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Areces-Gonzalez A, Paz-Linares D, Riaz U, Wang Y, Li M, Razzaq FA, Bosch-Bayard JF, Gonzalez-Moreira E, Ontivero-Ortega M, Galan-Garcia L, Martínez-Montes E, Minati L, Valdes-Sosa MJ, Bringas-Vega ML, Valdes-Sosa PA. CiftiStorm pipeline: facilitating reproducible EEG/MEG source connectomics. Front Neurosci 2024; 18:1237245. [PMID: 38680452 PMCID: PMC11047451 DOI: 10.3389/fnins.2024.1237245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 02/22/2024] [Indexed: 05/01/2024] Open
Abstract
We present CiftiStorm, an electrophysiological source imaging (ESI) pipeline incorporating recently developed methods to improve forward and inverse solutions. The CiftiStorm pipeline produces Human Connectome Project (HCP) and megconnectome-compliant outputs from dataset inputs with varying degrees of spatial resolution. The input data can range from low-sensor-density electroencephalogram (EEG) or magnetoencephalogram (MEG) recordings without structural magnetic resonance imaging (sMRI) to high-density EEG/MEG recordings with an HCP multimodal sMRI compliant protocol. CiftiStorm introduces a numerical quality control of the lead field and geometrical corrections to the head and source models for forward modeling. For the inverse modeling, we present a Bayesian estimation of the cross-spectrum of sources based on multiple priors. We facilitate ESI in the T1w/FSAverage32k high-resolution space obtained from individual sMRI. We validate this feature by comparing CiftiStorm outputs for EEG and MRI data from the Cuban Human Brain Mapping Project (CHBMP) acquired with technologies a decade before the HCP MEG and MRI standardized dataset.
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Affiliation(s)
- Ariosky Areces-Gonzalez
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- School of Technical Sciences, University “Hermanos Saiz Montes de Oca” of Pinar del Río, Pinar del Rio, Cuba
| | - Deirel Paz-Linares
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neurosciences Center, Havana, Cuba
| | - Usama Riaz
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ying Wang
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Li
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Hangzhou Dianzi University, Hangzhou, Zhejiang, China
| | - Fuleah A. Razzaq
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jorge F. Bosch-Bayard
- McGill Centre for Integrative Neurosciences MCIN, LudmerCentre for Mental Health, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Eduardo Gonzalez-Moreira
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | | | | | | | - Marlis Ontivero-Ortega
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neurosciences Center, Havana, Cuba
| | | | | | - Ludovico Minati
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy
| | | | - Maria L. Bringas-Vega
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neurosciences Center, Havana, Cuba
| | - Pedro A. Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neurosciences Center, Havana, Cuba
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2
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van Nifterick AM, Scheijbeler EP, Gouw AA, de Haan W, Stam CJ. Local signal variability and functional connectivity: Sensitive measures of the excitation-inhibition ratio? Cogn Neurodyn 2024; 18:519-537. [PMID: 38699618 PMCID: PMC11061092 DOI: 10.1007/s11571-023-10003-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 06/08/2023] [Accepted: 08/13/2023] [Indexed: 05/05/2024] Open
Abstract
A novel network version of permutation entropy, the inverted joint permutation entropy (JPEinv), holds potential as non-invasive biomarker of abnormal excitation-inhibition (E-I) ratio in Alzheimer's disease (AD). In this computational modelling study, we test the hypotheses that this metric, and related measures of signal variability and functional connectivity, are sensitive to altered E-I ratios. The E-I ratio in each neural mass of a whole-brain computational network model was systematically varied. We evaluated whether JPEinv, local signal variability (by permutation entropy) and functional connectivity (by weighted symbolic mutual information (wsMI)) were related to E-I ratio, on whole-brain and regional level. The hub disruption index can identify regions primarily affected in terms of functional connectivity strength (or: degree) by the altered E-I ratios. Analyses were performed for a range of coupling strengths, filter and time-delay settings. On whole-brain level, higher E-I ratios were associated with higher functional connectivity (by JPEinv and wsMI) and lower local signal variability. These relationships were nonlinear and depended on the coupling strength, filter and time-delay settings. On regional level, hub-like regions showed a selective decrease in functional degree (by JPEinv and wsMI) upon a lower E-I ratio, and non-hub-like regions showed a selective increase in degree upon a higher E-I ratio. These results suggest that abnormal functional connectivity and signal variability, as previously reported in patients across the AD continuum, can inform us about altered E-I ratios. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-023-10003-x.
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Affiliation(s)
- Anne M. van Nifterick
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Elliz P. Scheijbeler
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Alida A. Gouw
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Willem de Haan
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Cornelis J. Stam
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
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3
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Al‐Sa'd M, Vanhatalo S, Tokariev A. Multiplex dynamic networks in the newborn brain disclose latent links with neurobehavioral phenotypes. Hum Brain Mapp 2024; 45:e26610. [PMID: 38339895 PMCID: PMC10839739 DOI: 10.1002/hbm.26610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 01/08/2024] [Accepted: 01/16/2024] [Indexed: 02/12/2024] Open
Abstract
The higher brain functions arise from coordinated neural activity between distinct brain regions, but the spatial, temporal, and spectral complexity of these functional connectivity networks (FCNs) has challenged the identification of correlates with neurobehavioral phenotypes. Characterizing behavioral correlates of early life FCNs is important to understand the activity dependent emergence of neurodevelopmental performance and for improving health outcomes. Here, we develop an analysis pipeline for identifying multiplex dynamic FCNs that combine spectral and spatiotemporal characteristics of the newborn cortical activity. This data-driven approach automatically uncovers latent networks that show robust neurobehavioral correlations and consistent effects by in utero drug exposure. Altogether, the proposed pipeline provides a robust end-to-end solution for an objective assessment and quantitation of neurobehaviorally meaningful network constellations in the highly dynamic cortical functions.
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Affiliation(s)
- Mohammad Al‐Sa'd
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's Hospital, HUS imaging, HUS Diagnostic CenterUniversity of Helsinki and Helsinki University HospitalHelsinkiFinland
- Department of PhysiologyUniversity of HelsinkiHelsinkiFinland
- Faculty of Information Technology and Communication SciencesTampere UniversityTampereFinland
| | - Sampsa Vanhatalo
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's Hospital, HUS imaging, HUS Diagnostic CenterUniversity of Helsinki and Helsinki University HospitalHelsinkiFinland
- Department of PhysiologyUniversity of HelsinkiHelsinkiFinland
| | - Anton Tokariev
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's Hospital, HUS imaging, HUS Diagnostic CenterUniversity of Helsinki and Helsinki University HospitalHelsinkiFinland
- Department of PhysiologyUniversity of HelsinkiHelsinkiFinland
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Li Y, Gao J, Yang Y, Zhuang Y, Kang Q, Li X, Tian M, Lv H, He J. Temporal and spatial variability of dynamic microstate brain network in disorders of consciousness. CNS Neurosci Ther 2024; 30:e14641. [PMID: 38385681 PMCID: PMC10883110 DOI: 10.1111/cns.14641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 01/17/2024] [Accepted: 02/01/2024] [Indexed: 02/23/2024] Open
Abstract
BACKGROUND Accurately diagnosing patients with the vegetative state (VS) and the minimally conscious state (MCS) reached a misdiagnosis of approximately 40%. METHODS A method combined microstate and dynamic functional connectivity (dFC) to study the spatiotemporal variability of the brain in disorders of consciousness (DOC) patients was proposed. Resting-state EEG data were obtained from 16 patients with MCS and 16 patients with VS. Mutual information (MI) was used to assess the EEG connectivity in each microstate. MI-based features with statistical differences were selected as the total feature subset (TFS), then the TFS was utilized to feature selection and fed into the classifier, obtaining the optimal feature subsets (OFS) in each microstate. Subsequently, an OFS-based MI functional connectivity network (MIFCN) was constructed in the cortex. RESULTS The group-average MI connectivity matrix focused on all channels revealed that all five microstates exhibited stronger information interaction in the MCS when comparing with the VS. While OFS-based MIFCN, which only focused on a few channels, revealed greater MI flow in VS patients than in MCS patients under microstates A, B, C, and E, except for microstate D. Additionally, the average classification accuracy of OFS in the five microstates was 96.2%. CONCLUSION Constructing features based on microstates to distinguish between two categories of DOC patients had effectiveness.
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Affiliation(s)
- Yaqian Li
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical EngineeringSouth‐Central Minzu UniversityWuhanChina
| | - Junfeng Gao
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical EngineeringSouth‐Central Minzu UniversityWuhanChina
| | - Ying Yang
- College of Foreign LanguagesWuhan University of TechnologyWuhanChina
| | - Yvtong Zhuang
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Qianruo Kang
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical EngineeringSouth‐Central Minzu UniversityWuhanChina
| | - Xiang Li
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical EngineeringSouth‐Central Minzu UniversityWuhanChina
| | - Min Tian
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical EngineeringSouth‐Central Minzu UniversityWuhanChina
| | - Haoan Lv
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical EngineeringSouth‐Central Minzu UniversityWuhanChina
| | - Jianghong He
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
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5
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Nobukawa S, Ikeda T, Kikuchi M, Takahashi T. Atypical instantaneous spatio-temporal patterns of neural dynamics in Alzheimer's disease. Sci Rep 2024; 14:88. [PMID: 38167950 PMCID: PMC10761722 DOI: 10.1038/s41598-023-50265-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 12/18/2023] [Indexed: 01/05/2024] Open
Abstract
Cognitive functions produced by large-scale neural integrations are the most representative 'emergence phenomena' in complex systems. A novel approach focusing on the instantaneous phase difference of brain oscillations across brain regions has succeeded in detecting moment-to-moment dynamic functional connectivity. However, it is restricted to pairwise observations of two brain regions, contrary to large-scale spatial neural integration in the whole-brain. In this study, we introduce a microstate analysis to capture whole-brain instantaneous phase distributions instead of pairwise differences. Upon applying this method to electroencephalography signals of Alzheimer's disease (AD), which is characterised by progressive cognitive decline, the AD-specific state transition among the four states defined as the leading phase location due to the loss of brain regional interactions could be promptly characterised. In conclusion, our synthetic analysis approach, focusing on the microstate and instantaneous phase, enables the capture of the instantaneous spatiotemporal neural dynamics of brain activity and characterises its pathological conditions.
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Affiliation(s)
- Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, 275-0016, Chiba, Japan.
- Research Center for Mathematical Engineering, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, 275-0016, Chiba, Japan.
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, 187-8661, Tokyo, Japan.
| | - Takashi Ikeda
- Research Center for Child Mental Development, Kanazawa University, 13-1 Takaramachi, Kanazawa, 920-8640, Ishikawa, Japan
- United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, 2-2 Yamadaoka, Suita, 565-0871, Osaka, Japan
| | - Mitsuru Kikuchi
- Research Center for Child Mental Development, Kanazawa University, 13-1 Takaramachi, Kanazawa, 920-8640, Ishikawa, Japan
- Department of Psychiatry and Behavioral Science, Kanazawa University, 13-1 Takaramachi, Kanazawa, 920-8640, Ishikawa, Japan
| | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, 13-1 Takaramachi, Kanazawa, 920-8640, Ishikawa, Japan
- Department of Neuropsychiatry, University of Fukui, 23-3 Matsuoka, Yoshida, 910-1193, Fukui, Japan
- Uozu Shinkei Sanatorium, 1784-1 Eguchi, Uozu, 937-0017, Toyama, Japan
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6
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Sanchez-Bornot J, Sotero RC, Kelso JAS, Şimşek Ö, Coyle D. Solving large-scale MEG/EEG source localisation and functional connectivity problems simultaneously using state-space models. Neuroimage 2024; 285:120458. [PMID: 37993002 DOI: 10.1016/j.neuroimage.2023.120458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 09/28/2023] [Accepted: 11/14/2023] [Indexed: 11/24/2023] Open
Abstract
State-space models are widely employed across various research disciplines to study unobserved dynamics. Conventional estimation techniques, such as Kalman filtering and expectation maximisation, offer valuable insights but incur high computational costs in large-scale analyses. Sparse inverse covariance estimators can mitigate these costs, but at the expense of a trade-off between enforced sparsity and increased estimation bias, necessitating careful assessment in low signal-to-noise ratio (SNR) situations. To address these challenges, we propose a three-fold solution: (1) Introducing multiple penalised state-space (MPSS) models that leverage data-driven regularisation; (2) Developing novel algorithms derived from backpropagation, gradient descent, and alternating least squares to solve MPSS models; (3) Presenting a K-fold cross-validation extension for evaluating regularisation parameters. We validate this MPSS regularisation framework through lower and more complex simulations under varying SNR conditions, including a large-scale synthetic magneto- and electro-encephalography (MEG/EEG) data analysis. In addition, we apply MPSS models to concurrently solve brain source localisation and functional connectivity problems for real event-related MEG/EEG data, encompassing thousands of sources on the cortical surface. The proposed methodology overcomes the limitations of existing approaches, such as constraints to small-scale and region-of-interest analyses. Thus, it may enable a more accurate and detailed exploration of cognitive brain functions.
