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Yedurkar DP, Metkar SP, Stephan T. Multiresolution directed transfer function approach for segment-wise seizure classification of epileptic EEG signal. Cogn Neurodyn 2024; 18:301-315. [PMID: 38699601 PMCID: PMC11061070 DOI: 10.1007/s11571-021-09773-z] [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: 08/12/2021] [Revised: 11/10/2021] [Accepted: 12/13/2021] [Indexed: 11/03/2022] Open
Abstract
Currently, with the bloom in artificial intelligence (AI) algorithms, various human-centered smart systems can be utilized, especially in cognitive computing, for the detection of various chronic brain diseases such as epileptic seizure. The primary goal of this research article is to propose a novel human-centered cognitive computing (HCCC) method for segment-wise seizure classification by employing multiresolution extracted data with directed transfer function (DTF) features, termed as the multiresolution directed transfer function (MDTF) approach. Initially, the multiresolution information of the epileptic seizure signal is extracted using a multiresolution adaptive filtering (MRAF) method. These seizure details are passed to the DTF where the information flow of high frequency bands is computed. Thereafter, different measures of complexity such as approximate entropy (AEN) and sample entropy (SAEN) are computed from the extracted high frequency bands. Lastly, a k-nearest neighbor (k-NN) and support vector machine (SVM) are used for classifying the EEG signal into non-seizure and seizure data depending on the multiresolution based information flow characteristics. The MDTF approach is tested on a standard dataset and validated using a dataset from a local hospital. The proposed technique has obtained an average sensitivity of 98.31%, specificity of 96.13% and accuracy of 98.89% using SVM classifier. The average detection rate of the MDTF approach is 97.72% which is greater than the existing approaches. The proposed MDTF method will help neuro-specialists to locate seizure information drift which occurs within the consecutive segments and between two channels. The main advantage of the MDTF approach is its capability to locate the seizure activity contained by the EEG signal with accuracy. This will assist the neurologists with the precise localization of the epileptic seizure automatically and hence will reduce the burden of time-consuming epileptic seizure analysis.
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Affiliation(s)
- Dhanalekshmi P. Yedurkar
- Department of Electronics and Telecommunication Engineering, College of Engineering Pune, Pune, 411005 India
| | - Shilpa P. Metkar
- Department of Electronics and Telecommunication Engineering, College of Engineering Pune, Pune, 411005 India
| | - Thompson Stephan
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, M. S. Ramaiah University of Applied Sciences, Bangalore, Karnataka 560054 India
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2
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Liu H, Bai Y, Xu Z, Liu J, Ni G, Ming D. The scalp time-varying network of auditory spatial attention in "cocktail-party" situations. Hear Res 2024; 442:108946. [PMID: 38150794 DOI: 10.1016/j.heares.2023.108946] [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: 07/09/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 12/29/2023]
Abstract
Sound source localization in "cocktail-party" situations is a remarkable ability of the human auditory system. However, the neural mechanisms underlying auditory spatial attention are still largely unknown. In this study, the "cocktail-party" situations are simulated through multiple sound sources and presented through head-related transfer functions and headphones. Furthermore, the scalp time-varying network of auditory spatial attention is constructed using the high-temporal resolution electroencephalogram, and its network properties are measured quantitatively using graph theory analysis. The results show that the time-varying network of auditory spatial attention in "cocktail-party" situations is more complex and partially different than in simple acoustic situations, especially in the early- and middle-latency periods. The network coupling strength increases continuously over time, and the network hub shifts from the posterior temporal lobe to the parietal lobe and then to the frontal lobe region. In addition, the right hemisphere has a stronger network strength for processing auditory spatial information in "cocktail-party" situations, i.e., the right hemisphere has higher clustering levels, higher transmission efficiency, and more node degrees during the early- and middle-latency periods, while this phenomenon disappears and appears symmetrically during the late-latency period. These findings reveal different network patterns and properties of auditory spatial attention in "cocktail-party" situations during different periods and demonstrate the dominance of the right hemisphere in the dynamic processing of auditory spatial information.
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Affiliation(s)
- Hongxing Liu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072 China
| | - Yanru Bai
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072 China; Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin 300072 China; Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin 300392 China
| | - Zihao Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072 China
| | - Jihan Liu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072 China
| | - Guangjian Ni
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072 China; Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin 300072 China; Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin 300392 China.
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072 China; Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin 300072 China; Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin 300392 China
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3
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Knowlton RC. Ictal EEG Source Imaging. J Clin Neurophysiol 2024; 41:27-35. [PMID: 38181385 DOI: 10.1097/wnp.0000000000001033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2024] Open
Abstract
SUMMARY Ictal EEG source imaging (ESI) is an advancing and growing application for presurgical epilepsy evaluation. For far too long, localization of seizures with scalp EEG has continued to rely on visual inspection of tracings arranged in a variety of montages allowing, at best, rough estimates of seizure onset regions. This most critical step is arguably the weakest point in epilepsy localization for surgical decision-making in clinical practice today. This review covers the methods and strategies that have been developed and tested for the performance of ictal ESI. It highlights practical issues and solutions toward sound implementation while covering differing methods to tackle the challenges specific to ictal ESI-noise and artifact reduction, component analysis, and other tools to increase seizure-specific signal for analysis. Further, validation studies to date-those with both high and low density numbers of electrodes-are summarized, providing a glimpse at the relative accuracy of ictal ESI in all types of focal epilepsy patients. Finally, given the added noninvasive information (greater degree of spatial resolution compared with standard ictal EEG review), the role of ictal ESI and its clinical utility in the presurgical evaluation is discussed.
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Affiliation(s)
- Robert C Knowlton
- Departments of Neurology, Radiology, and Neurological Surgery, University of California San Francisco, San Francisco, California, U.S.A
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4
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López-Madrona VJ, Villalon SM, Velmurugan J, Semeux-Bernier A, Garnier E, Badier JM, Schön D, Bénar CG. Reconstruction and localization of auditory sources from intracerebral SEEG using independent component analysis. Neuroimage 2023; 269:119905. [PMID: 36720438 DOI: 10.1016/j.neuroimage.2023.119905] [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: 10/07/2022] [Revised: 01/11/2023] [Accepted: 01/26/2023] [Indexed: 01/30/2023] Open
Abstract
Stereo-electroencephalography (SEEG) is the surgical implantation of electrodes in the brain to better localize the epileptic network in pharmaco-resistant epileptic patients. This technique has exquisite spatial and temporal resolution. Still, the number and the position of the electrodes in the brain is limited and determined by the semiology and/or preliminary non-invasive examinations, leading to a large number of unexplored brain structures in each patient. Here, we propose a new approach to reconstruct the activity of non-sampled structures in SEEG, based on independent component analysis (ICA) and dipole source localization. We have tested this approach with an auditory stimulation dataset in ten patients. The activity directly recorded from the auditory cortex served as ground truth and was compared to the ICA applied on all non-auditory electrodes. Our results show that the activity from the auditory cortex can be reconstructed at the single trial level from contacts as far as ∼40 mm from the source. Importantly, this reconstructed activity is localized via dipole fitting in the proximity of the original source. In addition, we show that the size of the confidence interval of the dipole fitting is a good indicator of the reliability of the result, which depends on the geometry of the SEEG implantation. Overall, our approach allows reconstructing the activity of structures far from the electrode locations, partially overcoming the spatial sampling limitation of intracerebral recordings.
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Affiliation(s)
| | - Samuel Medina Villalon
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille 13005, France; APHM, Timone Hospital, Epileptology and cerebral rhythmology, Marseille 13005, France
| | - Jayabal Velmurugan
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille 13005, France
| | | | - Elodie Garnier
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille 13005, France
| | - Jean-Michel Badier
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille 13005, France
| | - Daniele Schön
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille 13005, France
| | - Christian-G Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille 13005, France.
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5
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An Explainable Statistical Method for Seizure Prediction Using Brain Functional Connectivity from EEG. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2183562. [DOI: 10.1155/2022/2183562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/19/2022] [Accepted: 09/28/2022] [Indexed: 12/13/2022]
Abstract
Background. Epilepsy is a group of chronic neurological disorders characterized by recurrent and abrupt seizures. The accurate prediction of seizures can reduce the burdens of this disorder. Now, existing studies use brain network features to classify patients’ preictal or interictal states, enabling seizure prediction. However, most predicting methods are based on deep learning techniques, which have weak interpretability and high computational complexity. To address these issues, in this study, we proposed a novel two-stage statistical method that is interpretable and easy to compute. Methods. We used two datasets to evaluate the performance of the proposed method, including the well-known public dataset CHB-MIT. In the first stage, we estimated the dynamic brain functional connectivity network for each epoch. Then, in the second stage, we used the derived network predictor for seizure prediction. Results. We illustrated the results of our method in seizure prediction in two datasets separately. For the FH-PKU dataset, our approach achieved an AUC value of 0.963, a prediction sensitivity of 93.1%, and a false discovery rate of 7.7%. For the CHB-MIT dataset, our approach achieved an AUC value of 0.940, a prediction sensitivity of 93.0%, and a false discovery rate of 11.1%, outperforming existing state-of-the-art methods. Significance. This study proposed an explainable statistical method, which can estimate the brain network using the scalp EEG method and use the net-work predictor to predict epileptic seizures. Availability and Implementation. R Source code is available at https://github.com/HaoChen1994/Seizure-Prediction.
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Ahn MH, Alsabbagh N, Lee HJ, Kim HJ, Jung MH, Hong SK. Neurobiological Signatures of Auditory False Perception and Phantom Perception as a Consequence of Sensory Prediction Errors. BIOLOGY 2022; 11:biology11101501. [PMID: 36290405 PMCID: PMC9598671 DOI: 10.3390/biology11101501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 10/07/2022] [Accepted: 10/09/2022] [Indexed: 11/06/2022]
Abstract
In this study, we hypothesized that top-down sensory prediction error due to peripheral hearing loss might influence sensorimotor integration using the efference copy (EC) signals as functional connections between auditory and motor brain areas. Using neurophysiological methods, we demonstrated that the auditory responses to self-generated sound were not suppressed in a group of patients with tinnitus accompanied by significant hearing impairment and in a schizophrenia group. However, the response was attenuated in a group with tinnitus accompanied by mild hearing impairment, similar to a healthy control group. The bias of attentional networks to self-generated sound was also observed in the subjects with tinnitus with significant hearing impairment compared to those with mild hearing impairment and healthy subjects, but it did not reach the notable disintegration found in those in the schizophrenia group. Even though the present study had significant constraints in that we did not include hearing loss subjects without tinnitus, these results might suggest that auditory deafferentation (hearing loss) may influence sensorimotor integration process using EC signals. However, the impaired sensorimotor integration in subjects with tinnitus with significant hearing impairment may have resulted from aberrant auditory signals due to sensory loss, not fundamental deficits in the reafference system, as the auditory attention network to self-generated sound is relatively well preserved in these subjects.
