department, Clinica Universidad de Pamplona, Navarra, Spain
| | - M Seeck
- EEG and Epilepsy, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland
| | - C M Korff
- Pediatric Neurology Unit, University Hospitals of Geneva, Geneva, Switzerland
| | - K Schaller
- Neurosurgery, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland
| | - D Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - S Vulliemoz
- EEG and Epilepsy, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland
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14
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Simões M, Abreu R, Direito B, Sayal A, Castelhano J, Carvalho P, Castelo-Branco M. How much of the BOLD-fMRI signal can be approximated from simultaneous EEG data: relevance for the transfer and dissemination of neurofeedback interventions. J Neural Eng 2020; 17:046007. [DOI: 10.1088/1741-2552/ab9a98] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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15
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Xiao G, Song Y, Zhang Y, Xing Y, Xu S, Lu Z, Wang M, Cai X. Cellular-Scale Microelectrode Arrays to Monitor Movement-Related Neuron Activities in the Epileptic Hippocampus of Awake Mice. IEEE Trans Biomed Eng 2020; 68:19-25. [PMID: 32356734 DOI: 10.1109/tbme.2020.2990356] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Epilepsy affects 50 million people worldwide and its pathogenesis is still unknown. In particular, the movement-related neural activities involving glutamate (Glu) and electrophysiological signals at cellular level remains unclear. METHODS A cellular-scale implantable microelectrode array (MEA) was fabricated to detect the movement-related neural activities involving Glu concentration and electrophysiological signals. Platinum and reduced graphene oxide nanocomposites were deposited to enhance the surface area. Glu oxidase (Gluox) were coated to effectively recognize Glu molecule. RESULTS Neural activities in the hippocampus of normal and epileptic mice is different, and the changes are closely connected with movement. Glu concentration and spike firing rate in the epileptic mice were much higher than those in the normal ones. And the neural activities with significant synchronization were detected in the epileptic mice even without seizure occurrence. Meanwhile, the spikes fire more intensively and Glu level became much higher during the movement of the mice compared to the stationary state. CONCLUSION The existing abnormality of neural activities in the epileptic mice are potential factors to induce a seizure. Movement may impact the neural activities and the duration of seizure. SIGNIFICANCE The MEA can monitor changes of movement, Glu and neuron discharges synchronously and provides us an effective technology to understand the neuronal disease.
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Bucho T, Caetano G, Vourvopoulos A, Accoto F, Esteves I, I Badia SB, Rosa A, Figueiredo P. Comparison of Visual and Auditory Modalities for Upper-Alpha EEG-Neurofeedback .. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:5960-5966. [PMID: 31947205 DOI: 10.1109/embc.2019.8856671] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Electroencephalography (EEG) neurofeedback (NF) training has been shown to produce long-lasting effects on the improvement of cognitive function as well as the normalization of aberrant brain activity in disease. However, the impact of the sensory modality used as the NF reinforcement signal on training effectiveness has not been systematically investigated. In this work, an EEG-based NF-training system was developed targeting the individual upper-alpha (UA) band and using either a visual or an auditory reinforcement signal, so as to compare the effects of the two sensory modalities. Sixteen healthy volunteers were randomly assigned to the Visual or Auditory group, where a radius-varying sphere or a volume-varying sound, respectively, reflected the relative amplitude of UA measured at EEG electrode Cz. Each participant underwent a total of four NF sessions, of approximately 40 min each, on consecutive days. Both groups showed significant increases in UA at Cz within sessions, and also across sessions. Effects subsequent to NF training were also found beyond the target frequency UA and scalp location Cz, namely in the lower-alpha and theta bands and in posterior brain regions, respectively. Only small differences were found on the EEG between the Visual and Auditory groups, suggesting that auditory reinforcement signals may be as effective as the more commonly used visual signals. The use of auditory NF may potentiate training protocols conducted under mobile conditions, which are now possible due to the increasing availability of wireless EEG systems.
