551
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Scorepochs: A Computer-Aided Scoring Tool for Resting-State M/EEG Epochs. SENSORS 2022; 22:s22082853. [PMID: 35458838 PMCID: PMC9031998 DOI: 10.3390/s22082853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/28/2022] [Accepted: 04/06/2022] [Indexed: 11/16/2022]
Abstract
M/EEG resting-state analysis often requires the definition of the epoch length and the criteria in order to select which epochs to include in the subsequent steps. However, the effects of epoch selection remain scarcely investigated and the procedure used to (visually) inspect, label, and remove bad epochs is often not documented, thereby hindering the reproducibility of the reported results. In this study, we present Scorepochs, a simple and freely available tool for the automatic scoring of resting-state M/EEG epochs that aims to provide an objective method to aid M/EEG experts during the epoch selection procedure. We tested our approach on a freely available EEG dataset containing recordings from 109 subjects using the BCI2000 64 channel system.
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552
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Karalunas SL, Ostlund BD, Alperin BR, Figuracion M, Gustafsson HC, Deming EM, Foti D, Antovich D, Dude J, Nigg J, Sullivan E. Electroencephalogram aperiodic power spectral slope can be reliably measured and predicts ADHD risk in early development. Dev Psychobiol 2022; 64:e22228. [PMID: 35312046 PMCID: PMC9707315 DOI: 10.1002/dev.22228] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 08/20/2021] [Accepted: 10/12/2021] [Indexed: 12/12/2022]
Abstract
The aperiodic exponent of the electroencephalogram (EEG) power spectrum has received growing attention as a physiological marker of neurodevelopmental psychopathology, including attention-deficit/hyperactivity disorder (ADHD). However, its use as a marker of ADHD risk across development, and particularly in very young children, is limited by unknown reliability, difficulty in aligning canonical band-based measures across development periods, and unclear effects of treatment in later development. Here, we investigate the internal consistency of the aperiodic EEG power spectrum slope and its association with ADHD risk in both infants (n = 69, 1-month-old) and adolescents (n = 262, ages 11-17 years). Results confirm good to excellent internal consistency in infancy and adolescence. In infancy, a larger aperiodic exponent was associated with greater family history of ADHD. In contrast, in adolescence, ADHD diagnosis was associated with a smaller aperiodic exponent, but only in children with ADHD who had not received stimulant medication treatment. Results suggest that disruptions in cortical development associated with ADHD risk may be detectable shortly after birth via this approach. Together, findings imply a dynamic developmental shift in which the developmentally normative flattening of the EEG power spectrum is exaggerated in ADHD, potentially reflecting imbalances in cortical excitation and inhibition that could contribute to long-lasting differences in brain connectivity.
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Affiliation(s)
- Sarah L Karalunas
- Department of Psychological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Brendan D Ostlund
- Department of Psychology, Pennsylvania State University, State College, Pennsylvania, USA
| | - Brittany R Alperin
- Department of Psychology, University of Richmond, Richmond, Virginia, USA
| | - McKenzie Figuracion
- Department of Psychiatry, Oregon Health and Science University, Portland, Oregon, USA
| | - Hanna C Gustafsson
- Department of Psychiatry, Oregon Health and Science University, Portland, Oregon, USA
| | - Erika Michiko Deming
- Department of Psychological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Dan Foti
- Department of Psychological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Dylan Antovich
- Department of Psychiatry, Oregon Health and Science University, Portland, Oregon, USA
| | - Jason Dude
- Department of Psychological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Joel Nigg
- Department of Psychiatry, Oregon Health and Science University, Portland, Oregon, USA
| | - Elinor Sullivan
- Department of Psychiatry, Oregon Health and Science University, Portland, Oregon, USA
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553
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Ratcliffe O, Shapiro K, Staresina BP. Fronto-medial theta coordinates posterior maintenance of working memory content. Curr Biol 2022; 32:2121-2129.e3. [PMID: 35385693 PMCID: PMC9616802 DOI: 10.1016/j.cub.2022.03.045] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 10/11/2021] [Accepted: 03/15/2022] [Indexed: 12/25/2022]
Abstract
How does the human brain manage multiple bits of information to guide goal-directed behavior? Successful working memory (WM) functioning has consistently been linked to oscillatory power in the theta frequency band (4–8 Hz) over fronto-medial cortex (fronto-medial theta [FMT]). Specifically, FMT is thought to reflect the mechanism of an executive sub-system that coordinates maintenance of memory contents in posterior regions. However, direct evidence for the role of FMT in controlling specific WM content is lacking. Here, we collected high-density electroencephalography (EEG) data while participants engaged in WM-dependent tasks and then used multivariate decoding methods to examine WM content during the maintenance period. Engagement of WM was accompanied by a focal increase in FMT. Importantly, decoding of WM content was driven by posterior sites, which, in turn, showed increased functional theta coupling with fronto-medial channels. Finally, we observed a significant slowing of FMT frequency with increasing WM load, consistent with the hypothesized broadening of a theta “duty cycle” to accommodate additional WM items. Together, these findings demonstrate that frontal theta orchestrates posterior maintenance of WM content. Moreover, the observed frequency slowing elucidates the function of FMT oscillations by specifically supporting phase-coding accounts of WM. FMT power supports WM functions During WM performance, posterior/parietal regions are coupled with FMT Multivariate decoding of WM content is mediated by these same posterior channels Frontal theta frequency slows with WM load supporting phase-coding models
