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Nagy P, Tóth B, Winkler I, Boncz Á. The effects of spatial leakage correction on the reliability of EEG-based functional connectivity networks. Hum Brain Mapp 2024; 45:e26747. [PMID: 38825981 PMCID: PMC11144954 DOI: 10.1002/hbm.26747] [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: 07/04/2023] [Revised: 03/28/2024] [Accepted: 05/16/2024] [Indexed: 06/04/2024] Open
Abstract
Electroencephalography (EEG) functional connectivity (FC) estimates are confounded by the volume conduction problem. This effect can be greatly reduced by applying FC measures insensitive to instantaneous, zero-lag dependencies (corrected measures). However, numerous studies showed that FC measures sensitive to volume conduction (uncorrected measures) exhibit higher reliability and higher subject-level identifiability. We tested how source reconstruction contributed to the reliability difference of EEG FC measures on a large (n = 201) resting-state data set testing eight FC measures (including corrected and uncorrected measures). We showed that the high reliability of uncorrected FC measures in resting state partly stems from source reconstruction: idiosyncratic noise patterns define a baseline resting-state functional network that explains a significant portion of the reliability of uncorrected FC measures. This effect remained valid for template head model-based, as well as individual head model-based source reconstruction. Based on our findings we made suggestions how to best use spatial leakage corrected and uncorrected FC measures depending on the main goals of the study.
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Affiliation(s)
- Péter Nagy
- HUN‐REN Research Centre for Natural SciencesBudapestHungary
- Faculty of Electrical Engineering and Informatics, Department of Measurement and Information SystemsBudapest University of Technology and EconomicsBudapestHungary
| | - Brigitta Tóth
- HUN‐REN Research Centre for Natural SciencesBudapestHungary
| | - István Winkler
- HUN‐REN Research Centre for Natural SciencesBudapestHungary
| | - Ádám Boncz
- HUN‐REN Research Centre for Natural SciencesBudapestHungary
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2
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Simmatis L, Russo EE, Geraci J, Harmsen IE, Samuel N. Technical and clinical considerations for electroencephalography-based biomarkers for major depressive disorder. NPJ MENTAL HEALTH RESEARCH 2023; 2:18. [PMID: 38609518 PMCID: PMC10955915 DOI: 10.1038/s44184-023-00038-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 09/21/2023] [Indexed: 04/14/2024]
Abstract
Major depressive disorder (MDD) is a prevalent and debilitating psychiatric disease that leads to substantial loss of quality of life. There has been little progress in developing new MDD therapeutics due to a poor understanding of disease heterogeneity and individuals' responses to treatments. Electroencephalography (EEG) is poised to improve this, owing to the ease of large-scale data collection and the advancement of computational methods to address artifacts. This review summarizes the viability of EEG for developing brain-based biomarkers in MDD. We examine the properties of well-established EEG preprocessing pipelines and consider factors leading to the discovery of sensitive and reliable biomarkers.
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Affiliation(s)
- Leif Simmatis
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Cove Neurosciences Inc., Toronto, ON, Canada
| | - Emma E Russo
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Cove Neurosciences Inc., Toronto, ON, Canada
| | - Joseph Geraci
- Cove Neurosciences Inc., Toronto, ON, Canada
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada
| | - Irene E Harmsen
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Cove Neurosciences Inc., Toronto, ON, Canada
| | - Nardin Samuel
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
- Cove Neurosciences Inc., Toronto, ON, Canada.
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3
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van de Velden D, Stier C, Kotikalapudi R, Heide EC, Garnica-Agudelo D, Focke NK. Comparison of Resting-State EEG Network Analyses With and Without Parallel MRI in Genetic Generalized Epilepsy. Brain Topogr 2023; 36:750-765. [PMID: 37354244 PMCID: PMC10415462 DOI: 10.1007/s10548-023-00977-6] [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: 07/20/2022] [Accepted: 06/12/2023] [Indexed: 06/26/2023]
Abstract
Genetic generalized epilepsy (GGE) is conceptualized as a brain disorder involving distributed bilateral networks. To study these networks, simultaneous EEG-fMRI measurements can be used. However, inside-MRI EEG suffers from strong MR-related artifacts; it is not established whether EEG-based metrics in EEG-fMRI resting-state measurements are suitable for the analysis of group differences at source-level. We evaluated the impact of the inside-MR measurement condition on statistical group comparisons of EEG on source-level power and functional connectivity in patients with GGE versus healthy controls. We studied the cross-modal spatial relation of statistical group differences in seed-based FC derived from EEG and parallel fMRI. We found a significant increase in power and a frequency-specific change in functional connectivity for the inside MR-scanner compared to the outside MR-scanner condition. For power, we found reduced group difference between GGE and controls both in terms of statistical significance as well as effect size. Group differences for ImCoh remained similar both in terms of statistical significance as well as effect size. We found increased seed-based FC for GGE patients from the thalamus to the precuneus cortex region in fMRI, and in the theta band of simultaneous EEG. Our findings suggest that the analysis of EEG functional connectivity based on ImCoh is suitable for MR-EEG, and that relative group difference in a comparison of patients with GGE against controls are preserved. Spatial correspondence of seed-based FC group differences between the two modalities was found for the thalamus.
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Affiliation(s)
- Daniel van de Velden
- Clinic for Neurology, University Medical Center Göttingen, 37075, Göttingen, Germany.
| | - Christina Stier
- Clinic for Neurology, University Medical Center Göttingen, 37075, Göttingen, Germany
- Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research, University Medical Center Tübingen, University of Tübingen, 72076, Tübingen, Germany
| | - Raviteja Kotikalapudi
- Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research, University Medical Center Tübingen, University of Tübingen, 72076, Tübingen, Germany
- Clinic for Neurology, University Medical Center Essen/University Duisburg-Essen, 45147, Essen, Germany
| | - Ev-Christin Heide
- Clinic for Neurology, University Medical Center Göttingen, 37075, Göttingen, Germany
| | - David Garnica-Agudelo
- Clinic for Neurology, University Medical Center Göttingen, 37075, Göttingen, Germany
| | - Niels K Focke
- Clinic for Neurology, University Medical Center Göttingen, 37075, Göttingen, Germany.
- Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research, University Medical Center Tübingen, University of Tübingen, 72076, Tübingen, Germany.
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4
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Stier C, Braun C, Focke NK. Adult lifespan trajectories of neuromagnetic signals and interrelations with cortical thickness. Neuroimage 2023; 278:120275. [PMID: 37451375 PMCID: PMC10443236 DOI: 10.1016/j.neuroimage.2023.120275] [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: 05/19/2023] [Revised: 07/03/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023] Open
Abstract
Oscillatory power and phase synchronization map neuronal dynamics and are commonly studied to differentiate the healthy and diseased brain. Yet, little is known about the course and spatial variability of these features from early adulthood into old age. Leveraging magnetoencephalography (MEG) resting-state data in a cross-sectional adult sample (n = 350), we probed lifespan differences (18-88 years) in connectivity and power and interaction effects with sex. Building upon recent attempts to link brain structure and function, we tested the spatial correspondence between age effects on cortical thickness and those on functional networks. We further probed a direct structure-function relationship at the level of the study sample. We found MEG frequency-specific patterns with age and divergence between sexes in low frequencies. Connectivity and power exhibited distinct linear trajectories or turning points at midlife that might reflect different physiological processes. In the delta and beta bands, these age effects corresponded to those on cortical thickness, pointing to co-variation between the modalities across the lifespan. Structure-function coupling was frequency-dependent and observed in unimodal or multimodal regions. Altogether, we provide a comprehensive overview of the topographic functional profile of adulthood that can form a basis for neurocognitive and clinical investigations. This study further sheds new light on how the brain's structural architecture relates to fast oscillatory activity.