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Affiliation(s)
- Jose Sanchez-Bornot
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Magee campus, Derry∼Londonderry, United Kingdom.
| | - Roberto C Sotero
- Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - J A Scott Kelso
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Magee campus, Derry∼Londonderry, United Kingdom; Human Brain & Behavior laboratory, Center for Complex Systems & Brain Sciences, Florida Atlantic University, Boca Raton, FL, USA
| | - Özgür Şimşek
- Bath Institute for the Augmented Human, University of Bath, Bath, BA2 7AY, United Kingdom
| | - Damien Coyle
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Magee campus, Derry∼Londonderry, United Kingdom; Bath Institute for the Augmented Human, University of Bath, Bath, BA2 7AY, United Kingdom
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7
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Alonso S, Vidaurre D. Toward stability of dynamic FC estimates in neuroimaging and electrophysiology: Solutions and limits. Netw Neurosci 2023; 7:1389-1403. [PMID: 38144684 PMCID: PMC10713011 DOI: 10.1162/netn_a_00331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 07/27/2023] [Indexed: 12/26/2023] Open
Abstract
Time-varying functional connectivity (FC) methods are used to map the spatiotemporal organization of brain activity. However, their estimation can be unstable, in the sense that different runs of the inference may yield different solutions. But to draw meaningful relations to behavior, estimates must be robust and reproducible. Here, we propose two solutions using the hidden Markov model (HMM) as a descriptive model of time-varying FC. The first, best ranked HMM, involves running the inference multiple times and selecting the best model based on a quantitative measure combining fitness and model complexity. The second, hierarchical-clustered HMM, generates stable cluster state time series by applying hierarchical clustering to the state time series obtained from multiple runs. Experimental results on fMRI and magnetoencephalography data demonstrate that these approaches substantially improve the stability of time-varying FC estimations. Overall, hierarchical-clustered HMM is preferred when the inference variability is high, while the best ranked HMM performs better otherwise.
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Affiliation(s)
- Sonsoles Alonso
- Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark
| | - Diego Vidaurre
- Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark
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8
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Rodríguez-González V, Núñez P, Gómez C, Shigihara Y, Hoshi H, Tola-Arribas MÁ, Cano M, Guerrero Á, García-Azorín D, Hornero R, Poza J. Connectivity-based Meta-Bands: A new approach for automatic frequency band identification in connectivity analyses. Neuroimage 2023; 280:120332. [PMID: 37619796 DOI: 10.1016/j.neuroimage.2023.120332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 07/05/2023] [Accepted: 08/14/2023] [Indexed: 08/26/2023] Open
Abstract
The majority of electroencephalographic (EEG) and magnetoencephalographic (MEG) studies filter and analyse neural signals in specific frequency ranges, known as "canonical" frequency bands. However, this segmentation, is not exempt from limitations, mainly due to the lack of adaptation to the neural idiosyncrasies of each individual. In this study, we introduce a new data-driven method to automatically identify frequency ranges based on the topological similarity of the frequency-dependent functional neural network. The resting-state neural activity of 195 cognitively healthy subjects from three different databases (MEG: 123 subjects; EEG1: 27 subjects; EEG2: 45 subjects) was analysed. In a first step, MEG and EEG signals were filtered with a narrow-band filter bank (1 Hz bandwidth) from 1 to 70 Hz with a 0.5 Hz step. Next, the connectivity in each of these filtered signals was estimated using the orthogonalized version of the amplitude envelope correlation to obtain the frequency-dependent functional neural network. Finally, a community detection algorithm was used to identify communities in the frequency domain showing a similar network topology. We have called this approach the "Connectivity-based Meta-Bands" (CMB) algorithm. Additionally, two types of synthetic signals were used to configure the hyper-parameters of the CMB algorithm. We observed that the classical approaches to band segmentation are partially aligned with the underlying network topologies at group level for the MEG signals, but they are missing individual idiosyncrasies that may be biasing previous studies, as revealed by our methodology. On the other hand, the sensitivity of EEG signals to reflect this underlying frequency-dependent network structure is limited, revealing a simpler frequency parcellation, not aligned with that defined by the "canonical" frequency bands. To the best of our knowledge, this is the first study that proposes an unsupervised band segmentation method based on the topological similarity of functional neural network across frequencies. This methodology fully accounts for subject-specific patterns, providing more robust and personalized analyses, and paving the way for new studies focused on exploring the frequency-dependent structure of brain connectivity.
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Affiliation(s)
- Víctor Rodríguez-González
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain.
| | - Pablo Núñez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain; Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium
| | - Carlos Gómez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain
| | | | | | - Miguel Ángel Tola-Arribas
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain; Servicio de Neurología. Hospital Universitario Río Hortega, Valladolid, Spain
| | - Mónica Cano
- Servicio de Neurología. Hospital Universitario Río Hortega, Valladolid, Spain
| | - Ángel Guerrero
- Hospital Clínico Universitario, Valladolid, Spain; Department of Medicine, University of Valladolid, Spain
| | | | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
| | - Jesús Poza
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
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9
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Kulik SD, Douw L, van Dellen E, Steenwijk MD, Geurts JJG, Stam CJ, Hillebrand A, Schoonheim MM, Tewarie P. Comparing individual and group-level simulated neurophysiological brain connectivity using the Jansen and Rit neural mass model. Netw Neurosci 2023; 7:950-965. [PMID: 37781149 PMCID: PMC10473283 DOI: 10.1162/netn_a_00303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 12/24/2022] [Indexed: 10/03/2023] Open
Abstract
Computational models are often used to assess how functional connectivity (FC) patterns emerge from neuronal population dynamics and anatomical brain connections. It remains unclear whether the commonly used group-averaged data can predict individual FC patterns. The Jansen and Rit neural mass model was employed, where masses were coupled using individual structural connectivity (SC). Simulated FC was correlated to individual magnetoencephalography-derived empirical FC. FC was estimated using phase-based (phase lag index (PLI), phase locking value (PLV)), and amplitude-based (amplitude envelope correlation (AEC)) metrics to analyze their goodness of fit for individual predictions. Individual FC predictions were compared against group-averaged FC predictions, and we tested whether SC of a different participant could equally well predict participants' FC patterns. The AEC provided a better match between individually simulated and empirical FC than phase-based metrics. Correlations between simulated and empirical FC were higher using individual SC compared to group-averaged SC. Using SC from other participants resulted in similar correlations between simulated and empirical FC compared to using participants' own SC. This work underlines the added value of FC simulations using individual instead of group-averaged SC for this particular computational model and could aid in a better understanding of mechanisms underlying individual functional network trajectories.
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Affiliation(s)
- S. D. Kulik
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neuroscience, Amsterdam Neuroscience, Amsterdam The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, Brain Tumour Center Amsterdam, Amsterdam, The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam, The Netherlands
| | - L. Douw
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neuroscience, Amsterdam Neuroscience, Amsterdam The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, Brain Tumour Center Amsterdam, Amsterdam, The Netherlands
| | - E. van Dellen
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Utrecht, The Netherlands
| | - M. D. Steenwijk
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neuroscience, Amsterdam Neuroscience, Amsterdam The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam, The Netherlands
| | - J. J. G. Geurts
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neuroscience, Amsterdam Neuroscience, Amsterdam The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam, The Netherlands
| | - C. J. Stam
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology and Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam The Netherlands
| | - A. Hillebrand
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology and Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam The Netherlands
| | - M. M. Schoonheim
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neuroscience, Amsterdam Neuroscience, Amsterdam The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam, The Netherlands
| | - P. Tewarie
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology and Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam The Netherlands
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10
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Paz-Linares D, Gonzalez-Moreira E, Areces-Gonzalez A, Wang Y, Li M, Martinez-Montes E, Bosch-Bayard J, Bringas-Vega ML, Valdes-Sosa M, Valdes-Sosa PA. Identifying oscillatory brain networks with hidden Gaussian graphical spectral models of MEEG. Sci Rep 2023; 13:11466. [PMID: 37454235 PMCID: PMC10349891 DOI: 10.1038/s41598-023-38513-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023] Open
Abstract
Identifying the functional networks underpinning indirectly observed processes poses an inverse problem for neurosciences or other fields. A solution of such inverse problems estimates as a first step the activity emerging within functional networks from EEG or MEG data. These EEG or MEG estimates are a direct reflection of functional brain network activity with a temporal resolution that no other in vivo neuroimage may provide. A second step estimating functional connectivity from such activity pseudodata unveil the oscillatory brain networks that strongly correlate with all cognition and behavior. Simulations of such MEG or EEG inverse problem also reveal estimation errors of the functional connectivity determined by any of the state-of-the-art inverse solutions. We disclose a significant cause of estimation errors originating from misspecification of the functional network model incorporated into either inverse solution steps. We introduce the Bayesian identification of a Hidden Gaussian Graphical Spectral (HIGGS) model specifying such oscillatory brain networks model. In human EEG alpha rhythm simulations, the estimation errors measured as ROC performance do not surpass 2% in our HIGGS inverse solution and reach 20% in state-of-the-art methods. Macaque simultaneous EEG/ECoG recordings provide experimental confirmation for our results with 1/3 times larger congruence according to Riemannian distances than state-of-the-art methods.
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Affiliation(s)
- Deirel Paz-Linares
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba
| | - Eduardo Gonzalez-Moreira
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- School of Electrical Engineering, Central University "Marta Abreu" of Las Villas, Santa Clara, Cuba
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Ariosky Areces-Gonzalez
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- School of Technical Sciences, University of Pinar del Río "Hermanos Saiz Montes de Oca", Pinar del Rio, Cuba
| | - Ying Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Jorge Bosch-Bayard
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba
- McGill Centre for Integrative Neurosciences MCIN, Ludmer Centre for Mental Health, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Maria L Bringas-Vega
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba
| | - Mitchell Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba
| | - Pedro A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba.
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11
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Wagner F, Rogenz J, Opitz L, Maas J, Schmidt A, Brodoehl S, Ullsperger M, Klingner CM. Reward network dysfunction is associated with cognitive impairment after stroke. Neuroimage Clin 2023; 39:103446. [PMID: 37307650 PMCID: PMC10276182 DOI: 10.1016/j.nicl.2023.103446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 05/23/2023] [Accepted: 05/27/2023] [Indexed: 06/14/2023]
Abstract
Stroke survivors not only suffer from severe motor, speech and neurocognitive deficits, but in many cases also from a "lack of pleasure" and a reduced motivational level. Especially apathy and anhedonic symptoms can be linked to a dysfunction of the reward system. Rewards are considered as important co-factor for learning, so the question arises as to why and how this affects the rehabilitation of stroke patients. We investigated reward behaviour, learning ability and brain network connectivity in acute (3-7d) mild to moderate stroke patients (n = 28) and age-matched healthy controls (n = 26). Reward system activity was assessed using the Monetary Incentive Delay task (MID) during magnetoencephalography (MEG). Coherence analyses were used to demonstrate reward effects on brain functional network connectivity. The MID-task showed that stroke survivors had lower reward sensitivity and required greater monetary incentives to improve performance and showed deficits in learning improvement. MEG-analyses showed a reduced network connectivity in frontal and temporoparietal regions. All three effects (reduced reward sensitivity, reduced learning ability and altered cerebral connectivity) were found to be closely related and differed strongly from the healthy group. Our results reinforce the notion that acute stroke induces reward network dysfunction, leading to functional impairment of behavioural systems. These findings are representative of a general pattern in mild strokes and are independent of the specific lesion localisation. For stroke rehabilitation, these results represent an important point to identify the reduced learning capacity after stroke and to implement individualised recovery exercises accordingly.
