151
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Beltrachini L, von Ellenrieder N, Eichardt R, Haueisen J. Optimal design of on-scalp electromagnetic sensor arrays for brain source localisation. Hum Brain Mapp 2021; 42:4869-4879. [PMID: 34245061 PMCID: PMC8449117 DOI: 10.1002/hbm.25586] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 06/09/2021] [Accepted: 06/25/2021] [Indexed: 11/11/2022] Open
Abstract
Optically pumped magnetometers (OPMs) are quickly widening the scopes of noninvasive neurophysiological imaging. The possibility of placing these magnetic field sensors on the scalp allows not only to acquire signals from people in movement, but also to reduce the distance between the sensors and the brain, with a consequent gain in the signal‐to‐noise ratio. These advantages make the technique particularly attractive to characterise sources of brain activity in demanding populations, such as children and patients with epilepsy. However, the technology is currently in an early stage, presenting new design challenges around the optimal sensor arrangement and their complementarity with other techniques as electroencephalography (EEG). In this article, we present an optimal array design strategy focussed on minimising the brain source localisation error. The methodology is based on the Cramér‐Rao bound, which provides lower error bounds on the estimation of source parameters regardless of the algorithm used. We utilise this framework to compare whole head OPM arrays with commercially available electro/magnetoencephalography (E/MEG) systems for localising brain signal generators. In addition, we study the complementarity between EEG and OPM‐based MEG, and design optimal whole head systems based on OPMs only and a combination of OPMs and EEG electrodes for characterising deep and superficial sources alike. Finally, we show the usefulness of the approach to find the nearly optimal sensor positions minimising the estimation error bound in a given cortical region when a limited number of OPMs are available. This is of special interest for maximising the performance of small scale systems to ad hoc neurophysiological experiments, a common situation arising in most OPM labs.
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Affiliation(s)
- Leandro Beltrachini
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff
| | | | - Roland Eichardt
- Institute of Biomedical Engineering and Informatics, Ilmenau University of Technology, Ilmenau, Germany
| | - Jens Haueisen
- Institute of Biomedical Engineering and Informatics, Ilmenau University of Technology, Ilmenau, Germany
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152
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Allouch S, Yochum M, Kabbara A, Duprez J, Khalil M, Wendling F, Hassan M, Modolo J. Mean-Field Modeling of Brain-Scale Dynamics for the Evaluation of EEG Source-Space Networks. Brain Topogr 2021; 35:54-65. [PMID: 34244910 DOI: 10.1007/s10548-021-00859-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 06/18/2021] [Indexed: 01/04/2023]
Abstract
Understanding the dynamics of brain-scale functional networks at rest and during cognitive tasks is the subject of intense research efforts to unveil fundamental principles of brain functions. To estimate these large-scale brain networks, the emergent method called "electroencephalography (EEG) source connectivity" has generated increasing interest in the network neuroscience community, due to its ability to identify cortical brain networks with satisfactory spatio-temporal resolution, while reducing mixing and volume conduction effects. However, no consensus has been reached yet regarding a unified EEG source connectivity pipeline, and several methodological issues have to be carefully accounted to avoid pitfalls. Thus, a validation toolbox that provides flexible "ground truth" models is needed for an objective methods/parameters evaluation and, thereby an optimization of the EEG source connectivity pipeline. In this paper, we show how a recently developed large-scale model of brain-scale activity, named COALIA, can provide to some extent such ground truth by providing realistic simulations of source-level and scalp-level activity. Using a bottom-up approach, the model bridges cortical micro-circuitry and large-scale network dynamics. Here, we provide an example of the potential use of COALIA to analyze, in the context of epileptiform activity, the effect of three key factors involved in the "EEG source connectivity" pipeline: (i) EEG sensors density, (ii) algorithm used to solve the inverse problem, and (iii) functional connectivity measure. Results showed that a high electrode density (at least 64 channels) is required to accurately estimate cortical networks. Regarding the inverse solution/connectivity measure combination, the best performance at high electrode density was obtained using the weighted minimum norm estimate (wMNE) combined with the weighted phase lag index (wPLI). Although those results are specific to the considered aforementioned context (epileptiform activity), we believe that this model-based approach can be successfully applied to other experimental questions/contexts. We aim at presenting a proof-of-concept of the interest of COALIA in the network neuroscience field, and its potential use in optimizing the EEG source-space network estimation pipeline.
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Affiliation(s)
- Sahar Allouch
- Univ Rennes, LTSI - INSERM U1099, 35000, Rennes, France. .,Azm Center for Research in Biotechnology and Its Applications, EDST, Tripoli, Lebanon.
| | - Maxime Yochum
- Univ Rennes, LTSI - INSERM U1099, 35000, Rennes, France
| | - Aya Kabbara
- Univ Rennes, LTSI - INSERM U1099, 35000, Rennes, France
| | - Joan Duprez
- Univ Rennes, LTSI - INSERM U1099, 35000, Rennes, France
| | - Mohamad Khalil
- Azm Center for Research in Biotechnology and Its Applications, EDST, Tripoli, Lebanon.,CRSI Research Center, Faculty of Engineering, Lebanese University, Beirut, Lebanon
| | | | | | - Julien Modolo
- Univ Rennes, LTSI - INSERM U1099, 35000, Rennes, France
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153
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Khanmohammadi S, Laurido-Soto O, Eisenman LN, Kummer TT, Ching S. Localizing focal brain injury via EEG spectral variance. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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154
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Moridera T, Rashed EA, Mizutani S, Hirata A. High-Resolution EEG Source Localization in Segmentation-Free Head Models Based on Finite-Difference Method and Matching Pursuit Algorithm. Front Neurosci 2021; 15:695668. [PMID: 34262433 PMCID: PMC8273249 DOI: 10.3389/fnins.2021.695668] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 06/04/2021] [Indexed: 11/23/2022] Open
Abstract
Electroencephalogram (EEG) is a method to monitor electrophysiological activity on the scalp, which represents the macroscopic activity of the brain. However, it is challenging to identify EEG source regions inside the brain based on data measured by a scalp-attached network of electrodes. The accuracy of EEG source localization significantly depends on the type of head modeling and inverse problem solver. In this study, we adopted different models with a resolution of 0.5 mm to account for thin tissues/fluids, such as the cerebrospinal fluid (CSF) and dura. In particular, a spatially dependent conductivity (segmentation-free) model created using deep learning was developed and used for more realist representation of electrical conductivity. We then adopted a multi-grid-based finite-difference method (FDM) for forward problem analysis and a sparse-based algorithm to solve the inverse problem. This enabled us to perform efficient source localization using high-resolution model with a reasonable computational cost. Results indicated that the abrupt spatial change in conductivity, inherent in conventional segmentation-based head models, may trigger source localization error accumulation. The accurate modeling of the CSF, whose conductivity is the highest in the head, was an important factor affecting localization accuracy. Moreover, computational experiments with different noise levels and electrode setups demonstrate the robustness of the proposed method with segmentation-free head model.
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Affiliation(s)
- Takayoshi Moridera
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, Japan
| | - Essam A Rashed
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, Japan.,Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia, Egypt
| | - Shogo Mizutani
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, Japan
| | - Akimasa Hirata
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, Japan.,Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya, Japan
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155
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Advances in Electrical Source Imaging: A Review of the Current Approaches, Applications and Challenges. SIGNALS 2021. [DOI: 10.3390/signals2030024] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Brain source localization has been consistently implemented over the recent years to elucidate complex brain operations, pairing the high temporal resolution of the EEG with the high spatial estimation of the estimated sources. This review paper aims to present the basic principles of Electrical source imaging (ESI) in the context of the recent progress for solving the forward and the inverse problems, and highlight the advantages and limitations of the different approaches. As such, a synthesis of the current state-of-the-art methodological aspects is provided, offering a complete overview of the present advances with regard to the ESI solutions. Moreover, the new dimensions for the analysis of the brain processes are indicated in terms of clinical and cognitive ESI applications, while the prevailing challenges and limitations are thoroughly discussed, providing insights for future approaches that could help to alleviate methodological and technical shortcomings.
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156
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Al-Ezzi A, Kamel N, Faye I, Gunaseli E. Analysis of Default Mode Network in Social Anxiety Disorder: EEG Resting-State Effective Connectivity Study. SENSORS (BASEL, SWITZERLAND) 2021; 21:4098. [PMID: 34203578 PMCID: PMC8232236 DOI: 10.3390/s21124098] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 04/07/2021] [Accepted: 04/09/2021] [Indexed: 12/27/2022]
Abstract
Recent brain imaging findings by using different methods (e.g., fMRI and PET) have suggested that social anxiety disorder (SAD) is correlated with alterations in regional or network-level brain function. However, due to many limitations associated with these methods, such as poor temporal resolution and limited number of samples per second, neuroscientists could not quantify the fast dynamic connectivity of causal information networks in SAD. In this study, SAD-related changes in brain connections within the default mode network (DMN) were investigated using eight electroencephalographic (EEG) regions of interest. Partial directed coherence (PDC) was used to assess the causal influences of DMN regions on each other and indicate the changes in the DMN effective network related to SAD severity. The DMN is a large-scale brain network basically composed of the mesial prefrontal cortex (mPFC), posterior cingulate cortex (PCC)/precuneus, and lateral parietal cortex (LPC). The EEG data were collected from 88 subjects (22 control, 22 mild, 22 moderate, 22 severe) and used to estimate the effective connectivity between DMN regions at different frequency bands: delta (1-3 Hz), theta (4-8 Hz), alpha (8-12 Hz), low beta (13-21 Hz), and high beta (22-30 Hz). Among the healthy control (HC) and the three considered levels of severity of SAD, the results indicated a higher level of causal interactions for the mild and moderate SAD groups than for the severe and HC groups. Between the control and the severe SAD groups, the results indicated a higher level of causal connections for the control throughout all the DMN regions. We found significant increases in the mean PDC in the delta (p = 0.009) and alpha (p = 0.001) bands between the SAD groups. Among the DMN regions, the precuneus exhibited a higher level of causal influence than other regions. Therefore, it was suggested to be a major source hub that contributes to the mental exploration and emotional content of SAD. In contrast to the severe group, HC exhibited higher resting-state connectivity at the mPFC, providing evidence for mPFC dysfunction in the severe SAD group. Furthermore, the total Social Interaction Anxiety Scale (SIAS) was positively correlated with the mean values of the PDC of the severe SAD group, r (22) = 0.576, p = 0.006 and negatively correlated with those of the HC group, r (22) = -0.689, p = 0.001. The reported results may facilitate greater comprehension of the underlying potential SAD neural biomarkers and can be used to characterize possible targets for further medication.
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Affiliation(s)
- Abdulhakim Al-Ezzi
- Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia; (A.A.-E.); (N.K.)
| | - Nidal Kamel
- Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia; (A.A.-E.); (N.K.)
| | - Ibrahima Faye
- Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia; (A.A.-E.); (N.K.)
| | - Esther Gunaseli
- Psychiatry Discipline Sub Unit, Universiti Kuala Lumpur Royal College of Medicine Perak, Ipoh 30450, Malaysia;
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157
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Hecker L, Rupprecht R, Tebartz Van Elst L, Kornmeier J. ConvDip: A Convolutional Neural Network for Better EEG Source Imaging. Front Neurosci 2021; 15:569918. [PMID: 34177438 PMCID: PMC8219905 DOI: 10.3389/fnins.2021.569918] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 04/14/2021] [Indexed: 11/30/2022] Open
Abstract
The electroencephalography (EEG) is a well-established non-invasive method in neuroscientific research and clinical diagnostics. It provides a high temporal but low spatial resolution of brain activity. To gain insight about the spatial dynamics of the EEG, one has to solve the inverse problem, i.e., finding the neural sources that give rise to the recorded EEG activity. The inverse problem is ill-posed, which means that more than one configuration of neural sources can evoke one and the same distribution of EEG activity on the scalp. Artificial neural networks have been previously used successfully to find either one or two dipole sources. These approaches, however, have never solved the inverse problem in a distributed dipole model with more than two dipole sources. We present ConvDip, a novel convolutional neural network (CNN) architecture, that solves the EEG inverse problem in a distributed dipole model based on simulated EEG data. We show that (1) ConvDip learned to produce inverse solutions from a single time point of EEG data and (2) outperforms state-of-the-art methods on all focused performance measures. (3) It is more flexible when dealing with varying number of sources, produces less ghost sources and misses less real sources than the comparison methods. It produces plausible inverse solutions for real EEG recordings from human participants. (4) The trained network needs <40 ms for a single prediction. Our results qualify ConvDip as an efficient and easy-to-apply novel method for source localization in EEG data, with high relevance for clinical applications, e.g., in epileptology and real-time applications.