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Affiliation(s)
- Oliver Ratcliffe
- School of Psychology, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Kimron Shapiro
- School of Psychology, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Bernhard P Staresina
- Department of Experimental Psychology, University of Oxford, Oxford, UK; Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK.
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554
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Ostlund B, Donoghue T, Anaya B, Gunther KE, Karalunas SL, Voytek B, Pérez-Edgar KE. Spectral parameterization for studying neurodevelopment: How and why. Dev Cogn Neurosci 2022; 54:101073. [PMID: 35074579 PMCID: PMC8792072 DOI: 10.1016/j.dcn.2022.101073] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 12/07/2021] [Accepted: 01/15/2022] [Indexed: 12/12/2022] Open
Abstract
A growing body of literature suggests that the explicit parameterization of neural power spectra is important for the appropriate physiological interpretation of periodic and aperiodic electroencephalogram (EEG) activity. In this paper, we discuss why parameterization is an imperative step for developmental cognitive neuroscientists interested in cognition and behavior across the lifespan, as well as how parameterization can be readily accomplished with an automated spectral parameterization ("specparam") algorithm (Donoghue et al., 2020a). We provide annotated code for power spectral parameterization, via specparam, in Jupyter Notebook and R Studio. We then apply this algorithm to EEG data in childhood (N = 60; Mage = 9.97, SD = 0.95) to illustrate its utility for developmental cognitive neuroscientists. Ultimately, the explicit parameterization of EEG power spectra may help us refine our understanding of how dynamic neural communication contributes to normative and aberrant cognition across the lifespan. Data and annotated analysis code for this manuscript are available on GitHub as a supplement to the open-access specparam toolbox.
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Affiliation(s)
- Brendan Ostlund
- Department of Psychology, The Pennsylvania State University, USA.
| | - Thomas Donoghue
- Department of Cognitive Science, University of California, San Diego, USA
| | - Berenice Anaya
- Department of Psychology, The Pennsylvania State University, USA
| | - Kelley E Gunther
- Department of Psychology, The Pennsylvania State University, USA
| | | | - Bradley Voytek
- Department of Cognitive Science, University of California, San Diego, USA; Halıcıoğlu Data Science Institute, University of California, San Diego, USA; Neurosciences Graduate Program, University of California, San Diego, USA
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555
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An efficient multi-scale CNN model with intrinsic feature integration for motor imagery EEG subject classification in brain-machine interfaces. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103496] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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556
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Hill AT, Clark GM, Bigelow FJ, Lum JAG, Enticott PG. Periodic and aperiodic neural activity displays age-dependent changes across early-to-middle childhood. Dev Cogn Neurosci 2022; 54:101076. [PMID: 35085871 PMCID: PMC8800045 DOI: 10.1016/j.dcn.2022.101076] [Citation(s) in RCA: 62] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 01/10/2022] [Accepted: 01/21/2022] [Indexed: 11/27/2022] Open
Abstract
The neurodevelopmental period spanning early-to-middle childhood represents a time of significant growth and reorganisation throughout the cortex. Such changes are critical for the emergence and maturation of a range of social and cognitive processes. Here, we utilised both eyes open and eyes closed resting-state electroencephalography (EEG) to examine maturational changes in both oscillatory (i.e., periodic) and non-oscillatory (aperiodic, '1/f-like') activity in a large cohort of participants ranging from 4-to-12 years of age (N = 139, average age=9.41 years, SD=1.95). The EEG signal was parameterised into aperiodic and periodic components, and linear regression models were used to evaluate if chronological age could predict aperiodic exponent and offset, as well as well as peak frequency and power within the alpha and beta ranges. Exponent and offset were found to both decrease with age, while aperiodic-adjusted alpha peak frequency increased with age; however, there was no association between age and peak frequency for the beta band. Age was also unrelated to aperiodic-adjusted spectral power within either the alpha or beta bands, despite both frequency ranges being correlated with the aperiodic signal. Overall, these results highlight the capacity for both periodic and aperiodic features of the EEG to elucidate age-related functional changes within the developing brain.