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Affiliation(s)
- Franziska Wagner
- Department of Neurology, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany; Biomagnetic Centre, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany.
| | - Jenny Rogenz
- Department of Neurology, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany; Biomagnetic Centre, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany
| | - Laura Opitz
- Department of Neurology, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany; Biomagnetic Centre, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany
| | - Johanna Maas
- Department of Neurology, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany; Biomagnetic Centre, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany
| | - Alexander Schmidt
- Department of Neurology, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany; Biomagnetic Centre, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany
| | - Stefan Brodoehl
- Department of Neurology, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany; Biomagnetic Centre, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany
| | - Markus Ullsperger
- Faculty of Natural Sciences, Institute of Psychology, 39106 Magdeburg, Germany; Center for Behavioral Brain Sciences, Magdeburg, Otto-von-Guericke University Magdeburg, Germany
| | - Carsten M Klingner
- Department of Neurology, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany; Biomagnetic Centre, 07747 Jena University Hospital, Friedrich Schiller University Jena, Germany
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12
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Sihn D, Kim JS, Kwon OS, Kim SP. Breakdown of long-range spatial correlations of infraslow amplitude fluctuations of EEG oscillations in patients with current and past major depressive disorder. Front Psychiatry 2023; 14:1132996. [PMID: 37181866 PMCID: PMC10169687 DOI: 10.3389/fpsyt.2023.1132996] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 04/05/2023] [Indexed: 05/16/2023] Open
Abstract
Introduction Identifying biomarkers for depression from brain activity is important for the diagnosis and treatment of depression disorders. We investigated spatial correlations of the amplitude fluctuations of electroencephalography (EEG) oscillations as a potential biomarker of depression. The amplitude fluctuations of EEG oscillations intrinsically reveal both temporal and spatial correlations, indicating rapid and functional organization of the brain networks. Amid these correlations, long-range temporal correlations are reportedly impaired in patients with depression, exhibiting amplitude fluctuations closer to a random process. Based on this occurrence, we hypothesized that the spatial correlations of amplitude fluctuations would also be altered by depression. Methods In the present study, we extracted the amplitude fluctuations of EEG oscillations by filtering them through infraslow frequency band (0.05-0.1 Hz). Results We found that the amplitude fluctuations of theta oscillations during eye-closed rest depicted lower levels of spatial correlation in patients with major depressive disorder (MDD) compared to control individuals. This breakdown of spatial correlations was most prominent in the left fronto - temporal network, specifically in patients with current MDD rather than in those with past MDD. We also found that the amplitude fluctuations of alpha oscillations during eye-open rest exhibited lower levels of spatial correlation in patients with past MDD compared to control individuals or patients with current MDD. Discussion Our results suggest that breakdown of long-range spatial correlations may offer a biomarker for the diagnosis of depression (current MDD), as well as the tracking of the recovery from depression (past MDD).
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Affiliation(s)
- Duho Sihn
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Ji Sun Kim
- Department of Psychiatry, College of Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
| | - Oh-Sang Kwon
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Sung-Phil Kim
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
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13
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Hindriks R, Tewarie PKB. Dissociation between phase and power correlation networks in the human brain is driven by co-occurrent bursts. Commun Biol 2023; 6:286. [PMID: 36934153 PMCID: PMC10024695 DOI: 10.1038/s42003-023-04648-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 03/02/2023] [Indexed: 03/20/2023] Open
Abstract
Well-known haemodynamic resting-state networks are better mirrored in power correlation networks than phase coupling networks in electrophysiological data. However, what do these power correlation networks reflect? We address this long-outstanding question in neuroscience using rigorous mathematical analysis, biophysical simulations with ground truth and application of these mathematical concepts to empirical magnetoencephalography (MEG) data. Our mathematical derivations show that for two non-Gaussian electrophysiological signals, their power correlation depends on their coherence, cokurtosis and conjugate-coherence. Only coherence and cokurtosis contribute to power correlation networks in MEG data, but cokurtosis is less affected by artefactual signal leakage and better mirrors haemodynamic resting-state networks. Simulations and MEG data show that cokurtosis may reflect co-occurrent bursting events. Our findings shed light on the origin of the complementary nature of power correlation networks to phase coupling networks and suggests that the origin of resting-state networks is partly reflected in co-occurent bursts in neuronal activity.
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Affiliation(s)
- Rikkert Hindriks
- Department of Mathematics, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Prejaas K B Tewarie
- Clinical Neurophysiology Group, University of Twente, Enschede, The Netherlands
- Sir Peter Mansfield Imaging Center, School of Physics, University of Nottingham, Nottingham, UK
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14
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Paz-Linares D, Gonzalez-Moreira E, Areces-Gonzalez A, Wang Y, Li M, Vega-Hernandez M, Wang Q, Bosch-Bayard J, Bringas-Vega ML, Martinez-Montes E, Valdes-Sosa MJ, Valdes-Sosa PA. Minimizing the distortions in electrophysiological source imaging of cortical oscillatory activity via Spectral Structured Sparse Bayesian Learning. Front Neurosci 2023; 17:978527. [PMID: 37008210 PMCID: PMC10050575 DOI: 10.3389/fnins.2023.978527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 02/07/2023] [Indexed: 03/17/2023] Open
Abstract
Oscillatory processes at all spatial scales and on all frequencies underpin brain function. Electrophysiological Source Imaging (ESI) is the data-driven brain imaging modality that provides the inverse solutions to the source processes of the EEG, MEG, or ECoG data. This study aimed to carry out an ESI of the source cross-spectrum while controlling common distortions of the estimates. As with all ESI-related problems under realistic settings, the main obstacle we faced is a severely ill-conditioned and high-dimensional inverse problem. Therefore, we opted for Bayesian inverse solutions that posited a priori probabilities on the source process. Indeed, rigorously specifying both the likelihoods and a priori probabilities of the problem leads to the proper Bayesian inverse problem of cross-spectral matrices. These inverse solutions are our formal definition for cross-spectral ESI (cESI), which requires a priori of the source cross-spectrum to counter the severe ill-condition and high-dimensionality of matrices. However, inverse solutions for this problem were NP-hard to tackle or approximated within iterations with bad-conditioned matrices in the standard ESI setup. We introduce cESI with a joint a priori probability upon the source cross-spectrum to avoid these problems. cESI inverse solutions are low-dimensional ones for the set of random vector instances and not random matrices. We achieved cESI inverse solutions through the variational approximations via our Spectral Structured Sparse Bayesian Learning (ssSBL) algorithm https://github.com/CCC-members/Spectral-Structured-Sparse-Bayesian-Learning. We compared low-density EEG (10-20 system) ssSBL inverse solutions with reference cESIs for two experiments: (a) high-density MEG that were used to simulate EEG and (b) high-density macaque ECoG that were recorded simultaneously with EEG. The ssSBL resulted in two orders of magnitude with less distortion than the state-of-the-art ESI methods. Our cESI toolbox, including the ssSBL method, is available at https://github.com/CCC-members/BC-VARETA_Toolbox.
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Affiliation(s)
- Deirel Paz-Linares
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
| | - Eduardo Gonzalez-Moreira
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
- Research Unit for Neurodevelopment, Institute of Neurobiology, Autonomous University of Mexico, Querétaro, Mexico
- Faculty of Electrical Engineering, Central University “Marta Abreu” of Las Villas, Santa Clara, Cuba
| | - Ariosky Areces-Gonzalez
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Faculty of Technical Sciences, University of Pinar del Río “Hermanos Saiz Montes de Oca”, Pinar del Rio, Cuba
| | - Ying Wang
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Li
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Qing Wang
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- McGill Centre for Integrative Neurosciences MCIN, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Ludmer Centre for Mental Health, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Jorge Bosch-Bayard
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- McGill Centre for Integrative Neurosciences MCIN, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Ludmer Centre for Mental Health, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Maria L. Bringas-Vega
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
| | | | - Mitchel J. Valdes-Sosa
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
| | - Pedro A. Valdes-Sosa
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
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15
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Partamian H, Tabbal J, Hassan M, Karameh F. Analysis of task-related MEG functional brain networks using dynamic mode decomposition. J Neural Eng 2023; 20. [PMID: 36538817 DOI: 10.1088/1741-2552/acad28] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022]
Abstract
Objective.Functional connectivity networks explain the different brain states during the diverse motor, cognitive, and sensory functions. Extracting connectivity network configurations and their temporal evolution is crucial for understanding brain function during diverse behavioral tasks.Approach.In this study, we introduce the use of dynamic mode decomposition (DMD) to extract the dynamics of brain networks. We compared DMD with principal component analysis (PCA) using real magnetoencephalography data during motor and memory tasks.Main results.The framework generates dominant connectivity brain networks and their time dynamics during simple tasks, such as button press and left-hand movement, as well as more complex tasks, such as picture naming and memory tasks. Our findings show that the proposed methodology with both the PCA-based and DMD-based approaches extracts similar dominant connectivity networks and their corresponding temporal dynamics.Significance.We believe that the proposed methodology with both the PCA and the DMD approaches has a very high potential for deciphering the spatiotemporal dynamics of electrophysiological brain network states during tasks.
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Affiliation(s)
- Hmayag Partamian
- Electrical and Computer Engineering, American University of Beirut (AUB), Beirut, Lebanon
| | - Judie Tabbal
- MINDig, Rennes F-35000, France.,Institut des Neurosciences Cliniques de Rennes (INCR), Rennes, France
| | - Mahmoud Hassan
- Institut des Neurosciences Cliniques de Rennes (INCR), Rennes, France.,School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
| | - Fadi Karameh
- Electrical and Computer Engineering, American University of Beirut (AUB), Beirut, Lebanon
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16
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Fathian A, Jamali Y, Raoufy MR. The trend of disruption in the functional brain network topology of Alzheimer's disease. Sci Rep 2022; 12:14998. [PMID: 36056059 PMCID: PMC9440254 DOI: 10.1038/s41598-022-18987-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/23/2022] [Indexed: 12/19/2022] Open
Abstract
Alzheimer's disease (AD) is a progressive disorder associated with cognitive dysfunction that alters the brain's functional connectivity. Assessing these alterations has become a topic of increasing interest. However, a few studies have examined different stages of AD from a complex network perspective that cover different topological scales. This study used resting state fMRI data to analyze the trend of functional connectivity alterations from a cognitively normal (CN) state through early and late mild cognitive impairment (EMCI and LMCI) and to Alzheimer's disease. The analyses had been done at the local (hubs and activated links and areas), meso (clustering, assortativity, and rich-club), and global (small-world, small-worldness, and efficiency) topological scales. The results showed that the trends of changes in the topological architecture of the functional brain network were not entirely proportional to the AD progression. There were network characteristics that have changed non-linearly regarding the disease progression, especially at the earliest stage of the disease, i.e., EMCI. Further, it has been indicated that the diseased groups engaged somatomotor, frontoparietal, and default mode modules compared to the CN group. The diseased groups also shifted the functional network towards more random architecture. In the end, the methods introduced in this paper enable us to gain an extensive understanding of the pathological changes of the AD process.
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Affiliation(s)
- Alireza Fathian
- Biomathematics Laboratory, Department of Applied Mathematics, School of Mathematical Science, Tarbiat Modares University, Tehran, Iran
| | - Yousef Jamali
- Biomathematics Laboratory, Department of Applied Mathematics, School of Mathematical Science, Tarbiat Modares University, Tehran, Iran.
- Applied Systems Biology, Leibniz-Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Jena, Germany.
| | - Mohammad Reza Raoufy
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
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17
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Gohil C, Roberts E, Timms R, Skates A, Higgins C, Quinn A, Pervaiz U, van Amersfoort J, Notin P, Gal Y, Adaszewski S, Woolrich M. Mixtures of large-scale dynamic functional brain network modes. Neuroimage 2022; 263:119595. [PMID: 36041643 DOI: 10.1016/j.neuroimage.2022.119595] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 08/12/2022] [Accepted: 08/26/2022] [Indexed: 10/31/2022] Open
Abstract
Accurate temporal modelling of functional brain networks is essential in the quest for understanding how such networks facilitate cognition. Researchers are beginning to adopt time-varying analyses for electrophysiological data that capture highly dynamic processes on the order of milliseconds. Typically, these approaches, such as clustering of functional connectivity profiles and Hidden Markov Modelling (HMM), assume mutual exclusivity of networks over time. Whilst a powerful constraint, this assumption may be compromising the ability of these approaches to describe the data effectively. Here, we propose a new generative model for functional connectivity as a time-varying linear mixture of spatially distributed statistical "modes". The temporal evolution of this mixture is governed by a recurrent neural network, which enables the model to generate data with a rich temporal structure. We use a Bayesian framework known as amortised variational inference to learn model parameters from observed data. We call the approach DyNeMo (for Dynamic Network Modes), and show using simulations it outperforms the HMM when the assumption of mutual exclusivity is violated. In resting-state MEG, DyNeMo reveals a mixture of modes that activate on fast time scales of 100-150 ms, which is similar to state lifetimes found using an HMM. In task MEG data, DyNeMo finds modes with plausible, task-dependent evoked responses without any knowledge of the task timings. Overall, DyNeMo provides decompositions that are an approximate remapping of the HMM's while showing improvements in overall explanatory power. However, the magnitude of the improvements suggests that the HMM's assumption of mutual exclusivity can be reasonable in practice. Nonetheless, DyNeMo provides a flexible framework for implementing and assessing future modelling developments.