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Affiliation(s)
- Christina Stier
- Clinic of Neurology, University Medical Center Göttingen, Göttingen, Germany; Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany.
| | - Christoph Braun
- MEG-Center, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany; CIMeC, Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy
| | - Niels K Focke
- Clinic of Neurology, University Medical Center Göttingen, Göttingen, Germany
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5
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Maura RM, Rueda Parra S, Stevens RE, Weeks DL, Wolbrecht ET, Perry JC. Literature review of stroke assessment for upper-extremity physical function via EEG, EMG, kinematic, and kinetic measurements and their reliability. J Neuroeng Rehabil 2023; 20:21. [PMID: 36793077 PMCID: PMC9930366 DOI: 10.1186/s12984-023-01142-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 01/19/2023] [Indexed: 02/17/2023] Open
Abstract
BACKGROUND Significant clinician training is required to mitigate the subjective nature and achieve useful reliability between measurement occasions and therapists. Previous research supports that robotic instruments can improve quantitative biomechanical assessments of the upper limb, offering reliable and more sensitive measures. Furthermore, combining kinematic and kinetic measurements with electrophysiological measurements offers new insights to unlock targeted impairment-specific therapy. This review presents common methods for analyzing biomechanical and neuromuscular data by describing their validity and reporting their reliability measures. METHODS This paper reviews literature (2000-2021) on sensor-based measures and metrics for upper-limb biomechanical and electrophysiological (neurological) assessment, which have been shown to correlate with clinical test outcomes for motor assessment. The search terms targeted robotic and passive devices developed for movement therapy. Journal and conference papers on stroke assessment metrics were selected using PRISMA guidelines. Intra-class correlation values of some of the metrics are recorded, along with model, type of agreement, and confidence intervals, when reported. RESULTS A total of 60 articles are identified. The sensor-based metrics assess various aspects of movement performance, such as smoothness, spasticity, efficiency, planning, efficacy, accuracy, coordination, range of motion, and strength. Additional metrics assess abnormal activation patterns of cortical activity and interconnections between brain regions and muscle groups; aiming to characterize differences between the population who had a stroke and the healthy population. CONCLUSION Range of motion, mean speed, mean distance, normal path length, spectral arc length, number of peaks, and task time metrics have all demonstrated good to excellent reliability, as well as provide a finer resolution compared to discrete clinical assessment tests. EEG power features for multiple frequency bands of interest, specifically the bands relating to slow and fast frequencies comparing affected and non-affected hemispheres, demonstrate good to excellent reliability for populations at various stages of stroke recovery. Further investigation is needed to evaluate the metrics missing reliability information. In the few studies combining biomechanical measures with neuroelectric signals, the multi-domain approaches demonstrated agreement with clinical assessments and provide further information during the relearning phase. Combining the reliable sensor-based metrics in the clinical assessment process will provide a more objective approach, relying less on therapist expertise. This paper suggests future work on analyzing the reliability of metrics to prevent biasedness and selecting the appropriate analysis.
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Affiliation(s)
- Rene M. Maura
- Mechanical Engineering Department, University of Idaho, Moscow, ID USA
| | | | - Richard E. Stevens
- Engineering and Physics Department, Whitworth University, Spokane, WA USA
| | - Douglas L. Weeks
- College of Medicine, Washington State University, Spokane, WA USA
| | - Eric T. Wolbrecht
- Mechanical Engineering Department, University of Idaho, Moscow, ID USA
| | - Joel C. Perry
- Mechanical Engineering Department, University of Idaho, Moscow, ID USA
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6
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Smith EE, Bel-Bahar TS, Kayser J. A systematic data-driven approach to analyze sensor-level EEG connectivity: Identifying robust phase-synchronized network components using surface Laplacian with spectral-spatial PCA. Psychophysiology 2022; 59:e14080. [PMID: 35478408 PMCID: PMC9427703 DOI: 10.1111/psyp.14080] [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: 08/10/2021] [Revised: 04/04/2022] [Accepted: 04/07/2022] [Indexed: 11/27/2022]
Abstract
Although conventional averaging across predefined frequency bands reduces the complexity of EEG functional connectivity (FC), it obscures the identification of resting-state brain networks (RSN) and impedes accurate estimation of FC reliability. Extending prior work, we combined scalp current source density (CSD; spherical spline surface Laplacian) and spectral-spatial PCA to identify FC components. Phase-based FC was estimated via debiased-weighted phase-locking index from CSD-transformed resting EEGs (71 sensors, 8 min, eyes open/closed, 35 healthy adults, 1-week retest). Spectral PCA extracted six robust alpha and theta components (86.6% variance). Subsequent spatial PCA for each spectral component revealed seven robust regionally focused (posterior, central, and frontal) and long-range (posterior-anterior) alpha components (peaks at 8, 10, and 13 Hz) and a midfrontal theta (6 Hz) component, accounting for 37.0% of FC variance. These spatial FC components were consistent with well-known networks (e.g., default mode, visual, and sensorimotor), and four were sensitive to eyes open/closed conditions. Most FC components had good-to-excellent internal consistency (odd/even epochs, eyes open/closed) and test-retest reliability (ICCs ≥ .8). Moreover, the FC component structure was generally present in subsamples (session × odd/even epoch, or smaller subgroups [n = 7-10]), as indicated by high similarity of component loadings across PCA solutions. Apart from systematically reducing FC dimensionality, our approach avoids arbitrary thresholds and allows quantification of meaningful and reliable network components that may prove to be of high relevance for basic and clinical research applications.
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Affiliation(s)
- Ezra E. Smith
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, NY, USA
| | - Tarik S. Bel-Bahar
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, NY, USA
| | - Jürgen Kayser
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
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7
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Strijbis EMM, Timar YSS, Schoonhoven DN, Nauta IM, Kulik SD, de Ruiter LRJ, Schoonheim MM, Hillebrand A, Stam CJ. State Changes During Resting-State (Magneto)encephalographic Studies: The Effect of Drowsiness on Spectral, Connectivity, and Network Analyses. Front Neurosci 2022; 16:782474. [PMID: 35784839 PMCID: PMC9245543 DOI: 10.3389/fnins.2022.782474] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Abstract
Background A common problem in resting-state neuroimaging studies is that subjects become drowsy or fall asleep. Although this could drastically affect neurophysiological measurements, such as magnetoencephalography (MEG), its specific impact remains understudied. We aimed to systematically investigate how often drowsiness is present during resting-state MEG recordings, and how the state changes alter quantitative estimates of oscillatory activity, functional connectivity, and network topology. Methods About 8-min MEG recordings of 19 healthy subjects, split into ~13-s epochs, were scored for the presence of eyes-open (EO), alert eyes-closed (A-EC), or drowsy eyes-closed (D-EC) states. After projection to source-space, results of spectral, functional connectivity, and network analyses in 6 canonical frequency bands were compared between these states on a global and regional levels. Functional connectivity was analyzed using the phase lag index (PLI) and corrected amplitude envelope correlation (AECc), and network topology was analyzed using the minimum spanning tree (MST). Results Drowsiness was present in >55% of all epochs that did not fulfill the AASM criteria for sleep. There were clear differences in spectral results between the states (A-EC vs. D-EC) and conditions (EO vs. A-EC). The influence of state and condition was far less pronounced for connectivity analyses, with only minimal differences between D-EC and EO in the AECc in the delta band. There were no effects of drowsiness on any of the MST measures. Conclusions Drowsiness during eyes-closed resting-state MEG recordings is present in the majority of epochs, despite the instructions to stay awake. This has considerable influence on spectral properties, but much less so on functional connectivity and network topology. These findings are important for interpreting the results of EEG/MEG studies using spectral analyses in neurological disease, where recordings should be evaluated for the presence of drowsiness. For connectivity analyses or studies on network topology, this seems of far less importance.