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Affiliation(s)
- Aron T Hill
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, Australia.
| | - Gillian M Clark
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, Australia
| | - Felicity J Bigelow
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, Australia
| | - Jarrad A G Lum
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, Australia
| | - Peter G Enticott
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, Australia
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557
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Rayson H, Debnath R, Alavizadeh S, Fox N, Ferrari PF, Bonaiuto JJ. Detection and analysis of cortical beta bursts in developmental EEG data. Dev Cogn Neurosci 2022; 54:101069. [PMID: 35114447 PMCID: PMC8816670 DOI: 10.1016/j.dcn.2022.101069] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 11/14/2021] [Accepted: 01/13/2022] [Indexed: 01/10/2023] Open
Abstract
Developmental EEG research often involves analyzing signals within various frequency bands, based on the assumption that these signals represent oscillatory neural activity. However, growing evidence suggests that certain frequency bands are dominated by transient burst events in single trials rather than sustained oscillations. This is especially true for the beta band, with adult 'beta burst' timing a better predictor of motor behavior than slow changes in average beta amplitude. No developmental research thus far has looked at beta bursts, with techniques used to investigate frequency-specific activity structure rarely even applied to such data. Therefore, we aimed to: i) provide a tutorial for developmental EEG researchers on the application of methods for evaluating the rhythmic versus transient nature of frequency-specific activity; and ii) use these techniques to investigate the existence of sensorimotor beta bursts in infants. We found that beta activity in 12-month-olds did occur in bursts, however differences were also revealed in terms of duration, amplitude, and rate during grasping compared to adults. Application of the techniques illustrated here will be critical for clarifying the functional roles of frequency-specific activity across early development, including the role of beta activity in motor processing and its contribution to differing developmental motor trajectories.
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Affiliation(s)
- Holly Rayson
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR5229, Bron, France; Université Claude Bernard Lyon 1, Université de Lyon, France.
| | | | - Sanaz Alavizadeh
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR5229, Bron, France; Université Claude Bernard Lyon 1, Université de Lyon, France
| | - Nathan Fox
- University of Maryland College Park, MD, USA
| | - Pier F Ferrari
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR5229, Bron, France; Université Claude Bernard Lyon 1, Université de Lyon, France
| | - James J Bonaiuto
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR5229, Bron, France; Université Claude Bernard Lyon 1, Université de Lyon, France
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558
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Férat V, Seeber M, Michel CM, Ros T. Beyond broadband: Towards a spectral decomposition of electroencephalography microstates. Hum Brain Mapp 2022; 43:3047-3061. [PMID: 35324021 PMCID: PMC9188972 DOI: 10.1002/hbm.25834] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 02/22/2022] [Accepted: 02/28/2022] [Indexed: 11/06/2022] Open
Abstract
Originally applied to alpha oscillations in the 1970s, microstate (MS) analysis has since been used to decompose mainly broadband electroencephalogram (EEG) signals (e.g., 1-40 Hz). We hypothesised that MS decomposition within separate, narrow frequency bands could provide more fine-grained information for capturing the spatio-temporal complexity of multichannel EEG. In this study, using a large open-access dataset (n = 203), we first filtered EEG recordings into four classical frequency bands (delta, theta, alpha and beta) and thereafter compared their individual MS segmentations using mutual information as well as traditional MS measures (e.g., mean duration and time coverage). Firstly, we confirmed that MS topographies were spatially equivalent across all frequencies, matching the canonical broadband maps (A, B, C, D and C'). Interestingly, however, we observed strong informational independence of MS temporal sequences between spectral bands, together with significant divergence in traditional MS measures. For example, relative to broadband, alpha/beta band dynamics displayed greater time coverage of maps A and B, while map D was more prevalent in delta/theta bands. Moreover, using a frequency-specific MS taxonomy (e.g., ϴA and αC), we were able to predict the eyes-open versus eyes-closed behavioural state significantly better using alpha-band MS features compared with broadband ones (80 vs. 73% accuracy). Overall, our findings demonstrate the value and validity of spectrally specific MS analyses, which may prove useful for identifying new neural mechanisms in fundamental research and/or for biomarker discovery in clinical populations.