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Affiliation(s)
- Chetan Gohil
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
| | - Evan Roberts
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
| | - Ryan Timms
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
| | - Alex Skates
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
| | - Cameron Higgins
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
| | - Andrew Quinn
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
| | - Usama Pervaiz
- Oxford Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom
| | - Joost van Amersfoort
- Oxford Applied and Theoretical Machine Learning (OATML), Department of Computer Science, University of Oxford, Oxford, OX1 3QD, United Kingdom
| | - Pascal Notin
- Oxford Applied and Theoretical Machine Learning (OATML), Department of Computer Science, University of Oxford, Oxford, OX1 3QD, United Kingdom
| | - Yarin Gal
- Oxford Applied and Theoretical Machine Learning (OATML), Department of Computer Science, University of Oxford, Oxford, OX1 3QD, United Kingdom
| | - Stanislaw Adaszewski
- Pharma Research and Early Development Operations, Roche Innovation Center Basel, F. Hoffman - La Roche AG, Basel CH-4070, Switzerland
| | - Mark Woolrich
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
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18
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Zhu J, Chen M, Lu J, Zhao K, Cui E, Zhang Z, Wan H. A Fast and Efficient Ensemble Transfer Entropy and Applications in Neural Signals. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1118. [PMID: 36010782 PMCID: PMC9407540 DOI: 10.3390/e24081118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/27/2022] [Accepted: 08/09/2022] [Indexed: 06/15/2023]
Abstract
The ensemble transfer entropy (TEensemble) refers to the transfer entropy estimated from an ensemble of realizations. Due to its time-resolved analysis, it is adapted to analyze the dynamic interaction between brain regions. However, in the traditional TEensemble, multiple sets of surrogate data should be used to construct the null hypothesis distribution, which dramatically increases the computational complexity. To reduce the computational cost, a fast, efficient TEensemble with a simple statistical test method is proposed here, in which just one set of surrogate data is involved. To validate the improved efficiency, the simulated neural signals are used to compare the characteristics of the novel TEensemble with those of the traditional TEensemble. The results show that the time consumption is reduced by two or three magnitudes in the novel TEensemble. Importantly, the proposed TEensemble could accurately track the dynamic interaction process and detect the strength and the direction of interaction robustly even in the presence of moderate noises. The novel TEensemble reaches its steady state with the increased samples, which is slower than the traditional method. Furthermore, the effectiveness of the novel TEensemble was verified in the actual neural signals. Accordingly, the TEensemble proposed in this work may provide a suitable way to investigate the dynamic interactions between brain regions.
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Affiliation(s)
- Junyao Zhu
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
| | - Mingming Chen
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
| | - Junfeng Lu
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
| | - Kun Zhao
- School of Intelligent Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450001, China
| | - Enze Cui
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
| | - Zhiheng Zhang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
| | - Hong Wan
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China
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19
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Liu L, Ren J, Li Z, Yang C. A review of MEG dynamic brain network research. Proc Inst Mech Eng H 2022; 236:763-774. [PMID: 35465768 DOI: 10.1177/09544119221092503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The dynamic description of neural networks has attracted the attention of researchers for dynamic networks may carry more information compared with resting-state networks. As a non-invasive electrophysiological data with high temporal and spatial resolution, magnetoencephalogram (MEG) can provide rich information for the analysis of dynamic functional brain networks. In this review, the development of MEG brain network was summarized. Several analysis methods such as sliding window, Hidden Markov model, and time-frequency based methods used in MEG dynamic brain network studies were discussed. Finally, the current research about multi-modal brain network analysis and their applications with MEG neurophysiology, which are prospected to be one of the research directions in the future, were concluded.
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Affiliation(s)
- Lu Liu
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Jiechuan Ren
- Department of Internal Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhimei Li
- Department of Internal Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chunlan Yang
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
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20
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Liu J, Tan G, Wang J, Wei Y, Sheng Y, Chang H, Xie Q, Liu H. Closed-Loop Construction and Analysis of Cortico-Muscular-Cortical Functional Network After Stroke. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:1575-1586. [PMID: 35030075 DOI: 10.1109/tmi.2022.3143133] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Brain networks allow a topological understanding into the pathophysiology of stroke-induced motor deficits, and have been an influential tool for investigating brain functions. Unfortunately, currently applied methods generally lack in the recognition of the dynamic changes in the cortical networks related to muscle activity, which is crucial to clarify the alterations of the cooperative working patterns in the motor control system after stroke. In this study, we integrate corticomuscular and intermuscular interactions to cortico-cortical network and propose a novel closed-loop construction of cortico-muscular-cortical functional network, named closed-loop network (CLN). Directional characteristic in terms of differentiating causal interactions is endowed on basis of the CLN framework, further expanding the definition of functional connectivity (FC) and effective connectivity (EC) dedicated to CLN. Next, CLN is applied to stroke patients to reveal the underlying after-effects mechanism of low frequency repetitive transcranial magnetic stimulation (rTMS) induced alterations of cortical physiologic functions during movement. Results show that the short-term modulation of rTMS is reflected in the enhancement of information interaction within the interhemispheric primary motor regions and inhibition of the coupling between motor cortex and effector muscles. CLN provides a new perspective for the study of motor-related cortical networks with muscle activities involvement instead of being restricted to brain network analysis of behaviors.
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21
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Tewarie P, Prasse B, Meier J, Mandke K, Warrington S, Stam CJ, Brookes MJ, Van Mieghem P, Sotiropoulos SN, Hillebrand A. Predicting time-resolved electrophysiological brain networks from structural eigenmodes. Hum Brain Mapp 2022; 43:4475-4491. [PMID: 35642600 PMCID: PMC9435022 DOI: 10.1002/hbm.25967] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 04/25/2022] [Accepted: 05/16/2022] [Indexed: 01/20/2023] Open
Abstract
How temporal modulations in functional interactions are shaped by the underlying anatomical connections remains an open question. Here, we analyse the role of structural eigenmodes, in the formation and dissolution of temporally evolving functional brain networks using resting-state magnetoencephalography and diffusion magnetic resonance imaging data at the individual subject level. Our results show that even at short timescales, phase and amplitude connectivity can partly be expressed by structural eigenmodes, but hardly by direct structural connections. Albeit a stronger relationship was found between structural eigenmodes and time-resolved amplitude connectivity. Time-resolved connectivity for both phase and amplitude was mostly characterised by a stationary process, superimposed with very brief periods that showed deviations from this stationary process. For these brief periods, dynamic network states were extracted that showed different expressions of eigenmodes. Furthermore, the eigenmode expression was related to overall cognitive performance and co-occurred with fluctuations in community structure of functional networks. These results implicate that ongoing time-resolved resting-state networks, even at short timescales, can to some extent be understood in terms of activation and deactivation of structural eigenmodes and that these eigenmodes play a role in the dynamic integration and segregation of information across the cortex, subserving cognitive functions.
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Affiliation(s)
- Prejaas Tewarie
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Bastian Prasse
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Jil Meier
- Department of Neurology, Brain Simulation Section, Charité-Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Kanad Mandke
- Centre for Neuroscience in Education, Department of Psychology, University of Cambridge, Cambridge, UK
| | - Shaun Warrington
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Piet Van Mieghem
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK.,Wellcome Centre for Integrative Neuroimaging (WIN-FMRIB), University of Oxford, Oxford, UK.,NIHR Biomedical Research Centre, University of Nottingham, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
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22
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Duprez J, Tabbal J, Hassan M, Modolo J, Kabbara A, Mheich A, Drapier S, Vérin M, Sauleau P, Wendling F, Benquet P, Houvenaghel JF. Spatio-temporal dynamics of large-scale electrophysiological networks during cognitive action control in healthy controls and Parkinson's disease patients. Neuroimage 2022; 258:119331. [PMID: 35660459 DOI: 10.1016/j.neuroimage.2022.119331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 05/16/2022] [Accepted: 05/23/2022] [Indexed: 10/18/2022] Open
Abstract
Among the cognitive symptoms that are associated with Parkinson's disease (PD), alterations in cognitive action control (CAC) are commonly reported in patients. CAC enables the suppression of an automatic action, in favor of a goal-directed one. The implementation of CAC is time-resolved and arguably associated with dynamic changes in functional brain networks. However, the electrophysiological functional networks involved, their dynamic changes, and how these changes are affected by PD, still remain unknown. In this study, to address this gap of knowledge, 10 PD patients and 10 healthy controls (HC) underwent a Simon task while high-density electroencephalography (HD-EEG) was recorded. Source-level dynamic connectivity matrices were estimated using the phase-locking value in the beta (12-25 Hz) and gamma (30-45 Hz) frequency bands. Temporal independent component analyses were used as a dimension reduction tool to isolate the task-related brain network states. Typical microstate metrics were quantified to investigate the presence of these states at the subject-level. Our results first confirmed that PD patients experienced difficulties in inhibiting automatic responses during the task. At the group-level, we found three functional network states in the beta band that involved fronto-temporal, temporo-cingulate and fronto-frontal connections with typical CAC-related prefrontal and cingulate nodes (e.g., inferior frontal cortex). The presence of these networks did not differ between PD patients and HC when analyzing microstates metrics, and no robust correlations with behavior were found. In the gamma band, five networks were found, including one fronto-temporal network that was identical to the one found in the beta band. These networks also included CAC-related nodes previously identified in different neuroimaging modalities. Similarly to the beta networks, no subject-level differences were found between PD patients and HC. Interestingly, in both frequency bands, the dominant network at the subject-level was never the one that was the most durably modulated by the task. Altogether, this study identified the dynamic functional brain networks observed during CAC, but did not highlight PD-related changes in these networks that might explain behavioral changes. Although other new methods might be needed to investigate the presence of task-related networks at the subject-level, this study still highlights that task-based dynamic functional connectivity is a promising approach in understanding the cognitive dysfunctions observed in PD and beyond.
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Key Words
- Cognitive control
- DIFFIT, Difference in data fitting
- DLPFC, Dorso-lateral prefrontal cortex
- EEG, Electroencephalography
- FC, Functional connectivity
- Functional connectivity
- HC, Healthy controls
- HD-EEG, High-density EEG
- ICA, Independent component analysis
- IFC, Inferior frontal cortex
- MEG, Magnetoencephalography
- Networks, Dynamics
- PD, Parkinson's disease
- PLV, Phase locking value
- Parkinson's disease Abbreviations CAC, Cognitive action control
- ROIS, Regions of interest
- RT, Reaction time
- Simon task
- dBNS, Dynamic brain network state
- dFC, Dynamic functional connectivity
- fMRI, Functional magnetic resonance imaging
- high density EEG
- pre-SMA, Pre-supplementary motor area
- tICA, Temporal ICA
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Affiliation(s)
- Joan Duprez
- Univ Rennes, LTSI - U1099, F-35000 Rennes, France
| | - Judie Tabbal
- Univ Rennes, LTSI - U1099, F-35000 Rennes, France; Azm Center for Research in Biotechnology and Its Applications, EDST, Lebanese University, Beirut, Lebanon
| | - Mahmoud Hassan
- MINDig, F-35000 Rennes, France; School of Engineering, Reykjavik University, Iceland
| | | | | | | | - Sophie Drapier
- CIC INSERM 1414, Rennes, France; Neurology Department, Pontchaillou Hospital, Rennes University Hospital, France
| | - Marc Vérin
- Neurology Department, Pontchaillou Hospital, Rennes University Hospital, France; Behavioral and Basal Ganglia' Research Unit, University of Rennes 1-Rennes University Hospital, France
| | - Paul Sauleau
- Behavioral and Basal Ganglia' Research Unit, University of Rennes 1-Rennes University Hospital, France; Neurophysiology department, Rennes University Hospital, France
| | | | | | - Jean-François Houvenaghel
- Neurology Department, Pontchaillou Hospital, Rennes University Hospital, France; Behavioral and Basal Ganglia' Research Unit, University of Rennes 1-Rennes University Hospital, France
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23
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Turk E, Vroomen J, Fonken Y, Levy J, van den Heuvel MI. In sync with your child: The potential of parent-child electroencephalography in developmental research. Dev Psychobiol 2022; 64:e22221. [PMID: 35312051 DOI: 10.1002/dev.22221] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 09/29/2021] [Accepted: 10/29/2021] [Indexed: 12/25/2022]
Abstract
Healthy interaction between parent and child is foundational for the child's socioemotional development. Recently, an innovative paradigm shift in electroencephalography (EEG) research has enabled the simultaneous measurement of neural activity in caregiver and child. This dual-EEG or hyperscanning approach, termed parent-child dual-EEG, combines the strength of both behavioral observations and EEG methods. In this review, we aim to inform on the potential of dual-EEG in parents and children (0-6 years) for developmental researchers. We first provide a general overview of the dual-EEG technique and continue by reviewing the first empirical work on the emerging field of parent-child dual-EEG, discussing the limited but fascinating findings on parent-child brain-to-behavior and brain-to-brain synchrony. We then continue by providing an overview of dual-EEG analysis techniques, including the technical challenges and solutions one may encounter. We finish by discussing the potential of parent-child dual-EEG for the future of developmental research. The analysis of multiple EEG data is technical and challenging, but when performed well, parent-child EEG may transform the way we understand how caregiver and child connect on a neurobiological level. Importantly, studying objective physiological measures of parent-child interactions could lead to the identification of novel brain-to-brain synchrony markers of interaction quality.