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Affiliation(s)
- Lukas Hecker
- Department of Psychiatry and Psychotherapy, University of Freiburg Medical Center, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Institute for Frontier Areas of Psychology and Mental Health (IGPP), Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
| | | | - Ludger Tebartz Van Elst
- Department of Psychiatry and Psychotherapy, University of Freiburg Medical Center, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jürgen Kornmeier
- Department of Psychiatry and Psychotherapy, University of Freiburg Medical Center, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Institute for Frontier Areas of Psychology and Mental Health (IGPP), Freiburg, Germany
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158
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Neural Correlates of Motor Recovery after Robot-Assisted Training in Chronic Stroke: A Multimodal Neuroimaging Study. Neural Plast 2021; 2021:8866613. [PMID: 34211549 PMCID: PMC8208881 DOI: 10.1155/2021/8866613] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 04/19/2021] [Accepted: 05/29/2021] [Indexed: 11/17/2022] Open
Abstract
Stroke is a leading cause of motor disability worldwide, and robot-assisted therapies have been increasingly applied to facilitate the recovery process. However, the underlying mechanism and induced neuroplasticity change remain partially understood, and few studies have investigated this from a multimodality neuroimaging perspective. The current study adopted BCI-guided robot hand therapy as the training intervention and combined multiple neuroimaging modalities to comprehensively understand the potential association between motor function alteration and various neural correlates. We adopted EEG-informed fMRI technique to understand the functional regions sensitive to training intervention. Additionally, correlation analysis among training effects, nonlinear property change quantified by fractal dimension (FD), and integrity of M1-M1 (M1: primary motor cortex) anatomical connection were performed. EEG-informed fMRI analysis indicated that for iM1 (iM1: ipsilesional M1) regressors, regions with significantly increased partial correlation were mainly located in contralesional parietal, prefrontal, and sensorimotor areas and regions with significantly decreased partial correlation were mainly observed in the ipsilesional supramarginal gyrus and superior temporal gyrus. Pearson's correlations revealed that the interhemispheric asymmetry change significantly correlated with the training effect as well as the integrity of M1-M1 anatomical connection. In summary, our study suggested that multiple functional brain regions not limited to motor areas were involved during the recovery process from multimodality perspective. The correlation analyses suggested the essential role of interhemispheric interaction in motor rehabilitation. Besides, the underlying structural substrate of the bilateral M1-M1 connection might relate to the interhemispheric change. This study might give some insights in understanding the neuroplasticity induced by the integrated BCI-guided robot hand training intervention and further facilitate the design of therapies for chronic stroke patients.
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159
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Anzolin A, Toppi J, Petti M, Cincotti F, Astolfi L. SEED-G: Simulated EEG Data Generator for Testing Connectivity Algorithms. SENSORS 2021; 21:s21113632. [PMID: 34071124 PMCID: PMC8197139 DOI: 10.3390/s21113632] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 05/14/2021] [Accepted: 05/19/2021] [Indexed: 11/16/2022]
Abstract
EEG signals are widely used to estimate brain circuits associated with specific tasks and cognitive processes. The testing of connectivity estimators is still an open issue because of the lack of a ground-truth in real data. Existing solutions such as the generation of simulated data based on a manually imposed connectivity pattern or mass oscillators can model only a few real cases with limited number of signals and spectral properties that do not reflect those of real brain activity. Furthermore, the generation of time series reproducing non-ideal and non-stationary ground-truth models is still missing. In this work, we present the SEED-G toolbox for the generation of pseudo-EEG data with imposed connectivity patterns, overcoming the existing limitations and enabling control of several parameters for data simulation according to the user’s needs. We first described the toolbox including guidelines for its correct use and then we tested its performances showing how, in a wide range of conditions, datasets composed by up to 60 time series were successfully generated in less than 5 s and with spectral features similar to real data. Then, SEED-G is employed for studying the effect of inter-trial variability Partial Directed Coherence (PDC) estimates, confirming its robustness.
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Affiliation(s)
- Alessandra Anzolin
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
- Department of Computer, Control, and Management Engineering, Sapienza University of Rome, 00185 Roma, Italy; (J.T.); (M.P.); (F.C.); (L.A.)
- IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
- Correspondence:
| | - Jlenia Toppi
- Department of Computer, Control, and Management Engineering, Sapienza University of Rome, 00185 Roma, Italy; (J.T.); (M.P.); (F.C.); (L.A.)
- IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
| | - Manuela Petti
- Department of Computer, Control, and Management Engineering, Sapienza University of Rome, 00185 Roma, Italy; (J.T.); (M.P.); (F.C.); (L.A.)
- IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
| | - Febo Cincotti
- Department of Computer, Control, and Management Engineering, Sapienza University of Rome, 00185 Roma, Italy; (J.T.); (M.P.); (F.C.); (L.A.)
- IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
| | - Laura Astolfi
- Department of Computer, Control, and Management Engineering, Sapienza University of Rome, 00185 Roma, Italy; (J.T.); (M.P.); (F.C.); (L.A.)
- IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
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160
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Samadzadehaghdam N, MakkiAbadi B, Eqlimi E, Mohagheghian F, Khajehpoor H, Harirchian MH. Developing a Multi-channel Beamformer by Enhancing Spatially Constrained ICA for Recovery of Correlated EEG Sources. J Biomed Phys Eng 2021; 11:205-214. [PMID: 33937127 PMCID: PMC8064133 DOI: 10.31661/jbpe.v0i0.801] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2017] [Accepted: 10/14/2017] [Indexed: 11/23/2022]
Abstract
Background: Brain source imaging based on electroencephalogram (EEG) data aims to recover the neuron populations’ activity producing the scalp potentials. This procedure is known as the EEG inverse problem. Recently, beamformers have gained a lot of consideration in the EEG inverse problem. Objective: Beamformers lack acceptable performance in the case of correlated brain sources. These sources happen when some regions of the brain have simultaneous or correlated activities such as auditory stimulation or moving left and right extremities of the body at the same time. In this paper, we have developed a multichannel beamformer robust to correlated sources. Material and Methods: In this simulation study, we have looked at the problem of brain source imaging and beamforming from a blind source separation point of view. We focused on the spatially constraint independent component analysis (scICA) algorithm, which generally benefits from the pre-known partial information of mixing matrix, and modified the steps of the algorithm in a way that makes it more robust to correlated sources. We called the modified scICA algorithm Multichannel ICA based EEG Beamformer (MIEB). Results: We evaluated the proposed algorithm on simulated EEG data and compared its performance quantitatively with three algorithms scICA, linearly-constrained minimum-variance (LCMV) and Dual-Core beamformers; it is considered that the latter is specially designed to reconstruct correlated sources. Conclusion: The MIEB algorithm has much better performance in terms of normalized mean squared error in recovering the correlated/uncorrelated sources both in noise free and noisy synthetic EEG signals. Therefore, it could be used as a robust beamformer in recovering correlated brain sources.
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Affiliation(s)
- Nasser Samadzadehaghdam
- PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- PhD, Department of Medical Bioengineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Bahador MakkiAbadi
- PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- PhD, Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Ehsan Eqlimi
- PhD Candidate, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- PhD Candidate, Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Fahimeh Mohagheghian
- PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hassan Khajehpoor
- PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- PhD, Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Mohammad Hossein Harirchian
- MD, Iranian Centre of Neurological Research, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
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161
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Rosjat N, Wang BA, Liu L, Fink GR, Daun S. Stimulus transformation into motor action: Dynamic graph analysis reveals a posterior-to-anterior shift in brain network communication of older subjects. Hum Brain Mapp 2021; 42:1547-1563. [PMID: 33305871 PMCID: PMC7927305 DOI: 10.1002/hbm.25313] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 09/11/2020] [Accepted: 11/29/2020] [Indexed: 11/08/2022] Open
Abstract
Cognitive performance slows down with increasing age. This includes cognitive processes that are essential for the performance of a motor act, such as the slowing down in response to an external stimulus. The objective of this study was to identify aging-associated functional changes in the brain networks that are involved in the transformation of external stimuli into motor action. To investigate this topic, we employed dynamic graphs based on phase-locking of Electroencephalography signals recorded from healthy younger and older subjects while performing a simple visually-cued finger-tapping task. The network analysis yielded specific age-related network structures varying in time in the low frequencies (2-7 Hz), which are closely connected to stimulus processing, movement initiation and execution in both age groups. The networks in older subjects, however, contained several additional, particularly interhemispheric, connections and showed an overall increased coupling density. Cluster analyses revealed reduced variability of the subnetworks in older subjects, particularly during movement preparation. In younger subjects, occipital, parietal, sensorimotor and central regions were-temporally arranged in this order-heavily involved in hub nodes. Whereas in older subjects, a hub in frontal regions preceded the noticeably delayed occurrence of sensorimotor hubs, indicating different neural information processing in older subjects. All observed changes in brain network organization, which are based on neural synchronization in the low frequencies, provide a possible neural mechanism underlying previous fMRI data, which report an overactivation, especially in the prefrontal and pre-motor areas, associated with a loss of hemispheric lateralization in older subjects.
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Affiliation(s)
- Nils Rosjat
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM‐3)JülichGermany
- Institute of Zoology, University of CologneCologneGermany
| | - Bin A. Wang
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM‐3)JülichGermany
- Department of NeurologyBG University Hospital BergmannsheilBochumGermany
| | - Liqing Liu
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM‐3)JülichGermany
- Institute of Zoology, University of CologneCologneGermany
- Faculty of Psychology, Key Research Base of Humanities and Social Sciences of Ministry of EducationTianjin Normal UniversityTianjinChina
| | - Gereon R. Fink
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM‐3)JülichGermany
- Department of NeurologyFaculty of Medicine and University Hospital Cologne, University of CologneCologneGermany
| | - Silvia Daun
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM‐3)JülichGermany
- Department of NeurologyFaculty of Medicine and University Hospital Cologne, University of CologneCologneGermany
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162
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Trumpis M, Chiang CH, Orsborn AL, Bent B, Li J, Rogers JA, Pesaran B, Cogan G, Viventi J. Sufficient sampling for kriging prediction of cortical potential in rat, monkey, and human µECoG. J Neural Eng 2021; 18. [PMID: 33326943 DOI: 10.1088/1741-2552/abd460] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 12/16/2020] [Indexed: 12/22/2022]
Abstract
Objective. Large channel count surface-based electrophysiology arrays (e.g. µECoG) are high-throughput neural interfaces with good chronic stability. Electrode spacing remains ad hoc due to redundancy and nonstationarity of field dynamics. Here, we establish a criterion for electrode spacing based on the expected accuracy of predicting unsampled field potential from sampled sites.Approach. We applied spatial covariance modeling and field prediction techniques based on geospatial kriging to quantify sufficient sampling for thousands of 500 ms µECoG snapshots in human, monkey, and rat. We calculated a probably approximately correct (PAC) spacing based on kriging that would be required to predict µECoG fields at≤10% error for most cases (95% of observations).Main results. Kriging theory accurately explained the competing effects of electrode density and noise on predicting field potential. Across five frequency bands from 4-7 to 75-300 Hz, PAC spacing was sub-millimeter for auditory cortex in anesthetized and awake rats, and posterior superior temporal gyrus in anesthetized human. At 75-300 Hz, sub-millimeter PAC spacing was required in all species and cortical areas.Significance. PAC spacing accounted for the effect of signal-to-noise on prediction quality and was sensitive to the full distribution of non-stationary covariance states. Our results show that µECoG arrays should sample at sub-millimeter resolution for applications in diverse cortical areas and for noise resilience.