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Affiliation(s)
- Eva M. M. Strijbis
- Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- *Correspondence: Eva M. M. Strijbis
| | - Yannick S. S. Timar
- Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Deborah N. Schoonhoven
- Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Ilse M. Nauta
- Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Shanna D. Kulik
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Lodewijk R. J. de Ruiter
- Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Menno M. Schoonheim
- Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Arjan Hillebrand
- Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Cornelis J. Stam
- Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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8
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Stier C, Loose M, Kotikalapudi R, Elshahabi A, Li Hegner Y, Marquetand J, Braun C, Lerche H, Focke NK. Combined electrophysiological and morphological phenotypes in patients with genetic generalized epilepsy and their healthy siblings. Epilepsia 2022; 63:1643-1657. [PMID: 35416282 DOI: 10.1111/epi.17258] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 04/11/2022] [Accepted: 04/11/2022] [Indexed: 11/27/2022]
Abstract
OBJECTIVE Genetic generalized epilepsy is characterized by aberrant neuronal dynamics and subtle structural alterations. We evaluated whether a combination of magnetic and electrical neuronal signals and cortical thickness would provide complementary information about network pathology in GGE. We also investigated if these imaging phenotypes were present in healthy siblings of the patients to test for genetic influence. METHODS In this cross-sectional study, we analyzed five minutes of resting-state data acquired using electroencephalography (EEG) and magnetoencephalography (MEG) in patients, their siblings, and controls, matched for age and sex. We computed source-reconstructed power and connectivity in six frequency bands (1-40 Hz) and cortical thickness (derived from magnetic resonance imaging (MRI)). Group differences were assessed using permutation analysis of linear models for each modality separately and jointly for all modalities using a non-parametric combination. RESULTS Patients with GGE (n = 23) had higher power than controls (n = 35) in all frequencies, with a more posterior focus in MEG than EEG. Connectivity was also increased, particularly in frontotemporal and central regions in theta (strongest in EEG) and low beta frequencies (strongest in MEG), which was eminent in the joint EEG/MEG analysis. EEG showed weaker connectivity differences in higher frequencies, possibly related to drug effects. The inclusion of cortical thickness reinforced group differences in connectivity and power. Siblings (n = 18) had functional and structural patterns intermediate between those of patients and controls. SIGNIFICANCE EEG detected increased connectivity and power in GGE similar to MEG, but with different spectral sensitivity, highlighting the importance of theta and beta oscillations. Cortical thickness reductions in GGE corresponded to functional imaging patterns. Our multimodal approach extends the understanding of the resting-state in GGE and points to genetic underpinnings of the imaging markers studied, providing new insights into the causes and consequences of epilepsy.
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Affiliation(s)
- Christina Stier
- Clinic of Neurology, University Medical Center Göttingen, Göttingen, Germany.,Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Markus Loose
- Clinic of Neurology, University Medical Center Göttingen, Göttingen, Germany
| | - Raviteja Kotikalapudi
- Clinic of Neurology, University Medical Center Göttingen, Göttingen, Germany.,Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,Institute of Psychology, University of Bern, Bern, Switzerland
| | - Adham Elshahabi
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,Department of Neurology, University Hospital Zurich, Zurich, Switzerland
| | - Yiwen Li Hegner
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Justus Marquetand
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,Department of Neural Dynamics and Magnetoencephalography, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Christoph Braun
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,MEG-Center, University of Tübingen, Tübingen, Germany.,CIMeC, Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy
| | - Holger Lerche
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Niels K Focke
- Clinic of Neurology, University Medical Center Göttingen, Göttingen, Germany.,Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
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9
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Candelaria-Cook FT, Solis I, Schendel ME, Wang YP, Wilson TW, Calhoun VD, Stephen JM. Developmental trajectory of MEG resting-state oscillatory activity in children and adolescents: a longitudinal reliability study. Cereb Cortex 2022; 32:5404-5419. [PMID: 35225334 PMCID: PMC9712698 DOI: 10.1093/cercor/bhac023] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 01/02/2022] [Accepted: 01/03/2022] [Indexed: 12/27/2022] Open
Abstract
Neural oscillations may be sensitive to aspects of brain maturation such as myelination and synaptic density changes. Better characterization of developmental trajectories and reliability is necessary for understanding typical and atypical neurodevelopment. Here, we examined reliability in 110 typically developing children and adolescents (aged 9-17 years) across 2.25 years. From 10 min of magnetoencephalography resting-state data, normalized source spectral power and intraclass correlation coefficients were calculated. We found sex-specific differences in global normalized power, with males showing age-related decreases in delta and theta, along with age-related increases in beta and gamma. Females had fewer significant age-related changes. Structural magnetic resonance imaging revealed that males had more total gray, subcortical gray, and cortical white matter volume. There were significant age-related changes in total gray matter volume with sex-specific and frequency-specific correlations to normalized power. In males, increased total gray matter volume correlated with increased theta and alpha, along with decreased gamma. Split-half reliability was excellent in all frequency bands and source regions. Test-retest reliability ranged from good (alpha) to fair (theta) to poor (remaining bands). While resting-state neural oscillations can have fingerprint-like quality in adults, we show here that neural oscillations continue to evolve in children and adolescents due to brain maturation and neurodevelopmental change.
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Affiliation(s)
- Felicha T Candelaria-Cook
- The Mind Research Network, a Division of Lovelace Biomedical Research Institute, 1101 Yale Blvd NE, Albuquerque, NM 87106, United States
| | - Isabel Solis
- The Mind Research Network, a Division of Lovelace Biomedical Research Institute, 1101 Yale Blvd NE, Albuquerque, NM 87106, United States,Department of Psychology, University of New Mexico, 1 University of New Mexico, Albuquerque, NM 87131, United States
| | - Megan E Schendel
- The Mind Research Network, a Division of Lovelace Biomedical Research Institute, 1101 Yale Blvd NE, Albuquerque, NM 87106, United States
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, 6823 St. Charles Avenue, New Orleans, LA 70118, United States
| | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, 14090 Mother Teresa Lane, Boys Town, NE 68010, United States
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, 55 Park Pl NE, Atlanta, GA 30303, United States
| | - Julia M Stephen
- Corresponding author: The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, NM 87106, United States.
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10
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Schoonhoven DN, Briels CT, Hillebrand A, Scheltens P, Stam CJ, Gouw AA. Sensitive and reproducible MEG resting-state metrics of functional connectivity in Alzheimer's disease. Alzheimers Res Ther 2022; 14:38. [PMID: 35219327 PMCID: PMC8881826 DOI: 10.1186/s13195-022-00970-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 01/30/2022] [Indexed: 01/08/2023]
Abstract
Background Analysis of functional brain networks in Alzheimer’s disease (AD) has been hampered by a lack of reproducible, yet valid metrics of functional connectivity (FC). This study aimed to assess both the sensitivity and reproducibility of the corrected amplitude envelope correlation (AEC-c) and phase lag index (PLI), two metrics of FC that are insensitive to the effects of volume conduction and field spread, in two separate cohorts of patients with dementia due to AD versus healthy elderly controls. Methods Subjects with a clinical diagnosis of AD dementia with biomarker proof, and a control group of subjective cognitive decline (SCD), underwent two 5-min resting-state MEG recordings. Data consisted of a test (AD = 28; SCD = 29) and validation (AD = 29; SCD = 27) cohort. Time-series were estimated for 90 regions of interest (ROIs) in the automated anatomical labelling (AAL) atlas. For each of five canonical frequency bands, the AEC-c and PLI were calculated between all 90 ROIs, and connections were averaged per ROI. General linear models were constructed to compare the global FC differences between the groups, assess the reproducibility, and evaluate the effects of age and relative power. Reproducibility of the regional FC differences was assessed using the Mann-Whitney U tests, with correction for multiple testing using the false discovery rate (FDR). Results The AEC-c showed significantly and reproducibly lower global FC for the AD group compared to SCD, in the alpha (8–13 Hz) and beta (13–30 Hz) bands, while the PLI revealed reproducibly lower FC for the AD group in the delta (0.5–4 Hz) band and higher FC for the theta (4–8 Hz) band. Regionally, the beta band AEC-c showed reproducibility for almost all ROIs (except for 13 ROIs in the frontal and temporal lobes). For the other bands, the AEC-c and PLI did not show regional reproducibility after FDR correction. The theta band PLI was susceptible to the effect of relative power. Conclusion For MEG, the AEC-c is a sensitive and reproducible metric, able to distinguish FC differences between patients with AD dementia and cognitively healthy controls. These two measures likely reflect different aspects of neural activity and show differential sensitivity to changes in neural dynamics. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-022-00970-4.