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Affiliation(s)
- Elise Turk
- Department of Cognitive Neuropsychology, Tilburg University, Tilburg, The Netherlands
| | - Jean Vroomen
- Department of Cognitive Neuropsychology, Tilburg University, Tilburg, The Netherlands
| | - Yvonne Fonken
- Department of Cognitive Neuropsychology, Tilburg University, Tilburg, The Netherlands
| | - Jonathan Levy
- Baruch Ivcher School of Psychology, Interdisciplinary Center Herzliya (IDC), Herzliya, Israel.,Department of Neuroscience and Biomedical Engineering, Aalto University, Aalto, Finland
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24
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Bahrami M, Laurienti PJ, Shappell HM, Dagenbach D, Simpson SL. A mixed-modeling framework for whole-brain dynamic network
analysis. Netw Neurosci 2022; 6:591-613. [PMID: 35733427 PMCID: PMC9208000 DOI: 10.1162/netn_a_00238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 02/09/2022] [Indexed: 11/15/2022] Open
Abstract
The emerging area of dynamic brain network analysis has gained considerable attention in recent years. However, development of multivariate statistical frameworks that allow for examining the associations between phenotypic traits and dynamic patterns of system-level properties of the brain, and drawing statistical inference about such associations, has largely lagged behind. To address this need we developed a mixed-modeling framework that allows for assessing the relationship between any desired phenotype and dynamic patterns of whole-brain connectivity and topology. This novel framework also allows for simulating dynamic brain networks with respect to desired covariates. Unlike current tools, which largely use data-driven methods, our model-based method enables aligning neuroscientific hypotheses with the analytic approach. We demonstrate the utility of this model in identifying the relationship between fluid intelligence and dynamic brain networks by using resting-state fMRI (rfMRI) data from 200 participants in the Human Connectome Project (HCP) study. We also demonstrate the utility of this model to simulate dynamic brain networks at both group and individual levels. To our knowledge, this approach provides the first model-based statistical method for examining dynamic patterns of system-level properties of the brain and their relationships to phenotypic traits as well as simulating dynamic brain networks. In recent years, a growing body of studies have aimed at analyzing the brain as a complex dynamic system by using various neuroimaging data. This has opened new avenues to answer compelling questions about the brain function in health and disease. However, methods that allow for providing statistical inference about how the complex interactions of the brain are associated with desired phenotypes are to be developed for a more profound insight. This study introduces a promising regression-based model to relate dynamic brain networks to desired phenotypes and provide statistical inference. Moreover, it can be used for simulating dynamic brain networks with respect to desired phenotypes at the group and individual levels.
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Affiliation(s)
- Mohsen Bahrami
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Paul J. Laurienti
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Heather M. Shappell
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Dale Dagenbach
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Psychology, Wake Forest University, Winston-Salem, NC, USA
| | - Sean L. Simpson
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
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25
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Tait L, Zhang J. MEG cortical microstates: spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses. Neuroimage 2022; 251:119006. [PMID: 35181551 PMCID: PMC8961001 DOI: 10.1016/j.neuroimage.2022.119006] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 01/29/2022] [Accepted: 02/14/2022] [Indexed: 12/12/2022] Open
Abstract
EEG microstate analysis is a method to study brain states in health and disease. We present a source-space microstate pipeline, giving additional anatomical insight. Simulations validate source-space microstates. We identify 10 resting microstates and their patterns of functional connectivity. Microstate activation likelihoods change in response to auditory stimulus.
EEG microstate analysis is an approach to study brain states and their fast transitions in healthy cognition and disease. A key limitation of conventional microstate analysis is that it must be performed at the sensor level, and therefore gives limited anatomical insight. Here, we generalise the microstate methodology to be applicable to source-reconstructed electrophysiological data. Using simulations of a neural-mass network model, we first established the validity and robustness of the proposed method. Using MEG resting-state data, we uncovered ten microstates with distinct spatial distributions of cortical activation. Multivariate pattern analysis demonstrated that source-level microstates were associated with distinct functional connectivity patterns. We further demonstrated that the occurrence probability of MEG microstates were altered by auditory stimuli, exhibiting a hyperactivity of the microstate including the auditory cortex. Our results support the use of source-level microstates as a method for investigating brain dynamic activity and connectivity at the millisecond scale.
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Affiliation(s)
- Luke Tait
- Centre for Systems Modelling & Quantitative Biomedicine (SMQB), University of Birmingham, Birmingham, UK; Cardiff University Brain Research Imaging Centre, Cardiff, UK
| | - Jiaxiang Zhang
- Cardiff University Brain Research Imaging Centre, Cardiff, UK
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26
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Basti A, Chella F, Guidotti R, Ermolova M, D'Andrea A, Stenroos M, Romani GL, Pizzella V, Marzetti L. Looking through the windows: a study about the dependency of phase-coupling estimates on the data length. J Neural Eng 2022; 19. [PMID: 35147515 DOI: 10.1088/1741-2552/ac542f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 02/08/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Being able to characterize functional connectivity (FC) state dynamics in a real-time setting, such as in brain-computer interface, neurofeedback or closed-loop neurostimulation frameworks, requires the rapid detection of the statistical dependencies that quantify FC in short windows of data. The aim of this study is to characterize, through extensive realistic simulations, the reliability of FC estimation as a function of the data length. In particular, we focused on FC as measured by phase-coupling (PC) of neuronal oscillations, one of the most functionally relevant neural coupling modes. APPROACH We generated synthetic data corresponding to different scenarios by varying the data length, the signal-to-noise ratio, the phase difference value, the spectral analysis approach (Hilbert or Fourier) and the fractional bandwidth. We compared seven PC metrics, i.e. imaginary part of phase locking value (PLV), PLV of orthogonalized signals, phase lag index (PLI), debiased weighted PLI, imaginary part of coherency, coherence of orthogonalized signals and lagged coherence. MAIN RESULTS Our findings show that, for a signal-to-noise-ratio of at least 10 dB, a data window that contains 5 to 8 cycles of the oscillation of interest (e.g. a 500-800ms window at 10Hz) is generally required to achieve reliable PC estimates. In general, Hilbert-based approaches were associated with higher performance than Fourier-based approaches. Furthermore, the results suggest that, when the analysis is performed in a narrow frequency range, a larger window is required. SIGNIFICANCE The achieved results pave the way to the introduction of best-practice guidelines to be followed when a real-time frequency-specific PC assessment is at target.
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Affiliation(s)
- Alessio Basti
- NEUROSCIENCE. IMAGING AND CLINICAL SCIENCE, Universita degli Studi Gabriele d\'Annunzio Chieti e Pescara, Via Luigi Polacchi 11, Chieti, Chieti, 66100, ITALY
| | - Federico Chella
- Neuroscience, Imaging and Clinical Sciences, Gabriele d'Annunzio University of Chieti and Pescara, Via Luigi Polacchi 11, Chieti, Abruzzo, 66100, ITALY
| | - Roberto Guidotti
- Neuroscience, Imaging and Clinical Sciences, Universita degli Studi Gabriele d'Annunzio Chieti e Pescara, Via Luigi Polacchi 11, Chieti Scalo, CH, 66100, ITALY
| | - Maria Ermolova
- Eberhard Karls University Tubingen Hertie Institute for Clinical Brain Research, Hoppe-Seyler Str. 3, Tubingen, Baden-Württemberg, 72076, GERMANY
| | - Antea D'Andrea
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele d'Annunzio University of Chieti and Pescara, Via Luigi Polacchi 11, Chieti, Abruzzo, 66100, ITALY
| | - Matti Stenroos
- Department of Biomedical Engineering and Computational Science, Aalto University, PO Box 12200, FI-00076 AALTO, Espoo, 00076, FINLAND
| | - Gian-Luca Romani
- Institute for Advanced Biomedical Technologies, Gabriele d'Annunzio University of Chieti and Pescara, Via Luigi Polacchi 11, 66013 Chieti, Chieti, Abruzzo, 66100, ITALY
| | - Vittorio Pizzella
- Neuroscience, Imaging and Clinical Sciences, Gabriele d'Annunzio University of Chieti and Pescara, Via Luigi Polacchi 11, Chieti, 66100, ITALY
| | - Laura Marzetti
- NEUROSCIENCE. IMAGING AND CLINICAL SCIENCE, Universita degli Studi Gabriele d\'Annunzio Chieti e Pescara, Via Luigi Polacchi 11, Chieti, Chieti, 66100, ITALY
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27
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Núñez P, Gomez C, Rodríguez-González V, Hillebrand A, Tewarie P, Gomez-Pilar J, Molina V, Hornero R, Poza J. Schizophrenia induces abnormal frequency-dependent patterns of dynamic brain network reconfiguration during an auditory oddball task. J Neural Eng 2022; 19. [PMID: 35108688 DOI: 10.1088/1741-2552/ac514e] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 02/02/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Schizophrenia is a psychiatric disorder that has been shown to disturb the dynamic top-down processing of sensory information. Various imaging techniques have revealed abnormalities in brain activity associated with this disorder, both locally and between cerebral regions. However, there is increasing interest in investigating dynamic network response to novel and relevant events at the network level during an attention-demanding task with high-temporal-resolution techniques. The aim of the work was: (i) to test the capacity of a novel algorithm to detect recurrent brain meta-states from auditory oddball task recordings; and (ii) to evaluate how the dynamic activation and behavior of the aforementioned meta-states were altered in schizophrenia, since it has been shown to impair top-down processing of sensory information. APPROACH A novel unsupervised method for the detection of brain meta-states based on recurrence plots and community detection algorithms, previously tested on resting-state data, was used on auditory oddball task recordings. Brain meta-states and several properties related to their activation during target trials in the task were extracted from electroencephalography (EEG) data from patients with schizophrenia and cognitively healthy controls. MAIN RESULTS The methodology successfully detected meta-states during an auditory oddball task, and they appeared to show both frequency-dependent time-locked and non-time-locked activity with respect to the stimulus onset. Moreover, patients with schizophrenia displayed higher network diversity, and showed more sluggish meta-state transitions, reflected in increased dwell times, less complex meta-state sequences, decreased meta-state space speed, and abnormal ratio of negative meta-state correlations. SIGNIFICANCE Abnormal cognition in schizophrenia is also reflected in decreased brain flexibility at the dynamic network level, which may hamper top-down processing, possibly indicating impaired decision-making linked to dysfunctional predictive coding. Moreover, the results showed the ability of the methodology to find meaningful and task-relevant changes in dynamic connectivity and pathology-related group differences.
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Affiliation(s)
- Pablo Núñez
- Teoría de la señal y comunicaciones e ingeniería telemática, Universidad de Valladolid, E.T.S. Ingenieros de Telecomunicacion, Paseo de Belen 15, 47011 - Valladolid, Valladolid, 47002, SPAIN
| | - Carlos Gomez
- Grupo de Ingeniería Biomédica, Universidad de Valladolid, E. T. S. Ingenieros de Telecomunicación, Universidad de Valladolid, Paseo Belén, 15, Valladolid, Valladolid, 47011, SPAIN
| | - Víctor Rodríguez-González
- Teoría de la señal y comunicaciones e ingeniería telemática, Universidad de Valladolid, E.T.S. Ingenieros de Telecomunicacion, Paseo de Belen 15, 47011 - Valladolid, Valladolid, 47011, SPAIN
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Centre, VU University Medical Centre, VU University Medical Centre, 1081 HV Amsterdam, Netherlands, Amsterdam, 1081 HV, NETHERLANDS
| | - Prejaas Tewarie
- Department of Clinical Neurophysiology and MEG Centre, VU University Medical Centre Amsterdam, VU University Medical Centre, 1081 HV Amsterdam, Netherlands, Amsterdam, Noord-Holland, 1081 HV, NETHERLANDS
| | - Javier Gomez-Pilar
- Communications and Signal Theory, Universidad de Valladolid, E.T.S. Ingenieros de Telecomunicacion, Paseo de Belen 15, 47011 - Valladolid, Valladolid, Valladolid, 47011, SPAIN
| | - Vicente Molina
- Universidad de Valladolid, School of Medicine, University of Valladolid, 47005 - Valladolid, Valladolid, 47002, SPAIN
| | - Roberto Hornero
- Biomedical Engineering Group, Universidad de Valladolid, ETSI Telecomunicacion, Paseo Belen 15, Valladolid, 47011, SPAIN
| | - Jesus Poza
- Communications and Signal Theory, University of Valladolid, E.T.S. Ingenieros de Telecomunicacion, Paseo de Belen 15, 47011 - Valladolid, Valladolid, 47002, SPAIN
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28
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Nunez P, Rodriguez-Gonzalez V, Gutierrez-de Pablo V, Gomez C, Shigihara Y, Hoshi H, Hornero R, Poza J. Effect of segment length, sampling frequency, and imaging modality on the estimation of measures of brain meta-state activation: an MEG/EEG study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:315-318. [PMID: 34891299 DOI: 10.1109/embc46164.2021.9630583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The main objective of this study was to examine the influence that recording length, sampling frequency, and imaging modality have on the estimation and characterization of spontaneous brain meta-states during rest. To this end, a recently developed method of meta-state extraction and characterization was applied to a subset of 16 healthy elderly subjects from two independent electroencephalographic and magnetoencephalographic (EEG/MEG) databases. The recordings were segmented into the first 5, 10, 15, 20, 25, 30, 60 and 90-s of artifact-free activity and meta-states were extracted. Temporal activation sequence (TAS) complexity, which characterizes the complexity of the metastateactivation sequences during rest, was calculated. Then, its stability as a function of segment length, sampling frequency, and imaging modality was assessed. The results showed that, in general, the minimum segment length needed to fully characterize resting-state meta-state activation complexity ranged from 15 to 25 seconds. Moreover, it was found that the sampling frequency has a slight influence on the complexity measure, and that results were similar across EEG and MEG. The findings indicate that the proposed methodology can be applied to both EEG and MEG recordings and displays stable behavior with relatively short segments. However, methodological choices, such as sampling frequency, should be carefully considered.