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Affiliation(s)
- Min-Hee Ahn
- Laboratory of Brain & Cognitive Sciences for Convergence Medicine, Hallym University College of Medicine, Anyang 14068, Korea
| | - Nour Alsabbagh
- Laboratory of Brain & Cognitive Sciences for Convergence Medicine, Hallym University College of Medicine, Anyang 14068, Korea
| | - Hyo-Jeong Lee
- Laboratory of Brain & Cognitive Sciences for Convergence Medicine, Hallym University College of Medicine, Anyang 14068, Korea
- Department of Otorhinolaryngology-Head and Neck Surgery, Hallym University College of Medicine, Anyang 14068, Korea
| | - Hyung-Jong Kim
- Department of Otorhinolaryngology-Head and Neck Surgery, Hallym University College of Medicine, Anyang 14068, Korea
| | - Myung-Hun Jung
- Department of Psychiatry, Hallym University College of Medicine, Anyang 14068, Korea
- Correspondence: (M.-H.J.); (S.-K.H.); Tel.: +82-31-380-3849 (S.-K.H.)
| | - Sung-Kwang Hong
- Laboratory of Brain & Cognitive Sciences for Convergence Medicine, Hallym University College of Medicine, Anyang 14068, Korea
- Department of Otorhinolaryngology-Head and Neck Surgery, Hallym University College of Medicine, Anyang 14068, Korea
- Correspondence: (M.-H.J.); (S.-K.H.); Tel.: +82-31-380-3849 (S.-K.H.)
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7
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Soleimani B, Das P, Dushyanthi Karunathilake IM, Kuchinsky SE, Simon JZ, Babadi B. NLGC: Network Localized Granger Causality with Application to MEG Directional Functional Connectivity Analysis. Neuroimage 2022; 260:119496. [PMID: 35870697 PMCID: PMC9435442 DOI: 10.1016/j.neuroimage.2022.119496] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 06/21/2022] [Accepted: 07/19/2022] [Indexed: 11/25/2022] Open
Abstract
Identifying the directed connectivity that underlie networked activity between different cortical areas is critical for understanding the neural mechanisms behind sensory processing. Granger causality (GC) is widely used for this purpose in functional magnetic resonance imaging analysis, but there the temporal resolution is low, making it difficult to capture the millisecond-scale interactions underlying sensory processing. Magnetoencephalography (MEG) has millisecond resolution, but only provides low-dimensional sensor-level linear mixtures of neural sources, which makes GC inference challenging. Conventional methods proceed in two stages: First, cortical sources are estimated from MEG using a source localization technique, followed by GC inference among the estimated sources. However, the spatiotemporal biases in estimating sources propagate into the subsequent GC analysis stage, may result in both false alarms and missing true GC links. Here, we introduce the Network Localized Granger Causality (NLGC) inference paradigm, which models the source dynamics as latent sparse multivariate autoregressive processes and estimates their parameters directly from the MEG measurements, integrated with source localization, and employs the resulting parameter estimates to produce a precise statistical characterization of the detected GC links. We offer several theoretical and algorithmic innovations within NLGC and further examine its utility via comprehensive simulations and application to MEG data from an auditory task involving tone processing from both younger and older participants. Our simulation studies reveal that NLGC is markedly robust with respect to model mismatch, network size, and low signal-to-noise ratio, whereas the conventional two-stage methods result in high false alarms and mis-detections. We also demonstrate the advantages of NLGC in revealing the cortical network-level characterization of neural activity during tone processing and resting state by delineating task- and age-related connectivity changes.
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Affiliation(s)
- Behrad Soleimani
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA; Institute for Systems Research, University of Maryland, College Park, MD, USA.
| | - Proloy Das
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA.
| | - I M Dushyanthi Karunathilake
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA; Institute for Systems Research, University of Maryland, College Park, MD, USA.
| | - Stefanie E Kuchinsky
- Audiology and Speech Pathology Center, Walter Reed National Military Medical Center, Bethesda, MD, USA.
| | - Jonathan Z Simon
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA; Institute for Systems Research, University of Maryland, College Park, MD, USA; Department of Biology, University of Maryland College Park, MD, USA.
| | - Behtash Babadi
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA; Institute for Systems Research, University of Maryland, College Park, MD, USA.
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8
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Jiang H, Kokkinos V, Ye S, Urban A, Bagić A, Richardson M, He B. Interictal SEEG Resting-State Connectivity Localizes the Seizure Onset Zone and Predicts Seizure Outcome. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2200887. [PMID: 35545899 PMCID: PMC9218648 DOI: 10.1002/advs.202200887] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Indexed: 05/23/2023]
Abstract
Localization of epileptogenic zone currently requires prolonged intracranial recordings to capture seizure, which may take days to weeks. The authors developed a novel method to identify the seizure onset zone (SOZ) and predict seizure outcome using short-time resting-state stereotacticelectroencephalography (SEEG) data. In a cohort of 27 drug-resistant epilepsy patients, the authors estimated the information flow via directional connectivity and inferred the excitation-inhibition ratio from the 1/f power slope. They hypothesized that the antagonism of information flow at multiple frequencies between SOZ and non-SOZ underlying the relatively stable epilepsy resting state could be related to the disrupted excitation-inhibition balance. They found flatter 1/f power slope in non-SOZ regions compared to the SOZ, with dominant information flow from non-SOZ to SOZ regions. Greater differences in resting-state information flow between SOZ and non-SOZ regions are associated with favorable seizure outcome. By integrating a balanced random forest model with resting-state connectivity, their method localized the SOZ with an accuracy of 88% and predicted the seizure outcome with an accuracy of 92% using clinically determined SOZ. Overall, this study suggests that brief resting-state SEEG data can significantly facilitate the identification of SOZ and may eventually predict seizure outcomes without requiring long-term ictal recordings.
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Affiliation(s)
- Haiteng Jiang
- Department of Biomedical EngineeringCarnegie Mellon UniversityPittsburghPA15213USA
- Department of NeurobiologyAffiliated Mental Health Center & Hangzhou Seventh People's HospitalZhejiang University School of MedicineHangzhou310013P. R. China
- NHC and CAMS Key Laboratory of Medical NeurobiologyMOE Frontier Science Center for Brain Science and Brain‐machine IntegrationSchool of Brain Science and Brain MedicineZhejiang UniversityHangzhou310058P. R. China
| | - Vasileios Kokkinos
- University of Pittsburgh Comprehensive Epilepsy CenterDepartment of NeurologyUniversity of Pittsburgh School of MedicinePittsburghPA15232USA
- Massachusetts General HospitalBostonMA02114USA
| | - Shuai Ye
- Department of Biomedical EngineeringCarnegie Mellon UniversityPittsburghPA15213USA
| | - Alexandra Urban
- University of Pittsburgh Comprehensive Epilepsy CenterDepartment of NeurologyUniversity of Pittsburgh School of MedicinePittsburghPA15232USA
| | - Anto Bagić
- University of Pittsburgh Comprehensive Epilepsy CenterDepartment of NeurologyUniversity of Pittsburgh School of MedicinePittsburghPA15232USA
| | - Mark Richardson
- University of Pittsburgh Comprehensive Epilepsy CenterDepartment of NeurologyUniversity of Pittsburgh School of MedicinePittsburghPA15232USA
- Massachusetts General HospitalBostonMA02114USA
| | - Bin He
- Department of Biomedical EngineeringCarnegie Mellon UniversityPittsburghPA15213USA
- Neuroscience InstituteCarnegie Mellon UniversityPittsburghPA15213USA
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9
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Cao M, Vogrin SJ, Peterson ADH, Woods W, Cook MJ, Plummer C. Dynamical Network Models From EEG and MEG for Epilepsy Surgery—A Quantitative Approach. Front Neurol 2022; 13:837893. [PMID: 35422755 PMCID: PMC9001937 DOI: 10.3389/fneur.2022.837893] [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: 12/17/2021] [Accepted: 03/01/2022] [Indexed: 11/16/2022] Open
Abstract
There is an urgent need for more informative quantitative techniques that non-invasively and objectively assess strategies for epilepsy surgery. Invasive intracranial electroencephalography (iEEG) remains the clinical gold standard to investigate the nature of the epileptogenic zone (EZ) before surgical resection. However, there are major limitations of iEEG, such as the limited spatial sampling and the degree of subjectivity inherent in the analysis and clinical interpretation of iEEG data. Recent advances in network analysis and dynamical network modeling provide a novel aspect toward a more objective assessment of the EZ. The advantage of such approaches is that they are data-driven and require less or no human input. Multiple studies have demonstrated success using these approaches when applied to iEEG data in characterizing the EZ and predicting surgical outcomes. However, the limitations of iEEG recordings equally apply to these studies—limited spatial sampling and the implicit assumption that iEEG electrodes, whether strip, grid, depth or stereo EEG (sEEG) arrays, are placed in the correct location. Therefore, it is of interest to clinicians and scientists to see whether the same analysis and modeling techniques can be applied to whole-brain, non-invasive neuroimaging data (from MRI-based techniques) and neurophysiological data (from MEG and scalp EEG recordings), thus removing the limitation of spatial sampling, while safely and objectively characterizing the EZ. This review aims to summarize current state of the art non-invasive methods that inform epilepsy surgery using network analysis and dynamical network models. We also present perspectives on future directions and clinical applications of these promising approaches.
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Affiliation(s)
- Miao Cao
- Center for MRI Research, Peking University, Beijing, China
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
| | - Simon J. Vogrin
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
- School of Health Sciences, Swinburne University of Technology, Melbourne, VIC, Australia
| | - Andre D. H. Peterson
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
| | - William Woods
- School of Health Sciences, Swinburne University of Technology, Melbourne, VIC, Australia
| | - Mark J. Cook
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
| | - Chris Plummer
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
- School of Health Sciences, Swinburne University of Technology, Melbourne, VIC, Australia
- *Correspondence: Chris Plummer
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10
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Bosch-Bayard J, Razzaq FA, Lopez-Naranjo C, Wang Y, Li M, Galan-Garcia L, Calzada-Reyes A, Virues-Alba T, Rabinowitz AG, Suarez-Murias C, Guo Y, Sanchez-Castillo M, Rogers K, Gallagher A, Prichep L, Anderson SG, Michel CM, Evans AC, Bringas-Vega ML, Galler JR, Valdes-Sosa PA. Early protein energy malnutrition impacts life-long developmental trajectories of the sources of EEG rhythmic activity. Neuroimage 2022; 254:119144. [PMID: 35342003 DOI: 10.1016/j.neuroimage.2022.119144] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 03/20/2022] [Accepted: 03/23/2022] [Indexed: 02/07/2023] Open
Abstract
Protein Energy Malnutrition (PEM) has lifelong consequences on brain development and cognitive function. We studied the lifelong developmental trajectories of resting-state EEG source activity in 66 individuals with histories of Protein Energy Malnutrition (PEM) limited to the first year of life and in 83 matched classmate controls (CON) who are all participants of the 49 years longitudinal Barbados Nutrition Study (BNS). qEEGt source z-spectra measured deviation from normative values of EEG rhythmic activity sources at 5-11 years of age and 40 years later at 45-51 years of age. The PEM group showed qEEGt abnormalities in childhood, including a developmental delay in alpha rhythm maturation and an insufficient decrease in beta activity. These profiles may be correlated with accelerated cognitive decline.