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Affiliation(s)
- Victor Férat
- Functional Brain Mapping Laboratory, Department of Basic Neurosciences, Campus Biotech, University of Geneva, Geneva, Switzerland
| | - Martin Seeber
- Functional Brain Mapping Laboratory, Department of Basic Neurosciences, Campus Biotech, University of Geneva, Geneva, Switzerland
| | - Christoph M Michel
- Functional Brain Mapping Laboratory, Department of Basic Neurosciences, Campus Biotech, University of Geneva, Geneva, Switzerland.,Centre for Biomedical Imaging (CIBM), Lausanne-Geneva, Geneva, Switzerland
| | - Tomas Ros
- Functional Brain Mapping Laboratory, Department of Basic Neurosciences, Campus Biotech, University of Geneva, Geneva, Switzerland.,Centre for Biomedical Imaging (CIBM), Lausanne-Geneva, Geneva, Switzerland
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559
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Zis P, Liampas A, Artemiadis A, Tsalamandris G, Neophytou P, Unwin Z, Kimiskidis VK, Hadjigeorgiou GM, Varrassi G, Zhao Y, Sarrigiannis PG. EEG Recordings as Biomarkers of Pain Perception: Where Do We Stand and Where to Go? Pain Ther 2022; 11:369-380. [PMID: 35322392 PMCID: PMC9098726 DOI: 10.1007/s40122-022-00372-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 03/07/2022] [Indexed: 11/25/2022] Open
Abstract
Introduction The universality and complexity of pain, which is highly prevalent, yield its significance to both patients and researchers. Developing a non-invasive tool that can objectively measure pain is of the utmost importance for clinical and research purposes. Traditionally electroencephalography (EEG) has been mostly used in epilepsy; however, over the recent years EEG has become an important non-invasive clinical tool that has helped increase our understanding of brain network complexities and for the identification of areas of dysfunction. This review aimed to investigate the role of EEG recordings as potential biomarkers of pain perception. Methods A systematic search of the PubMed database led to the identification of 938 papers, of which 919 were excluded as a result of not meeting the eligibility criteria, and one article was identified through screening of the reference lists of the 19 eligible studies. Ultimately, 20 papers were included in this systematic review. Results Changes of the cortical activation have potential, though the described changes are not always consistent. The most consistent finding is the increase in the delta and gamma power activity. Only a limited number of studies have looked into brain networks encoding pain perception. Conclusion Although no robust EEG biomarkers of pain perception have been identified yet, EEG has potential and future research should be attempted. Designing strong research protocols, controlling for potential risk of biases, as well as investigating brain networks rather than isolated cortical changes will be crucial in this attempt. Supplementary Information The online version contains supplementary material available at 10.1007/s40122-022-00372-2.