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29
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Nij Bijvank JA, Strijbis EMM, Nauta IM, Kulik SD, Balk LJ, Stam CJ, Hillebrand A, Geurts JJG, Uitdehaag BMJ, van Rijn LJ, Petzold A, Schoonheim MM. Impaired saccadic eye movements in multiple sclerosis are related to altered functional connectivity of the oculomotor brain network. Neuroimage Clin 2021; 32:102848. [PMID: 34624635 PMCID: PMC8503580 DOI: 10.1016/j.nicl.2021.102848] [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: 03/17/2021] [Revised: 09/17/2021] [Accepted: 09/28/2021] [Indexed: 11/28/2022]
Abstract
Impaired eye movements in multiple sclerosis (MS) and functional connectivity (FC) Eye movements related to altered FC of the oculomotor brain network. Lower (beta band) and higher (theta/delta band) FC related to abnormal eye movements. Regional changes were more informative than whole-network measures. Eye movement parameters also related to disability and cognitive dysfunction.
Background Impaired eye movements in multiple sclerosis (MS) are common and could represent a non-invasive and accurate measure of (dys)functioning of interconnected areas within the complex brain network. The aim of this study was to test whether altered saccadic eye movements are related to changes in functional connectivity (FC) in patients with MS. Methods Cross-sectional eye movement (pro-saccades and anti-saccades) and magnetoencephalography (MEG) data from the Amsterdam MS cohort were included from 176 MS patients and 33 healthy controls. FC was calculated between all regions of the Brainnetome atlas in six conventional frequency bands. Cognitive function and disability were evaluated by previously validated measures. The relationships between saccadic parameters and both FC and clinical scores in MS patients were analysed using multivariate linear regression models. Results In MS pro- and anti-saccades were abnormal compared to healthy controls A relationship of saccadic eye movements was found with FC of the oculomotor network, which was stronger for regional than global FC. In general, abnormal eye movements were related to higher delta and theta FC but lower beta FC. Strongest associations were found for pro-saccadic latency and FC of the precuneus (beta band β = -0.23, p = .006), peak velocity and FC of the parietal eye field (theta band β = -0.25, p = .005) and gain and FC of the inferior frontal eye field (theta band β = -0.25, p = .003). Pro-saccadic latency was also strongly associated with disability scores and cognitive dysfunction. Conclusions Impaired saccadic eye movements were related to functional connectivity of the oculomotor network and clinical performance in MS. This study also showed that, in addition to global network connectivity, studying regional changes in MEG studies could yield stronger correlations.
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Affiliation(s)
- J A Nij Bijvank
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology, MS Center and Neuro-ophthalmology Expertise Center, Amsterdam Neuroscience, Amsterdam, the Netherlands; Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Ophthalmology, Neuro-ophthalmology Expertise Center, Amsterdam Neuroscience, Amsterdam, the Netherlands.
| | - E M M Strijbis
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology, MS Center and Neuro-ophthalmology Expertise Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - I M Nauta
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology, MS Center and Neuro-ophthalmology Expertise Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - S D Kulik
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, Amsterdam, the Netherlands
| | - L J Balk
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology, MS Center and Neuro-ophthalmology Expertise Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - C J Stam
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology and Magnetoencephalography Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - A Hillebrand
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology and Magnetoencephalography Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - J J G Geurts
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, Amsterdam, the Netherlands
| | - B M J Uitdehaag
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology, MS Center and Neuro-ophthalmology Expertise Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - L J van Rijn
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Ophthalmology, Neuro-ophthalmology Expertise Center, Amsterdam Neuroscience, Amsterdam, the Netherlands; Onze Lieve Vrouwe Gasthuis, Department of Ophthalmology, Amsterdam, the Netherlands
| | - A Petzold
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology, MS Center and Neuro-ophthalmology Expertise Center, Amsterdam Neuroscience, Amsterdam, the Netherlands; Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Ophthalmology, Neuro-ophthalmology Expertise Center, Amsterdam Neuroscience, Amsterdam, the Netherlands; Moorfields Eye Hospital, The National Hospital for Neurology and Neurosurgery and the UCL Queen Square Institute of Neurology, London, United Kingdom
| | - M M Schoonheim
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neurosciences, Amsterdam, the Netherlands
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30
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Wei HT, Francois-Nienaber A, Deschamps T, Bellana B, Hebscher M, Sivaratnam G, Zadeh M, Meltzer JA. Sensitivity of amplitude and phase based MEG measures of interhemispheric connectivity during unilateral finger movements. Neuroimage 2021; 242:118457. [PMID: 34363959 DOI: 10.1016/j.neuroimage.2021.118457] [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: 04/16/2021] [Revised: 06/03/2021] [Accepted: 08/04/2021] [Indexed: 11/16/2022] Open
Abstract
Interactions between different brain regions can be revealed by dependencies between their neuronal oscillations. We examined the sensitivity of different oscillatory connectivity measures in revealing interhemispheric interactions between primary motor cortices (M1s) during unilateral finger movements. Based on frequency, amplitude, and phase of the oscillations, a number of metrics have been developed to measure connectivity between brain regions, and each metric has its own strengths, weaknesses, and pitfalls. Taking advantage of the well-known movement-related modulations of oscillatory amplitude in M1s, this study compared and contrasted a number of leading connectivity metrics during distinct phases of oscillatory power changes. Between M1s during unilateral movements, we found that phase-based metrics were effective at revealing connectivity during the beta (15-35 Hz) rebound period linked to movement termination, but not during the early period of beta desynchronization occurring during the movement itself. Amplitude correlation metrics revealed robust connectivity during both periods. Techniques for estimating the direction of connectivity had limited success. Granger Causality was not well suited to studying these connections because it was strongly confounded by differences in signal-to-noise ratio linked to modulation of beta amplitude occurring during the task. Phase slope index was suggestive but not conclusive of a unidirectional influence between motor cortices during the beta rebound. Our findings suggest that a combination of amplitude and phase-based metrics is likely required to fully characterize connectivity during task protocols that involve modulation of oscillatory power, and that amplitude-based metrics appear to be more sensitive despite the lack of directional information.
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Affiliation(s)
- Hsi T Wei
- Department of Psychology, University of Toronto, Canada; Rotman Research Institute, Baycrest Hospital, Canada.
| | | | | | - Buddhika Bellana
- Rotman Research Institute, Baycrest Hospital, Canada; Department of Psychological and Brain Sciences, Johns Hopkins University, United States
| | - Melissa Hebscher
- Rotman Research Institute, Baycrest Hospital, Canada; Feinberg School of Medicine, Northwestern University, United States
| | | | - Maryam Zadeh
- Rotman Research Institute, Baycrest Hospital, Canada
| | - Jed A Meltzer
- Department of Psychology, University of Toronto, Canada; Rotman Research Institute, Baycrest Hospital, Canada; Department of Speech-Language Pathology, University of Toronto, Canada
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31
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Hämäläinen M, Huang M, Bowyer SM. Magnetoencephalography Signal Processing, Forward Modeling, Magnetoencephalography Inverse Source Imaging, and Coherence Analysis. Neuroimaging Clin N Am 2021; 30:125-143. [PMID: 32336402 DOI: 10.1016/j.nic.2020.02.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Magnetoencephalography (MEG) is a noninvasive functional imaging technique for the brain. MEG directly measures the magnetic signal due to neuronal activation in gray matter with high spatial localization accuracy. The first part of this article covers the overall concepts of MEG and the forward and inverse modeling techniques. It is followed by examples of analyzing evoked and resting-state MEG signals using a high-resolution MEG source imaging technique. Next, different techniques for connectivity and network analysis are reviewed with examples showing connectivity estimates from resting-state and epileptic activity.
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Affiliation(s)
- Matti Hämäläinen
- Department of Radiology, Athinoula A. Martinos Center, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA; Harvard Medical School, Boston, MA, USA
| | - Mingxiong Huang
- Department of Radiology, UCSD Radiology Imaging Lab, University of California, San Diego, 3510 Dunhill Street, San Diego, CA 92121, USA
| | - Susan M Bowyer
- Department of Neurology, MEG Lab, Henry Ford Hospital, 2799 West Grand Boulevard, CFP 079, Detroit, MI 48202, USA; Wayne State University School of Medicine, Detroit, MI, USA; Department of Physics, Oakland University, Rochester, MI, USA.
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32
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Núñez P, Poza J, Gómez C, Rodríguez-González V, Hillebrand A, Tewarie P, Tola-Arribas MÁ, Cano M, Hornero R. Abnormal meta-state activation of dynamic brain networks across the Alzheimer spectrum. Neuroimage 2021; 232:117898. [PMID: 33621696 DOI: 10.1016/j.neuroimage.2021.117898] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 01/19/2021] [Accepted: 02/16/2021] [Indexed: 02/06/2023] Open
Abstract
The characterization of the distinct dynamic functional connectivity (dFC) patterns that activate in the brain during rest can help to understand the underlying time-varying network organization. The presence and behavior of these patterns (known as meta-states) have been widely studied by means of functional magnetic resonance imaging (fMRI). However, modalities with high-temporal resolution, such as electroencephalography (EEG), enable the characterization of fast temporally evolving meta-state sequences. Mild cognitive impairment (MCI) and dementia due to Alzheimer's disease (AD) have been shown to disrupt spatially localized activation and dFC between different brain regions, but not much is known about how they affect meta-state network topologies and their network dynamics. The main hypothesis of the study was that MCI and dementia due to AD alter normal meta-state sequences by inducing a loss of structure in their patterns and a reduction of their dynamics. Moreover, we expected that patients with MCI would display more flexible behavior compared to patients with dementia due to AD. Thus, the aim of the current study was twofold: (i) to find repeating, distinctly organized network patterns (meta-states) in neural activity; and (ii) to extract information about meta-state fluctuations and how they are influenced by MCI and dementia due to AD. To accomplish these goals, we present a novel methodology to characterize dynamic meta-states and their temporal fluctuations by capturing aspects based on both their discrete activation and the continuous evolution of their individual strength. These properties were extracted from 60-s resting-state EEG recordings from 67 patients with MCI due to AD, 50 patients with dementia due to AD, and 43 cognitively healthy controls. First, the instantaneous amplitude correlation (IAC) was used to estimate instantaneous functional connectivity with a high temporal resolution. We then extracted meta-states by means of graph community detection based on recurrence plots (RPs), both at the individual- and group-level. Subsequently, a diverse set of properties of the continuous and discrete fluctuation patterns of the meta-states was extracted and analyzed. The main novelty of the methodology lies in the usage of Louvain GJA community detection to extract meta-states from IAC-derived RPs and the extended analysis of their discrete and continuous activation. Our findings showed that distinct dynamic functional connectivity meta-states can be found on the EEG time-scale, and that these were not affected by the oscillatory slowing induced by MCI or dementia due to AD. However, both conditions displayed a loss of meta-state modularity, coupled with shorter dwell times and higher complexity of the meta-state sequences. Furthermore, we found evidence that meta-state sequencing is not entirely random; it shows an underlying structure that is partially lost in MCI and dementia due to AD. These results show evidence that AD progression is associated with alterations in meta-state switching, and a degradation of dynamic brain flexibility.
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Affiliation(s)
- Pablo Núñez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.
| | - Jesús Poza
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Madrid, Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
| | - Carlos Gómez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Madrid, Spain
| | | | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Prejaas Tewarie
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Miguel Ángel Tola-Arribas
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Madrid, Spain
| | - Mónica Cano
- Department of Clinical Neurophysiology, "Río Hortega" University Hospital, Valladolid, Spain
| | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Madrid, Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
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33
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Tabbal J, Kabbara A, Khalil M, Benquet P, Hassan M. Dynamics of task-related electrophysiological networks: a benchmarking study. Neuroimage 2021; 231:117829. [PMID: 33549758 DOI: 10.1016/j.neuroimage.2021.117829] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 01/25/2021] [Accepted: 01/29/2021] [Indexed: 12/29/2022] Open
Abstract
Motor, sensory and cognitive functions rely on dynamic reshaping of functional brain networks. Tracking these rapid changes is crucial to understand information processing in the brain, but challenging due to the great variety of dimensionality reduction methods used at the network-level and the limited evaluation studies. Using Magnetoencephalography (MEG) combined with Source Separation (SS) methods, we present an integrated framework to track fast dynamics of electrophysiological brain networks. We evaluate nine SS methods applied to three independent MEG databases (N=95) during motor and memory tasks. We report differences between these methods at the group and subject level. We seek to help researchers in choosing objectively the appropriate SS method when tracking fast reconfiguration of functional brain networks, due to its enormous benefits in cognitive and clinical neuroscience.