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Affiliation(s)
- Jorge Bosch-Bayard
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; McGill Center for Integrative Neuroscience Center MCIN. Ludmer Center for Mental Health. Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Fuleah Abdul Razzaq
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.
| | - Carlos Lopez-Naranjo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Ying Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | | | | | | | - Arielle G Rabinowitz
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | | | - Yanbo Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Kassandra Rogers
- LION Lab, Sainte-Justine University Hospital Research Centre, University of Montreal, Montreal, QC, Canada
| | - Anne Gallagher
- LION Lab, Sainte-Justine University Hospital Research Centre, University of Montreal, Montreal, QC, Canada
| | | | - Simon G Anderson
- Caribbean Institute for Health Research, University of the West Indies, Barbados
| | | | - Alan C Evans
- McGill Center for Integrative Neuroscience Center MCIN. Ludmer Center for Mental Health. Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Maria L Bringas-Vega
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; Cuban Neuroscience Center, La Habana, Cuba
| | - Janina R Galler
- Division of Pediatric Gastroenterology and Nutrition, Mucosal Immunology and Biology Research Center, Mass General Hospital for Children, Boston, MA, USA
| | - Pedro A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; McGill Center for Integrative Neuroscience Center MCIN. Ludmer Center for Mental Health. Montreal Neurological Institute, McGill University, Montreal, Canada; Cuban Neuroscience Center, La Habana, Cuba.
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11
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Cao M, Galvis D, Vogrin SJ, Woods WP, Vogrin S, Wang F, Woldman W, Terry JR, Peterson A, Plummer C, Cook MJ. Virtual intracranial EEG signals reconstructed from MEG with potential for epilepsy surgery. Nat Commun 2022; 13:994. [PMID: 35194035 PMCID: PMC8863890 DOI: 10.1038/s41467-022-28640-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 01/28/2022] [Indexed: 12/17/2022] Open
Abstract
Modelling the interactions that arise from neural dynamics in seizure genesis is challenging but important in the effort to improve the success of epilepsy surgery. Dynamical network models developed from physiological evidence offer insights into rapidly evolving brain networks in the epileptic seizure. A limitation of previous studies in this field is the dependence on invasive cortical recordings with constrained spatial sampling of brain regions that might be involved in seizure dynamics. Here, we propose virtual intracranial electroencephalography (ViEEG), which combines non-invasive ictal magnetoencephalographic imaging (MEG), dynamical network models and a virtual resection technique. In this proof-of-concept study, we show that ViEEG signals reconstructed from MEG alone preserve critical temporospatial characteristics for dynamical approaches to identify brain areas involved in seizure generation. We show the non-invasive ViEEG approach may have some advantage over intracranial electroencephalography (iEEG). Future work may be designed to test the potential of the virtual iEEG approach for use in surgical management of epilepsy.
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Affiliation(s)
- Miao Cao
- Department of Medicine St Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, Australia
| | - Daniel Galvis
- Translational Research Exchange at Exeter, University of Exeter, Exeter, UK.,Living Systems Institute, University of Exeter, Exeter, UK.,Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, UK.,Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - Simon J Vogrin
- Department of Medicine St Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, Australia.,Faculty of Health, Art and Design, Swinburne University of Technology, Melbourne, Australia
| | - William P Woods
- Faculty of Health, Art and Design, Swinburne University of Technology, Melbourne, Australia
| | - Sara Vogrin
- Department of Medicine St Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Department of Medicine Western Health, The University of Melbourne, Melbourne, Australia
| | - Fan Wang
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.,CAS Centre for Excellence in Brain Science and Intelligence Technology, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Wessel Woldman
- Translational Research Exchange at Exeter, University of Exeter, Exeter, UK.,Living Systems Institute, University of Exeter, Exeter, UK.,Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, UK.,Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - John R Terry
- Translational Research Exchange at Exeter, University of Exeter, Exeter, UK.,Living Systems Institute, University of Exeter, Exeter, UK.,Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, UK.,Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - Andre Peterson
- Department of Medicine St Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, Australia.,Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia
| | - Chris Plummer
- Department of Medicine St Vincent's Hospital, The University of Melbourne, Melbourne, Australia. .,Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, Australia. .,Faculty of Health, Art and Design, Swinburne University of Technology, Melbourne, Australia.
| | - Mark J Cook
- Department of Medicine St Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, Australia.,Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia
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12
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Emotion discrimination using source connectivity analysis based on dynamic ROI identification. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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13
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Royer J, Bernhardt BC, Larivière S, Gleichgerrcht E, Vorderwülbecke BJ, Vulliémoz S, Bonilha L. Epilepsy and brain network hubs. Epilepsia 2022; 63:537-550. [DOI: 10.1111/epi.17171] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 01/03/2022] [Accepted: 01/10/2022] [Indexed: 02/06/2023]
Affiliation(s)
- Jessica Royer
- Multimodal Imaging and Connectome Analysis Laboratory Montreal Neurological Institute and Hospital McGill University Montreal Quebec Canada
| | - Boris C. Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory Montreal Neurological Institute and Hospital McGill University Montreal Quebec Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory Montreal Neurological Institute and Hospital McGill University Montreal Quebec Canada
| | - Ezequiel Gleichgerrcht
- Department of Neurology Medical University of South Carolina Charleston South Carolina USA
| | - Bernd J. Vorderwülbecke
- EEG and Epilepsy Unit University Hospitals and Faculty of Medicine Geneva Geneva Switzerland
- Department of Neurology Epilepsy Center Berlin‐Brandenburg Charité–Universitätsmedizin Berlin Berlin Germany
| | - Serge Vulliémoz
- EEG and Epilepsy Unit University Hospitals and Faculty of Medicine Geneva Geneva Switzerland
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14
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Bao X, Qi C, Liu T, Zheng X. Information transmission in mPFC-BLA network during exploratory behavior in the open field. Behav Brain Res 2021; 414:113483. [PMID: 34302874 DOI: 10.1016/j.bbr.2021.113483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 07/18/2021] [Accepted: 07/19/2021] [Indexed: 12/30/2022]
Abstract
Exploratory behavior plays a fundamental role in motivation, learning, and well-being of organisms. The open field test (OFT) is a classic method to investigate the exploratory behavior in rodents, also a widely adopted and pharmacologically validated procedure for evaluating anxiety and depression. Several lines of evidence have shown that medial prefrontal cortex (mPFC) and basolateral amygdala (BLA) play crucial roles in anxiety-like or depression-like exploratory behavior. However, the dynamic characterization of the mPFC-BLA network in exploratory behavior is less well understood. Therefore, this study aimed to investigate the information transmission mechanism in the mPFC-BLA network during exploratory behavior. Local field potentials (LFPs) from mPFC and BLA were simultaneously recorded while the rats performed the OFT. Directed transfer function (DTF), which was derived from Granger causal connectivity analysis, was applied to measure the functional connectivity among LFPs. Information flow (IF) was calculated to explore the dynamics of information transmission in the mPFC-BLA network. Our results revealed that, for both mPFC and BLA, the theta-band functional connectivity in periphery was significantly higher than that in center of the open field. The IF from BLA to mPFC in the open field task was significantly higher than that from mPFC to BLA. These results suggest that the functional connectivity and IF in the mPFC-BLA network are related to the exploratory behavior, and information transmission from BLA to mPFC could be predominant for exploratory behavior.
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Affiliation(s)
- Xuehui Bao
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, 300070, China
| | - Chengxi Qi
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, 300070, China
| | - Tiaotiao Liu
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, 300070, China
| | - Xuyuan Zheng
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, 300070, China.
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15
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Wang D, Liu Z, Tao Y, Chen W, Chen B, Wang Q, Yan X, Wang G. Improvement in EEG Source Imaging Accuracy by Means of Wavelet Packet Transform and Subspace Component Selection. IEEE Trans Neural Syst Rehabil Eng 2021; 29:650-661. [PMID: 33687844 DOI: 10.1109/tnsre.2021.3064665] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The electroencephalograph (EEG) source imaging (ESI) method is a non-invasive method that provides high temporal resolution imaging of brain electrical activity on the cortex. However, because the accuracy of EEG source imaging is often affected by unwanted signals such as noise or other source-irrelevant signals, the results of ESI are often incongruous with the real sources of brain activities. This study presents a novel ESI method (WPESI) that is based on wavelet packet transform (WPT) and subspace component selection to image the cerebral activities of EEG signals on the cortex. First, the original EEG signals are decomposed into several subspace components by WPT. Second, the subspaces associated with brain sources are selected and the relevant signals are reconstructed by WPT. Finally, the current density distribution in the cerebral cortex is obtained by establishing a boundary element model (BEM) from head MRI and applying the appropriate inverse calculation. In this study, the localization results obtained by this proposed approach were better than those of the original sLORETA approach (OESI) in the computer simulations and visual evoked potential (VEP) experiments. For epilepsy patients, the activity sources estimated by this proposed algorithm conformed to the seizure onset zones. The WPESI approach is easy to implement achieved favorable accuracy in terms of EEG source imaging. This demonstrates the potential for use of the WPESI algorithm to localize epileptogenic foci from scalp EEG signals.
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16
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Ahn MH, Park JH, Jeon H, Lee HJ, Kim HJ, Hong SK. Temporal Dynamics of Visually Induced Motion Perception and Neural Evidence of Alterations in the Motion Perception Process in an Immersive Virtual Reality Environment. Front Neurosci 2020; 14:600839. [PMID: 33328873 PMCID: PMC7710904 DOI: 10.3389/fnins.2020.600839] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 10/29/2020] [Indexed: 01/10/2023] Open
Abstract
Even though reciprocal inhibitory vestibular interactions following visual stimulation have been understood as sensory-reweighting mechanisms to stabilize motion perception; this hypothesis has not been thoroughly investigated with temporal dynamic measurements. Recently, virtual reality technology has been implemented in different medical domains. However, exposure in virtual reality environments can cause discomfort, including nausea or headache, due to visual-vestibular conflicts. We speculated that self-motion perception could be altered by accelerative visual motion stimulation in the virtual reality situation because of the absence of vestibular signals (visual-vestibular sensory conflict), which could result in the sickness. The current study investigated spatio-temporal profiles for motion perception using immersive virtual reality. We demonstrated alterations in neural dynamics under the sensory mismatch condition (accelerative visual motion stimulation) and in participants with high levels of sickness after driving simulation. Additionally, an event-related potentials study revealed that the high-sickness group presented with higher P3 amplitudes in sensory mismatch conditions, suggesting that it would be a substantial demand of cognitive resources for motion perception on sensory mismatch conditions.