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Affiliation(s)
- Panagiotis Zis
- Medical School, University of Cyprus, Nicosia, Cyprus
- Medical School, University of Sheffield, Sheffield, UK
- Department of Neurology, Nicosia General Hospital, Nicosia, Cyprus
| | - Andreas Liampas
- Department of Neurology, Nicosia General Hospital, Nicosia, Cyprus
| | - Artemios Artemiadis
- Medical School, University of Cyprus, Nicosia, Cyprus
- Department of Neurology, Nicosia General Hospital, Nicosia, Cyprus
| | | | | | - Zoe Unwin
- Department of Clinical Neurophysiology, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Vasilios K. Kimiskidis
- 1st Department of Neurology, AHEPA University Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Georgios M. Hadjigeorgiou
- Medical School, University of Cyprus, Nicosia, Cyprus
- Department of Neurology, Nicosia General Hospital, Nicosia, Cyprus
| | | | - Yifan Zhao
- School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, UK
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560
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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: 16] [Impact Index Per Article: 8.0] [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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561
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Schaworonkow N, Nikulin VV. Is sensor space analysis good enough? Spatial patterns as a tool for assessing spatial mixing of EEG/MEG rhythms. Neuroimage 2022; 253:119093. [PMID: 35288283 DOI: 10.1016/j.neuroimage.2022.119093] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 03/07/2022] [Accepted: 03/10/2022] [Indexed: 12/25/2022] Open
Abstract
Analyzing non-invasive recordings of electroencephalography (EEG) and magnetoencephalography (MEG) directly in sensor space, using the signal from individual sensors, is a convenient and standard way of working with this type of data. However, volume conduction introduces considerable challenges for sensor space analysis. While the general idea of signal mixing due to volume conduction in EEG/MEG is recognized, the implications have not yet been clearly exemplified. Here, we illustrate how different types of activity overlap on the level of individual sensors. We show spatial mixing in the context of alpha rhythms, which are known to have generators in different areas of the brain. Using simulations with a realistic 3D head model and lead field and data analysis of a large resting-state EEG dataset, we show that electrode signals can be differentially affected by spatial mixing by computing a sensor complexity measure. While prominent occipital alpha rhythms result in less heterogeneous spatial mixing on posterior electrodes, central electrodes show a diversity of rhythms present. This makes the individual contributions, such as the sensorimotor mu-rhythm and temporal alpha rhythms, hard to disentangle from the dominant occipital alpha. Additionally, we show how strong occipital rhythms can contribute the majority of activity to frontal channels, potentially compromising analyses that are solely conducted in sensor space. We also outline specific consequences of signal mixing for frequently used assessment of power, power ratios and connectivity profiles in basic research and for neurofeedback application. With this work, we hope to illustrate the effects of volume conduction in a concrete way, such that the provided practical illustrations may be of use to EEG researchers to in order to evaluate whether sensor space is an appropriate choice for their topic of investigation.
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Affiliation(s)
- Natalie Schaworonkow
- Ernst Strüngmann Institute for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main 60528, Germany.
| | - Vadim V Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany
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562
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Spencer ER, Chinappen D, Emerton BC, Morgan AK, Hämäläinen MS, Manoach DS, Eden UT, Kramer MA, Chu CJ. Source EEG reveals that Rolandic epilepsy is a regional epileptic encephalopathy. Neuroimage Clin 2022; 33:102956. [PMID: 35151039 PMCID: PMC8844714 DOI: 10.1016/j.nicl.2022.102956] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 01/10/2022] [Accepted: 02/03/2022] [Indexed: 01/15/2023]
Abstract
Children with RE have fewer spindles but they have typical time–frequency features. Spindle deficits extend to multiple cortical regions in Rolandic epilepsy. Cognitive deficits are predicted by spindle rate in Rolandic epilepsy. Regional spindle rate predicts motor deficits better than Rolandic spindle deficit. Spindle features in RE identify a regional thalamocortical epileptic encephalopathy.