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Affiliation(s)
- Judie Tabbal
- Univ Rennes, LTSI - U1099, F-35000 Rennes, France; Azm Center for Research in Biotechnology and Its Applications, EDST, Lebanese University, Beirut, Lebanon.
| | - Aya Kabbara
- Univ Rennes, LTSI - U1099, F-35000 Rennes, France
| | - Mohamad Khalil
- Azm Center for Research in Biotechnology and Its Applications, EDST, Lebanese University, Beirut, Lebanon; CRSI Lab, Engineering Faculty, Lebanese University, Beirut, Lebanon
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34
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Sjøgård M, Wens V, Van Schependom J, Costers L, D'hooghe M, D'haeseleer M, Woolrich M, Goldman S, Nagels G, De Tiège X. Brain dysconnectivity relates to disability and cognitive impairment in multiple sclerosis. Hum Brain Mapp 2020; 42:626-643. [PMID: 33242237 PMCID: PMC7814767 DOI: 10.1002/hbm.25247] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 09/10/2020] [Accepted: 09/29/2020] [Indexed: 12/27/2022] Open
Abstract
The pathophysiology of cognitive dysfunction in multiple sclerosis (MS) is still unclear. This magnetoencephalography (MEG) study investigates the impact of MS on brain resting-state functional connectivity (rsFC) and its relationship to disability and cognitive impairment. We investigated rsFC based on power envelope correlation within and between different frequency bands, in a large cohort of participants consisting of 99 MS patients and 47 healthy subjects. Correlations were investigated between rsFC and outcomes on disability, disease duration and 7 neuropsychological scores within each group, while stringently correcting for multiple comparisons and possible confounding factors. Specific dysconnections correlating with MS-induced physical disability and disease duration were found within the sensorimotor and language networks, respectively. Global network-level reductions in within- and cross-network rsFC were observed in the default-mode network. Healthy subjects and patients significantly differed in their scores on cognitive fatigue and verbal fluency. Healthy subjects and patients showed different correlation patterns between rsFC and cognitive fatigue or verbal fluency, both of which involved a shift in patients from the posterior default-mode network to the language network. Introducing electrophysiological rsFC in a regression model of verbal fluency and cognitive fatigue in MS patients significantly increased the explained variance compared to a regression limited to structural MRI markers (relative thalamic volume and lesion load). This MEG study demonstrates that MS induces distinct changes in the resting-state functional brain architecture that relate to disability, disease duration and specific cognitive functioning alterations. It highlights the potential value of electrophysiological intrinsic rsFC for monitoring the cognitive impairment in patients with MS.
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Affiliation(s)
- Martin Sjøgård
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Vincent Wens
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB-Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Jeroen Van Schependom
- Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium.,National MS Center, Belgium
| | - Lars Costers
- Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Marie D'hooghe
- Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium.,National MS Center, Belgium
| | - Miguel D'haeseleer
- Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium.,National MS Center, Belgium
| | - Mark Woolrich
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Serge Goldman
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB-Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Guy Nagels
- Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium.,National MS Center, Belgium.,St Edmund Hall, University of Oxford, Oxford, UK
| | - Xavier De Tiège
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB-Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium
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35
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High-dimensional brain-wide functional connectivity mapping in magnetoencephalography. J Neurosci Methods 2020; 348:108991. [PMID: 33181166 DOI: 10.1016/j.jneumeth.2020.108991] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 09/06/2020] [Accepted: 10/22/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND Brain functional connectivity (FC) analyses based on magneto/electroencephalography (M/EEG) signals have yet to exploit the intrinsic high-dimensional information. Typically, these analyses are constrained to regions of interest to avoid the curse of dimensionality, with the latter leading to conservative hypothesis testing. NEW METHOD We removed such constraint by estimating high-dimensional source-based M/EEG-FC using cluster-permutation statistic (CPS) and demonstrated the feasibility of this approach by identifying resting-state changes in mild cognitive impairment (MCI), a prodromal stage of Alzheimer's disease. Particularly, we proposed a unified framework for CPS analysis together with a novel neighbourhood measure to estimate more compact and neurophysiological plausible neural communication. As clusters could more confidently reveal interregional communication, we proposed and tested a cluster-strength index to demonstrate other advantages of CPS analysis. RESULTS We found clusters of increased communication or hypersynchronization in MCI compared to healthy controls in delta (1-4 Hz) and higher-theta (6-8 Hz) bands oscillations. These mainly consisted of interactions between occipitofrontal and occipitotemporal regions in the left hemisphere, which may be critically affected in the early stages of Alzheimer's disease. CONCLUSIONS Our approach could be important to create high-resolution FC maps from neuroimaging studies in general, allowing the multimodal analysis of neural communication across multiple spatial scales. Particularly, FC clusters more robustly represent the interregional communication by identifying dense bundles of connections that are less sensitive to inter-individual anatomical and functional variability. Overall, this approach could help to better understand neural information processing in healthy and disease conditions as needed for developing biomarker research.
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36
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Zhang C, Qiu S, Wang S, Wei W, He H. Temporal Dynamics on Decoding Target Stimuli in Rapid Serial Visual Presentation using Magnetoencephalography. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2954-2958. [PMID: 33018626 DOI: 10.1109/embc44109.2020.9176174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Rapid serial visual presentation (RSVP) is a high efficient paradigm in brain-computer interface (BCI). Target detection accuracy is the first consideration of RSVP-BCI. But the influence of different frequency bands and time ranges on decoding accuracy are still an open questions. Moreover, the underlying neural dynamic of the rapid target detecting process is still unclear. Methods: This work focused the temporal dynamic of the responses triggered by target stimuli in a static RSVP paradigm using paired structural Magnetic Resonance Imaging (MRI) and magnetoencephalography (MEG) signals with different frequency bands. Multivariate pattern analysis (MVPA) was applied on the MEG signal with different frequency bands and time points after stimuli onset. Cortical neuronal activation estimation technology was also applied to present the temporal-spatial dynamic on cortex surface. Results: The MVPA results showed that the low frequency signals (0.1 - 7 Hz) yield highest decoding accuracy, and the decoding power reached its peak at 0.4 second after target stimuli onset. The cortical neuronal activation method identified the target stimuli triggered regions, like bilateral parahippocampal cortex, precentral gyrus and insula cortex, and the averaged time series were presented.
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37
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Issues and recommendations from the OHBM COBIDAS MEEG committee for reproducible EEG and MEG research. Nat Neurosci 2020; 23:1473-1483. [DOI: 10.1038/s41593-020-00709-0] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 08/18/2020] [Indexed: 11/08/2022]
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38
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Briels CT, Eertink JJ, Stam CJ, van der Flier WM, Scheltens P, Gouw AA. Profound regional spectral, connectivity, and network changes reflect visual deficits in posterior cortical atrophy: an EEG study. Neurobiol Aging 2020; 96:1-11. [PMID: 32905950 DOI: 10.1016/j.neurobiolaging.2020.07.029] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 07/20/2020] [Accepted: 07/29/2020] [Indexed: 10/23/2022]
Abstract
Patients with posterior cortical atrophy (PCA-AD) show more severe visuospatial and perceptual deficits than those with typical AD (tAD). The aim of this study was to investigate whether functional alterations measured by electroencephalography can help understand the mechanisms that explain this clinical heterogeneity. 21-channel electroencephalography recordings of 29 patients with PCA-AD were compared with 29 patients with tAD and 29 controls matched for age, gender, and disease severity. Patients with PCA-AD and tAD both showed a global decrease in fast and increase in slow oscillatory activity compared with controls. This pattern was, however, more profound in patients with PCA-AD which was driven by more extensive slowing of the posterior regions. Alpha band functional connectivity showed a similar decrease in PCA-AD and tAD. Compared with controls, a less integrated network topology was observed in PCA-AD, with a decrease of posterior and an increase of frontal hubness. In PCA-AD, decreased right parietal peak frequency correlated with worse performance on visual tasks. Regional vulnerability of the posterior network might explain the atypical pattern of neurodegeneration in PCA-AD.
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Affiliation(s)
- Casper T Briels
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands; Department of Clinical Neurophysiology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.
| | - Jakoba J Eertink
- Department of Clinical Neurophysiology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands; Department of Epidemiology and Biostatistics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Alida A Gouw
- Department of Clinical Neurophysiology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
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Connectome spectral analysis to track EEG task dynamics on a subsecond scale. Neuroimage 2020; 221:117137. [PMID: 32652217 DOI: 10.1016/j.neuroimage.2020.117137] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 06/10/2020] [Accepted: 07/02/2020] [Indexed: 12/11/2022] Open
Abstract
We present an approach for tracking fast spatiotemporal cortical dynamics in which we combine white matter connectivity data with source-projected electroencephalographic (EEG) data. We employ the mathematical framework of graph signal processing in order to derive the Fourier modes of the brain structural connectivity graph, or "network harmonics". These network harmonics are naturally ordered by smoothness. Smoothness in this context can be understood as the amount of variation along the cortex, leading to a multi-scale representation of brain connectivity. We demonstrate that network harmonics provide a sparse representation of the EEG signal, where, at certain times, the smoothest 15 network harmonics capture 90% of the signal power. This suggests that network harmonics are functionally meaningful, which we demonstrate by using them as a basis for the functional EEG data recorded from a face detection task. There, only 13 network harmonics are sufficient to track the large-scale cortical activity during the processing of the stimuli with a 50 ms resolution, reproducing well-known activity in the fusiform face area as well as revealing co-activation patterns in somatosensory/motor and frontal cortices that an unconstrained ROI-by-ROI analysis fails to capture. The proposed approach is simple and fast, provides a means of integration of multimodal datasets, and is tied to a theoretical framework in mathematics and physics. Thus, network harmonics point towards promising research directions both theoretically - for example in exploring the relationship between structure and function in the brain - and practically - for example for network tracking in different tasks and groups of individuals, such as patients.
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Yuk V, Anagnostou E, Taylor MJ. Altered Connectivity During a False-Belief Task in Adults With Autism Spectrum Disorder. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:901-912. [PMID: 32600899 DOI: 10.1016/j.bpsc.2020.04.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 04/15/2020] [Accepted: 04/15/2020] [Indexed: 01/04/2023]
Abstract
BACKGROUND Deficits in social communication are one of the main features of autism spectrum disorder (ASD). Adults with ASD show atypical brain activity during false-belief understanding, an aspect of social communication involving the ability to infer that an individual can have an incorrect belief about a situation. Our study is the first to investigate whether adults with ASD exhibit differences in frequency-specific functional connectivity patterns during false-belief reasoning. METHODS We used magnetoencephalography to contrast functional connectivity underlying false-belief understanding between 40 adults with ASD and 39 control adults. We examined whole-brain phase synchrony measures during a false-belief task in 3 frequency bands: theta (4-7 Hz), alpha (8-14 Hz), and beta (15-30 Hz). RESULTS Adults with ASD demonstrated reduced theta-band connectivity compared with control adults between several right-lateralized and midline regions such as the medial prefrontal cortex, right temporoparietal junction, right inferior frontal gyrus, and right superior temporal gyrus. During false-belief trials, they also recruited a network in the beta band that included primary visual regions such as the bilateral inferior occipital gyri and the left anterior temporoparietal junction. CONCLUSIONS Reduced theta-band synchrony between areas associated with mentalizing, inhibition, and visual processing implies some difficulty in communication among these functions in ASD. This impairment in top-down control in the theta band may be counterbalanced by their engagement of a beta-band network because both the left anterior temporoparietal junction and beta-band oscillations are associated with attentional processes. Thus, adults with ASD demonstrate alternative neural mechanisms for successful false-belief reasoning.
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Affiliation(s)
- Veronica Yuk
- Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, SickKids Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada; Department of Psychology, University of Toronto, Toronto, Ontario, Canada.
| | - Evdokia Anagnostou
- Department of Neurology, The Hospital for Sick Children, Toronto, Ontario, Canada; Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
| | - Margot J Taylor
- Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, SickKids Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada; Department of Psychology, University of Toronto, Toronto, Ontario, Canada; Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
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Discovering dynamic task-modulated functional networks with specific spectral modes using MEG. Neuroimage 2020; 218:116924. [PMID: 32445878 DOI: 10.1016/j.neuroimage.2020.116924] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Revised: 03/31/2020] [Accepted: 05/04/2020] [Indexed: 11/20/2022] Open
Abstract
Efficient neuronal communication between brain regions through oscillatory synchronization at certain frequencies is necessary for cognition. Such synchronized networks are transient and dynamic, established on the timescale of milliseconds in order to support ongoing cognitive operations. However, few studies characterizing dynamic electrophysiological brain networks have simultaneously accounted for temporal non-stationarity, spectral structure, and spatial properties. Here, we propose an analysis framework for characterizing the large-scale phase-coupling network dynamics during task performance using magnetoencephalography (MEG). We exploit the high spatiotemporal resolution of MEG to measure time-frequency dynamics of connectivity between parcellated brain regions, yielding data in tensor format. We then use a tensor component analysis (TCA)-based procedure to identify the spatio-temporal-spectral modes of covariation among separate regions in the human brain. We validate our pipeline using MEG data recorded during a hand movement task, extracting a transient motor network with beta-dominant spectral mode, which is significantly modulated by the movement task. Next, we apply the proposed pipeline to explore brain networks that support cognitive operations during a working memory task. The derived results demonstrate the temporal formation and dissolution of multiple phase-coupled networks with specific spectral modes, which are associated with face recognition, vision, and movement. The proposed pipeline can characterize the spectro-temporal dynamics of functional connectivity in the brain on the subsecond timescale, commensurate with that of cognitive performance.