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Affiliation(s)
- Deborah N Schoonhoven
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands. .,Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
| | - Casper T Briels
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, 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
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Alida A Gouw
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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11
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Functional connectivity using high density EEG shows competitive reliability and agreement across test/retest sessions. J Neurosci Methods 2022; 367:109424. [PMID: 34826504 DOI: 10.1016/j.jneumeth.2021.109424] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 10/26/2021] [Accepted: 11/18/2021] [Indexed: 11/24/2022]
Abstract
BACKGROUND Electrophysiological resting state functional connectivity using high density electroencephalography (hdEEG) is gaining momentum. The increased resolution offered by hdEEG, usually either 128 or 256 channels, permits source localization of EEG signals on the cortical surface. However, the number of methodological options for the acquisition and analysis of resting state hdEEG is extremely large. These include acquisition duration, eyes open/closed, channel density, source localization methods, and functional connectivity metric. NEW METHODS We undertake an extensive examination of the test-retest reliability and methodological agreement of all these options for regional measures of functional connectivity. RESULTS Power envelope connectivity shows larger test-retest reliability than imaginary coherence across all bands. While channel density doesn't strongly impact reliability or agreement, source localization methods produce systematically different functional connectivity, highlighting an important obstacle for replicating results in the literature. Most importantly, reliability and agreement often plateaus at or after 6 minutes of acquisition, well beyond the typical duration of 3 minutes. Finally, our study demonstrates that resting EEG can be as or more reliable than resting fMRI acquired in the same individuals. CONCLUSIONS The competitive reliability and agreement of power envelope connectivity greatly increases our confidence in measuring resting state connectivity using EEG and its capacity to find individual differences.
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12
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MEG-Derived Symptom-Sensitive Biomarkers with Long-Term Test-Retest Reliability. Diagnostics (Basel) 2021; 12:diagnostics12010084. [PMID: 35054252 PMCID: PMC8775104 DOI: 10.3390/diagnostics12010084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 12/20/2021] [Accepted: 12/23/2021] [Indexed: 11/17/2022] Open
Abstract
Neuroelectric measures derived from human magnetoencephalographic (MEG) recordings hold promise as aides to diagnosis and treatment monitoring and targeting for chronic sequelae of traumatic brain injury (TBI). This study tests novel MEG-derived regional brain measures of tonic neuroelectric activation for long-term test-retest reliability and sensitivity to symptoms. Resting state MEG recordings were obtained from a normative cohort (CamCAN, baseline: n = 613; mean 16-month follow-up: n = 245) and a chronic symptomatic TBI cohort (TEAM-TBI, baseline: n = 62; mean 6-month follow-up: n = 40). The MEG-derived neuroelectric measures were corrected for the empty-room contribution using a random forest classifier. The mean 16-month correlation between baseline and 16-month follow-up CamCAN measures was 0.67; test-retest reliability was markedly improved in this study compared with previous work. The TEAM-TBI cohort was screened for depression, somatization, and anxiety with the Brief Symptom Inventory and for insomnia with the Insomnia Severity Index and was assessed via adjudication for six clinical syndromes: chronic pain, psychological health, and oculomotor, vestibular, cognitive, and sleep dysfunction. Linear classifiers constructed from the 136 regional measures from each TEAM-TBI cohort member distinguished those with and without each symptom, p < 0.0003 for each, i.e., the tonic regional neuroelectric measures of activation are sensitive to the presence/absence of these symptoms and clinical syndromes. The novel regional MEG-derived neuroelectric measures obtained and tested in this study demonstrate the necessary and sufficient properties to be clinically useful, i.e., good test-retest reliability, sensitivity to symptoms in each individual, and obtainable using automatic processing without human judgement or intervention.
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13
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Wiesman AI, da Silva Castanheira J, Baillet S. Stability of spectral estimates in resting-state magnetoencephalography: Recommendations for minimal data duration with neuroanatomical specificity. Neuroimage 2021; 247:118823. [PMID: 34923132 PMCID: PMC8852336 DOI: 10.1016/j.neuroimage.2021.118823] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/19/2021] [Accepted: 12/14/2021] [Indexed: 12/20/2022] Open
Abstract
The principle of resting-state paradigms is appealing and practical for collecting data from impaired patients and special populations, especially if data collection times can be minimized. To achieve this goal, researchers need to ensure estimated signal features of interest are robust. In electro- and magnetoencephalography (EEG, MEG) we are not aware of any studies of the minimal length of data required to yield a robust one-session snapshot of the frequency-spectrum derivatives that are typically used to characterize the complex dynamics of the brain’s resting-state. We aimed to fill this knowledge gap by studying the stability of common spectra measures of resting-state MEG source time series obtained from large samples of single-session recordings from shared data repositories featuring different recording conditions and instrument technologies (OMEGA: N = 107; Cam-CAN: N = 50). We discovered that the rhythmic and arrhythmic spectral properties of intrinsic brain activity can be robustly estimated in most cortical regions when derived from relatively short segments of 30-s to 120-s of resting-state data, regardless of instrument technology and resting-state paradigm. Using an adapted leave-one-out approach and Bayesian analysis, we also provide evidence that the stability of spectral features over time is unaffected by age, sex, handedness, and general cognitive function. In summary, short MEG sessions are sufficient to yield robust estimates of frequency-defined brain activity during resting-state. This study may help guide future empirical designs in the field, particularly when recording times need to be minimized, such as with patient or special populations.
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Affiliation(s)
- Alex I Wiesman
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal QC, Canada.
| | | | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal QC, Canada
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14
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Vogel S, Kaltenhäuser M, Kim C, Müller-Voggel N, Rössler K, Dörfler A, Schwab S, Hamer H, Buchfelder M, Rampp S. MEG Node Degree Differences in Patients with Focal Epilepsy vs. Controls-Influence of Experimental Conditions. Brain Sci 2021; 11:1590. [PMID: 34942895 PMCID: PMC8699109 DOI: 10.3390/brainsci11121590] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/25/2021] [Accepted: 11/27/2021] [Indexed: 11/16/2022] Open
Abstract
Drug-resistant epilepsy can be most limiting for patients, and surgery represents a viable therapy option. With the growing research on the human connectome and the evidence of epilepsy being a network disorder, connectivity analysis may be able to contribute to our understanding of epilepsy and may be potentially developed into clinical applications. In this magnetoencephalographic study, we determined the whole-brain node degree of connectivity levels in patients and controls. Resting-state activity was measured at five frequency bands in 15 healthy controls and 15 patients with focal epilepsy of different etiologies. The whole-brain all-to-all imaginary part of coherence in source space was then calculated. Node degree was determined and parcellated and was used for further statistical evaluation. In comparison to controls, we found a significantly higher overall node degree in patients with lesional and non-lesional epilepsy. Furthermore, we examined the conditions of high/reduced vigilance and open/closed eyes in controls, to analyze whether patient node degree levels can be achieved. We evaluated intraclass-correlation statistics (ICC) to evaluate the reproducibility. Connectivity and specifically node degree analysis could present new tools for one of the most common neurological diseases, with potential applications in epilepsy diagnostics.
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Affiliation(s)
- Stephan Vogel
- Department of Neurosurgery, University Hospital Erlangen, 91054 Erlangen, Germany; (M.K.); (C.K.); (N.M.-V.); (M.B.); (S.R.)