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Affiliation(s)
- Michael Trumpis
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America
| | - Chia-Han Chiang
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America
| | - Amy L Orsborn
- Center for Neural Science, New York University, New York, NY 10003, United States of America.,Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, United States of America.,Department of Bioengineering, University of Washington, Seattle, Washington 98105, United States of America.,Washington National Primate Research Center, Seattle, Washington 98195, United States of America
| | - Brinnae Bent
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America
| | - Jinghua Li
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL 60208, United States of America.,Department of Materials Science and Engineering, The Ohio State University, Columbus, OH 43210, United States of America.,Chronic Brain Injury Program, The Ohio State University, Columbus, OH 43210, United States of America
| | - John A Rogers
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL 60208, United States of America.,Simpson Querrey Institute, Northwestern University, Chicago, IL 60611, United States of America.,Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, United States of America.,Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, United States of America
| | - Bijan Pesaran
- Center for Neural Science, New York University, New York, NY 10003, United States of America
| | - Gregory Cogan
- Department of Neurosurgery, Duke School of Medicine, Durham, NC 27710, United States of America.,Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, United States of America.,Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, United States of America.,Duke Comprehensive Epilepsy Center, Duke School of Medicine, Durham, NC 27710, United States of America
| | - Jonathan Viventi
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, United States of America.,Department of Neurosurgery, Duke School of Medicine, Durham, NC 27710, United States of America.,Duke Comprehensive Epilepsy Center, Duke School of Medicine, Durham, NC 27710, United States of America.,Department of Neurobiology, Duke School of Medicine, Durham, NC 27710, United States of America
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163
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Ieracitano C, Mammone N, Hussain A, Morabito FC. A novel explainable machine learning approach for EEG-based brain-computer interface systems. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05624-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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164
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Mohammed S, Dey DK. Scalable spatio‐temporal Bayesian analysis of high‐dimensional electroencephalography data. CAN J STAT 2021. [DOI: 10.1002/cjs.11592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Shariq Mohammed
- Department of Statistics University of Connecticut Storrs Connecticut 06269 U.S.A
| | - Dipak K. Dey
- Department of Statistics University of Connecticut Storrs Connecticut 06269 U.S.A
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165
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Hamid L, Habboush N, Stern P, Japaridze N, Aydin Ü, Wolters CH, Claussen JC, Heute U, Stephani U, Galka A, Siniatchkin M. Source imaging of deep-brain activity using the regional spatiotemporal Kalman filter. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 200:105830. [PMID: 33250282 DOI: 10.1016/j.cmpb.2020.105830] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Accepted: 10/31/2020] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE The human brain displays rich and complex patterns of interaction within and among brain networks that involve both cortical and subcortical brain regions. Due to the limited spatial resolution of surface electroencephalography (EEG), EEG source imaging is used to reconstruct brain sources and investigate their spatial and temporal dynamics. The majority of EEG source imaging methods fail to detect activity from subcortical brain structures. The reconstruction of subcortical sources is a challenging task because the signal from these sources is weakened and mixed with artifacts and other signals from cortical sources. In this proof-of-principle study we present a novel EEG source imaging method, the regional spatiotemporal Kalman filter (RSTKF), that can detect deep brain activity. METHODS The regional spatiotemporal Kalman filter (RSTKF) is a generalization of the spatiotemporal Kalman filter (STKF), which allows for the characterization of different regional dynamics in the brain. It is based on state-space modeling with spatially heterogeneous dynamical noise variances, since models with spatial and temporal homogeneity fail to describe the dynamical complexity of brain activity. First, RSTKF is tested using simulated EEG data from sources in the frontal lobe, putamen, and thalamus. After that, it is applied to non-averaged interictal epileptic spikes from a presurgical epilepsy patient with focal epileptic activity in the amygdalo-hippocampal complex. The results of RSTKF are compared to those of low-resolution brain electromagnetic tomography (LORETA) and of standard STKF. RESULTS Only RSTKF is successful in consistently and accurately localizing the sources in deep brain regions. Additionally, RSTKF shows improved spatial resolution compared to LORETA and STKF. CONCLUSIONS RSTKF is a generalization of STKF that allows for accurate, focal, and consistent localization of sources, especially in the deeper brain areas. In contrast to standard source imaging methods, RSTKF may find application in the localization of the epileptogenic zone in deeper brain structures, such as mesial frontal and temporal lobe epilepsies, especially in EEG recordings for which no reliable averaged spike shape can be obtained due to lack of the necessary number of spikes required to reach a certain signal-to-noise ratio level after averaging.
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Affiliation(s)
- Laith Hamid
- Department of Medical Psychology and Medical Sociology, University of Kiel, D-24113 Kiel, Germany.
| | - Nawar Habboush
- Department of Medical Psychology and Medical Sociology, University of Kiel, D-24113 Kiel, Germany
| | - Philipp Stern
- Institute of Theoretical Physics and Astrophysics, University of Kiel, D-24098 Kiel, Germany
| | - Natia Japaridze
- Department of Neuropediatrics, University of Kiel, D-24098 Kiel, Germany
| | - Ümit Aydin
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, D-48149 Münster, Germany; Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montreal, Canada
| | - Carsten H Wolters
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, D-48149 Münster, Germany
| | - Jens Christian Claussen
- Institute of Theoretical Physics and Astrophysics, University of Kiel, D-24098 Kiel, Germany; Institute for Neuro- and Bioinformatics, University of Lübeck, D-23562 Lübeck, Germany; Mathematics EAS, Aston University, Aston Triangle, Birmingham B3 7ET, United Kingdom
| | - Ulrich Heute
- Digital Signal Processing and System Theory Group, Faculty of Engineering, University of Kiel, D-24143 Kiel, Germany
| | - Ulrich Stephani
- Department of Neuropediatrics, University of Kiel, D-24098 Kiel, Germany
| | - Andreas Galka
- Department of Medical Psychology and Medical Sociology, University of Kiel, D-24113 Kiel, Germany
| | - Michael Siniatchkin
- Department of Medical Psychology and Medical Sociology, University of Kiel, D-24113 Kiel, Germany; Department of Child and Adolescent Psychiatry and Psychotherapy, Evangelisches Klinikum Bethel gGmbH, D-33617 Bielefeld, Germany
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166
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Völker JM, Arguissain FG, Andersen OK, Biurrun Manresa J. Variability and effect sizes of intracranial current source density estimations during pain: Systematic review, experimental findings, and future perspectives. Hum Brain Mapp 2021; 42:2461-2476. [PMID: 33605512 PMCID: PMC8090781 DOI: 10.1002/hbm.25380] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 02/08/2021] [Accepted: 02/09/2021] [Indexed: 12/14/2022] Open
Abstract
Pain arises from the integration of sensory and cognitive processes in the brain, resulting in specific patterns of neural oscillations that can be characterized by measuring electrical brain activity. Current source density (CSD) estimation from low-resolution brain electromagnetic tomography (LORETA) and its standardized (sLORETA) and exact (eLORETA) variants, is a common approach to identify the spatiotemporal dynamics of the brain sources in physiological and pathological pain-related conditions. However, there is no consensus on the magnitude and variability of clinically or experimentally relevant effects for CSD estimations. Here, we systematically examined reports of sample size calculations and effect size estimations in all studies that included the keywords pain, and LORETA, sLORETA, or eLORETA in Scopus and PubMed. We also assessed the reliability of LORETA CSD estimations during non-painful and painful conditions to estimate hypothetical sample sizes for future experiments using CSD estimations. We found that none of the studies included in the systematic review reported sample size calculations, and less than 20% reported measures of central tendency and dispersion, which are necessary to estimate effect sizes. Based on these data and our experimental results, we determined that sample sizes commonly used in pain studies using CSD estimations are suitable to detect medium and large effect sizes in crossover designs and only large effects in parallel designs. These results provide a comprehensive summary of the effect sizes observed using LORETA in pain research, and this information can be used by clinicians and researchers to improve settings and designs of future pain studies.
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Affiliation(s)
- Juan Manuel Völker
- Integrative Neuroscience Group, Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Federico Gabriel Arguissain
- Integrative Neuroscience Group, Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Ole Kaeseler Andersen
- Integrative Neuroscience Group, Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - José Biurrun Manresa
- Integrative Neuroscience Group, Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.,Institute for Research and Development in Bioengineering and Bioinformatics (IBB), National Scientific and Technical Research Council (CONICET) and National University of Entre Ríos (UNER), Oro Verde, Argentina
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167
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Farahani ED, Wouters J, van Wieringen A. Brain mapping of auditory steady-state responses: A broad view of cortical and subcortical sources. Hum Brain Mapp 2021; 42:780-796. [PMID: 33166050 PMCID: PMC7814770 DOI: 10.1002/hbm.25262] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 10/13/2020] [Accepted: 10/15/2020] [Indexed: 12/21/2022] Open
Abstract
Auditory steady-state responses (ASSRs) are evoked brain responses to modulated or repetitive acoustic stimuli. Investigating the underlying neural generators of ASSRs is important to gain in-depth insight into the mechanisms of auditory temporal processing. The aim of this study is to reconstruct an extensive range of neural generators, that is, cortical and subcortical, as well as primary and non-primary ones. This extensive overview of neural generators provides an appropriate basis for studying functional connectivity. To this end, a minimum-norm imaging (MNI) technique is employed. We also present a novel extension to MNI which facilitates source analysis by quantifying the ASSR for each dipole. Results demonstrate that the proposed MNI approach is successful in reconstructing sources located both within (primary) and outside (non-primary) of the auditory cortex (AC). Primary sources are detected in different stimulation conditions (four modulation frequencies and two sides of stimulation), thereby demonstrating the robustness of the approach. This study is one of the first investigations to identify non-primary sources. Moreover, we show that the MNI approach is also capable of reconstructing the subcortical activities of ASSRs. Finally, the results obtained using the MNI approach outperform the group-independent component analysis method on the same data, in terms of detection of sources in the AC, reconstructing the subcortical activities and reducing computational load.
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Affiliation(s)
- Ehsan Darestani Farahani
- Research Group Experimental ORL, Department of NeurosciencesKatholieke Universiteit LeuvenLeuvenBelgium
| | - Jan Wouters
- Research Group Experimental ORL, Department of NeurosciencesKatholieke Universiteit LeuvenLeuvenBelgium
| | - Astrid van Wieringen
- Research Group Experimental ORL, Department of NeurosciencesKatholieke Universiteit LeuvenLeuvenBelgium
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168
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Sip V, Scholly J, Guye M, Bartolomei F, Jirsa V. Evidence for spreading seizure as a cause of theta-alpha activity electrographic pattern in stereo-EEG seizure recordings. PLoS Comput Biol 2021; 17:e1008731. [PMID: 33635864 PMCID: PMC7946361 DOI: 10.1371/journal.pcbi.1008731] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 03/10/2021] [Accepted: 01/21/2021] [Indexed: 02/07/2023] Open
Abstract
Intracranial electroencephalography is a standard tool in clinical evaluation of patients with focal epilepsy. Various early electrographic seizure patterns differing in frequency, amplitude, and waveform of the oscillations are observed. The pattern most common in the areas of seizure propagation is the so-called theta-alpha activity (TAA), whose defining features are oscillations in the θ - α range and gradually increasing amplitude. A deeper understanding of the mechanism underlying the generation of the TAA pattern is however lacking. In this work we evaluate the hypothesis that the TAA patterns are caused by seizures spreading across the cortex. To do so, we perform simulations of seizure dynamics on detailed patient-derived cortical surfaces using the spreading seizure model as well as reference models with one or two homogeneous sources. We then detect the occurrences of the TAA patterns both in the simulated stereo-electroencephalographic signals and in the signals of recorded epileptic seizures from a cohort of fifty patients, and we compare the features of the groups of detected TAA patterns to assess the plausibility of the different models. Our results show that spreading seizure hypothesis is qualitatively consistent with the evidence available in the seizure recordings, and it can explain the features of the detected TAA groups best among the examined models.