Rolandic epilepsy is the most common form of epileptic encephalopathy, characterized by sleep-potentiated inferior Rolandic epileptiform spikes, seizures, and cognitive deficits in school-age children that spontaneously resolve by adolescence. We recently identified a paucity of sleep spindles, physiological thalamocortical rhythms associated with sleep-dependent learning, in the Rolandic cortex during the active phase of this disease. Because spindles are generated in the thalamus and amplified through regional thalamocortical circuits, we hypothesized that: 1) deficits in spindle rate would involve but extend beyond the inferior Rolandic cortex in active epilepsy and 2) regional spindle deficits would better predict cognitive function than inferior Rolandic spindle deficits alone. To test these hypotheses, we obtained high-resolution MRI, high-density EEG recordings, and focused neuropsychological assessments in children with Rolandic epilepsy during active (n = 8, age 9–14.7 years, 3F) and resolved (seizure free for > 1 year, n = 10, age 10.3–16.7 years, 1F) stages of disease and age-matched controls (n = 8, age 8.9–14.5 years, 5F). Using a validated spindle detector applied to estimates of electrical source activity in 31 cortical regions, including the inferior Rolandic cortex, during stages 2 and 3 of non-rapid eye movement sleep, we compared spindle rates in each cortical region across groups. Among detected spindles, we compared spindle features (power, duration, coherence, bilateral synchrony) between groups. We then used regression models to examine the relationship between spindle rate and cognitive function (fine motor dexterity, phonological processing, attention, and intelligence, and a global measure of all functions). We found that spindle rate was reduced in the inferior Rolandic cortices in active but not resolved disease (active P = 0.007; resolved P = 0.2) compared to controls. Spindles in this region were less synchronous between hemispheres in the active group (P = 0.005; resolved P = 0.1) compared to controls; but there were no differences in spindle power, duration, or coherence between groups. Compared to controls, spindle rate in the active group was also reduced in the prefrontal, insular, superior temporal, and posterior parietal regions (i.e., “regional spindle rate”, P < 0.039 for all). Independent of group, regional spindle rate positively correlated with fine motor dexterity (P < 1e-3), attention (P = 0.02), intelligence (P = 0.04), and global cognitive performance (P < 1e-4). Compared to the inferior Rolandic spindle rate alone, models including regional spindle rate trended to improve prediction of global cognitive performance (P = 0.052), and markedly improved prediction of fine motor dexterity (P = 0.006). These results identify a spindle disruption in Rolandic epilepsy that extends beyond the epileptic cortex and a potential mechanistic explanation for the broad cognitive deficits that can be observed in this epileptic encephalopathy.
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Affiliation(s)
- Elizabeth R Spencer
- Graduate Program in Neuroscience, Boston University, Boston, MA 02215; Department of Neurology, Massachusetts General Hospital, Boston, MA 02114
| | - Dhinakaran Chinappen
- Graduate Program in Neuroscience, Boston University, Boston, MA 02215; Department of Neurology, Massachusetts General Hospital, Boston, MA 02114
| | - Britt C Emerton
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114
| | - Amy K Morgan
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114
| | - Matti S Hämäläinen
- Harvard Medical School, Boston, MA 02115; Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129; Massachusetts General Hospital, Department of Radiology, Boston, MA 02114
| | - Dara S Manoach
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114; Harvard Medical School, Boston, MA 02115; Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129
| | - Uri T Eden
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215; Center for Systems Neuroscience, Boston University, Boston, MA 02215
| | - Mark A Kramer
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215; Center for Systems Neuroscience, Boston University, Boston, MA 02215
| | - Catherine J Chu
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114; Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114.
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563
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Ibarra-Lecue I, Haegens S, Harris AZ. Breaking Down a Rhythm: Dissecting the Mechanisms Underlying Task-Related Neural Oscillations. Front Neural Circuits 2022; 16:846905. [PMID: 35310550 PMCID: PMC8931663 DOI: 10.3389/fncir.2022.846905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 02/10/2022] [Indexed: 11/13/2022] Open
Abstract
A century worth of research has linked multiple cognitive, perceptual and behavioral states to various brain oscillations. However, the mechanistic roles and circuit underpinnings of these oscillations remain an area of active study. In this review, we argue that the advent of optogenetic and related systems neuroscience techniques has shifted the field from correlational to causal observations regarding the role of oscillations in brain function. As a result, studying brain rhythms associated with behavior can provide insight at different levels, such as decoding task-relevant information, mapping relevant circuits or determining key proteins involved in rhythmicity. We summarize recent advances in this field, highlighting the methods that are being used for this purpose, and discussing their relative strengths and limitations. We conclude with promising future approaches that will help unravel the functional role of brain rhythms in orchestrating the repertoire of complex behavior.