- Friedrich Alexander University Erlangen Nürnberg (FAU), 91054 Erlangen, Germany
| | - Martin Kaltenhäuser
- Department of Neurosurgery, University Hospital Erlangen, 91054 Erlangen, Germany; (M.K.); (C.K.); (N.M.-V.); (M.B.); (S.R.)
| | - Cora Kim
- Department of Neurosurgery, University Hospital Erlangen, 91054 Erlangen, Germany; (M.K.); (C.K.); (N.M.-V.); (M.B.); (S.R.)
| | - Nadia Müller-Voggel
- Department of Neurosurgery, University Hospital Erlangen, 91054 Erlangen, Germany; (M.K.); (C.K.); (N.M.-V.); (M.B.); (S.R.)
| | - Karl Rössler
- Department of Neurosurgery, Medical University Vienna, 1090 Vienna, Austria;
| | - Arnd Dörfler
- Department of Neuroradiology, University Hospital Erlangen, 91054 Erlangen, Germany;
| | - Stefan Schwab
- Department of Neurology, University Hospital Erlangen, 91054 Erlangen, Germany; (S.S.); (H.H.)
| | - Hajo Hamer
- Department of Neurology, University Hospital Erlangen, 91054 Erlangen, Germany; (S.S.); (H.H.)
| | - Michael Buchfelder
- Department of Neurosurgery, University Hospital Erlangen, 91054 Erlangen, Germany; (M.K.); (C.K.); (N.M.-V.); (M.B.); (S.R.)
| | - Stefan Rampp
- Department of Neurosurgery, University Hospital Erlangen, 91054 Erlangen, Germany; (M.K.); (C.K.); (N.M.-V.); (M.B.); (S.R.)
- Department of Neurosurgery, University Hospital Halle (Saale), 06120 Halle (Saale), Germany
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15
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Büchel D, Lehmann T, Sandbakk Ø, Baumeister J. EEG-derived brain graphs are reliable measures for exploring exercise-induced changes in brain networks. Sci Rep 2021; 11:20803. [PMID: 34675312 PMCID: PMC8531386 DOI: 10.1038/s41598-021-00371-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 10/11/2021] [Indexed: 11/25/2022] Open
Abstract
The interaction of acute exercise and the central nervous system evokes increasing interest in interdisciplinary research fields of neuroscience. Novel approaches allow to monitor large-scale brain networks from mobile electroencephalography (EEG) applying graph theory, but it is yet uncertain whether brain graphs extracted after exercise are reliable. We therefore aimed to investigate brain graph reliability extracted from resting state EEG data before and after submaximal exercise twice within one week in male participants. To obtain graph measures, we extracted global small-world-index (SWI), clustering coefficient (CC) and characteristic path length (PL) based on weighted phase leg index (wPLI) and spectral coherence (Coh) calculation. For reliability analysis, Intraclass-Correlation-Coefficient (ICC) and Coefficient of Variation (CoV) were computed for graph measures before (REST) and after POST) exercise. Overall results revealed poor to excellent measures at PRE and good to excellent ICCs at POST in the theta, alpha-1 and alpha-2, beta-1 and beta-2 frequency band. Based on bootstrap-analysis, a positive effect of exercise on reliability of wPLI based measures was observed, while exercise induced a negative effect on reliability of Coh-based graph measures. Findings indicate that brain graphs are a reliable tool to analyze brain networks in exercise contexts, which might be related to the neuroregulating effect of exercise inducing functional connections within the connectome. Relative and absolute reliability demonstrated good to excellent reliability after exercise. Chosen graph measures may not only allow analysis of acute, but also longitudinal studies in exercise-scientific contexts.
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Affiliation(s)
- Daniel Büchel
- Department Sport & Health, Exercise Science & Neuroscience Unit, Paderborn University, Warburger Str. 100, 33098, Paderborn, Germany.
| | - Tim Lehmann
- Department Sport & Health, Exercise Science & Neuroscience Unit, Paderborn University, Warburger Str. 100, 33098, Paderborn, Germany
| | - Øyvind Sandbakk
- Department of Neuromedicine and Movement Science, Centre for Elite Sports Research, Norwegian University of Science and Technology, Trondheim, Norway
| | - Jochen Baumeister
- Department Sport & Health, Exercise Science & Neuroscience Unit, Paderborn University, Warburger Str. 100, 33098, Paderborn, Germany
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16
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Zhang K, Sun J, Sun Y, Niu K, Wang P, Wu C, Chen Q, Wang X. Pretreatment Source Location and Functional Connectivity Network Correlated With Therapy Response in Childhood Absence Epilepsy: A Magnetoencephalography Study. Front Neurol 2021; 12:692126. [PMID: 34413824 PMCID: PMC8368437 DOI: 10.3389/fneur.2021.692126] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 06/07/2021] [Indexed: 11/30/2022] Open
Abstract
Objective: This study aims to investigate the differences between antiepileptic drug (AED) responders and nonresponders among patients with childhood absence epilepsy (CAE) using magnetoencephalography (MEG) and to additionally evaluate whether the neuromagnetic signals of the brain neurons were correlated with the response to therapy. Methods: Twenty-four drug-naïve patients were subjected to MEG under six frequency bandwidths during ictal periods. The source location and functional connectivity were analyzed using accumulated source imaging and correlation analysis, respectively. All patients were treated with appropriate AED, at least 1 year after their MEG recordings, their outcome was assessed, and they were consequently divided into responders and nonresponders. Results: The source location of the nonresponders was mainly in the frontal cortex at a frequency range of 8–12 and 30–80 Hz, especially 8–12 Hz, while the source location of the nonresponders was mostly in the medial frontal cortex, which was chosen as the region of interest. The nonresponders showed strong positive local frontal connections and deficient anterior and posterior connections at 80–250 Hz. Conclusion: The frontal cortex and especially the medial frontal cortex at α band might be relevant to AED-nonresponsive CAE patients. The local frontal positive epileptic network at 80–250 Hz in our study might further reveal underlying cerebral abnormalities even before treatment in CAE patients, which could cause them to be nonresponsive to AED. One single mechanism cannot explain AED resistance; the nonresponders may represent a subgroup of CAE who is refractory to several antiepileptic drugs.
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Affiliation(s)
- Ke Zhang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Jintao Sun
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Yulei Sun
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Kai Niu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Pengfei Wang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Caiyun Wu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Qiqi Chen
- MEG Center, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xiaoshan Wang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
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17
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Xu N, Shan W, Qi J, Wu J, Wang Q. Presurgical Evaluation of Epilepsy Using Resting-State MEG Functional Connectivity. Front Hum Neurosci 2021; 15:649074. [PMID: 34276321 PMCID: PMC8283278 DOI: 10.3389/fnhum.2021.649074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Accepted: 06/07/2021] [Indexed: 11/21/2022] Open
Abstract
Epilepsy is caused by abnormal electrical discharges (clinically identified by electrophysiological recording) in a specific part of the brain [originating in only one part of the brain, namely, the epileptogenic zone (EZ)]. Epilepsy is now defined as an archetypical hyperexcited neural network disorder. It can be investigated through the network analysis of interictal discharges, ictal discharges, and resting-state functional connectivity. Currently, there is an increasing interest in embedding resting-state connectivity analysis into the preoperative evaluation of epilepsy. Among the various neuroimaging technologies employed to achieve brain functional networks, magnetoencephalography (MEG) with the excellent temporal resolution is an ideal tool for estimating the resting-state connectivity between brain regions, which can reveal network abnormalities in epilepsy. What value does MEG resting-state functional connectivity offer for epileptic presurgical evaluation? Regarding this topic, this paper introduced the origin of MEG and the workflow of constructing source-space functional connectivity based on MEG signals. Resting-state functional connectivity abnormalities correlate with epileptogenic networks, which are defined by the brain regions involved in the production and propagation of epileptic activities. This paper reviewed the evidence of altered epileptic connectivity based on low- or high-frequency oscillations (HFOs) and the evidence of the advantage of using simultaneous MEG and intracranial electroencephalography (iEEG) recordings. More importantly, this review highlighted that MEG-based resting-state functional connectivity has the potential to predict postsurgical outcomes. In conclusion, resting-state MEG functional connectivity has made a substantial progress toward serving as a candidate biomarker included in epileptic presurgical evaluations.