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Affiliation(s)
- Viktor Sip
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Julia Scholly
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, CEMEREM, Pôle d’Imagerie Médicale, CHU, Marseille, France
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, Service de Neurophysiologie Clinique, CHU, Marseille, France
| | - Maxime Guye
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, CEMEREM, Pôle d’Imagerie Médicale, CHU, Marseille, France
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
| | - Fabrice Bartolomei
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, Service de Neurophysiologie Clinique, CHU, Marseille, France
| | - Viktor Jirsa
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
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169
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Sadat-Nejad Y, Beheshti S. Efficient high resolution sLORETA in brain source localization. J Neural Eng 2021; 18. [DOI: 10.1088/1741-2552/abcc48] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 11/19/2020] [Indexed: 11/12/2022]
Abstract
Abstract
Objective. Estimation of the source location within the brain from electroencephalography (EEG) and magnetoencephalography measures is a challenging task. Among the existing techniques in the field, which are known as brain imaging methods, standardized low-resolution brain electromagnetic tomography (sLORETA) is the most popular method due to its simplicity and high accuracy. However, in this work we illustrate that sLORETA is still noisy and the additive noise is causing the blurry image. The existing pre-fixed/manual thresholding process after sLORETA can partially take care of denoising. However, this ad-hoc theresholding can either remove so much of the desired data or leave much of the noise in the process. Manual correction to avoid such extreme cases can be time-consuming. The objective of this paper is to automate the denoising process in the form of adaptive thresholding. Approach. The proposed method, denoted by efficient high-resolution sLORETA (EHR-sLORETA), is based on minimizing the error between the desired denoised source and the source estimates. Main results. The approach is evaluated using synthetic EEG and real EEG data. spatial dispersion (SD), and mean square error (MSE) are used as metrics to provide the quantitative performance of the method. In addition, qualitative analysis of the method is provided for real EEG data. This proposed model demonstrates advantages over the existing methods in sense of accuracy and robustness with SD and MSE comparison. Significance. EHR-sLORETA could have a significant impact on clinical studies with source estimation task, as it improves the accuracy of source estimation and eliminates the need for manual thresholding.
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170
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Jiricek S, Koudelka V, Lacik J, Vejmola C, Kuratko D, Wójcik DK, Raida Z, Hlinka J, Palenicek T. Electrical Source Imaging in Freely Moving Rats: Evaluation of a 12-Electrode Cortical Electroencephalography System. Front Neuroinform 2021; 14:589228. [PMID: 33568980 PMCID: PMC7868391 DOI: 10.3389/fninf.2020.589228] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 12/28/2020] [Indexed: 11/23/2022] Open
Abstract
This work presents and evaluates a 12-electrode intracranial electroencephalography system developed at the National Institute of Mental Health (Klecany, Czech Republic) in terms of an electrical source imaging (ESI) technique in rats. The electrode system was originally designed for translational research purposes. This study demonstrates that it is also possible to use this well-established system for ESI, and estimates its precision, accuracy, and limitations. Furthermore, this paper sets a methodological basis for future implants. Source localization quality is evaluated using three approaches based on surrogate data, physical phantom measurements, and in vivo experiments. The forward model for source localization is obtained from the FieldTrip-SimBio pipeline using the finite-element method. Rat brain tissue extracted from a magnetic resonance imaging template is approximated by a single-compartment homogeneous tetrahedral head model. Four inverse solvers were tested: standardized low-resolution brain electromagnetic tomography, exact low-resolution brain electromagnetic tomography (eLORETA), linear constrained minimum variance (LCMV), and dynamic imaging of coherent sources. Based on surrogate data, this paper evaluates the accuracy and precision of all solvers within the brain volume using error distance and reliability maps. The mean error distance over the whole brain was found to be the lowest in the eLORETA solution through signal to noise ratios (SNRs) (0.2 mm for 25 dB SNR). The LCMV outperformed eLORETA under higher SNR conditions, and exhibiting higher spatial precision. Both of these inverse solvers provided accurate results in a phantom experiment (1.6 mm mean error distance across shallow and 2.6 mm across subcortical testing dipoles). Utilizing the developed technique in freely moving rats, an auditory steady-state response experiment provided results in line with previously reported findings. The obtained results support the idea of utilizing a 12-electrode system for ESI and using it as a solid basis for the development of future ESI dedicated implants.
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Affiliation(s)
- Stanislav Jiricek
- National Institute of Mental Health, Klecany, Czechia
- Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czechia
- Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Prague, Czechia
| | | | - Jaroslav Lacik
- Department of Radioengineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czechia
| | - Cestmir Vejmola
- National Institute of Mental Health, Klecany, Czechia
- Third Faculty of Medicine, Charles University, Prague, Czechia
| | - David Kuratko
- Department of Radioengineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czechia
| | - Daniel K. Wójcik
- Department of Radioengineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czechia
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of the Polish Academy of Sciences, Warsaw, Poland
| | - Zbynek Raida
- Department of Radioengineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czechia
| | - Jaroslav Hlinka
- National Institute of Mental Health, Klecany, Czechia
- Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Prague, Czechia
| | - Tomas Palenicek
- National Institute of Mental Health, Klecany, Czechia
- Third Faculty of Medicine, Charles University, Prague, Czechia
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171
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Abstract
Due to the proliferation of brain and neurological disorders (World Health Organization 2006), EEG (Blinowska and Durka 2006) is gaining attention as a support for decision making in the fields of neurology, psychology, and psychiatry. But EEG data are not always easy to understand. Therefore, extracting the desired information from EEG data in different contexts is an important requirement. This article analyses state-of-the-art EEG signal processing tools and proposes a new one: Signaleeg. This addresses the limitations of previous tools. It has been designed with the aim of helping users to build predictive models from EEG signals in a process that is called signal-data mining (DM). Moreover, Signaleeg is user friendly and multi-threaded, with optimisation facilities for finding the best predictive model. It has been implemented and tested in three scenarios: schizophrenia diagnosis, alcoholism detection, and emotion recognition. The tool provided good results in each case, thus demonstrating its versatility.
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172
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Liu F, Wang L, Lou Y, Li RC, Purdon PL. Probabilistic Structure Learning for EEG/MEG Source Imaging With Hierarchical Graph Priors. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:321-334. [PMID: 32956052 DOI: 10.1109/tmi.2020.3025608] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Brain source imaging is an important method for noninvasively characterizing brain activity using Electroencephalogram (EEG) or Magnetoencephalography (MEG) recordings. Traditional EEG/MEG Source Imaging (ESI) methods usually assume the source activities at different time points are unrelated, and do not utilize the temporal structure in the source activation, making the ESI analysis sensitive to noise. Some methods may encourage very similar activation patterns across the entire time course and may be incapable of accounting the variation along the time course. To effectively deal with noise while maintaining flexibility and continuity among brain activation patterns, we propose a novel probabilistic ESI model based on a hierarchical graph prior. Under our method, a spanning tree constraint ensures that activity patterns have spatiotemporal continuity. An efficient algorithm based on an alternating convex search is presented to solve the resulting problem of the proposed model with guaranteed convergence. Comprehensive numerical studies using synthetic data on a realistic brain model are conducted under different levels of signal-to-noise ratio (SNR) from both sensor and source spaces. We also examine the EEG/MEG datasets in two real applications, in which our ESI reconstructions are neurologically plausible. All the results demonstrate significant improvements of the proposed method over benchmark methods in terms of source localization performance, especially at high noise levels.
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173
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Campanella S, Arikan K, Babiloni C, Balconi M, Bertollo M, Betti V, Bianchi L, Brunovsky M, Buttinelli C, Comani S, Di Lorenzo G, Dumalin D, Escera C, Fallgatter A, Fisher D, Giordano GM, Guntekin B, Imperatori C, Ishii R, Kajosch H, Kiang M, López-Caneda E, Missonnier P, Mucci A, Olbrich S, Otte G, Perrottelli A, Pizzuti A, Pinal D, Salisbury D, Tang Y, Tisei P, Wang J, Winkler I, Yuan J, Pogarell O. Special Report on the Impact of the COVID-19 Pandemic on Clinical EEG and Research and Consensus Recommendations for the Safe Use of EEG. Clin EEG Neurosci 2021; 52:3-28. [PMID: 32975150 PMCID: PMC8121213 DOI: 10.1177/1550059420954054] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
INTRODUCTION The global COVID-19 pandemic has affected the economy, daily life, and mental/physical health. The latter includes the use of electroencephalography (EEG) in clinical practice and research. We report a survey of the impact of COVID-19 on the use of clinical EEG in practice and research in several countries, and the recommendations of an international panel of experts for the safe application of EEG during and after this pandemic. METHODS Fifteen clinicians from 8 different countries and 25 researchers from 13 different countries reported the impact of COVID-19 on their EEG activities, the procedures implemented in response to the COVID-19 pandemic, and precautions planned or already implemented during the reopening of EEG activities. RESULTS Of the 15 clinical centers responding, 11 reported a total stoppage of all EEG activities, while 4 reduced the number of tests per day. In research settings, all 25 laboratories reported a complete stoppage of activity, with 7 laboratories reopening to some extent since initial closure. In both settings, recommended precautions for restarting or continuing EEG recording included strict hygienic rules, social distance, and assessment for infection symptoms among staff and patients/participants. CONCLUSIONS The COVID-19 pandemic interfered with the use of EEG recordings in clinical practice and even more in clinical research. We suggest updated best practices to allow safe EEG recordings in both research and clinical settings. The continued use of EEG is important in those with psychiatric diseases, particularly in times of social alarm such as the COVID-19 pandemic.