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Shou G, Yuan H, Li C, Chen Y, Chen Y, Ding L. Whole-brain electrophysiological functional connectivity dynamics in resting-state EEG. J Neural Eng 2020; 17:026016. [PMID: 32106106 DOI: 10.1088/1741-2552/ab7ad3] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVE Functional connectivity (FC) dynamics have been studied in functional magnetic resonance imaging (fMRI) data, while it is largely unknown in electrophysiological data, e.g. EEG. APPROACH The present study proposed a novel analytic framework to study spatiotemporal dynamics of FC (dFC) in resting-state human EEG data, including independent component analysis, cortical source imaging, sliding-window correlation analysis, and k-means clustering. MAIN RESULTS Our results confirm that major fMRI intrinsic connectivity networks (ICNs) can be successfully reconstructed from EEG using our analytic framework. Prominent spatial and temporal variability were revealed in these ICNs. The mean dFC spatial patterns of individual ICNs resemble their corresponding static FC (sFC) patterns but show fewer cross-talks among distinct ICNs. Our investigation unveils evidences of time-domain variations in individual ICNs comparable to their mean FC level in terms of magnitude. The major contributors to these variations are from the frequency below 0.0156 Hz, in the similar range of FC dynamics from fMRI data. Among different ICNs, larger temporal variabilities are observed in the frontal attention and auditory/visual ICNs, while sensorimotor, salience, and default model networks showed less. Our analytic framework for the first time revealed quasi-stable states within individual EEG ICNs, with various strengths or spatial patterns that were reliably detected at both group and individual levels. These states all together reveal a more complete picture of EEG ICNs: (1) quasi-stable state spatial patterns as a whole for each EEG ICN are more consistent with the corresponding fMRI ICN in terms of the bilateral distribution and multi-nodes structure; (2) EEG ICNs reveal more transient patterns about within-ICN between-node communications than fMRI ICNs. SIGNIFICANCE The present findings highlight the fact that rich temporal and spatial dynamics exist in ICN that can be detected from EEG data. Future studies might extend investigations towards spectral dynamics of EEG ICNs.
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Affiliation(s)
- Guofa Shou
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, United States of America
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Núñez P, Poza J, Gómez C, Barroso-García V, Maturana-Candelas A, Tola-Arribas MA, Cano M, Hornero R. Characterization of the dynamic behavior of neural activity in Alzheimer's disease: exploring the non-stationarity and recurrence structure of EEG resting-state activity. J Neural Eng 2020; 17:016071. [PMID: 32000144 DOI: 10.1088/1741-2552/ab71e9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
OBJECTIVE Mild cognitive impairment (MCI) and dementia due to Alzheimer's disease (AD) have been shown to induce perturbations to normal neuronal behavior and disrupt neuronal networks. Recent work suggests that the dynamic properties of resting-state neuronal activity could be affected by MCI and AD-induced neurodegeneration. The aim of the study was to characterize these properties from different perspectives: (i) using the Kullback-Leibler divergence (KLD), a measure of non-stationarity derived from the continuous wavelet transform; and (ii) using the entropy of the recurrence point density ([Formula: see text]) and the median of the recurrence point density ([Formula: see text]), two novel metrics based on recurrence quantification analysis. APPROACH KLD, [Formula: see text] and [Formula: see text] were computed for 49 patients with dementia due to AD, 66 patients with MCI due to AD and 43 cognitively healthy controls from 60 s electroencephalographic (EEG) recordings with a 10 s sliding window with no overlap. Afterwards, we tested whether the measures reflected alterations to normal neuronal activity induced by MCI and AD. MAIN RESULTS Our results showed that frequency-dependent alterations to normal dynamic behavior can be found in patients with MCI and AD, both in non-stationarity and recurrence structure. Patients with MCI showed signs of patterns of abnormal state recurrence in the theta (4-8 Hz) and beta (13-30 Hz) frequency bands that became more marked in AD. Moreover, abnormal non-stationarity patterns were found in MCI patients, but not in patients with AD in delta (1-4 Hz), alpha (8-13 Hz), and gamma (30-70 Hz). SIGNIFICANCE The alterations in normal levels of non-stationarity in patients with MCI suggest an initial increase in cortical activity during the development of AD. This increase could possibly be due to an impairment in neuronal inhibition that is not present during later stages. MCI and AD induce alterations to the recurrence structure of cortical activity, suggesting that normal state switching during rest may be affected by these pathologies.
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Affiliation(s)
- Pablo Núñez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain. Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina CIBER-BBN, Valladolid, Spain. Author to whom any correspondence should be addressed
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Griffa A, Van De Ville D, Herrmann FR, Allali G. Neural circuits of idiopathic Normal Pressure Hydrocephalus: A perspective review of brain connectivity and symptoms meta-analysis. Neurosci Biobehav Rev 2020; 112:452-471. [PMID: 32088348 DOI: 10.1016/j.neubiorev.2020.02.023] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 01/09/2020] [Accepted: 02/17/2020] [Indexed: 12/13/2022]
Abstract
Idiopathic normal pressure hydrocephalus (iNPH) is a prevalent reversible neurological disorder characterized by impaired locomotion, cognition and urinary control with ventriculomegaly. Symptoms can be relieved with cerebrospinal fluid drainage, which makes iNPH the leading cause of reversible dementia. Because of a limited understanding of pathophysiological mechanisms, unspecific symptoms and the high prevalence of comorbidity (i.e. Alzheimer's disease), iNPH is largely underdiagnosed. For these reasons, there is an urgent need for developing noninvasive quantitative biomarkers for iNPH diagnosis and prognosis. Structural and functional changes of brain circuits in relation to symptoms and treatment response are expected to deliver major advances in this direction. We review structural and functional brain connectivity findings in iNPH and complement those findings with iNPH symptom meta-analyses in healthy populations. Our goal is to reinforce our conceptualization of iNPH as to brain network mechanisms and foster the development of new hypotheses for future research and treatment options.
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Affiliation(s)
- Alessandra Griffa
- Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland; Institute of Bioengineering, Center of Neuroprosthetics, Ecole Polytechnique Fédérale De Lausanne (EPFL), Lausanne, Switzerland.
| | - Dimitri Van De Ville
- Institute of Bioengineering, Center of Neuroprosthetics, Ecole Polytechnique Fédérale De Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
| | - François R Herrmann
- Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland.
| | - Gilles Allali
- Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland; Faculty of Medicine, University of Geneva, Geneva, Switzerland; Department of Neurology, Division of Cognitive & Motor Aging, Albert Einstein College of Medicine, Yeshiva University, Bronx, NY, USA.
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Yuk V, Urbain C, Anagnostou E, Taylor MJ. Frontoparietal Network Connectivity During an N-Back Task in Adults With Autism Spectrum Disorder. Front Psychiatry 2020; 11:551808. [PMID: 33033481 PMCID: PMC7509600 DOI: 10.3389/fpsyt.2020.551808] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 08/13/2020] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Short-term and working memory (STM and WM) deficits have been demonstrated in individuals with autism spectrum disorder (ASD) and may emerge through atypical functional activity and connectivity of the frontoparietal network, which exerts top-down control necessary for successful STM and WM processes. Little is known regarding the spectral properties of the frontoparietal network during STM or WM processes in ASD, although certain neural frequencies have been linked to specific neural mechanisms. METHODS We analysed magnetoencephalographic data from 39 control adults (26 males; 27.15 ± 5.91 years old) and 40 adults with ASD (26 males; 27.17 ± 6.27 years old) during a 1-back condition (STM) of an n-back task, and from a subset of this sample during a 2-back condition (WM). We performed seed-based connectivity analyses using regions of the frontoparietal network. Interregional synchrony in theta, alpha, and beta bands was assessed with the phase difference derivative and compared between groups during periods of maintenance and recognition. RESULTS During maintenance of newly presented vs. repeated stimuli, the two groups did not differ significantly in theta, alpha, or beta phase synchrony for either condition. Adults with ASD showed alpha-band synchrony in a network containing the right dorsolateral prefrontal cortex, bilateral inferior parietal lobules (IPL), and precuneus in both 1- and 2-back tasks, whereas controls demonstrated alpha-band synchrony in a sparser set of regions, including the left insula and IPL, in only the 1-back task. During recognition of repeated vs. newly presented stimuli, adults with ASD exhibited decreased theta-band connectivity compared to controls in a network with hubs in the right inferior frontal gyrus and left IPL in the 1-back condition. Whilst there were no group differences in connectivity in the 2-back condition, adults with ASD showed no frontoparietal network recruitment during recognition, whilst controls activated networks in the theta and beta bands. CONCLUSIONS Our findings suggest that since adults with ASD performed well on the n-back task, their appropriate, but effortful recruitment of alpha-band mechanisms in the frontoparietal network to maintain items in STM and WM may compensate for atypical modulation of this network in the theta band to recognise previously presented items in STM.
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Affiliation(s)
- Veronica Yuk
- Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, Canada.,Neurosciences & Mental Health Program, SickKids Research Institute, The Hospital for Sick Children, Toronto, ON, Canada.,Department of Psychology, University of Toronto, Toronto, ON, Canada
| | - Charline Urbain
- Neuropsychology and Functional Neuroimaging Research Group, Center for Research in Cognition & Neurosciences and ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Brussels, Belgium.,Laboratoire de Cartographie Fonctionnelle du Cerveau, Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium
| | - Evdokia Anagnostou
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada.,Department of Neurology, The Hospital for Sick Children, Toronto, ON, Canada.,Department of Paediatrics, University of Toronto, Toronto, ON, Canada
| | - Margot J Taylor
- Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, Canada.,Neurosciences & Mental Health Program, SickKids Research Institute, The Hospital for Sick Children, Toronto, ON, Canada.,Department of Psychology, University of Toronto, Toronto, ON, Canada.,Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
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Liuzzi L, Quinn AJ, O’Neill GC, Woolrich MW, Brookes MJ, Hillebrand A, Tewarie P. How Sensitive Are Conventional MEG Functional Connectivity Metrics With Sliding Windows to Detect Genuine Fluctuations in Dynamic Functional Connectivity? Front Neurosci 2019; 13:797. [PMID: 31427920 PMCID: PMC6688728 DOI: 10.3389/fnins.2019.00797] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 07/16/2019] [Indexed: 12/30/2022] Open
Abstract
Despite advances in the field of dynamic connectivity, fixed sliding window approaches for the detection of fluctuations in functional connectivity are still widely used. The use of conventional connectivity metrics in conjunction with a fixed sliding window comes with the arbitrariness of the chosen window lengths. In this paper we use multivariate autoregressive and neural mass models with a priori defined ground truths to systematically analyze the sensitivity of conventional metrics in combination with different window lengths to detect genuine fluctuations in connectivity for various underlying state durations. Metrics of interest are the coherence, imaginary coherence, phase lag index, phase locking value and the amplitude envelope correlation. We performed analysis for two nodes and at the network level. We demonstrate that these metrics show indeed higher variability for genuine temporal fluctuations in connectivity compared to a static connectivity state superimposed by noise. Overall, the error of the connectivity estimates themselves decreases for longer state durations (order of seconds), while correlations of the connectivity fluctuations with the ground truth was higher for longer state durations. In general, metrics, in combination with a sliding window, perform poorly for very short state durations. Increasing the SNR of the system only leads to a moderate improvement. In addition, at the network level, only longer window widths were sufficient to detect plausible resting state networks that matched the underlying ground truth, especially for the phase locking value, amplitude envelope correlation and coherence. The length of these longer window widths did not necessarily correspond to the underlying state durations. For short window widths resting state network connectivity patterns could not be retrieved. We conclude that fixed sliding window approaches for connectivity can detect modulations of connectivity, but mostly if the underlying dynamics operate on moderate to slow timescales. In practice, this can be a drawback, as state durations can vary significantly in empirical data.
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Affiliation(s)
- Lucrezia Liuzzi
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Andrew J. Quinn
- Oxford Centre for Human Brain Activity, University of Oxford, Warneford Hospital, Oxford, United Kingdom
| | - George C. O’Neill
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Mark W. Woolrich
- Oxford Centre for Human Brain Activity, University of Oxford, Warneford Hospital, Oxford, United Kingdom
- Oxford Centre for Functional MRI of the Brain, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Matthew J. Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Prejaas Tewarie
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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