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Affiliation(s)
- Na Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wei Shan
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jing Qi
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jianping Wu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Neurological Diseases, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Qun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Neurological Diseases, Beijing, China
- Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neuromodulation, Beijing, China
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18
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Stier C, Elshahabi A, Li Hegner Y, Kotikalapudi R, Marquetand J, Braun C, Lerche H, Focke NK. Heritability of Magnetoencephalography Phenotypes Among Patients With Genetic Generalized Epilepsy and Their Siblings. Neurology 2021; 97:e166-e177. [PMID: 34045271 PMCID: PMC8279565 DOI: 10.1212/wnl.0000000000012144] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 04/07/2021] [Indexed: 11/15/2022] Open
Abstract
Objective To assess whether neuronal signals in patients with genetic generalized epilepsy (GGE) are heritable, we examined magnetoencephalography resting-state recordings in patients and their healthy siblings. Methods In a prospective, cross-sectional design, we investigated source-reconstructed power and functional connectivity in patients, siblings, and controls. We analyzed 5 minutes of cleaned and awake data without epileptiform discharges in 6 frequency bands (1–40 Hz). We further calculated intraclass correlations to estimate heritability for the imaging patterns within families. Results Compared with controls (n = 45), patients with GGE (n = 25) showed widespread increased functional connectivity (θ to γ frequency bands) and power (δ to γ frequency bands) across the spectrum. Siblings (n = 18) fell between the levels of patients and controls. Heritability of the imaging metrics was observed in regions where patients strongly differed from controls, mainly in β frequencies, but also for δ and θ power. Network connectivity in GGE was heritable in frontal, central, and inferior parietal brain areas and power in central, temporo-parietal, and subcortical structures. Presence of generalized spike-wave activity during recordings and medication were associated with the network patterns, whereas other clinical factors such as age at onset, disease duration, or seizure control were not. Conclusion Metrics of brain oscillations are well suited to characterize GGE and likely relate to genetic factors rather than the active disease or treatment. High power and connectivity levels co-segregated in patients with GGE and healthy siblings, predominantly in the β band, representing an endophenotype of GGE.
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Affiliation(s)
- Christina Stier
- From the Clinic of Clinical Neurophysiology (C.S., R.K., N.K.F.), University Medical Center Göttingen; Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research (C.S., A.E., Y.L.H., R.K., J.M., H.L., N.K.F., C.B.), and MEG Center (C.B.), University of Tübingen, Germany; Department of Neurology (A.E.), University Hospital Zurich; Institute of Psychology (R.K.), University of Bern, Switzerland; and CIMeC (C.B.), Center for Mind/Brain Sciences, University of Trento, Italy
| | - Adham Elshahabi
- From the Clinic of Clinical Neurophysiology (C.S., R.K., N.K.F.), University Medical Center Göttingen; Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research (C.S., A.E., Y.L.H., R.K., J.M., H.L., N.K.F., C.B.), and MEG Center (C.B.), University of Tübingen, Germany; Department of Neurology (A.E.), University Hospital Zurich; Institute of Psychology (R.K.), University of Bern, Switzerland; and CIMeC (C.B.), Center for Mind/Brain Sciences, University of Trento, Italy
| | - Yiwen Li Hegner
- From the Clinic of Clinical Neurophysiology (C.S., R.K., N.K.F.), University Medical Center Göttingen; Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research (C.S., A.E., Y.L.H., R.K., J.M., H.L., N.K.F., C.B.), and MEG Center (C.B.), University of Tübingen, Germany; Department of Neurology (A.E.), University Hospital Zurich; Institute of Psychology (R.K.), University of Bern, Switzerland; and CIMeC (C.B.), Center for Mind/Brain Sciences, University of Trento, Italy
| | - Raviteja Kotikalapudi
- From the Clinic of Clinical Neurophysiology (C.S., R.K., N.K.F.), University Medical Center Göttingen; Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research (C.S., A.E., Y.L.H., R.K., J.M., H.L., N.K.F., C.B.), and MEG Center (C.B.), University of Tübingen, Germany; Department of Neurology (A.E.), University Hospital Zurich; Institute of Psychology (R.K.), University of Bern, Switzerland; and CIMeC (C.B.), Center for Mind/Brain Sciences, University of Trento, Italy
| | - Justus Marquetand
- From the Clinic of Clinical Neurophysiology (C.S., R.K., N.K.F.), University Medical Center Göttingen; Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research (C.S., A.E., Y.L.H., R.K., J.M., H.L., N.K.F., C.B.), and MEG Center (C.B.), University of Tübingen, Germany; Department of Neurology (A.E.), University Hospital Zurich; Institute of Psychology (R.K.), University of Bern, Switzerland; and CIMeC (C.B.), Center for Mind/Brain Sciences, University of Trento, Italy
| | - Christoph Braun
- From the Clinic of Clinical Neurophysiology (C.S., R.K., N.K.F.), University Medical Center Göttingen; Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research (C.S., A.E., Y.L.H., R.K., J.M., H.L., N.K.F., C.B.), and MEG Center (C.B.), University of Tübingen, Germany; Department of Neurology (A.E.), University Hospital Zurich; Institute of Psychology (R.K.), University of Bern, Switzerland; and CIMeC (C.B.), Center for Mind/Brain Sciences, University of Trento, Italy
| | - Holger Lerche
- From the Clinic of Clinical Neurophysiology (C.S., R.K., N.K.F.), University Medical Center Göttingen; Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research (C.S., A.E., Y.L.H., R.K., J.M., H.L., N.K.F., C.B.), and MEG Center (C.B.), University of Tübingen, Germany; Department of Neurology (A.E.), University Hospital Zurich; Institute of Psychology (R.K.), University of Bern, Switzerland; and CIMeC (C.B.), Center for Mind/Brain Sciences, University of Trento, Italy
| | - Niels K Focke
- From the Clinic of Clinical Neurophysiology (C.S., R.K., N.K.F.), University Medical Center Göttingen; Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research (C.S., A.E., Y.L.H., R.K., J.M., H.L., N.K.F., C.B.), and MEG Center (C.B.), University of Tübingen, Germany; Department of Neurology (A.E.), University Hospital Zurich; Institute of Psychology (R.K.), University of Bern, Switzerland; and CIMeC (C.B.), Center for Mind/Brain Sciences, University of Trento, Italy.
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19
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Krieger D, Shepard P, Soose R, Puccio AM, Beers S, Schneider W, Kontos AP, Collins MW, Okonkwo DO. Symptom-Dependent Changes in MEG-Derived Neuroelectric Brain Activity in Traumatic Brain Injury Patients with Chronic Symptoms. Med Sci (Basel) 2021; 9:medsci9020020. [PMID: 33806153 PMCID: PMC8103254 DOI: 10.3390/medsci9020020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 02/23/2021] [Accepted: 03/17/2021] [Indexed: 01/11/2023] Open
Abstract
Neuroelectric measures derived from human magnetoencephalographic (MEG) recordings hold promise as aides to diagnosis and treatment monitoring and targeting for chronic sequelae of traumatic brain injury (TBI). This study tests novel MEG-derived regional brain measures of tonic neuroelectric activation for long-term test-retest reliability and sensitivity to symptoms. Resting state MEG recordings were obtained from a normative cohort, Cambridge Centre for Ageing and Neuroscience (CamCAN), baseline: n = 619; mean 16-month follow-up: n = 253) and a chronic symptomatic TBI cohort, Targeted Evaluation, Action and Monitoring of Traumatic Brain Injury (TEAM-TBI), baseline: n = 64; mean 6-month follow-up: n = 39). For the CamCAN cohort, MEG-derived neuroelectric measures showed good long-term test-retest reliability for most of the 103 automatically identified stereotypic regions. The TEAM-TBI cohort was screened for depression, somatization, and anxiety with the Brief Symptom Inventory and for insomnia with the Insomnia Severity Index. Linear classifiers constructed from the 103 regional measures from each TEAM-TBI cohort member distinguished those with and without each symptom, with p < 0.01 for each-i.e., the tonic regional neuroelectric measures of activation are sensitive to the presence/absence of these symptoms. The novel regional MEG-derived neuroelectric measures obtained and tested in this study demonstrate the necessary and sufficient properties to be clinically useful-i.e., good test-retest reliability, sensitivity to symptoms in each individual, and obtainable using automatic processing without human judgement or intervention.