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Affiliation(s)
- Min-Hee Ahn
- Department of Otorhinolaryngology-Head and Neck Surgery, Hallym University College of Medicine, Anyang, South Korea.,Laboratory of Brain & Cognitive Sciences for Convergence Medicine, Hallym University College of Medicine, Anyang, South Korea
| | - Jeong Hye Park
- Department of Otorhinolaryngology-Head and Neck Surgery, Hallym University College of Medicine, Anyang, South Korea
| | - Hanjae Jeon
- Laboratory of Brain & Cognitive Sciences for Convergence Medicine, Hallym University College of Medicine, Anyang, South Korea
| | - Hyo-Jeong Lee
- Department of Otorhinolaryngology-Head and Neck Surgery, Hallym University College of Medicine, Anyang, South Korea.,Laboratory of Brain & Cognitive Sciences for Convergence Medicine, Hallym University College of Medicine, Anyang, South Korea
| | - Hyung-Jong Kim
- Department of Otorhinolaryngology-Head and Neck Surgery, Hallym University College of Medicine, Anyang, South Korea
| | - Sung Kwang Hong
- Department of Otorhinolaryngology-Head and Neck Surgery, Hallym University College of Medicine, Anyang, South Korea.,Laboratory of Brain & Cognitive Sciences for Convergence Medicine, Hallym University College of Medicine, Anyang, South Korea
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17
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Yu K, Niu X, He B. Neuromodulation Management of Chronic Neuropathic Pain in The Central Nervous system. ADVANCED FUNCTIONAL MATERIALS 2020; 30:1908999. [PMID: 34335132 PMCID: PMC8323399 DOI: 10.1002/adfm.201908999] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Indexed: 05/05/2023]
Abstract
Neuromodulation is becoming one of the clinical tools for treating chronic neuropathic pain by transmitting controlled physical energy to the pre-identified neural targets in the central nervous system. Its nature of drug-free, non-addictive and improved targeting have attracted increasing attention among neuroscience research and clinical practices. This article provides a brief overview of the neuropathic pain and pharmacological routines for treatment, summarizes both the invasive and non-invasive neuromodulation modalities for pain management, and highlights an emerging brain stimulation technology, transcranial focused ultrasound (tFUS) with a focus on ultrasound transducer devices and the achieved neuromodulation effects and applications on pain management. Practical considerations of spatial guidance for tFUS are discussed for clinical applications. The safety of transcranial ultrasound neuromodulation and its future prospectives on pain management are also discussed.
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Affiliation(s)
| | | | - Bin He
- Department of Biomedical Engineering, Carnegie Mellon University
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18
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van Mierlo P, Vorderwülbecke BJ, Staljanssens W, Seeck M, Vulliémoz S. Ictal EEG source localization in focal epilepsy: Review and future perspectives. Clin Neurophysiol 2020; 131:2600-2616. [PMID: 32927216 DOI: 10.1016/j.clinph.2020.08.001] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 07/12/2020] [Accepted: 08/04/2020] [Indexed: 11/25/2022]
Abstract
Electroencephalographic (EEG) source imaging localizes the generators of neural activity in the brain. During presurgical epilepsy evaluation, EEG source imaging of interictal epileptiform discharges is an established tool to estimate the irritative zone. However, the origin of interictal activity can be partly or fully discordant with the origin of seizures. Therefore, source imaging based on ictal EEG data to determine the seizure onset zone can provide precious clinical information. In this descriptive review, we address the importance of localizing the seizure onset zone based on noninvasive EEG recordings as a complementary analysis that might reduce the burden of the presurgical evaluation. We identify three major challenges (low signal-to-noise ratio of the ictal EEG data, spread of ictal activity in the brain, and validation of the developed methods) and discuss practical solutions. We provide an extensive overview of the existing clinical studies to illustrate the potential clinical utility of EEG-based localization of the seizure onset zone. Finally, we conclude with future perspectives and the needs for translating ictal EEG source imaging into clinical practice.
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Affiliation(s)
- Pieter van Mierlo
- Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University, Corneel Heymanslaan 10, 9000 Ghent, Belgium.
| | - Bernd J Vorderwülbecke
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland; Department of Neurology, Epilepsy-Center Berlin-Brandenburg, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany.
| | - Willeke Staljanssens
- Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University, Corneel Heymanslaan 10, 9000 Ghent, Belgium
| | - Margitta Seeck
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland.
| | - Serge Vulliémoz
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland.
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19
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Vespa S, Baroumand AG, Ferrao Santos S, Vrielynck P, de Tourtchaninoff M, Feys O, Strobbe G, Raftopoulos C, van Mierlo P, El Tahry R. Ictal EEG source imaging and connectivity to localize the seizure onset zone in extratemporal lobe epilepsy. Seizure 2020; 78:18-30. [DOI: 10.1016/j.seizure.2020.03.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 02/27/2020] [Accepted: 03/01/2020] [Indexed: 12/16/2022] Open
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20
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Zhang Y, Liu B, Gao X. Spatiotemporal dynamics of working memory under the influence of emotions based on EEG. J Neural Eng 2020; 17:026039. [PMID: 32163933 DOI: 10.1088/1741-2552/ab7f50] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Previous studies have reported that working memory (WM) may be affected by emotions and that the effect may exist in different stages of WM. However, at present it remains controversial whether emotions inhibit or facilitate WM, and how the mechanism of dynamic information transmission in the brain during WM is affected by emotions. APPROACH In this study, we used a video database to induce three emotions (negative, neutral, and positive) and adopted a change detection paradigm based on electroencephalography. Event-related potential (ERP) analysis, event-related spectral perturbation analysis, source location analysis based on the dipole localization method and the distributed source localization method, and effective connectivity analysis were performed. MAIN RESULTS Both behavioral and ERP results suggest that positive emotions have no significant effect on WM capacity, while negative emotions could facilitate WM capacity. Furthermore, the effective connectivity results based on two source location methods suggest that the long-range connectivity between the frontal and posterior areas can reflect the influence of positive and negative emotions on the WM network, in which the connectivity under the positive emotion condition occurs in the earlier period of WM maintenance, while the connectivity under the negative emotion condition occurs in the later period of WM maintenance. SIGNIFICANCE The consistency of the behavioral, ERP, and effective connectivity results suggests that under the negative emotion condition, the top-down attention modulation between the frontoparietal area and posterior area could promote the most relevant information storage during WM maintenance.
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Affiliation(s)
- Yuanyuan Zhang
- College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin 300350, People's Republic of China
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21
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Noninvasive electromagnetic source imaging of spatiotemporally distributed epileptogenic brain sources. Nat Commun 2020; 11:1946. [PMID: 32327635 PMCID: PMC7181775 DOI: 10.1038/s41467-020-15781-0] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 03/27/2020] [Indexed: 12/17/2022] Open
Abstract
Brain networks are spatiotemporal phenomena that dynamically vary over time. Functional imaging approaches strive to noninvasively estimate these underlying processes. Here, we propose a novel source imaging approach that uses high-density EEG recordings to map brain networks. This approach objectively addresses the long-standing limitations of conventional source imaging techniques, namely, difficulty in objectively estimating the spatial extent, as well as the temporal evolution of underlying brain sources. We validate our approach by directly comparing source imaging results with the intracranial EEG (iEEG) findings and surgical resection outcomes in a cohort of 36 patients with focal epilepsy. To this end, we analyzed a total of 1,027 spikes and 86 seizures. We demonstrate the capability of our approach in imaging both the location and spatial extent of brain networks from noninvasive electrophysiological measurements, specifically for ictal and interictal brain networks. Our approach is a powerful tool for noninvasively investigating large-scale dynamic brain networks. Noninvasive electromagnetic measurements are utilized effectively to estimate large scale dynamic brain networks. Sohrabpour et al. propose a novel electrophysiological source imaging approach to estimate the location and size of epileptogenic tissues in patients with epilepsy.
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22
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Ashourvan A, Pequito S, Khambhati AN, Mikhail F, Baldassano SN, Davis KA, Lucas TH, Vettel JM, Litt B, Pappas GJ, Bassett DS. Model-based design for seizure control by stimulation. J Neural Eng 2020; 17:026009. [PMID: 32103826 PMCID: PMC8341467 DOI: 10.1088/1741-2552/ab7a4e] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
OBJECTIVE Current brain stimulation paradigms are largely empirical rather than theoretical. An opportunity exists to improve upon their modest effectiveness in closed-loop control strategies with the development of theoretically grounded, model-based designs. APPROACH Inspired by this need, here we couple experimental data and mathematical modeling with a control-theoretic strategy for seizure termination. We begin by exercising a dynamical systems approach to model seizures (n = 94) recorded using intracranial EEG (iEEG) from 21 patients with medication-resistant, localization-related epilepsy. MAIN RESULTS Although each patient's seizures displayed unique spatial and temporal patterns, their evolution can be parsimoniously characterized by the same model form. Idiosyncracies of the model can inform individualized intervention strategies, specifically in iEEG samples with well-localized seizure onset zones. Temporal fluctuations in the spatial profiles of the oscillatory modes show that seizure onset marks a transition into a regime in which the underlying system supports prolonged rhythmic and focal activity. Based on these observations, we propose a control-theoretic strategy that aims to stabilize ictal activity using static output feedback for linear time-invariant switching systems. Finally, we demonstrate in silico that our proposed strategy allows us to dampen the emerging focal oscillatory sources using only a small set of electrodes. SIGNIFICANCE Our integrative study informs the development of modulation and control algorithms for neurostimulation that could improve the effectiveness of implantable, closed-loop anti-epileptic devices.
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Affiliation(s)
- Arian Ashourvan
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, United States of America. U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005, United States of America
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23
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Habib MA, Ibrahim F, Mohktar MS, Kamaruzzaman SB, Lim KS. Recursive independent component analysis (ICA)-decomposition of ictal EEG to select the best ictal component for EEG source imaging. Clin Neurophysiol 2020; 131:642-654. [DOI: 10.1016/j.clinph.2019.11.058] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Revised: 11/25/2019] [Accepted: 11/30/2019] [Indexed: 11/28/2022]
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24
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Jiang H, Cai Z, Worrell GA, He B. Multiple Oscillatory Push-Pull Antagonisms Constrain Seizure Propagation. Ann Neurol 2019; 86:683-694. [PMID: 31566799 PMCID: PMC6856814 DOI: 10.1002/ana.25583] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 08/06/2019] [Accepted: 08/18/2019] [Indexed: 02/03/2023]
Abstract
Objective Drug‐resistant focal epilepsy is widely recognized as a network disease in which epileptic seizure propagation is likely coordinated by different neuronal oscillations such as low‐frequency activity (LFA), high‐frequency activity (HFA), or low‐to‐high cross‐frequency coupling. However, the mechanism by which different oscillatory networks constrain the propagation of focal seizures remains unclear. Methods We studied focal epilepsy patients with invasive electrocorticography (ECoG) recordings and compared multilayer directional network interactions between focal seizures either with or without secondary generalization. Within‐frequency and cross‐frequency directional connectivity were estimated by an adaptive directed transfer function and cross‐frequency directionality, respectively. Results In the within‐frequency epileptic network, we found that the seizure onset zone (SOZ) always sent stronger information flow to the surrounding regions, and secondary generalization was accompanied by weaker information flow in the LFA from the surrounding regions to SOZ. In the cross‐frequency epileptic network, secondary generalization was associated with either decreased information flow from surrounding regions’ HFA to SOZ's LFA or increased information flow from SOZ's LFA to surrounding regions’ HFA. Interpretation Our results suggest that the secondary generalization of focal seizures is regulated by numerous within‐ and cross‐frequency push–pull dynamics, potentially reflecting impaired excitation–inhibition interactions of the epileptic network. ANN NEUROL 2019;86:683–694
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Affiliation(s)
- Haiteng Jiang
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA
| | - Zhengxiang Cai
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA
| | | | - Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA
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25
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van Mierlo P, Höller Y, Focke NK, Vulliemoz S. Network Perspectives on Epilepsy Using EEG/MEG Source Connectivity. Front Neurol 2019; 10:721. [PMID: 31379703 PMCID: PMC6651209 DOI: 10.3389/fneur.2019.00721] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 06/18/2019] [Indexed: 12/17/2022] Open
Abstract
The evolution of EEG/MEG source connectivity is both, a promising, and controversial advance in the characterization of epileptic brain activity. In this narrative review we elucidate the potential of this technology to provide an intuitive view of the epileptic network at its origin, the different brain regions involved in the epilepsy, without the limitation of electrodes at the scalp level. Several studies have confirmed the added value of using source connectivity to localize the seizure onset zone and irritative zone or to quantify the propagation of epileptic activity over time. It has been shown in pilot studies that source connectivity has the potential to obtain prognostic correlates, to assist in the diagnosis of the epilepsy type even in the absence of visually noticeable epileptic activity in the EEG/MEG, and to predict treatment outcome. Nevertheless, prospective validation studies in large and heterogeneous patient cohorts are still lacking and are needed to bring these techniques into clinical use. Moreover, the methodological approach is challenging, with several poorly examined parameters that most likely impact the resulting network patterns. These fundamental challenges affect all potential applications of EEG/MEG source connectivity analysis, be it in a resting, spiking, or ictal state, and also its application to cognitive activation of the eloquent area in presurgical evaluation. However, such method can allow unique insights into physiological and pathological brain functions and have great potential in (clinical) neuroscience.