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Affiliation(s)
- Salvatore Campanella
- Laboratoire de Psychologie Médicale et d'Addictologie, ULB Neuroscience Institute (UNI), CHU Brugmann-Université Libre de Bruxelles (U.L.B.), Belgium
| | - Kemal Arikan
- Kemal Arıkan Psychiatry Clinic, Istanbul, Turkey
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "Erspamer", Sapienza University of Rome, Italy.,San Raffaele Cassino, Cassino (FR), Italy
| | - Michela Balconi
- Research Unit in Affective and Social Neuroscience, Department of Psychology, Catholic University of Milan, Milan, Italy
| | - Maurizio Bertollo
- BIND-Behavioral Imaging and Neural Dynamics Center, Department of Neuroscience, Imaging and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Viviana Betti
- Department of Psychology, Sapienza University of Rome, Fondazione Santa Lucia, Rome, Italy
| | - Luigi Bianchi
- Dipartimento di Ingegneria Civile e Ingegneria Informatica (DICII), University of Rome Tor Vergata, Rome, Italy
| | - Martin Brunovsky
- National Institute of Mental Health, Klecany Czech Republic.,Third Medical Faculty, Charles University, Prague, Czech Republic
| | - Carla Buttinelli
- Department of Neurosciences, Public Health and Sense Organs (NESMOS), Sapienza University of Rome, Rome, Italy
| | - Silvia Comani
- BIND-Behavioral Imaging and Neural Dynamics Center, Department of Neuroscience, Imaging and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Giorgio Di Lorenzo
- Laboratory of Psychophysiology and Cognitive Neuroscience, Chair of Psychiatry, Department of Systems Medicine, School of Medicine and Surgery, University of Rome Tor Vergata, Rome, Italy.,IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Daniel Dumalin
- AZ Sint-Jan Brugge-Oostende AV, Campus Henri Serruys, Lab of Neurophysiology, Department Neurology-Psychiatry, Ostend, Belgium
| | - Carles Escera
- Brainlab-Cognitive Neuroscience Research Group, Department of Clinical Psychology and Psychobiology, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
| | - Andreas Fallgatter
- Department of Psychiatry, University of Tübingen, Germany; LEAD Graduate School and Training Center, Tübingen, Germany.,German Center for Neurodegenerative Diseases DZNE, Tübingen, Germany
| | - Derek Fisher
- Department of Psychology, Mount Saint Vincent University, and Department of Psychiatry, Nova Scotia Health Authority, Halifax, Nova Scotia, Canada
| | | | - Bahar Guntekin
- Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Claudio Imperatori
- Cognitive and Clinical Psychology Laboratory, Department of Human Science, European University of Rome, Rome, Italy
| | - Ryouhei Ishii
- Department of Psychiatry Osaka University Graduate School of Medicine, Osaka, Japan
| | - Hendrik Kajosch
- Laboratoire de Psychologie Médicale et d'Addictologie, ULB Neuroscience Institute (UNI), CHU Brugmann-Université Libre de Bruxelles (U.L.B.), Belgium
| | - Michael Kiang
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Eduardo López-Caneda
- Psychological Neuroscience Laboratory, Center for Research in Psychology, School of Psychology, University of Minho, Braga, Portugal
| | - Pascal Missonnier
- Mental Health Network Fribourg (RFSM), Sector of Psychiatry and Psychotherapy for Adults, Marsens, Switzerland
| | - Armida Mucci
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Sebastian Olbrich
- Psychotherapy and Psychosomatics, Department for Psychiatry, University Hospital Zurich, Zurich, Switzerland
| | | | - Andrea Perrottelli
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Alessandra Pizzuti
- Department of Psychology, Sapienza University of Rome, Fondazione Santa Lucia, Rome, Italy
| | - Diego Pinal
- Psychological Neuroscience Laboratory, Center for Research in Psychology, School of Psychology, University of Minho, Braga, Portugal
| | - Dean Salisbury
- Clinical Neurophysiology Research Laboratory, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Paolo Tisei
- Department of Neurosciences, Public Health and Sense Organs (NESMOS), Sapienza University of Rome, Rome, Italy
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Istvan Winkler
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
| | - Jiajin Yuan
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
| | - Oliver Pogarell
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
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174
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Zaboski BA, Stern EF, Skosnik PD, Pittenger C. Electroencephalographic Correlates and Predictors of Treatment Outcome in OCD: A Brief Narrative Review. Front Psychiatry 2021; 12:703398. [PMID: 34408681 PMCID: PMC8365146 DOI: 10.3389/fpsyt.2021.703398] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 06/21/2021] [Indexed: 12/28/2022] Open
Abstract
Electroencephalography (EEG) measures the brain's electrical activity with high temporal resolution. In comparison to neuroimaging modalities such as MRI or PET, EEG is relatively cheap, non-invasive, portable, and simple to administer, making it an attractive tool for clinical deployment. Despite this, studies utilizing EEG to investigate obsessive-compulsive disorder (OCD) are relatively sparse. This contrasts with a robust literature using other brain imaging methodologies. The present review examines studies that have used EEG to examine predictors and correlates of response in OCD and draws tentative conclusions that may guide much needed future work. Key findings include a limited literature base; few studies have attempted to predict clinical change from EEG signals, and they are confounded by the effects of both pharmacotherapy and psychotherapy. The most robust literature, consisting of several studies, has examined event-related potentials, including the P300, which several studies have reported to be abnormal at baseline in OCD and to normalize with treatment; but even here the literature is quite heterogeneous, and more work is needed. With more robust research, we suggest that the relatively low cost and convenience of EEG, especially in comparison to fMRI and PET, make it well-suited to the development of feasible personalized treatment algorithms.
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Affiliation(s)
- Brian A Zaboski
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Elisa F Stern
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Patrick D Skosnik
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Christopher Pittenger
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT, United States
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175
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Hindriks R. Lag-invariant detection of interactions in spatially-extended systems using linear inverse modeling. PLoS One 2020; 15:e0242715. [PMID: 33306719 PMCID: PMC7732350 DOI: 10.1371/journal.pone.0242715] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 11/07/2020] [Indexed: 11/18/2022] Open
Abstract
Measurements on physical systems result from the systems' activity being converted into sensor measurements by a forward model. In a number of cases, inversion of the forward model is extremely sensitive to perturbations such as sensor noise or numerical errors in the forward model. Regularization is then required, which introduces bias in the reconstruction of the systems' activity. One domain in which this is particularly problematic is the reconstruction of interactions in spatially-extended complex systems such as the human brain. Brain interactions can be reconstructed from non-invasive measurements such as electroencephalography (EEG) or magnetoencephalography (MEG), whose forward models are linear and instantaneous, but have large null-spaces and high condition numbers. This leads to incomplete unmixing of the forward models and hence to spurious interactions. This motivated the development of interaction measures that are exclusively sensitive to lagged, i.e. delayed interactions. The drawback of such measures is that they only detect interactions that have sufficiently large lags and this introduces bias in reconstructed brain networks. We introduce three estimators for linear interactions in spatially-extended systems that are uniformly sensitive to all lags. We derive some basic properties of and relationships between the estimators and evaluate their performance using numerical simulations from a simple benchmark model.
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Affiliation(s)
- Rikkert Hindriks
- Department of Mathematics, VU University Amsterdam, Amsterdam, The Netherlands
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176
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Fernandez-Corazza M, Feng R, Ma C, Hu J, Pan L, Luu P, Tucker D. Source localization of epileptic spikes using Multiple Sparse Priors. Clin Neurophysiol 2020; 132:586-597. [PMID: 33477100 PMCID: PMC7971150 DOI: 10.1016/j.clinph.2020.10.030] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 09/10/2020] [Accepted: 10/21/2020] [Indexed: 11/16/2022]
Abstract
OBJECTIVE To evaluate epileptic source estimation using multiple sparse priors (MSP) inverse method and high-resolution, individual electrical head models. METHODS Accurate source localization is dependent on accurate electrical head models and appropriate inverse solvers. Using high-resolution, individual electrical head models in fifteen epilepsy patients, with surgical resection and clinical outcome as criteria for accuracy, performance of MSP method was compared against standardized low-resolution brain electromagnetic tomography (sLORETA) and coherent maximum entropy on the mean (cMEM) methods. RESULTS The MSP method performed similarly to the sLORETA method and slightly better than the cMEM method in terms of success rate. The MSP and cMEM methods were more focal than sLORETA with the advantage of not requiring an arbitrary selection of a hyperparameter or thresholding of reconstructed current density values to determine focus. MSP and cMEM methods were better than sLORETA in terms of spatial dispersion. CONCLUSIONS Results suggest that the three methods are complementary and could be used together. In practice, the MSP method will be easier to use and interpret compared to sLORETA, and slightly more accurate and faster than the cMEM method. SIGNIFICANCE Source localization of interictal spikes from dense-array electroencephalography data has been shown to be a reliable marker of epileptic foci and useful for pre-surgical planning. The advantages of MSP make it a useful complement to other inverse solvers in clinical practice.
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Affiliation(s)
- Mariano Fernandez-Corazza
- LEICI Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales, Universidad Nacional de La Plata - CONICET, Argentina.
| | - Rui Feng
- Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai, China
| | - Chengxin Ma
- Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai, China
| | - Jie Hu
- Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai, China
| | - Li Pan
- Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai, China
| | - Phan Luu
- Brain Electrophysiology Laboratory (BEL) Company, Eugene, OR, USA; NeuroInformatics Center, University of Oregon, Eugene, OR, USA
| | - Don Tucker
- Brain Electrophysiology Laboratory (BEL) Company, Eugene, OR, USA; NeuroInformatics Center, University of Oregon, Eugene, OR, USA
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177
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Galaris E, Gallos I, Myatchin I, Lagae L, Siettos C. Electroencephalography source localization analysis in epileptic children during a visual working-memory task. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2020; 36:e3404. [PMID: 33029905 DOI: 10.1002/cnm.3404] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 06/15/2020] [Accepted: 09/28/2020] [Indexed: 06/11/2023]
Abstract
We localize the sources of brain activity of children with epilepsy based on electroencephalograph (EEG) recordings acquired during a visual discrimination working memory task. For the numerical solution of the inverse problem, with the aid of age-specific MRI scans processed from a publicly available database, we use and compare three regularization numerical methods, namely the standardized low resolution brain electromagnetic tomography (sLORETA), the weighted minimum norm estimation (wMNE) and the dynamic statistical parametric mapping (dSPM). We show that all three methods provide the same spatio-temporal patterns of differences between the groups of epileptic and control children. In particular, our analysis reveals statistically significant differences between the two groups in regions of the parietal cortex indicating that these may serve as "biomarkers" for diagnostic purposes and ultimately localized treatment.
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Affiliation(s)
- Evangelos Galaris
- Dipartimento di Matematica e Applicazioni "Renato Caccioppoli", Universita' degli Studi di Napoli Federico II, Napoli, Italy
| | - Ioannis Gallos
- School of Applied Mathematical and Physical Sciences, National Technical University of Athens, Athens, Greece
| | - Ivan Myatchin
- Department of Anesthesiology, Sint-Trudo Regional Hospital, Sint-Truiden, Belgium
| | - Lieven Lagae
- Department of Development and Regeneration, Section Paediatric Neurology, KU Leuven, Leuven, Belgium
| | - Constantinos Siettos
- Dipartimento di Matematica e Applicazioni "Renato Caccioppoli", Universita' degli Studi di Napoli Federico II, Napoli, Italy
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178
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Hirth LN, Stanley CJ, Damiano DL, Bulea TC. Algorithmic localization of high-density EEG electrode positions using motion capture. J Neurosci Methods 2020; 346:108919. [PMID: 32853593 DOI: 10.1016/j.jneumeth.2020.108919] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 08/19/2020] [Accepted: 08/19/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND Accurate source localization from electroencephalography (EEG) requires electrode co-registration to brain anatomy, a process that depends on precise measurement of 3D scalp locations. Stylus digitizers and camera-based scanners for such measurements require the subject to remain still and therefore are not ideal for young children or those with movement disorders. NEW METHOD Motion capture accurately measures electrode position in one frame but marker placement adds significant setup time, particularly in high-density EEG. We developed an algorithm, named MoLo and implemented as an open-source MATLAB toolbox, to compute 3D electrode coordinates from a subset of positions measured in motion capture using spline interpolation. Algorithm accuracy was evaluated across 5 different-sized head models. RESULTS MoLo interpolation reduced setup time by approximately 10 min for 64-channel EEG. Mean electrode interpolation error was 2.95 ± 1.3 mm (range: 0.38-7.98 mm). Source localization errors with interpolated compared to true electrode locations were below 1 mm and 0.1 mm in 75 % and 35 % of dipoles, respectively. COMPARISON WITH EXISTING METHODS MoLo location accuracy is comparable to stylus digitizers and camera-scanners, common in clinical research. The MoLo algorithm could be deployed with other tools beyond motion capture, e.g., a stylus, to extract high-density EEG electrode locations from a subset of measured positions. The algorithm is particularly useful for research involving young children and others who cannot remain still for extended time periods. CONCLUSIONS Electrode position and source localization errors with MoLo are similar to other modalities supporting its use to measure high-density EEG electrode positions in research and clinical settings.
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Affiliation(s)
- Lauren N Hirth
- Functional and Applied Biomechanics Section, Rehabilitation Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Christopher J Stanley
- Functional and Applied Biomechanics Section, Rehabilitation Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Diane L Damiano
- Functional and Applied Biomechanics Section, Rehabilitation Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Thomas C Bulea
- Functional and Applied Biomechanics Section, Rehabilitation Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD, USA.