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Van Eyndhoven S, Hunyadi B, Dupont P, Van Paesschen W, Van Huffel S. Semi-automated EEG Enhancement Improves Localization of Ictal Onset Zone With EEG-Correlated fMRI. Front Neurol 2019; 10:805. [PMID: 31428036 PMCID: PMC6688528 DOI: 10.3389/fneur.2019.00805] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 07/11/2019] [Indexed: 11/13/2022] Open
Abstract
Objective: To improve the accuracy of detecting the ictal onset zone, we propose to enhance the epilepsy-related activity present in the EEG signals, before mapping their BOLD correlates through EEG-correlated fMRI analysis. Methods: Based solely on a segmentation of interictal epileptic discharges (IEDs) on the EEG, we train multi-channel Wiener filters (MWF) which enhance IED-like waveforms, and suppress background activity and noisy influences. Subsequently, we use EEG-correlated fMRI to find the brain regions in which the BOLD signal fluctuation corresponds to the filtered signals' time-varying power (after convolving with the hemodynamic response function), and validate the identified regions by quantitatively comparing them to ground-truth maps of the (resected or hypothesized) ictal onset zone. We validate the performance of this novel predictor vs. that of commonly used unitary or power-weighted predictors and a recently introduced connectivity-based metric, on a cohort of 12 patients with refractory epilepsy. Results: The novel predictor, derived from the filtered EEG signals, allowed the detection of the ictal onset zone in a larger percentage of epileptic patients (92% vs. at most 83% for the other predictors), and with higher statistical significance, compared to existing predictors. At the same time, the new method maintains maximal specificity by not producing false positive activations in healthy controls. Significance: The findings of this study advocate for the use of the MWF to maximize the signal-to-noise ratio of IED-like events in the interictal EEG, and subsequently use time-varying power as a sensitive predictor of the BOLD signal, to localize the ictal onset zone.
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Affiliation(s)
- Simon Van Eyndhoven
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium
| | | | - Patrick Dupont
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium.,Leuven Brain Institute, Leuven, Belgium
| | - Wim Van Paesschen
- Laboratory for Epilepsy Research, KU Leuven, Leuven, Belgium.,Department of Neurology, University Hospitals Leuven, Leuven, Belgium
| | - Sabine Van Huffel
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium
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Qin Y, Jiang S, Zhang Q, Dong L, Jia X, He H, Yao Y, Yang H, Zhang T, Luo C, Yao D. BOLD-fMRI activity informed by network variation of scalp EEG in juvenile myoclonic epilepsy. NEUROIMAGE-CLINICAL 2019; 22:101759. [PMID: 30897433 PMCID: PMC6425117 DOI: 10.1016/j.nicl.2019.101759] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Revised: 02/22/2019] [Accepted: 03/10/2019] [Indexed: 01/14/2023]
Abstract
Epilepsy is marked by hypersynchronous bursts of neuronal activity, and seizures can propagate variably to any and all areas, leading to brain network dynamic organization. However, the relationship between the network characteristics of scalp EEG and blood oxygenation level-dependent (BOLD) responses in epilepsy patients is still not well known. In this study, simultaneous EEG and fMRI data were acquired in 18 juvenile myoclonic epilepsy (JME) patients. Then, the adapted directed transfer function (ADTF) values between EEG electrodes were calculated to define the time-varying network. The variation of network information flow within sliding windows was used as a temporal regressor in fMRI analysis to predict the BOLD response. To investigate the EEG-dependent functional coupling among the responding regions, modulatory interactions were analyzed for network variation of scalp EEG and BOLD time courses. The results showed that BOLD activations associated with high network variation were mainly located in the thalamus, cerebellum, precuneus, inferior temporal lobe and sensorimotor-related areas, including the middle cingulate cortex (MCC), supplemental motor area (SMA), and paracentral lobule. BOLD deactivations associated with medium network variation were found in the frontal, parietal, and occipital areas. In addition, modulatory interaction analysis demonstrated predominantly directional negative modulation effects among the thalamus, cerebellum, frontal and sensorimotor-related areas. This study described a novel method to link BOLD response with simultaneous functional network organization of scalp EEG. These findings suggested the validity of predicting epileptic activity using functional connectivity variation between electrodes. The functional coupling among the thalamus, frontal regions, cerebellum and sensorimotor-related regions may be characteristically involved in epilepsy generation and propagation, which provides new insight into the pathophysiological mechanisms and intervene targets for JME.