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Affiliation(s)
- Pieter van Mierlo
- Medical Image and Signal Processing Group, Ghent University, Ghent, Belgium
| | - Yvonne Höller
- Faculty of Psychology, University of Akureyri, Akureyri, Iceland
| | - Niels K Focke
- Clinical Neurophysiology, University Medicine Göttingen, Göttingen, Germany
| | - Serge Vulliemoz
- EEG and Epilepsy Unit, University Hospital of Geneva, Geneva, Switzerland
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26
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He B, Astolfi L, Valdés-Sosa PA, Marinazzo D, Palva SO, Bénar CG, Michel CM, Koenig T. Electrophysiological Brain Connectivity: Theory and Implementation. IEEE Trans Biomed Eng 2019; 66:10.1109/TBME.2019.2913928. [PMID: 31071012 PMCID: PMC6834897 DOI: 10.1109/tbme.2019.2913928] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
We review the theory and algorithms of electrophysiological brain connectivity analysis. This tutorial is aimed at providing an introduction to brain functional connectivity from electrophysiological signals, including electroencephalography (EEG), magnetoencephalography (MEG), electrocorticography (ECoG), stereoelectroencephalography (SEEG). Various connectivity estimators are discussed, and algorithms introduced. Important issues for estimating and mapping brain functional connectivity with electrophysiology are discussed.
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Affiliation(s)
- Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, USA
| | - Laura Astolfi
- Department of Computer, Control and Management Engineering, University of Rome Sapienza, and with IRCCS Fondazione Santa Lucia, Rome, Italy
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27
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Michel CM, Brunet D. EEG Source Imaging: A Practical Review of the Analysis Steps. Front Neurol 2019; 10:325. [PMID: 31019487 PMCID: PMC6458265 DOI: 10.3389/fneur.2019.00325] [Citation(s) in RCA: 273] [Impact Index Per Article: 54.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2019] [Accepted: 03/15/2019] [Indexed: 11/13/2022] Open
Abstract
The electroencephalogram (EEG) is one of the oldest technologies to measure neuronal activity of the human brain. It has its undisputed value in clinical diagnosis, particularly (but not exclusively) in the identification of epilepsy and sleep disorders and in the evaluation of dysfunctions in sensory transmission pathways. With the advancement of digital technologies, the analysis of EEG has moved from pure visual inspection of amplitude and frequency modulations over time to a comprehensive exploration of the temporal and spatial characteristics of the recorded signals. Today, EEG is accepted as a powerful tool to capture brain function with the unique advantage of measuring neuronal processes in the time frame in which these processes occur, namely in the sub-second range. However, it is generally stated that EEG suffers from a poor spatial resolution that makes it difficult to infer to the location of the brain areas generating the neuronal activity measured on the scalp. This statement has challenged a whole community of biomedical engineers to offer solutions to localize more precisely and more reliably the generators of the EEG activity. High-density EEG systems combined with precise information of the head anatomy and sophisticated source localization algorithms now exist that convert the EEG to a true neuroimaging modality. With these tools in hand and with the fact that EEG still remains versatile, inexpensive and portable, electrical neuroimaging has become a widely used technology to study the functions of the pathological and healthy human brain. However, several steps are needed to pass from the recording of the EEG to 3-dimensional images of neuronal activity. This review explains these different steps and illustrates them in a comprehensive analysis pipeline integrated in a stand-alone freely available academic software: Cartool. The information about how the different steps are performed in Cartool is only meant as a suggestion. Other EEG source imaging software may apply similar or different approaches to the different steps.
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Affiliation(s)
- Christoph M. Michel
- Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland
- Center for Biomedical Imaging Lausanne-Geneva (CIBM), Geneva, Switzerland
| | - Denis Brunet
- Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland
- Center for Biomedical Imaging Lausanne-Geneva (CIBM), Geneva, Switzerland
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Kouti M, Ansari-Asl K, Namjoo E. Epileptic source connectivity analysis based on estimating of dynamic time series of regions of interest. NETWORK (BRISTOL, ENGLAND) 2019; 30:1-30. [PMID: 31240983 DOI: 10.1080/0954898x.2019.1634290] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 04/30/2019] [Accepted: 06/17/2019] [Indexed: 06/09/2023]
Abstract
We propose a new source connectivity method by focusing on estimating time courses of the regions of interest (ROIs). To this aim, it is necessary to consider the strong inherent non-stationary behavior of neural activity. We develop an iterative dynamic approach to extract a single time course for each ROI encoding the temporal non-stationary features. The proposed approach explicitly includes dynamic constraints by taking into account the evolution of the sources activities for further dynamic connectivity analysis. We simulated an epileptic network with a non-stationary structure; accordingly, EEG source reconstruction using LORETA is performed. Using the reconstructed sources, the spatially compact ROIs are selected. Then, a single time course encoding the temporal non-stationarity is extracted for each ROI. An adaptive directed transfer function (ADTF) is applied to measure the information flow of underlying brain networks. Obtained results demonstrate that the contributed approach is more efficient to estimate the ROI time series and ROI to ROI information flow in comparison with existing methods. Our work is validated in three drug-resistance epilepsy patients. The proposed ROI time series estimation directly affects the quality of connectivity analysis, leading to the best possible seizure onset zone (SOZ) localization verified by electrocorticography and post-operational results.
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Affiliation(s)
- Mayadeh Kouti
- Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
| | - Karim Ansari-Asl
- Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
| | - Ehsan Namjoo
- Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
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Jafadideh AT, Asl BM. Modified Dominant Mode Rejection Beamformer for Localizing Brain Activities When Data Covariance Matrix Is Rank Deficient. IEEE Trans Biomed Eng 2018; 66:2241-2252. [PMID: 30561337 DOI: 10.1109/tbme.2018.2886251] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Minimum variance beamformer (MVB) and its extensions fail in localizing short time brain activities particularly evoked potentials because of rank deficiency or inaccurate estimation of a data covariance matrix. In this paper, the conventional dominant mode rejection (DMR) adaptive beamformer is modified to localize brain short time activities. METHODS In the modified DMR, it is attempted to obtain a well-conditioned covariance matrix by dividing the eigenvalues of the data covariance matrix into dominant, medium, and small eigenvalues and then modifying medium and small parts. The performance of the proposed approach is compared with diagonal loading MVB (DL_MVB) and fast fully adaptive (FFA) beamformer by using simulated event-related potentials and real event-related field data. Eigenspace versions of DL_MVB and modified DMR are also implemented. RESULTS In all simulations, the modified DMR obtains the least localization error (0-5 mm) and spread radius (0-8 mm) when the signal-to-noise ratio (SNR) varies from 0 to 10 dB with step 1 dB. In real data, the new approach in comparison to two other ones attains the most concentrated power spectrum. Eigenspace projection of DL_MVB presents better results than DL_MVB but worse results than the modified DMR. Applying eigenspace projection on the proposed method improves its performance at high SNR levels. CONCLUSION Empirical results illustrate the superiority of the proposed DMR method to the DL_MVB and FFA in localizing brain short time activities. SIGNIFICANCE The proposed method can be utilized in source localization of epilepsy for presurgical clinical evaluation purpose and also in applications dealing with the localization of evoked potentials and fields.
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Pagnotta MF, Dhamala M, Plomp G. Benchmarking nonparametric Granger causality: Robustness against downsampling and influence of spectral decomposition parameters. Neuroimage 2018; 183:478-494. [DOI: 10.1016/j.neuroimage.2018.07.046] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Revised: 07/14/2018] [Accepted: 07/18/2018] [Indexed: 12/19/2022] Open
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Stamoulis C, Connolly J, Axeen E, Kaulas H, Bolton J, Dorfman K, Halford JJ, Duffy FH, Treves ST, Pearl PL. Non-invasive Seizure Localization with Ictal Single-Photon Emission Computed Tomography is Impacted by Preictal/Early Ictal Network Dynamics. IEEE Trans Biomed Eng 2018; 66:1863-1871. [PMID: 30418877 DOI: 10.1109/tbme.2018.2880575] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
OBJECTIVE More than one third of children with epilepsy have medically intractable seizures. Promising therapies, including targeted neurostimulation and surgery, depend on accurate localization of the epileptogenic zone. Ictal perfusion Single-Photon Emission Computed Tomography (SPECT) can localize the seizure focus noninvasively, with comparable accuracy to that of invasive EEG. However, multiple factors including seizure dynamics may affect its spatial specificity. METHODS Using subtracted ictal from interictal SPECT and scalp EEG from 118 pediatric epilepsy patients (40 of whom had surgery after the SPECT studies), information theoretic measures of association and advanced statistical models, this study investigated the impact of preictal and ictal brain network dynamics on SPECT focality. RESULTS Network dynamics significantly impacted the SPECT localization ~30 s before to ~45 s following ictal onset. Distributed early ictal connectivity changes, indicative of a rapidly evolving seizure, were negatively associated with SPECT focality. Spatially localized connectivity changes later in the seizure, indicating slower seizure propagation, were positively associated with SPECT focality. In the first ~60 s of the seizure, significantly higher network connectivity was estimated in an area overlapping with the area of hyperperfusion. Finally, ~75% of patients with Engel class 1a/1b outcomes had SPECTs that were concordant with the resected area. CONCLUSION Slowly evolving seizures are more likely to be accurately imaged with SPECT, and the identified focus may overlap with brain regions where significant topological changes occur. SIGNIFICANCE Measures of preictal/early ictal network dynamics may help optimize the SPECT localization, leading to improved surgical and neurostimulation outcomes in refractory epilepsy.