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Affiliation(s)
- Don Krieger
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15232, USA; (A.M.P.); (D.O.O.)
- Correspondence:
| | - Paul Shepard
- Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA 15232, USA;
| | - Ryan Soose
- Department of Otolaryngology, University of Pittsburgh, Pittsburgh, PA 15232, USA;
| | - Ava M. Puccio
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15232, USA; (A.M.P.); (D.O.O.)
| | - Sue Beers
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15232, USA;
| | - Walter Schneider
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA 15232, USA;
| | - Anthony P. Kontos
- Department of Sports Medicine, University of Pittsburgh, Pittsburgh, PA 15232, USA; (A.P.K.); (M.W.C.)
| | - Michael W. Collins
- Department of Sports Medicine, University of Pittsburgh, Pittsburgh, PA 15232, USA; (A.P.K.); (M.W.C.)
| | - David O. Okonkwo
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15232, USA; (A.M.P.); (D.O.O.)
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20
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Wirsich J, Jorge J, Iannotti GR, Shamshiri EA, Grouiller F, Abreu R, Lazeyras F, Giraud AL, Gruetter R, Sadaghiani S, Vulliémoz S. The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T. Neuroimage 2021; 231:117864. [PMID: 33592241 DOI: 10.1016/j.neuroimage.2021.117864] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 01/21/2021] [Accepted: 02/09/2021] [Indexed: 01/01/2023] Open
Abstract
Both electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are non-invasive methods that show complementary aspects of human brain activity. Despite measuring different proxies of brain activity, both the measured blood-oxygenation (fMRI) and neurophysiological recordings (EEG) are indirectly coupled. The electrophysiological and BOLD signal can map the underlying functional connectivity structure at the whole brain scale at different timescales. Previous work demonstrated a moderate but significant correlation between resting-state functional connectivity of both modalities, however there is a wide range of technical setups to measure simultaneous EEG-fMRI and the reliability of those measures between different setups remains unknown. This is true notably with respect to different magnetic field strengths (low and high field) and different spatial sampling of EEG (medium to high-density electrode coverage). Here, we investigated the reproducibility of the bimodal EEG-fMRI functional connectome in the most comprehensive resting-state simultaneous EEG-fMRI dataset compiled to date including a total of 72 subjects from four different imaging centers. Data was acquired from 1.5T, 3T and 7T scanners with simultaneously recorded EEG using 64 or 256 electrodes. We demonstrate that the whole-brain monomodal connectivity reproducibly correlates across different datasets and that a moderate crossmodal correlation between EEG and fMRI connectivity of r ≈ 0.3 can be reproducibly extracted in low- and high-field scanners. The crossmodal correlation was strongest in the EEG-β frequency band but exists across all frequency bands. Both homotopic and within intrinsic connectivity network (ICN) connections contributed the most to the crossmodal relationship. This study confirms, using a considerably diverse range of recording setups, that simultaneous EEG-fMRI offers a consistent estimate of multimodal functional connectomes in healthy subjects that are dominantly linked through a functional core of ICNs across spanning across the different timescales measured by EEG and fMRI. This opens new avenues for estimating the dynamics of brain function and provides a better understanding of interactions between EEG and fMRI measures. This observed level of reproducibility also defines a baseline for the study of alterations of this coupling in pathological conditions and their role as potential clinical markers.
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Affiliation(s)
- Jonathan Wirsich
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva, Geneva, Switzerland.
| | - João Jorge
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Systems Division, Swiss Center for Electronics and Microtechnology (CSEM), Neuchâtel, Switzerland
| | - Giannina Rita Iannotti
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva, Geneva, Switzerland
| | - Elhum A Shamshiri
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva, Geneva, Switzerland
| | - Frédéric Grouiller
- Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland
| | - Rodolfo Abreu
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico - Universidade de Lisboa, Lisbon, Portugal; Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - François Lazeyras
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Anne-Lise Giraud
- Department of Neuroscience, University of Geneva, Geneva, Switzerland
| | - Rolf Gruetter
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland; Department of Radiology, University of Lausanne, Lausanne, Switzerland
| | - Sepideh Sadaghiani
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, United States; Psychology Department, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Serge Vulliémoz
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva, Geneva, Switzerland
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21
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Coomans EM, Schoonhoven DN, Tuncel H, Verfaillie SCJ, Wolters EE, Boellaard R, Ossenkoppele R, den Braber A, Scheper W, Schober P, Sweeney SP, Ryan JM, Schuit RC, Windhorst AD, Barkhof F, Scheltens P, Golla SSV, Hillebrand A, Gouw AA, van Berckel BNM. In vivo tau pathology is associated with synaptic loss and altered synaptic function. Alzheimers Res Ther 2021; 13:35. [PMID: 33546722 PMCID: PMC7866464 DOI: 10.1186/s13195-021-00772-0] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 01/11/2021] [Indexed: 01/08/2023]
Abstract
BACKGROUND The mechanism of synaptic loss in Alzheimer's disease is poorly understood and may be associated with tau pathology. In this combined positron emission tomography (PET) and magnetoencephalography (MEG) study, we aimed to investigate spatial associations between regional tau pathology ([18F]flortaucipir PET), synaptic density (synaptic vesicle 2A [11C]UCB-J PET) and synaptic function (MEG) in Alzheimer's disease. METHODS Seven amyloid-positive Alzheimer's disease subjects from the Amsterdam Dementia Cohort underwent dynamic 130-min [18F]flortaucipir PET, dynamic 60-min [11C]UCB-J PET with arterial sampling and 2 × 5-min resting-state MEG measurement. [18F]flortaucipir- and [11C]UCB-J-specific binding (binding potential, BPND) and MEG spectral measures (relative delta, theta and alpha power; broadband power; and peak frequency) were assessed in cortical brain regions of interest. Associations between regional [18F]flortaucipir BPND, [11C]UCB-J BPND and MEG spectral measures were assessed using Spearman correlations and generalized estimating equation models. RESULTS Across subjects, higher regional [18F]flortaucipir uptake was associated with lower [11C]UCB-J uptake. Within subjects, the association between [11C]UCB-J and [18F]flortaucipir depended on within-subject neocortical tau load; negative associations were observed when neocortical tau load was high, gradually changing into opposite patterns with decreasing neocortical tau burden. Both higher [18F]flortaucipir and lower [11C]UCB-J uptake were associated with altered synaptic function, indicative of slowing of oscillatory activity, most pronounced in the occipital lobe. CONCLUSIONS These results indicate that in Alzheimer's disease, tau pathology is closely associated with reduced synaptic density and synaptic dysfunction.