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Affiliation(s)
- Yun Qin
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Qiqi Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xiaoyan Jia
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yutong Yao
- Faculty of natural science, University of Stirling, Stirling, United Kingdom
| | - Huanghao Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Tao Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China.
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China.
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China.
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Yamazoe T, von Ellenrieder N, Khoo HM, Huang YH, Zazubovits N, Dubeau F, Gotman J. Widespread interictal epileptic discharge more likely than focal discharges to unveil the seizure onset zone in EEG-fMRI. Clin Neurophysiol 2019; 130:429-438. [PMID: 30769269 DOI: 10.1016/j.clinph.2018.12.014] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2018] [Revised: 12/12/2018] [Accepted: 12/25/2018] [Indexed: 11/28/2022]
Abstract
OBJECTIVE We hypothesized that the number of interictal epileptic discharges (IEDs) during scan and their spatial extent are contributing factors in obtaining appropriate activations that reveal the seizure onset zone (SOZ) in EEG-fMRI. METHODS 157 IED types, each corresponding to one EEG scalp distribution, in 64 consecutive EEG-fMRI studies from 64 patients with refractory localization-related epilepsy were reviewed. To determine reliable activation, we used the threshold corresponding to corrected whole-brain topological false discovery rate (FDR). The location with maximum activation was compared to the presumed SOZ as defined by a comprehensive evaluation for each patient. RESULTS The number of IEDs was significantly higher in the types with t-value above FDR than with t-value below FDR. The presumed SOZ could be delineated in 30 of the 64 patients. Among these patients, the types of IED concordant with the SOZ had significantly larger extent on scalp EEG than the IED types discordant with the SOZ. CONCLUSIONS The number of IEDs is important factor in obtaining reliable activations in EEG-fMRI. IEDs with larger spatial extent are more likely to reveal, on maximum BOLD, accurate location of the SOZ. SIGNIFICANCE Widespread discharges are more likely to yield a reliable activation for SOZ in EEG-fMRI.
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Affiliation(s)
- Tomohiro Yamazoe
- Montreal Neurological Institute and Hospital, McGill University, Canada; Seirei Hamamatsu General Hospital, Hamamatsu, Japan.
| | | | - Hui Ming Khoo
- Montreal Neurological Institute and Hospital, McGill University, Canada; Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Japan
| | - Yao-Hsien Huang
- Montreal Neurological Institute and Hospital, McGill University, Canada; Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei, Taiwan
| | | | - François Dubeau
- Montreal Neurological Institute and Hospital, McGill University, Canada
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, Canada
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20
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Abreu R, Leal A, Figueiredo P. Identification of epileptic brain states by dynamic functional connectivity analysis of simultaneous EEG-fMRI: a dictionary learning approach. Sci Rep 2019; 9:638. [PMID: 30679773 PMCID: PMC6345787 DOI: 10.1038/s41598-018-36976-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Accepted: 11/30/2018] [Indexed: 12/12/2022] Open
Abstract
Most fMRI studies of the brain's intrinsic functional connectivity (FC) have assumed that this is static; however, it is now clear that it changes over time. This is particularly relevant in epilepsy, which is characterized by a continuous interchange between epileptic and normal brain states associated with the occurrence of epileptic activity. Interestingly, recurrent states of dynamic FC (dFC) have been found in fMRI data using unsupervised learning techniques, assuming either their sparse or non-sparse combination. Here, we propose an l1-norm regularized dictionary learning (l1-DL) approach for dFC state estimation, which allows an intermediate and flexible degree of sparsity in time, and demonstrate its application in the identification of epilepsy-related dFC states using simultaneous EEG-fMRI data. With this l1-DL approach, we aim to accommodate a potentially varying degree of sparsity upon the interchange between epileptic and non-epileptic dFC states. The simultaneous recording of the EEG is used to extract time courses representative of epileptic activity, which are incorporated into the fMRI dFC state analysis to inform the selection of epilepsy-related dFC states. We found that the proposed l1-DL method performed best at identifying epilepsy-related dFC states, when compared with two alternative methods of extreme sparsity (k-means clustering, maximum; and principal component analysis, minimum), as well as an l0-norm regularization framework (l0-DL), with a fixed amount of temporal sparsity. We further showed that epilepsy-related dFC states provide novel insights into the dynamics of epileptic networks, which go beyond the information provided by more conventional EEG-correlated fMRI analysis, and which were concordant with the clinical profile of each patient. In addition to its application in epilepsy, our study provides a new dFC state identification method of potential relevance for studying brain functional connectivity dynamics in general.