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Wang X, Yao L, Zhao Y, Xing L, Qian Z, Li W, Yang Y. Effects of disparity on visual discomfort caused by short-term stereoscopic viewing based on electroencephalograph analysis. Biomed Eng Online 2018; 17:166. [PMID: 30390658 PMCID: PMC6215628 DOI: 10.1186/s12938-018-0595-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2018] [Accepted: 10/25/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Discomfort evoked by stereoscopic depth has been widely concerned. Previous studies have proposed a comfortable disparity range and considered that disparities exceed this range would cause visual discomfort. Brain activity recordings including Electroencephalograph (EEG) monitoring enable better understanding of perceptual and cognitive processes related to stereo depth-induced visual comfort. METHODS EEG data was collected using a stereo-visual evoked potential (VEP) test system by providing visual stimulus to subjects aged from 21 to 25 with normal stereoscopic vision. For each type of visual stimulus, data were processed using directed transfer function (DTF) and adaptive directed transfer function (ADTF) in combination with subjective feedbacks (comfort or discomfort). The topographies of information flow were constructed to compare responses stimulated by different stereoscopic depth, and to determine the difference in comfort and discomfort situations upon stimulation with same stereoscopic depth. RESULTS Based on EEG analysis results, we found that the occipital P270 was moderately related to the disparity. Moreover, the ADTF of P270 showed that the information flows at frontal lobe and central-parietal lobe changed when stimulation with different stereoscopic depth applied. As to the stereo images with same stereoscopic depth, the DTF outflows at the temporal and temporal-parietal lobes in δ band, central and central-parietal lobes in α and θ bands, and the comparison of inflows in these three bands could be considered as discriminated indexes for matching the stereoscopic effect with viewers' comfort or discomfort state impacted by disparity. The subjective feedbacks indicated that the comfort judgments remained as a result of cumulative effect. CONCLUSIONS This study proposed a short-term stereo-VEP experiment that shorted the duration of each stimulus in the experimental scheme to minimize the interference from other factors except the disparity. The occipital P270 had a mid-relevance to the disparity and its ADTF showed the affected areas when viewers are receiving stimulations with different disparities. DTF could be considered as discriminated indexes for matching the stereoscopic effect with viewers' comfort or discomfort state induced by disparity. This study proposed a preferable experiment to observe the single effect of disparity and provided an intuitive and easy-to-read result in a more convenient manner.
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Affiliation(s)
- Xiao Wang
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29 Jiangjun Avenue, Jiangning District, Nanjing, 211106, China
| | - Liuye Yao
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29 Jiangjun Avenue, Jiangning District, Nanjing, 211106, China
| | - Yuemei Zhao
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29 Jiangjun Avenue, Jiangning District, Nanjing, 211106, China
| | - Lidong Xing
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29 Jiangjun Avenue, Jiangning District, Nanjing, 211106, China
| | - Zhiyu Qian
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29 Jiangjun Avenue, Jiangning District, Nanjing, 211106, China.
| | - Weitao Li
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29 Jiangjun Avenue, Jiangning District, Nanjing, 211106, China
| | - Yamin Yang
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, No. 29 Jiangjun Avenue, Jiangning District, Nanjing, 211106, China
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Baroumand AG, van Mierlo P, Strobbe G, Pinborg LH, Fabricius M, Rubboli G, Leffers AM, Uldall P, Jespersen B, Brennum J, Henriksen OM, Beniczky S. Automated EEG source imaging: A retrospective, blinded clinical validation study. Clin Neurophysiol 2018; 129:2403-2410. [DOI: 10.1016/j.clinph.2018.09.015] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Revised: 08/21/2018] [Accepted: 09/15/2018] [Indexed: 11/16/2022]
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35
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Coito A, Michel CM, Vulliemoz S, Plomp G. Directed functional connections underlying spontaneous brain activity. Hum Brain Mapp 2018; 40:879-888. [PMID: 30367722 DOI: 10.1002/hbm.24418] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Revised: 09/13/2018] [Accepted: 10/02/2018] [Indexed: 11/06/2022] Open
Abstract
Neuroimaging studies have shown that spontaneous brain activity is characterized as changing networks of coherent activity across multiple brain areas. However, the directionality of functional interactions between the most active regions in our brain at rest remains poorly understood. Here, we examined, at the whole-brain scale, the main drivers and directionality of interactions that underlie spontaneous human brain activity by applying directed functional connectivity analysis to electroencephalography (EEG) source signals. We found that the main drivers of electrophysiological activity were the posterior cingulate cortex (PCC), the medial temporal lobes (MTL), and the anterior cingulate cortex (ACC). Among those regions, the PCC was the strongest driver and had both the highest integration and segregation importance, followed by the MTL regions. The driving role of the PCC and MTL resulted in an effective directed interaction directed from posterior toward anterior brain regions. Our results strongly suggest that the PCC and MTL structures are the main drivers of electrophysiological spontaneous activity throughout the brain and suggest that EEG-based directed functional connectivity analysis is a promising tool to better understand the dynamics of spontaneous brain activity in healthy subjects and in various brain disorders.
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Affiliation(s)
- Ana Coito
- Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland.,Epilepsy Unit, University Hospital of Geneva, Geneva, Switzerland
| | - Christoph M Michel
- Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Serge Vulliemoz
- Epilepsy Unit, University Hospital of Geneva, Geneva, Switzerland
| | - Gijs Plomp
- Perceptual Networks Group, Department of Psychology, University of Fribourg, Fribourg, Switzerland
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Abstract
Brain activity and connectivity are distributed in the three-dimensional space and evolve in time. It is important to image brain dynamics with high spatial and temporal resolution. Electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive measurements associated with complex neural activations and interactions that encode brain functions. Electrophysiological source imaging estimates the underlying brain electrical sources from EEG and MEG measurements. It offers increasingly improved spatial resolution and intrinsically high temporal resolution for imaging large-scale brain activity and connectivity on a wide range of timescales. Integration of electrophysiological source imaging and functional magnetic resonance imaging could further enhance spatiotemporal resolution and specificity to an extent that is not attainable with either technique alone. We review methodological developments in electrophysiological source imaging over the past three decades and envision its future advancement into a powerful functional neuroimaging technology for basic and clinical neuroscience applications.
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Affiliation(s)
- Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA;
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - Abbas Sohrabpour
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - Emery Brown
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Zhongming Liu
- Weldon School of Biomedical Engineering, School of Electrical and Computer Engineering, and Purdue Institute of Integrative Neuroscience, Purdue University, West Lafayette, Indiana 47906, USA
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37
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Spectral and phase-amplitude coupling signatures in human deep brain oscillations during propofol-induced anaesthesia. Br J Anaesth 2018; 121:303-313. [PMID: 29935585 DOI: 10.1016/j.bja.2018.04.031] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Revised: 03/19/2018] [Accepted: 04/30/2018] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Both the cerebral cortex and subcortical structures play important roles in consciousness. Some evidence points to general anaesthesia-induced unconsciousness being associated with distinct patterns of superficial cortical electrophysiological oscillations, but how general anaesthetics influence deep brain neural oscillations and interactions between oscillations in humans is poorly understood. METHODS Local field potentials were recorded in discrete deep brain regions, including anterior cingulate cortex, sensory thalamus, and periaqueductal grey, in humans with implanted deep brain electrodes during induction of unconsciousness with propofol. Power-frequency spectra, phase-amplitude coupling, coherence, and directed functional connectivity analysis were used to characterise local field potentials in the awake and unconscious states. RESULTS An increase in alpha (7-13 Hz) power and decrease in gamma (30-90 Hz) power were observed in both deep cortical (ACC, anterior cingulate cortex) and subcortical (sensory thalamus, periaqueductal grey) areas during propofol-induced unconsciousness. Robust alpha-low gamma (30-60 Hz) phase-amplitude coupling induced by general anaesthesia was observed in the anterior cingulate cortex but not in other regions studied. Moreover, alpha oscillations during unconsciousness were highly coherent within the anterior cingulate cortex, and this rhythm exhibited a bidirectional information flow between left and right anterior cingulate cortex but stronger left-to-right flow. CONCLUSION Propofol increases alpha oscillations and attenuates gamma oscillations in both cortical and subcortical areas. The alpha-gamma phase-amplitude coupling and the functional connectivity of alpha oscillations in the anterior cingulate cortex could be specific markers for loss of consciousness.
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van Mierlo P, Lie O, Staljanssens W, Coito A, Vulliémoz S. Influence of Time-Series Normalization, Number of Nodes, Connectivity and Graph Measure Selection on Seizure-Onset Zone Localization from Intracranial EEG. Brain Topogr 2018; 31:753-766. [PMID: 29700719 PMCID: PMC6097740 DOI: 10.1007/s10548-018-0646-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Accepted: 04/18/2018] [Indexed: 10/28/2022]
Abstract
We investigated the influence of processing steps in the estimation of multivariate directed functional connectivity during seizures recorded with intracranial EEG (iEEG) on seizure-onset zone (SOZ) localization. We studied the effect of (i) the number of nodes, (ii) time-series normalization, (iii) the choice of multivariate time-varying connectivity measure: Adaptive Directed Transfer Function (ADTF) or Adaptive Partial Directed Coherence (APDC) and (iv) graph theory measure: outdegree or shortest path length. First, simulations were performed to quantify the influence of the various processing steps on the accuracy to localize the SOZ. Afterwards, the SOZ was estimated from a 113-electrodes iEEG seizure recording and compared with the resection that rendered the patient seizure-free. The simulations revealed that ADTF is preferred over APDC to localize the SOZ from ictal iEEG recordings. Normalizing the time series before analysis resulted in an increase of 25-35% of correctly localized SOZ, while adding more nodes to the connectivity analysis led to a moderate decrease of 10%, when comparing 128 with 32 input nodes. The real-seizure connectivity estimates localized the SOZ inside the resection area using the ADTF coupled to outdegree or shortest path length. Our study showed that normalizing the time-series is an important pre-processing step, while adding nodes to the analysis did only marginally affect the SOZ localization. The study shows that directed multivariate Granger-based connectivity analysis is feasible with many input nodes (> 100) and that normalization of the time-series before connectivity analysis is preferred.
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Affiliation(s)
- Pieter van Mierlo
- Medical Image and Signal Processing Group, Ghent University, Ghent, Belgium. .,Functional Brain Mapping Lab, University of Geneva, Geneva, Switzerland. .,EEG and Epilepsy Unit, University Hospital of Geneva, Geneva, Switzerland.
| | - Octavian Lie
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | | | - Ana Coito
- Functional Brain Mapping Lab, University of Geneva, Geneva, Switzerland
| | - Serge Vulliémoz
- Functional Brain Mapping Lab, University of Geneva, Geneva, Switzerland.,EEG and Epilepsy Unit, University Hospital of Geneva, Geneva, Switzerland
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Koren J, Gritsch G, Pirker S, Herta J, Perko H, Kluge T, Baumgartner C. Automatic ictal onset source localization in presurgical epilepsy evaluation. Clin Neurophysiol 2018; 129:1291-1299. [PMID: 29680731 DOI: 10.1016/j.clinph.2018.03.020] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Revised: 03/07/2018] [Accepted: 03/24/2018] [Indexed: 10/17/2022]
Abstract
OBJECTIVE To test the diagnostic accuracy of a new automatic algorithm for ictal onset source localization (IOSL) during routine presurgical epilepsy evaluation following STARD (Standards for Reporting of Diagnostic Accuracy) criteria. METHODS We included 28 consecutive patients with refractory focal epilepsy (25 patients with temporal lobe epilepsy (TLE) and 3 with extratemporal epilepsy) who underwent resective epilepsy surgery. Ictal EEG patterns were analyzed with a novel automatic IOSL algorithm. IOSL source localizations on a sublobar level were validated by comparison with actual resection sites and seizure free outcome 2 years after surgery. RESULTS Sensitivity of IOSL was 92.3% (TLE: 92.3%); specificity 60% (TLE: 50%); positive predictive value 66.7% (TLE: 66.7%); and negative predictive value 90% (TLE: 85.7%). The likelihood ratio was more than ten times higher for concordant IOSL results as compared to discordant results (p = 0.013). CONCLUSIONS We demonstrated the clinical feasibility of our IOSL approach yielding reasonable high performance measures on a sublobar level. SIGNIFICANCE Our IOSL method may contribute to a correct localization of the seizure onset zone in temporal lobe epilepsy and can readily be used in standard epilepsy monitoring settings. Further studies are needed for validation in extratemporal epilepsy.