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Affiliation(s)
- Emma M Coomans
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
| | - Deborah N Schoonhoven
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Hayel Tuncel
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Sander C J Verfaillie
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Emma E Wolters
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Anouk den Braber
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Wiep Scheper
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, The Netherlands
- Center for Neurogenomics and Cognitive Research, Department of Functional Genomics, Faculty of Science, Vrije Universiteit, Amsterdam, The Netherlands
| | - Patrick Schober
- Department of Anaesthesiology, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | | | | | - Robert C Schuit
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Albert D Windhorst
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- UCL Institutes of Neurology and Healthcare Engineering, London, UK
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Sandeep S V Golla
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Alida A Gouw
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Bart N M van Berckel
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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22
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Haartsen R, van der Velde B, Jones EJH, Johnson MH, Kemner C. Using multiple short epochs optimises the stability of infant EEG connectivity parameters. Sci Rep 2020; 10:12703. [PMID: 32728099 PMCID: PMC7391718 DOI: 10.1038/s41598-020-68981-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 06/30/2020] [Indexed: 12/19/2022] Open
Abstract
Atypicalities in connectivity between brain regions have been implicated in a range of neurocognitive disorders. We require metrics to assess stable individual differences in connectivity in the developing brain, while facing the challenge of limited data quality and quantity. Here, we examine how varying core processing parameters can optimise the test–retest reliability of EEG connectivity measures in infants. EEG was recorded twice with a 1-week interval between sessions in 10-month-olds. EEG alpha connectivity was measured across different epoch lengths and numbers, with the phase lag index (PLI) and debiased weighted PLI (dbWPLI), for both whole-head connectivity and graph theory metrics. We calculated intra-class correlations between sessions for infants with sufficient data for both sessions (N’s = 19–41, depending on the segmentation method). Reliability for the whole brain dbWPLI was higher across many short epochs, whereas reliability for the whole brain PLI was higher across fewer long epochs. However, the PLI is confounded by the number of available segments. Reliability was higher for whole brain connectivity than graph theory metrics. Thus, segmenting available data into a high number of short epochs and calculating the dbWPLI is most appropriate for characterising connectivity in populations with limited availability of EEG data.
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Affiliation(s)
- Rianne Haartsen
- Department of Psychological Sciences (BMA), Centre for Brain and Cognitive Development, Birkbeck College, University of London, Malet Street, London, WC1E 7HX, UK.
| | - Bauke van der Velde
- Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Heidelberglaan 1, 3584 CS, Utrecht, The Netherlands.,Department of Developmental Psychology, Utrecht University, Utrecht, The Netherlands.,Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Heidelberglaan 100, 3508 GA, Utrecht, The Netherlands
| | - Emily J H Jones
- Department of Psychological Sciences (BMA), Centre for Brain and Cognitive Development, Birkbeck College, University of London, Malet Street, London, WC1E 7HX, UK
| | - Mark H Johnson
- Department of Psychological Sciences (BMA), Centre for Brain and Cognitive Development, Birkbeck College, University of London, Malet Street, London, WC1E 7HX, UK.,Department of Psychology, University of Cambridge, Cambridge, UK
| | - Chantal Kemner
- Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Heidelberglaan 1, 3584 CS, Utrecht, The Netherlands.,Department of Developmental Psychology, Utrecht University, Utrecht, The Netherlands.,Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Heidelberglaan 100, 3508 GA, Utrecht, The Netherlands
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23
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Sadaghiani S, Wirsich J. Intrinsic connectome organization across temporal scales: New insights from cross-modal approaches. Netw Neurosci 2020; 4:1-29. [PMID: 32043042 PMCID: PMC7006873 DOI: 10.1162/netn_a_00114] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 11/11/2019] [Indexed: 12/17/2022] Open
Abstract
The discovery of a stable, whole-brain functional connectivity organization that is largely independent of external events has drastically extended our view of human brain function. However, this discovery has been primarily based on functional magnetic resonance imaging (fMRI). The role of this whole-brain organization in fast oscillation-based connectivity as measured, for example, by electroencephalography (EEG) and magnetoencephalography (MEG) is only beginning to emerge. Here, we review studies of intrinsic connectivity and its whole-brain organization in EEG, MEG, and intracranial electrophysiology with a particular focus on direct comparisons to connectome studies in fMRI. Synthesizing this literature, we conclude that irrespective of temporal scale over four orders of magnitude, intrinsic neurophysiological connectivity shows spatial similarity to the connectivity organization commonly observed in fMRI. A shared structural connectivity basis and cross-frequency coupling are possible mechanisms contributing to this similarity. Acknowledging that a stable whole-brain organization governs long-range coupling across all timescales of neural processing motivates researchers to take "baseline" intrinsic connectivity into account when investigating brain-behavior associations, and further encourages more widespread exploration of functional connectomics approaches beyond fMRI by using EEG and MEG modalities.
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Affiliation(s)
- Sepideh Sadaghiani
- Psychology Department, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Jonathan Wirsich
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
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24
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Iraji A, Miller R, Adali T, Calhoun VD. Space: A Missing Piece of the Dynamic Puzzle. Trends Cogn Sci 2020; 24:135-149. [PMID: 31983607 DOI: 10.1016/j.tics.2019.12.004] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 11/15/2019] [Accepted: 12/03/2019] [Indexed: 01/24/2023]
Abstract
There has been growing interest in studying the temporal reconfiguration of brain functional connectivity to understand the role of dynamic interaction (e.g., integration and segregation) among neuronal populations in cognitive functions. However, it is crucial to differentiate between various dynamic properties because nearly all existing dynamic connectivity studies are presented as spatiotemporally dynamic, even though they fall into different categories. As a result, variation in the spatial patterns of functional structures are not well characterized. Here, we present the concepts of spatially, temporally, and spatiotemporally dynamics and use this terminology to categorize existing approaches. We review current spatially dynamic connectivity work, emphasizing that explicit incorporation of space into dynamic analyses can expand our understanding of brain function and disorder.
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Affiliation(s)
- Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Robyn Miller
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Tulay Adali
- Department of CSEE, University of Maryland Baltimore County, Baltimore, MD 21250, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA.
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25
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Candelaria-Cook FT, Stephen JM. Test-Retest Reliability of Magnetoencephalography Resting-State Functional Connectivity in Schizophrenia. Front Psychiatry 2020; 11:551952. [PMID: 33391043 PMCID: PMC7772354 DOI: 10.3389/fpsyt.2020.551952] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 11/23/2020] [Indexed: 12/17/2022] Open
Abstract
The reliability of magnetoencephalography (MEG) resting-state functional connectivity in schizophrenia (SZ) is unknown as previous research has focused on healthy controls (HC). Here, we examined reliability in 26 participants (13-SZ, 13-HC). Eyes opened and eyes closed resting-state data were collected on 4 separate occasions during 2 visits, 1 week apart. For source modeling, we used minimum norm software to apply dynamic statistical parametric mapping. Source analyses compared the following functional connectivity metrics from each data run: coherence (coh), imaginary coherence (imcoh), pairwise phase consistency (ppc), phase-locking value (plv), phase lag index (pli), weighted phase lag index (wpli), and weighted phase lag index debiased (wpli2). Intraclass correlation coefficients (ICCs) were calculated for whole brain, network, and network pair averages. For reliability, ICCs above 0.75 = excellent, above 0.60 = good, above 0.40 = fair, and below 0.40 = poor reliability. We found the reliability of these metrics varied greatly depending on frequency band, network, network pair, and participant group examined. Broadband (1-58 Hz) whole brain averages in both HC and SZ showed excellent reliability for wpli2, and good to fair reliability for ppc, plv, and coh. Broadband network averages showed excellent to good reliability across 1 hour and 1 week for coh, imcoh, ppc, plv, wpli within default mode, cognitive control, and visual networks in HC, while the same metrics had excellent to fair reliability in SZ. Regional network pair averages showed good to fair reliability for coh, ppc, plv within default mode, cognitive control and visual network pairs in HC and SZ. In general, HC had higher reliability compared to SZ, and the default mode, cognitive control, and visual networks had higher reliability compared to somatosensory and auditory networks. Similar reliability levels occurred for both eyes opened and eyes closed resting-states for most metrics. The functional connectivity metrics of coh, ppc, and plv performed best across 1 hour and 1 week in HC and SZ. We also found that SZ had reduced coh, plv, and ppc in the dmn average and pair values indicating dysconnectivity in SZ. These findings encourage collecting both eyes opened and eyes closed resting-state MEG, while demonstrating that clinical populations may differ in reliability.
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