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179
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Monin MY, Rahmouni L, Merlini A, Andriulli FP. A Hybrid Volume-Surface-Wire Integral Equation for the Anisotropic Forward Problem in Electroencephalography. ACTA ACUST UNITED AC 2020. [DOI: 10.1109/jerm.2020.2966121] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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180
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A Novel Bayesian Approach for EEG Source Localization. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:8837954. [PMID: 33178259 PMCID: PMC7647781 DOI: 10.1155/2020/8837954] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 09/28/2020] [Accepted: 10/15/2020] [Indexed: 12/01/2022]
Abstract
We propose a new method for EEG source localization. An efficient solution to this problem requires choosing an appropriate regularization term in order to constraint the original problem. In our work, we adopt the Bayesian framework to place constraints; hence, the regularization term is closely connected to the prior distribution. More specifically, we propose a new sparse prior for the localization of EEG sources. The proposed prior distribution has sparse properties favoring focal EEG sources. In order to obtain an efficient algorithm, we use the variational Bayesian (VB) framework which provides us with a tractable iterative algorithm of closed-form equations. Additionally, we provide extensions of our method in cases where we observe group structures and spatially extended EEG sources. We have performed experiments using synthetic EEG data and real EEG data from three publicly available datasets. The real EEG data are produced due to the presentation of auditory and visual stimulus. We compare the proposed method with well-known approaches of EEG source localization and the results have shown that our method presents state-of-the-art performance, especially in cases where we expect few activated brain regions. The proposed method can effectively detect EEG sources in various circumstances. Overall, the proposed sparse prior for EEG source localization results in more accurate localization of EEG sources than state-of-the-art approaches.
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181
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The Value of Source Localization for Clinical Magnetoencephalography: Beyond the Equivalent Current Dipole. J Clin Neurophysiol 2020; 37:537-544. [DOI: 10.1097/wnp.0000000000000487] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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182
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From shape to meaning: Evidence for multiple fast feedforward hierarchies of concept processing in the human brain. Neuroimage 2020; 221:117148. [PMID: 32659350 DOI: 10.1016/j.neuroimage.2020.117148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 06/10/2020] [Accepted: 07/06/2020] [Indexed: 11/23/2022] Open
Abstract
A number of fMRI studies have provided support for the existence of multiple concept representations in areas of the brain such as the anterior temporal lobe (ATL) and inferior parietal lobule (IPL). However, the interaction among different conceptual representations remains unclear. To better understand the dynamics of how the brain extracts meaning from sensory stimuli, we conducted a human high-density electroencephalography (EEG) study in which we first trained participants to associate pseudowords with various animal and tool concepts. After training, multivariate pattern classification of EEG signals in sensor and source space revealed the representation of both animal and tool concepts in the left ATL and tool concepts within the left IPL within 250 ms. Finally, we used Granger Causality analyses to show that orthography-selective sensors directly modulated activity in the parietal-tool selective cluster. Together, our results provide evidence for distinct but parallel "perceptual-to-conceptual" feedforward hierarchies in the brain.
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183
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Ludyga S, Möhring W, Budde H, Hirt N, Pühse U, Gerber M. Neurocognitive processes mediate the relation between children's motor skills, cardiorespiratory fitness and response inhibition: Evidence from source imaging. Psychophysiology 2020; 58:e13716. [PMID: 33128487 DOI: 10.1111/psyp.13716] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 08/26/2020] [Accepted: 10/02/2020] [Indexed: 01/20/2023]
Abstract
Accumulating evidence suggests an association between outcomes of sports participation, such as motor skills and cardiorespiratory fitness, and aspects of inhibitory control in children. However, it remains unclear if motor skills and cardiorespiratory fitness are related to different source activity patterns and if neurophysiological indices of response inhibition mediate the relation of these constructs with behavioral performance. We examined the relative contributions of motor skills and cardiorespiratory fitness to response inhibition and a potential mediation by the neurocognitive processes indexed by the N200 and P300 components of event-related potentials. About 92 children aged 9-13 years completed the Movement ABC-2, the PWC170 and a Go/NoGo task. We employed electroencephalography (EEG) to record the N200 and P300 components elicited by the task, which are considered to reflect conflict monitoring and the allocation of attentional resources toward task-relevant stimuli, respectively. Path-anlayses revealed a moderate association between motor skills and behavioral performance on the Go/NoGo task. This association was fully mediated by the P300 amplitude in the NoGo condition. In contrast, cardiorespiratory fitness was not related to behavioral performance, but accounted for variance in N200. Source analyses supported an association between cardiorespiratory fitness and N200 source activity in prefrontal and primary motor cortex, whereas motor skills were related to P300 source activity in the posterior cingulate cortex. Our findings provide novel insights into the neural mechanisms underlying the relation between motor skills and response inhibition. Moreover, we found that the neural generators of the P300 and N200 varied as a function of children's cardiorespiratory fitness and motor skills.
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Affiliation(s)
- Sebastian Ludyga
- Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Wenke Möhring
- Department of Psychology, University of Basel, Basel, Switzerland
| | - Henning Budde
- Faculty of Human Sciences, Medical School Hamburg, Hamburg, Germany
| | - Nick Hirt
- Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Uwe Pühse
- Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
| | - Markus Gerber
- Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland
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184
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Resting EEG Asymmetry Markers of Multiple Facets of the Behavioral Approach System: A LORETA Analysis. Symmetry (Basel) 2020. [DOI: 10.3390/sym12111794] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Previously published models of frontal activity linked high relative left frontal activity to the behavioral approach system (BAS) and impulsivity. Additionally, these models did not account for BAS facets encompassing the anticipation of reward, i.e., goal-driven persistence (BAS–GDP) and reward interest (BAS–RI), from those that deal with the actual hedonic experience of reward, i.e., reward reactivity (BAS–RR) and impulsivity (BAS–I). Using resting electroencephalographic (EEG) recordings, the source localization (LORETA) method allowed us to calculate the hemispheric asymmetry of the current density within the alpha band (7.5–13 Hz) in ten regions of interest. Compared to low BAS subtrait scorers, high BAS subtrait scorers (except for BAS–I) were correlated with greater relative left-sided activity in the superior frontal gyrus (BA10). Further, an isolated effective coherence (iCOH) analysis of the beta activity (21 Hz) disclosed that high impulsive scorers as compared to low impulsive ones had higher connectivity between the superior frontal gyrus and middle temporal gyrus, which was not compensated for by enhanced inhibitory alpha (11 Hz) connectivity between these regions. For the beta frequency, we also found in highly impulsive individuals that (i) both left and right middle temporal lobes directly influenced the activity of the left and right superior frontal lobes, and (ii) a clear decoupling between left and right superior frontal lobes. These findings could indicate reduced control by the supervisory system in more impulsive individuals.
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185
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Mazurek KA, Patelaki E, Foxe JJ, Freedman EG. Using the MoBI motion capture system to rapidly and accurately localize EEG electrodes in anatomic space. Eur J Neurosci 2020; 54:8396-8405. [PMID: 33103279 PMCID: PMC8573528 DOI: 10.1111/ejn.15019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 10/09/2020] [Accepted: 10/13/2020] [Indexed: 11/30/2022]
Abstract
During mobile brain/body imaging (MoBI) experiments, electroencephalography and motion capture systems are used in concert to record high temporal resolution neural activity and movement kinematics while participants perform demanding perceptual and cognitive tasks in a naturalistic environment. A typical MoBI setup involves positioning multi‐channel electrode caps based on anatomical fiducials as well as experimenter and participant intuition regarding the scalp midpoint location (i.e., Cz). Researchers often use the “template” electrode locations provided by the manufacturer, however, the “actual” electrode locations can vary based on each participant's head morphology. Accounting for differences in head morphologies could provide more accurate clinical diagnostic information when using MoBI to identify neurological deficits in patients with motor, sensory, or cognitive impairments. Here, we asked whether the existing motion capture system used in a MoBI setup could be easily adapted to improve spatial localization of electrodes across participants without requiring additional or specialized equipment that might impede clinical adoption. Using standard electrode configurations, infrared markers were placed on a subset of electrodes and anatomical fiducials, and the remaining electrode locations were estimated using spherical or ellipsoid models. We identified differences in event‐related potentials between “template” and “actual” electrode locations during a Go/No‐Go task (p < 9.8e–5) and an object‐manipulation task (p < 9.8e–5). Thus, the motion capture system already used in MoBI experiments can be effectively deployed to accurately register and quantify the neural activity. Improving the spatial localization without needing specialized hardware or additional setup time to the workflow has important real‐world implications for translating MoBI to clinical environments.
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Affiliation(s)
- Kevin A Mazurek
- The Cognitive Neurophysiology Laboratory, The Del Monte Institute for Neuroscience, Department of Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
| | - Eleni Patelaki
- Department of Biomedical Engineering, University of Rochester, Rochester, NY, USA
| | - John J Foxe
- The Cognitive Neurophysiology Laboratory, The Del Monte Institute for Neuroscience, Department of Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
| | - Edward G Freedman
- The Cognitive Neurophysiology Laboratory, The Del Monte Institute for Neuroscience, Department of Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
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186
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Koizumi K, Ueda K, Li Z, Nakao M. Effects of Transcranial Direct Current Stimulation on Brain Networks Related to Creative Thinking. Front Hum Neurosci 2020; 14:541052. [PMID: 33192387 PMCID: PMC7596331 DOI: 10.3389/fnhum.2020.541052] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 09/16/2020] [Indexed: 11/13/2022] Open
Abstract
Human creative thinking is unique and capable of generating novel and valuable ideas. Recent research has clarified the contribution of different brain networks (default mode network, DN; executive control network; salience network) to creative thinking. However, the effects of brain stimulation on brain networks during creative thinking and on creative performance have not been clarified. The present study was designed to examine the changes in functional connectivity (FC) and effective connectivity (EC) of the large-scale brain network, and the ensuing changes in creative performance, induced by transcranial direct current stimulation (tDCS). Fourteen healthy male students underwent two tDCS sessions, one with actual stimulation and one with sham stimulation, on two separate days. Participants underwent tDCS (anode over the left dorsolateral prefrontal cortex, DLPFC; cathode over the right inferior parietal lobule, IPL) for 20 min. Before and after the tDCS session, electroencephalography signals were acquired from 32 electrodes over the whole head during the creative thinking task. On FC analysis, the delta band FC between the posterior cingulate cortex and IPL significantly increased only after real stimulation. We also found that the change of flexibility score was significantly correlated with the change in: (i) delta band FC between mPFC and left lateral temporal cortex (LTC) and (ii) alpha band FC between IPL and right LTC. On EC analysis, decreased flow within the DN (from left LTC to right IPL) was observed. Our results reveal that tDCS could affect brain networks, particularly the DN, during creative thinking and modulate key FC in the generation of flexible creative ideas.
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Affiliation(s)
| | - Kazutaka Ueda
- Creative Design Laboratory, Department of Mechanical Engineering, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan
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187
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Rodríguez-González V, Gómez C, Shigihara Y, Hoshi H, Revilla-Vallejo M, Hornero R, Poza J. Consistency of local activation parameters at sensor- and source-level in neural signals. J Neural Eng 2020; 17:056020. [PMID: 33055364 DOI: 10.1088/1741-2552/abb582] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
OBJECTIVE Although magnetoencephalography and electroencephalography (M/EEG) signals at sensor level are robust and reliable, they suffer from different degrees of distortion due to changes in brain tissue conductivities, known as field spread and volume conduction effects. To estimate original neural generators from M/EEG activity acquired at sensor level, diverse source localisation algorithms have been proposed; however, they are not exempt from limitations and usually involve time-consuming procedures. Connectivity and network-based M/EEG analyses have been found to be affected by field spread and volume conduction effects; nevertheless, the influence of the aforementioned effects on widely used local activation parameters has not been assessed yet. The goal of this study is to evaluate the consistency of various local activation parameters when they are computed at sensor- and source-level. APPROACH Six spectral (relative power, median frequency, and individual alpha frequency) and non-linear parameters (Lempel-Ziv complexity, sample entropy, and central tendency measure) are computed from M/EEG signals at sensor- and source-level using four source inversion methods: weighted minimum norm estimate (wMNE), standardised low-resolution brain electromagnetic tomography (sLORETA), linear constrained minimum variance (LCMV), and dynamical statistical parametric mapping (dSPM). MAIN RESULTS Our results show that the spectral and non-linear parameters yield similar results at sensor- and source-level, showing high correlation values between them for all the source inversion methods evaluated and both modalities of signal, EEG and MEG. Furthermore, the correlation values remain high when performing coarse-grained spatial analyses. SIGNIFICANCE To the best of our knowledge, this is the first study analysing how field spread and volume conduction effects impact on local activation parameters computed from resting-state neural activity. Our findings evidence that local activation parameters are robust against field spread and volume conduction effects and provide equivalent information at sensor- and source-level even when performing regional analyses.