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Affiliation(s)
- Johannes Koren
- Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Neurological Department, General Hospital Hietzing with Neurological Center Rosenhügel, Vienna, Austria
| | - Gerhard Gritsch
- Austrian Institute of Technology GmbH (AIT), Safety & Security Department, Vienna, Austria
| | - Susanne Pirker
- Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Neurological Department, General Hospital Hietzing with Neurological Center Rosenhügel, Vienna, Austria
| | - Johannes Herta
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Hannes Perko
- Austrian Institute of Technology GmbH (AIT), Safety & Security Department, Vienna, Austria
| | - Tilmann Kluge
- Austrian Institute of Technology GmbH (AIT), Safety & Security Department, Vienna, Austria
| | - Christoph Baumgartner
- Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Neurological Department, General Hospital Hietzing with Neurological Center Rosenhügel, Vienna, Austria; Department of Epileptology and Clinical Neurophysiology, Sigmund Freud University, Vienna, Austria.
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40
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Sperdin HF, Coito A, Kojovic N, Rihs TA, Jan RK, Franchini M, Plomp G, Vulliemoz S, Eliez S, Michel CM, Schaer M. Early alterations of social brain networks in young children with autism. eLife 2018; 7:31670. [PMID: 29482718 PMCID: PMC5828667 DOI: 10.7554/elife.31670] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Accepted: 01/22/2018] [Indexed: 11/30/2022] Open
Abstract
Social impairments are a hallmark of Autism Spectrum Disorders (ASD), but empirical evidence for early brain network alterations in response to social stimuli is scant in ASD. We recorded the gaze patterns and brain activity of toddlers with ASD and their typically developing peers while they explored dynamic social scenes. Directed functional connectivity analyses based on electrical source imaging revealed frequency specific network atypicalities in the theta and alpha frequency bands, manifesting as alterations in both the driving and the connections from key nodes of the social brain associated with autism. Analyses of brain-behavioural relationships within the ASD group suggested that compensatory mechanisms from dorsomedial frontal, inferior temporal and insular cortical regions were associated with less atypical gaze patterns and lower clinical impairment. Our results provide strong evidence that directed functional connectivity alterations of social brain networks is a core component of atypical brain development at early stages of ASD. Newborns are attracted to voices, faces and social gestures. Paying attention to these social cues in everyday life helps infants and young children learn how to interact with others. During this period of development, a network of connections forms between different parts of the brain that helps children to understand other people’s social behaviors. During their first year of life, infants who later develop autism spectrum disorders (ASD) pay less attention to social cues. This early indifference to these important signals leads to social deficits in children with ASD. They are less able to understand other people’s behaviors or engage in typical social interactions. It’s not yet clear why children with ASD are less attuned to social cues. But is likely that the development of brain networks essential for understanding social behavior suffers as a result. Studying how such networks develop in typical very young children and those with ASD may help scientist learn more. Now, Sperdin et al. confirm there are differences in the social brain-networks of very young children with ASD compared with their typical peers. In the experiment, 3-year-old children with ASD and without watched videos of other children playing, while Sperdin et al. recorded what they looked at and what happened in their brains. Eyemovements were measured with a tracker, and the brain activity was recorded using an electroencephalogram (EEG), which uses sensors placed on the scalp to measure electrical signals. What children with ASD looked at was different than their typical peers, and these differences corresponded with alterations in the brain networks that process social information. Children with ASD who had less severe symptoms had stronger activity in these brain networks. What they looked at also was more similar to typical children. This suggests less severely affected children with ASD may be able to compensate that way. Identifying ASD-like behaviors and brain differences early in life may help scientists to better understand what causes the condition. It may also help clinicians provide more individualized therapies early in life when the brain is most adaptable. Long-term studies of these brain-network differences in children with ASD are necessary to better understand how therapies can influence these changes.
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Affiliation(s)
- Holger Franz Sperdin
- Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, University of Geneva, Geneva, Switzerland
| | - Ana Coito
- Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland
| | - Nada Kojovic
- Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, University of Geneva, Geneva, Switzerland
| | - Tonia Anahi Rihs
- Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland
| | - Reem Kais Jan
- Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland.,College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
| | - Martina Franchini
- Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, University of Geneva, Geneva, Switzerland
| | - Gijs Plomp
- Department of Psychology, University of Fribourg, Fribourg, Switzerland
| | - Serge Vulliemoz
- EEG and Epilepsy Unit, Neurology, University Hospitals of Geneva, Geneva, Switzerland
| | - Stephan Eliez
- Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, University of Geneva, Geneva, Switzerland
| | - Christoph Martin Michel
- Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland
| | - Marie Schaer
- Developmental Imaging and Psychopathology Laboratory, Department of Psychiatry, University of Geneva, Geneva, Switzerland
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Lp (p ≤ 1) Norm Partial Directed Coherence for Directed Network Analysis of Scalp EEGs. Brain Topogr 2018; 31:738-752. [DOI: 10.1007/s10548-018-0624-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Accepted: 01/17/2018] [Indexed: 10/18/2022]
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42
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Hosseini SAH, Sohrabpour A, He B. Electromagnetic source imaging using simultaneous scalp EEG and intracranial EEG: An emerging tool for interacting with pathological brain networks. Clin Neurophysiol 2018; 129:168-187. [PMID: 29190523 PMCID: PMC5743592 DOI: 10.1016/j.clinph.2017.10.027] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Revised: 09/05/2017] [Accepted: 10/11/2017] [Indexed: 10/18/2022]
Abstract
OBJECTIVE The goal of this study is to investigate the performance, merits and limitations of source imaging using intracranial EEG (iEEG) recordings and to compare its accuracy to the results of EEG source imaging. Accuracy in this study, is measured both by determining the location and inter-nodal connectivity of underlying brain networks. METHODS Systematic computer simulation studies are conducted to evaluate iEEG-based source imaging vs. EEG-based source imaging, and source imaging using both EEG and iEEG. To test the source imaging models, networks of inter-connected nodes (in terms of activity) are simulated. The location of the network nodes is randomly selected within a realistic geometry head model and a connectivity link is created among these nodes based on a multi-variate auto-regressive (MVAR) model. Then the forward problem is solved to calculate the potentials at the electrodes and noise (white and correlated) is added to these simulated potentials to simulate realistic measurements. Subsequently, the inverse problem is solved and an algorithm based on principle component analysis is performed on the estimated source activities to determine the location of the simulated network nodes. The activity of these nodes (over time), is then extracted, and used to estimate the connectivity links among the mentioned nodes using Granger causality analysis. RESULTS Source imaging based on iEEG recordings may or may not improve the accuracy in localization, depending on the number and location of active nodes relative to iEEG electrodes and to other nodes within the network. However, our simulation results suggest that combining EEG and iEEG modalities (simultaneous scalp and intracranial recordings) can improve the imaging accuracy significantly. CONCLUSIONS While iEEG source imaging is useful in estimating the exact location of sources near the iEEG electrodes, combining EEG and iEEG recordings can achieve a more accurate imaging due to the high spatial coverage of the scalp electrodes and the added near field information provided by the iEEG electrodes. SIGNIFICANCE The present results suggest the feasibility of localizing brain electrical sources from iEEG recordings and improving EEG source localization using simultaneous EEG and iEEG recordings to cover the whole brain. The hybrid EEG and iEEG source imaging can assist the clinicians when unequivocal decisions about determining the epileptogenic zone cannot be reached using a single modality.
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Affiliation(s)
| | - Abbas Sohrabpour
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Bin He
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA; Institute for Engineering in Medicine, University of Minnesota, Minneapolis, MN, USA.
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Gilson M, Tauste Campo A, Chen X, Thiele A, Deco G. Nonparametric test for connectivity detection in multivariate autoregressive networks and application to multiunit activity data. Netw Neurosci 2017; 1:357-380. [PMID: 30090871 PMCID: PMC6063719 DOI: 10.1162/netn_a_00019] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Accepted: 06/01/2017] [Indexed: 01/22/2023] Open
Abstract
Directed connectivity inference has become a cornerstone in neuroscience to analyze multivariate data from neuroimaging and electrophysiological techniques. Here we propose a nonparametric significance method to test the nonzero values of multivariate autoregressive model to infer interactions in recurrent networks. We use random permutations or circular shifts of the original time series to generate the null-hypothesis distributions. The underlying network model is the same as used in multivariate Granger causality, but our test relies on the autoregressive coefficients instead of error residuals. By means of numerical simulation over multiple network configurations, we show that this method achieves a good control of false positives (type 1 error) and detects existing pairwise connections more accurately than using the standard parametric test for the ratio of error residuals. In practice, our method aims to detect temporal interactions in real neuronal networks with nodes possibly exhibiting redundant activity. As a proof of concept, we apply our method to multiunit activity (MUA) recorded from Utah electrode arrays in a monkey and examine detected interactions between 25 channels. We show that during stimulus presentation our method detects a large number of interactions that cannot be solely explained by the increase in the MUA level.
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Affiliation(s)
- M. Gilson
- Computational Neuroscience Group, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona, Spain
| | - A. Tauste Campo
- Computational Neuroscience Group, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona, Spain
- Epilepsy Monitoring Unit, Department of Neurology, Hospital del Mar Medical Research Institute, Barcelona, Spain
| | - X. Chen
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - A. Thiele
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - G. Deco
- Computational Neuroscience Group, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
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44
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Ahn MH, Hong SK, Min BK. The absence of resting-state high-gamma cross-frequency coupling in patients with tinnitus. Hear Res 2017; 356:63-73. [PMID: 29097049 DOI: 10.1016/j.heares.2017.10.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Revised: 10/17/2017] [Accepted: 10/23/2017] [Indexed: 12/17/2022]
Abstract
Tinnitus is a psychoacoustic phantom perception of currently unknown neuropathology. Despite a growing number of post-stimulus tinnitus studies, uncertainty still exists regarding the neural signature of tinnitus in the resting-state brain. In the present study, we used high-gamma cross-frequency coupling and a Granger causality analysis to evaluate resting-state electroencephalographic (EEG) data in healthy participants and patients with tinnitus. Patients with tinnitus lacked robust frontal delta-phase/central high-gamma-amplitude coupling that was otherwise clearly observed in healthy participants. Since low-frequency phase and high-frequency amplitude coupling reflects inter-regional communication during cognitive processing, and given the absence of frontal modulation in patients with tinnitus, we hypothesized that tinnitus might be related to impaired prefrontal top-down inhibitory control. A Granger causality analysis consistently showed abnormally pronounced functional connectivity of low-frequency activity in patients with tinnitus, possibly reflecting a deficiency in large-scale communication during the resting state. Moreover, different causal neurodynamics were characterized across two subgroups of patients with tinnitus; the T1 group (with higher P300 amplitudes) showed abnormal frontal-to-auditory cortical information flow, whereas the T2 group (with lower P300 amplitudes) exhibited abnormal auditory-to-frontal cortical information control. This dissociation in resting-state low-frequency causal connectivity is consistent with recent post-stimulus observations. Taken together, our findings suggest that maladaptive neuroplasticity or abnormal reorganization occurs in the auditory default mode network of patients with tinnitus. Additionally, our data highlight the utility of resting-state EEG for the quantitative diagnosis of tinnitus symptoms and the further characterization of tinnitus subtypes.