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Affiliation(s)
- Rodolfo Abreu
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
| | - Alberto Leal
- Department of Neurophysiology, Centro Hospitalar Psiquiátrico de Lisboa, Lisbon, Portugal
| | - Patrícia Figueiredo
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
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Oldan JD, Shin HW, Khandani AH, Zamora C, Benefield T, Jewells V. Subsequent experience in hybrid PET-MRI for evaluation of refractory focal onset epilepsy. Seizure 2018; 61:128-134. [DOI: 10.1016/j.seizure.2018.07.022] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2018] [Revised: 07/19/2018] [Accepted: 07/31/2018] [Indexed: 11/28/2022] Open
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22
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Adaptive optimal basis set for BCG artifact removal in simultaneous EEG-fMRI. Sci Rep 2018; 8:8902. [PMID: 29891929 PMCID: PMC5995808 DOI: 10.1038/s41598-018-27187-6] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Accepted: 05/30/2018] [Indexed: 11/13/2022] Open
Abstract
Electroencephalography (EEG) signals recorded during simultaneous functional magnetic resonance imaging (fMRI) are contaminated by strong artifacts. Among these, the ballistocardiographic (BCG) artifact is the most challenging, due to its complex spatio-temporal dynamics associated with ongoing cardiac activity. The presence of BCG residuals in EEG data may hide true, or generate spurious correlations between EEG and fMRI time-courses. Here, we propose an adaptive Optimal Basis Set (aOBS) method for BCG artifact removal. Our method is adaptive, as it can estimate the delay between cardiac activity and BCG occurrence on a beat-to-beat basis. The effective creation of an optimal basis set by principal component analysis (PCA) is therefore ensured by a more accurate alignment of BCG occurrences. Furthermore, aOBS can automatically estimate which components produced by PCA are likely to be BCG artifact-related and therefore need to be removed. The aOBS performance was evaluated on high-density EEG data acquired with simultaneous fMRI in healthy subjects during visual stimulation. As aOBS enables effective reduction of BCG residuals while preserving brain signals, we suggest it may find wide application in simultaneous EEG-fMRI studies.
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Abreu R, Leal A, Figueiredo P. EEG-Informed fMRI: A Review of Data Analysis Methods. Front Hum Neurosci 2018; 12:29. [PMID: 29467634 PMCID: PMC5808233 DOI: 10.3389/fnhum.2018.00029] [Citation(s) in RCA: 71] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Accepted: 01/18/2018] [Indexed: 01/17/2023] Open
Abstract
The simultaneous acquisition of electroencephalography (EEG) with functional magnetic resonance imaging (fMRI) is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD) fMRI signal, the so-called EEG-informed fMRI integration strategy, the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest.
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Affiliation(s)
- Rodolfo Abreu
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico - Universidade de Lisboa, Lisbon, Portugal
| | - Alberto Leal
- Department of Neurophysiology, Centro Hospitalar Psiquiátrico de Lisboa, Lisbon, Portugal
| | - Patrícia Figueiredo
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico - Universidade de Lisboa, Lisbon, Portugal
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