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188
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Anzalone C, Luedke JC, Green JJ, Decker SL. QEEG coherence patterns related to mathematics ability in children. APPLIED NEUROPSYCHOLOGY-CHILD 2020; 11:328-338. [PMID: 33052731 DOI: 10.1080/21622965.2020.1830403] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
The current study investigates the utility of resting-state EEG coherence values in predicting standardized math scores in children. Quantitative EEG and standardized academic achievement analyses were performed on 60 school-aged children. Analyses assessing intrahemispheric coherence at rest were conducted across the sample of participants and several coherence networks were extracted and compared to standardized math achievement values. Specifically, networks that included Brodmann area 40 (a brain region involved in the cognitive processes responsible for mathematics performance) and whose coherence values were significantly correlated with standardized math scores were examined. Results indicate a total of four coherence networks, two in each hemisphere, that have utility in predicting general math skills in children. In addition to BA 40, these coherence networks include BAs in the right temporal lobe, right frontoparietal lobe, left superior temporal lobe, and the left medial prefrontal cortex. These findings address the current dearth of research on the neurological connectivity patterns that are foundational for mathematics abilities in children. Further, these results lay a foundation for the supplementary use of EEG in the assessment and identification practices surrounding math learning disabilities in children and additionally provide a neurocognitive framework upon which intervention research may be targeted.
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Affiliation(s)
- Christopher Anzalone
- Department of Psychology, University of South Carolina, Columbia, South Carolina, USA
| | - Jessica C Luedke
- Department of Psychology, University of South Carolina, Columbia, South Carolina, USA
| | - Jessica J Green
- Department of Psychology, University of South Carolina, Columbia, South Carolina, USA
| | - Scott L Decker
- Department of Psychology, University of South Carolina, Columbia, South Carolina, USA
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189
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Mora-Gonzalez J, Esteban-Cornejo I, Migueles JH, Rodriguez-Ayllon M, Molina-Garcia P, Cadenas-Sanchez C, Solis-Urra P, Plaza-Florido A, Kramer AF, Erickson KI, Hillman CH, Catena A, Ortega FB. Physical fitness and brain source localization during a working memory task in children with overweight/obesity: The ActiveBrains project. Dev Sci 2020; 24:e13048. [PMID: 33037758 DOI: 10.1111/desc.13048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 06/02/2020] [Accepted: 10/05/2020] [Indexed: 11/26/2022]
Abstract
The present study aims (i) to examine the association of physical fitness components (i.e., cardiorespiratory fitness, speed-agility, and muscular fitness) with brain current source density during working memory; and (ii) to examine whether fitness-related current density was associated to working memory performance and academic achievement. Eighty-five children with overweight/obesity aged 8-11 years participated in this cross-sectional study. Physical fitness components were assessed using the ALPHA test battery. Electroencephalography recordings were performed during a Delayed Non-Match-to-Sample task that assessed working memory. Brain source analysis was carried out using sLORETA to estimate regional current source density differences between high and low (H-L) working memory loads. Academic achievement was measured by the Spanish version of the Woodcock-Johnson III test battery. The main results showed that higher cardiorespiratory fitness was associated with higher H-L current density differences in frontal, limbic, and occipital regions during encoding and maintenance task's phases (β≥0.412, p ≤ 0.019). A limbic area was further related to better working memory performance (β=0.267, p = 0.005). During retrieval, higher cardiorespiratory fitness was also associated with higher current density in temporal regions (β=0.265, p = 0.013), whereas lower muscular fitness was associated with higher current density in frontal regions (β=-0.261, p = 0.016). Our results suggest that cardiorespiratory fitness, but not speed-agility nor muscular fitness, is positively associated with brain current source density during working memory processes in children with overweight/obesity. Fitness-related current density differences in limbic regions were associated with better working memory.
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Affiliation(s)
- Jose Mora-Gonzalez
- PROFITH "PROmoting FITness and Health through physical activity" Research Group, Department of Physical Education and Sports, Faculty of Sports Science, University of Granada, Granada, Spain.,College of Health and Human Services, University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Irene Esteban-Cornejo
- PROFITH "PROmoting FITness and Health through physical activity" Research Group, Department of Physical Education and Sports, Faculty of Sports Science, University of Granada, Granada, Spain
| | - Jairo H Migueles
- PROFITH "PROmoting FITness and Health through physical activity" Research Group, Department of Physical Education and Sports, Faculty of Sports Science, University of Granada, Granada, Spain
| | - María Rodriguez-Ayllon
- PROFITH "PROmoting FITness and Health through physical activity" Research Group, Department of Physical Education and Sports, Faculty of Sports Science, University of Granada, Granada, Spain
| | - Pablo Molina-Garcia
- PROFITH "PROmoting FITness and Health through physical activity" Research Group, Department of Physical Education and Sports, Faculty of Sports Science, University of Granada, Granada, Spain.,Department of Rehabilitation Sciences, KU Leuven - University of Leuven, Leuven, Belgium
| | - Cristina Cadenas-Sanchez
- PROFITH "PROmoting FITness and Health through physical activity" Research Group, Department of Physical Education and Sports, Faculty of Sports Science, University of Granada, Granada, Spain.,Institute for Innovation & Sustainable Development in Food Chain (IS-FOOD), Public University of Navarra, Pamplona, Spain
| | - Patricio Solis-Urra
- PROFITH "PROmoting FITness and Health through physical activity" Research Group, Department of Physical Education and Sports, Faculty of Sports Science, University of Granada, Granada, Spain.,IRyS Research Group, School of Physical Education, Pontificia Universidad Católica de Valparaiso, Valparaiso, Chile
| | - Abel Plaza-Florido
- PROFITH "PROmoting FITness and Health through physical activity" Research Group, Department of Physical Education and Sports, Faculty of Sports Science, University of Granada, Granada, Spain
| | - Arthur F Kramer
- Department of Psychology, Northeastern University, Boston, MA, USA.,Beckman Institute, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Kirk I Erickson
- Department of Psychology, University of Pittsburgh, Sennott Square, Pittsburgh, PA, USA
| | - Charles H Hillman
- Department of Psychology, Northeastern University, Boston, MA, USA.,Department of Physical Therapy, Movement & Rehabilitation Sciences, Northeastern University, Boston, MA, USA
| | - Andrés Catena
- Department of Experimental Psychology, Mind, Brain and Behaviour Research Centre (CIMCYC), University of Granada, Granada, Spain
| | - Francisco B Ortega
- PROFITH "PROmoting FITness and Health through physical activity" Research Group, Department of Physical Education and Sports, Faculty of Sports Science, University of Granada, Granada, Spain
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190
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Hindriks R. A methodological framework for inverse-modeling of propagating cortical activity using MEG/EEG. Neuroimage 2020; 223:117345. [PMID: 32896634 DOI: 10.1016/j.neuroimage.2020.117345] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 08/18/2020] [Accepted: 09/01/2020] [Indexed: 11/16/2022] Open
Abstract
The prevailing view on the dynamics of large-scale electrical activity in the human cortex is that it constitutes a functional network of discrete and localized circuits. Within this view, a natural way to analyse magnetoencephalographic (MEG) and electroencephalographic (EEG) data is by adopting methods from network theory. Invasive recordings, however, demonstrate that cortical activity is spatially continuous, rather than discrete, and exhibits propagation behavior. Furthermore, human cortical activity is known to propagate under a variety of conditions such as non-REM sleep, general anesthesia, and coma. Although several MEG/EEG studies have investigated propagating cortical activity, not much is known about the conditions under which such activity can be successfully reconstructed from MEG/EEG sensor-data. This study provides a methodological framework for inverse-modeling of propagating cortical activity. Within this framework, cortical activity is represented in the spatial frequency domain, which is more natural than the dipole domain when dealing with spatially continuous activity. We define angular power spectra, which show how the power of cortical activity is distributed across spatial frequencies, angular gain/phase spectra, which characterize the spatial filtering properties of linear inverse operators, and angular resolution matrices, which summarize how linear inverse operators leak signal within and across spatial frequencies. We adopt the framework to provide insight into the performance of several linear inverse operators in reconstructing propagating cortical activity from MEG/EEG sensor-data. We also describe how prior spatial frequency information can be incorporated into the inverse-modeling to obtain better reconstructions.
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Affiliation(s)
- Rikkert Hindriks
- Department of Mathematics, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
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191
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Fahimi Hnazaee M, Wittevrongel B, Khachatryan E, Libert A, Carrette E, Dauwe I, Meurs A, Boon P, Van Roost D, Van Hulle MM. Localization of deep brain activity with scalp and subdural EEG. Neuroimage 2020; 223:117344. [PMID: 32898677 DOI: 10.1016/j.neuroimage.2020.117344] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 07/27/2020] [Accepted: 08/31/2020] [Indexed: 01/11/2023] Open
Abstract
To what extent electrocorticography (ECoG) and electroencephalography (scalp EEG) differ in their capability to locate sources of deep brain activity is far from evident. Compared to EEG, the spatial resolution and signal-to-noise ratio of ECoG is superior but its spatial coverage is more restricted, as is arguably the volume of tissue activity effectively measured from. Moreover, scalp EEG studies are providing evidence of locating activity from deep sources such as the hippocampus using high-density setups during quiet wakefulness. To address this question, we recorded a multimodal dataset from 4 patients with refractory epilepsy during quiet wakefulness. This data comprises simultaneous scalp, subdural and depth EEG electrode recordings. The latter was located in the hippocampus or insula and provided us with our "ground truth" for source localization of deep activity. We applied independent component analysis (ICA) for the purpose of separating the independent sources in theta, alpha and beta frequency band activity. In all patients subdural- and scalp EEG components were observed which had a significant zero-lag correlation with one or more contacts of the depth electrodes. Subsequent dipole modeling of the correlating components revealed dipole locations that were significantly closer to the depth electrodes compared to the dipole location of non-correlating components. These findings support the idea that components found in both recording modalities originate from neural activity in close proximity to the depth electrodes. Sources localized with subdural electrodes were ~70% closer to the depth electrode than sources localized with EEG with an absolute improvement of around ~2cm. In our opinion, this is not a considerable improvement in source localization accuracy given that, for clinical purposes, ECoG electrodes were implanted in close proximity to the depth electrodes. Furthermore, the ECoG grid attenuates the scalp EEG, due to the electrically isolating silastic sheets in which the ECoG electrodes are embedded. Our results on dipole modeling show that the deep source localization accuracy of scalp EEG is comparable to that of ECoG. SIGNIFICANCE STATEMENT: Deep and subcortical regions play an important role in brain function. However, as joint recordings at multiple spatial scales to study brain function in humans are still scarce, it is still unresolved to what extent ECoG and EEG differ in their capability to locate sources of deep brain activity. To the best of our knowledge, this is the first study presenting a dataset of simultaneously recorded EEG, ECoG and depth electrodes in the hippocampus or insula, with a focus on non-epileptiform activity (quiet wakefulness). Furthermore, we are the first study to provide experimental findings on the comparison of source localization of deep cortical structures between invasive and non-invasive brain activity measured from the cortical surface.