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Affiliation(s)
- Min-Hee Ahn
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea
| | - Sung Kwang Hong
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea; Department of Otolaryngology, Hallym University College of Medicine, Anyang 14068, South Korea
| | - Byoung-Kyong Min
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea.
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Ahmedt-Aristizabal D, Fookes C, Dionisio S, Nguyen K, Cunha JPS, Sridharan S. Automated analysis of seizure semiology and brain electrical activity in presurgery evaluation of epilepsy: A focused survey. Epilepsia 2017; 58:1817-1831. [DOI: 10.1111/epi.13907] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/28/2017] [Indexed: 11/28/2022]
Affiliation(s)
- David Ahmedt-Aristizabal
- The Speech, Audio, Image and Video Technologies (SAIVT) and Science and Engineering Faculty; Queensland University of Technology; Brisbane Queensland Australia
| | - Clinton Fookes
- The Speech, Audio, Image and Video Technologies (SAIVT) and Science and Engineering Faculty; Queensland University of Technology; Brisbane Queensland Australia
| | - Sasha Dionisio
- Mater Centre for Neurosciences; Brisbane Queensland Australia
| | - Kien Nguyen
- The Speech, Audio, Image and Video Technologies (SAIVT) and Science and Engineering Faculty; Queensland University of Technology; Brisbane Queensland Australia
| | - João Paulo S. Cunha
- The Institute of Systems and Computer Engineering; Technology and Science; and Faculty of Engineering; University of Porto; Porto Portugal
| | - Sridha Sridharan
- The Speech, Audio, Image and Video Technologies (SAIVT) and Science and Engineering Faculty; Queensland University of Technology; Brisbane Queensland Australia
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Staljanssens W, Strobbe G, Van Holen R, Keereman V, Gadeyne S, Carrette E, Meurs A, Pittau F, Momjian S, Seeck M, Boon P, Vandenberghe S, Vulliemoz S, Vonck K, van Mierlo P. EEG source connectivity to localize the seizure onset zone in patients with drug resistant epilepsy. NEUROIMAGE-CLINICAL 2017; 16:689-698. [PMID: 29034162 PMCID: PMC5633847 DOI: 10.1016/j.nicl.2017.09.011] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Revised: 09/01/2017] [Accepted: 09/12/2017] [Indexed: 11/25/2022]
Abstract
Electrical source imaging (ESI) from interictal scalp EEG is increasingly validated and used as a valuable tool in the presurgical evaluation of epilepsy as a reflection of the irritative zone. ESI of ictal scalp EEG to localize the seizure onset zone (SOZ) remains challenging. We investigated the value of an approach for ictal imaging using ESI and functional connectivity analysis (FC). Ictal scalp EEG from 111 seizures in 27 patients who had Engel class I outcome at least 1 year following resective surgery was analyzed. For every seizure, an artifact-free epoch close to the seizure onset was selected and ESI using LORETA was applied. In addition, the reconstructed sources underwent FC using the spectrum-weighted Adaptive Directed Transfer Function. This resulted in the estimation of the SOZ in two ways: (i) the source with maximal power after ESI, (ii) the source with the strongest outgoing connections after combined ESI and FC. Next, we calculated the distance between the estimated SOZ and the border of the resected zone (RZ) for both approaches and called this the localization error ((i) LEpow and (ii) LEconn respectively). By comparing LEpow and LEconn, we assessed the added value of FC. The source with maximal power after ESI was inside the RZ (LEpow = 0 mm) in 31% of the seizures and estimated within 10 mm from the border of the RZ (LEpow ≤ 10 mm) in 42%. Using ESI and FC, these numbers increased to 72% for LEconn = 0 mm and 94% for LEconn ≤ 10 mm. FC provided a significant added value to ESI alone (p < 0.001). ESI combined with subsequent FC is able to localize the SOZ in a non-invasive way with high accuracy. Therefore it could be a valuable tool in the presurgical evaluation of epilepsy. ESI + functional connectivity analysis allows localizing the SOZ with high accuracy. Functional connectivity analysis offered a significant added value to ESI. The method is robust for inter- and intra-patient variability. The method could be a useful tool in the presurgical evaluation of epilepsy.
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Affiliation(s)
- Willeke Staljanssens
- Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University - imec, De Pintelaan 185, 9000 Ghent, Belgium
| | | | - Roel Van Holen
- Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University - imec, De Pintelaan 185, 9000 Ghent, Belgium
| | - Vincent Keereman
- Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University - imec, De Pintelaan 185, 9000 Ghent, Belgium.,Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and Neuropsychology, Department of Neurology, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium
| | - Stefanie Gadeyne
- Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and Neuropsychology, Department of Neurology, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium
| | - Evelien Carrette
- Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and Neuropsychology, Department of Neurology, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium
| | - Alfred Meurs
- Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and Neuropsychology, Department of Neurology, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium
| | - Francesca Pittau
- EEG and Epilepsy Unit, Neurology Department, University Hospitals and Faculty of Medicine of Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland
| | - Shahan Momjian
- Department of Neurosurgery, University Hospitals of Geneva and University of Geneva, rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland
| | - Margitta Seeck
- Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and Neuropsychology, Department of Neurology, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium
| | - Paul Boon
- Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and Neuropsychology, Department of Neurology, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium
| | - Stefaan Vandenberghe
- Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University - imec, De Pintelaan 185, 9000 Ghent, Belgium
| | - Serge Vulliemoz
- EEG and Epilepsy Unit, Neurology Department, University Hospitals and Faculty of Medicine of Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland.,Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland
| | - Kristl Vonck
- Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and Neuropsychology, Department of Neurology, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium
| | - Pieter van Mierlo
- Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University - imec, De Pintelaan 185, 9000 Ghent, Belgium.,Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland
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47
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Exploring the Epileptic Brain Network Using Time-Variant Effective Connectivity and Graph Theory. IEEE J Biomed Health Inform 2017; 21:1411-1421. [DOI: 10.1109/jbhi.2016.2607802] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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48
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Artoni F, Fanciullacci C, Bertolucci F, Panarese A, Makeig S, Micera S, Chisari C. Unidirectional brain to muscle connectivity reveals motor cortex control of leg muscles during stereotyped walking. Neuroimage 2017; 159:403-416. [PMID: 28782683 PMCID: PMC6698582 DOI: 10.1016/j.neuroimage.2017.07.013] [Citation(s) in RCA: 105] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2017] [Revised: 07/01/2017] [Accepted: 07/09/2017] [Indexed: 01/20/2023] Open
Abstract
In lower mammals, locomotion seems to be mainly regulated by subcortical and spinal networks. On the contrary, recent evidence suggests that in humans the motor cortex is also significantly engaged during complex locomotion tasks. However, a detailed understanding of cortical contribution to locomotion is still lacking especially during stereotyped activities. Here, we show that cortical motor areas finely control leg muscle activation during treadmill stereotyped walking. Using a novel technique based on a combination of Reliable Independent Component Analysis, source localization and effective connectivity, and by combining electroencephalographic (EEG) and electromyographic (EMG) recordings in able-bodied adults we were able to examine for the first time cortical activation patterns and cortico-muscular connectivity including information flow direction. Results not only provided evidence of cortical activity associated with locomotion, but demonstrated significant causal unidirectional drive from contralateral motor cortex to muscles in the swing leg. These insights overturn the traditional view that human cortex has a limited role in the control of stereotyped locomotion, and suggest useful hypotheses concerning mechanisms underlying gait under other conditions. ONE SENTENCE SUMMARY Motor cortex proactively drives contralateral swing leg muscles during treadmill walking, counter to the traditional view of stereotyped human locomotion.
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Affiliation(s)
- Fiorenzo Artoni
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy; Translational Neural Engineering Laboratory, Center for Neuroprosthetics and Institute of Bioengineering, EPFL, Lausanne, Switzerland.
| | - Chiara Fanciullacci
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy; Pisa University Hospital, Pisa, Italy
| | | | | | - Scott Makeig
- Swartz Center for Computational Neuroscience, University of California, San Diego, La Jolla, CA, United States
| | - Silvestro Micera
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy; Translational Neural Engineering Laboratory, Center for Neuroprosthetics and Institute of Bioengineering, EPFL, Lausanne, Switzerland
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Nemtsas P, Birot G, Pittau F, Michel CM, Schaller K, Vulliemoz S, Kimiskidis VK, Seeck M. Source localization of ictal epileptic activity based on high-density scalp EEG data. Epilepsia 2017; 58:1027-1036. [DOI: 10.1111/epi.13749] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/11/2017] [Indexed: 02/05/2023]
Affiliation(s)
- Petros Nemtsas
- Laboratory of Clinical Neurophysiology; AHEPA Hospital; Aristotle University of Thessaloniki; Thessaloniki Greece
| | - Gwenael Birot
- Department of Fundamental Neurosciences; Functional Brain Mapping Lab; University of Geneva; Geneva Switzerland
| | - Francesca Pittau
- EEG & Epilepsy Unit; Department of Clinical Neurosciences; University Hospital of Geneva; Geneva Switzerland
| | - Christoph M. Michel
- Department of Fundamental Neurosciences; Functional Brain Mapping Lab; University of Geneva; Geneva Switzerland
- Center for Biomedical Imaging (CIBM) Lausanne; Geneva Switzerland
| | - Karl Schaller
- Department of Clinical Neurosciences; Neurosurgery Clinic; University Hospital of Geneva; Geneva Switzerland
| | - Serge Vulliemoz
- EEG & Epilepsy Unit; Department of Clinical Neurosciences; University Hospital of Geneva; Geneva Switzerland
| | - Vasilios K. Kimiskidis
- Laboratory of Clinical Neurophysiology; AHEPA Hospital; Aristotle University of Thessaloniki; Thessaloniki Greece
| | - Margitta Seeck
- EEG & Epilepsy Unit; Department of Clinical Neurosciences; University Hospital of Geneva; Geneva Switzerland
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50
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Top-down and bottom-up neurodynamic evidence in patients with tinnitus. Hear Res 2016; 342:86-100. [DOI: 10.1016/j.heares.2016.10.002] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Revised: 09/12/2016] [Accepted: 10/06/2016] [Indexed: 12/14/2022]
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