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Affiliation(s)
| | - Benjamin Wittevrongel
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven, Belgium
| | - Elvira Khachatryan
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven, Belgium
| | - Arno Libert
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven, Belgium
| | - Evelien Carrette
- Faculty of Medicine and Health Sciences, Ghent University Hospital, Ghent, Belgium
| | - Ine Dauwe
- Faculty of Medicine and Health Sciences, Ghent University Hospital, Ghent, Belgium
| | - Alfred Meurs
- Faculty of Medicine and Health Sciences, Ghent University Hospital, Ghent, Belgium
| | - Paul Boon
- Faculty of Medicine and Health Sciences, Ghent University Hospital, Ghent, Belgium
| | - Dirk Van Roost
- Faculty of Medicine and Health Sciences, Ghent University Hospital, Ghent, Belgium
| | - Marc M Van Hulle
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven, Belgium
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192
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Lanzone J, Imperatori C, Assenza G, Ricci L, Farina B, Di Lazzaro V, Tombini M. Power Spectral Differences between Transient Epileptic and Global Amnesia: An eLORETA Quantitative EEG Study. Brain Sci 2020; 10:brainsci10090613. [PMID: 32899970 PMCID: PMC7563784 DOI: 10.3390/brainsci10090613] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 08/28/2020] [Accepted: 09/04/2020] [Indexed: 11/23/2022] Open
Abstract
Transient epileptic amnesia (TEA) is a rare epileptic condition, often confused with transient global amnesia (TGA). In a real-life scenario, differential diagnosis between these two conditions can be hard. In this study we use power spectral analysis empowered by exact Low Resolution Brain Electromagnetic Tomography (eLORETA) to evidence the differences between TEA and TGA. Fifteen patients affected by TEA (64.2 ± 5.2 y.o.; 11 female/4 male; 10 left and 5 right temporal epileptic focus) and 15 patients affected by TGA (65.8 ± 7.2 y.o.; 11 females/4 males) were retrospectively identified in our clinical records. All patients recorded EEGs after symptoms offset. EEGs were analyzed with eLORETA to evidence power spectral contrast between the two conditions. We used an inverse problem solution to localize the source of spectral differences. We found a significant increase in beta band power over the affected hemisphere of TEA patients. Significant results corresponded to the uncus and para-hippocampal gyrus, respectively Brodmann’s Areas: 36, 35, 28, 34. We present original evidence of an increase in beta power in the affected hemisphere (AH) of TEA as compared to TGA. These differences involve key areas of the memory network located in the mesial temporal lobe. Spectral asymmetries could be used in the future to recognize cases of amnesia with a high risk of epilepsy.
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Affiliation(s)
- Jacopo Lanzone
- Neurology, Neurophysiology and Neurobiology Unit, Department of Medicine, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (G.A.); (L.R.); (V.D.L.); (M.T.)
- Correspondence:
| | - Claudio Imperatori
- Cognitive and Clinical Psychology Laboratory, Department of Human Sciences, European University of Rome, Via degli Aldobrandeschi 190, 00163 Rome, Italy; (C.I.); (B.F.)
| | - Giovanni Assenza
- Neurology, Neurophysiology and Neurobiology Unit, Department of Medicine, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (G.A.); (L.R.); (V.D.L.); (M.T.)
| | - Lorenzo Ricci
- Neurology, Neurophysiology and Neurobiology Unit, Department of Medicine, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (G.A.); (L.R.); (V.D.L.); (M.T.)
| | - Benedetto Farina
- Cognitive and Clinical Psychology Laboratory, Department of Human Sciences, European University of Rome, Via degli Aldobrandeschi 190, 00163 Rome, Italy; (C.I.); (B.F.)
| | - Vincenzo Di Lazzaro
- Neurology, Neurophysiology and Neurobiology Unit, Department of Medicine, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (G.A.); (L.R.); (V.D.L.); (M.T.)
| | - Mario Tombini
- Neurology, Neurophysiology and Neurobiology Unit, Department of Medicine, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (G.A.); (L.R.); (V.D.L.); (M.T.)
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193
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Malovichko M, Koshev N, Yavich N, Razorenova A, Fedorov M. Electroencephalographic Source Reconstruction by the Finite-Element Approximation of the Elliptic Cauchy Problem. IEEE Trans Biomed Eng 2020; 68:1811-1819. [PMID: 32877329 DOI: 10.1109/tbme.2020.3021359] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE This paper develops a novel approach for fast and reliable reconstruction of EEG sources in MRI-based head models. METHODS The inverse EEG problem is reduced to the Cauchy problem for an elliptic partial-derivative equation. The problem is transformed into a regularized minimax problem, which is directly approximated in a finite-element space. The resulting numerical method is efficient and easy to program. It eliminates the need to solve forward problems, which can be a tedious task. The method applies to complex anatomical head models, possibly containing holes in surfaces, anisotropic conductivity, and conductivity variations inside each tissue. The method has been verified on a spherical shell model and an MRI-based head. RESULTS Numerical experiments indicate high accuracy of localization of brain activations (both cortical potential and current) and rapid execution time. CONCLUSION This study demonstrates that the proposed approach is feasible for EEG source analysis and can serve as a rapid and reliable tool for EEG source analysis. SIGNIFICANCE The significance of this study is that it develops a fast, accurate, and simple numerical method of EEG source analysis, applicable to almost arbitrary complex head models.
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194
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Deolindo CS, Ribeiro MW, Aratanha MA, Afonso RF, Irrmischer M, Kozasa EH. A Critical Analysis on Characterizing the Meditation Experience Through the Electroencephalogram. Front Syst Neurosci 2020; 14:53. [PMID: 32848645 PMCID: PMC7427581 DOI: 10.3389/fnsys.2020.00053] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 07/06/2020] [Indexed: 11/13/2022] Open
Abstract
Meditation practices, originated from ancient traditions, have increasingly received attention due to their potential benefits to mental and physical health. The scientific community invests efforts into scrutinizing and quantifying the effects of these practices, especially on the brain. There are methodological challenges in describing the neural correlates of the subjective experience of meditation. We noticed, however, that technical considerations on signal processing also don't follow standardized approaches, which may hinder generalizations. Therefore, in this article, we discuss the usage of the electroencephalogram (EEG) as a tool to study meditation experiences in healthy individuals. We describe the main EEG signal processing techniques and how they have been translated to the meditation field until April 2020. Moreover, we examine in detail the limitations/assumptions of these techniques and highlight some good practices, further discussing how technical specifications may impact the interpretation of the outcomes. By shedding light on technical features, this article contributes to more rigorous approaches to evaluate the construct of meditation.
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Affiliation(s)
| | | | | | | | - Mona Irrmischer
- Department of Integrative Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, VU Amsterdam, Amsterdam, Netherlands
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195
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196
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Diminished large-scale functional brain networks in absolute pitch during the perception of naturalistic music and audiobooks. Neuroimage 2020; 216:116513. [DOI: 10.1016/j.neuroimage.2019.116513] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 12/16/2019] [Accepted: 12/31/2019] [Indexed: 12/26/2022] Open
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197
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Rajabioun M. Motor imagery classification by active source dynamics. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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198
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Campbell J, Sharma A. Frontal Cortical Modulation of Temporal Visual Cross-Modal Re-organization in Adults with Hearing Loss. Brain Sci 2020; 10:brainsci10080498. [PMID: 32751543 PMCID: PMC7465622 DOI: 10.3390/brainsci10080498] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 07/24/2020] [Accepted: 07/27/2020] [Indexed: 11/19/2022] Open
Abstract
Recent research has demonstrated frontal cortical involvement to co-occur with visual re-organization, suggestive of top-down modulation of cross-modal mechanisms. However, it is unclear whether top-down modulation of visual re-organization takes place in mild hearing loss, or is dependent upon greater degrees of hearing loss severity. Thus, the purpose of this study was to determine if frontal top-down modulation of visual cross-modal re-organization increased across hearing loss severity. We recorded visual evoked potentials (VEPs) in response to apparent motion stimuli in 17 adults with mild-moderate hearing loss using 128-channel high-density electroencephalography (EEG). Current density reconstructions (CDRs) were generated using sLORETA to visualize VEP generators in both groups. VEP latency and amplitude in frontal regions of interest (ROIs) were compared between groups and correlated with auditory behavioral measures. Activation of frontal networks in response to visual stimulation increased across mild to moderate hearing loss, with simultaneous activation of the temporal cortex. In addition, group differences in VEP latency and amplitude correlated with auditory behavioral measures. Overall, these findings support the hypothesis that frontal top-down modulation of visual cross-modal re-organization is dependent upon hearing loss severity.
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Affiliation(s)
- Julia Campbell
- Central Sensory Processes Laboratory, Department of Communication Sciences and Disorders, University of Texas at Austin, 2504 Whitis Ave a1100, Austin, TX 78712, USA;
| | - Anu Sharma
- Anu Sharma, Brain and Behavior Laboratory, Institute of Cognitive Science, Department of Speech, Language and Hearing Science, University of Colorado at Boulder, 409 UCB, 2501 Kittredge Loop Drive, Boulder, CO 80309, USA
- Correspondence:
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199
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Gaul A, O'Keeffe C, Dominguez MC, O'Rourke E, Reilly RB. Quantification of Neural Activity in FMR1 Premutation Carriers during a Dynamic Sway Task using Source Localization. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2909-2912. [PMID: 33018615 DOI: 10.1109/embc44109.2020.9176566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Fragile X-associated Tremor/Ataxia Syndrome is a genetic neurodegenerative disorder affecting carriers of the FMR1 premutation. Not all carriers develop the condition and the age of onset is somewhat variable. A greater understanding of disease progression would be beneficial. Eight carriers and five controls matched by age, sex, and dominant hand volunteered to perform a sway task on a force platform while EEG was simultaneously recorded. Sway parameters were extracted from the movement data at important timepoints throughout their sway cycles and matched to their EEG activity. Distributed source analysis was applied. While there initially appeared to be differences in neural activity between the two groups in the anterior lobe, the right posterior lobe, the right superior parietal lobule and the right parietal lobe, these differences did not survive correction for multiple comparisons.
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200
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Santhana Gopalan PR, Loberg O, Lohvansuu K, McCandliss B, Hämäläinen J, Leppänen P. Attentional Processes in Children With Attentional Problems or Reading Difficulties as Revealed Using Brain Event-Related Potentials and Their Source Localization. Front Hum Neurosci 2020; 14:160. [PMID: 32536857 PMCID: PMC7227392 DOI: 10.3389/fnhum.2020.00160] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 04/14/2020] [Indexed: 01/22/2023] Open
Abstract
Visual attention-related processes include three functional sub-processes: alerting, orienting, and inhibition. We examined these sub-processes using reaction times, event-related potentials (ERPs), and their neuronal source activations during the Attention Network Test (ANT) in control children, attentional problems (AP) children, and reading difficulties (RD) children. During the ANT, electroencephalography was measured using 128 electrodes on three groups of Finnish sixth-graders aged 12–13 years (control = 77; AP = 15; RD = 23). Participants were asked to detect the direction of a middle target fish within a group of five fish. The target stimulus was either preceded by a cue (center, double, or spatial), or without a cue, to manipulate the alerting and orienting sub-processes of attention. The direction of the target fish was either congruent or incongruent in relation to the flanker fish, thereby manipulating the inhibition sub-processes of attention. Reaction time performance showed no differences between groups in alerting, orienting, and inhibition effects. The group differences in ERPs were only found at the source level. Neuronal source analysis in the AP children revealed a larger alerting effect (double-cued vs. non-cued target stimuli) than control and RD children in the left occipital lobe. Control children showed a smaller orienting effect (spatially cued vs. center-cued target stimuli) in the left occipital lobe than AP and RD children. No group differences were found for the neuronal sources related to the inhibition effect. The neuronal activity differences related to sub-processes of attention in the AP and RD groups suggest different underlying mechanisms for attentional and reading problems.
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Affiliation(s)
| | - Otto Loberg
- Department of Psychology, University of Jyväskylä, Jyväskylä, Finland
| | - Kaisa Lohvansuu
- Department of Psychology, University of Jyväskylä, Jyväskylä, Finland
| | - Bruce McCandliss
- Graduate School of Education, Stanford University, Stanford, CA, United States
| | - Jarmo Hämäläinen
- Department of Psychology, University of Jyväskylä, Jyväskylä, Finland
| | - Paavo Leppänen
- Department of Psychology, University of Jyväskylä, Jyväskylä, Finland
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