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Affiliation(s)
- Inés Ibarra-Lecue
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY, United States
- New York State Psychiatric Institute, New York, NY, United States
| | - Saskia Haegens
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY, United States
- New York State Psychiatric Institute, New York, NY, United States
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Alexander Z. Harris
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY, United States
- New York State Psychiatric Institute, New York, NY, United States
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564
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Espinoso A, Andrzejak RG. Phase irregularity: A conceptually simple and efficient approach to characterize electroencephalographic recordings from epilepsy patients. Phys Rev E 2022; 105:034212. [PMID: 35428047 DOI: 10.1103/physreve.105.034212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 03/06/2022] [Indexed: 06/14/2023]
Abstract
The severe neurological disorder epilepsy affects almost 1% of the world population. For patients who suffer from pharmacoresistant focal-onset epilepsy, electroencephalographic (EEG) recordings are essential for the localization of the brain area where seizures start. Apart from the visual inspection of the recordings, quantitative EEG signal analysis techniques proved to be useful for this purpose. Among other features, regularity versus irregularity and phase coherence versus phase independence allowed characterizing brain dynamics from the measured EEG signals. Can phase irregularities also characterize brain dynamics? To address this question, we use the univariate coefficient of phase velocity variation, defined as the ratio of phase velocity standard deviation and the mean phase velocity. Beyond that, as a bivariate measure we use the classical mean phase coherence to quantify the degree of phase locking. All phase-based measures are combined with surrogates to test null hypotheses about the dynamics underlying the signals. In the first part of our analysis, we use the Rössler model system to study our approach under controlled conditions. In the second part, we use the Bern-Barcelona EEG database which consists of focal and nonfocal signals extracted from seizure-free recordings. Focal signals are recorded from brain areas where the first seizure EEG signal changes can be detected, and nonfocal signals are recorded from areas that are not involved in the seizure at its onset. Our results show that focal signals have less phase variability and more phase coherence than nonfocal signals. Once combined with surrogates, the mean phase velocity proved to have the highest discriminative power between focal and nonfocal signals. In conclusion, conceptually simple and easy to compute phase-based measures can help to detect features induced by epilepsy from EEG signals. This holds not only for the classical mean phase coherence but even more so for univariate measures of phase irregularity.
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Affiliation(s)
- Anaïs Espinoso
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer Roc Boronat 138, 08018 Barcelona, Catalonia, Spain and Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Carrer Baldiri Reixac 10-12, 08028 Barcelona, Catalonia, Spain
| | - Ralph G Andrzejak
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer Roc Boronat 138, 08018 Barcelona, Catalonia, Spain
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565
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Eymann V, Beck AK, Jaarsveld S, Lachmann T, Czernochowski D. Alpha oscillatory evidence for shared underlying mechanisms of creativity and fluid intelligence above and beyond working memory-related activity. INTELLIGENCE 2022. [DOI: 10.1016/j.intell.2022.101630] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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566
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Frohlich F, Riddle J. Transcranial alternating current stimulation (tACS) as a treatment for fibromyalgia syndrome? Eur Arch Psychiatry Clin Neurosci 2022; 272:349-350. [PMID: 33715022 PMCID: PMC9332915 DOI: 10.1007/s00406-021-01253-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 03/03/2021] [Indexed: 11/25/2022]
Affiliation(s)
- Flavio Frohlich
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. .,Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. .,Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. .,Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. .,Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. .,Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Justin Riddle
- Department of Psychiatry, University of North Carolina at
Chapel Hill, Chapel Hill, NC, USA,Carolina Center for Neurostimulation, University of North
Carolina at Chapel Hill, Chapel Hill, NC, USA
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567
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Zeraati R, Engel TA, Levina A. A flexible Bayesian framework for unbiased estimation of timescales. NATURE COMPUTATIONAL SCIENCE 2022; 2:193-204. [PMID: 36644291 PMCID: PMC9835171 DOI: 10.1038/s43588-022-00214-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 02/15/2022] [Indexed: 01/19/2023]
Abstract
Timescales characterize the pace of change for many dynamic processes in nature. Timescales are usually estimated by fitting the exponential decay of data autocorrelation in the time or frequency domain. Here we show that this standard procedure often fails to recover the correct timescales due to a statistical bias arising from the finite sample size. We develop an alternative approach which estimates timescales by fitting the sample autocorrelation or power spectrum with a generative model based on a mixture of Ornstein-Uhlenbeck (OU) processes using adaptive approximate Bayesian computations (aABC). Our method accounts for finite sample size and noise in data and returns a posterior distribution of timescales that quantifies the estimation uncertainty and can be used for model selection. We demonstrate the accuracy of our method on synthetic data and illustrate its application to recordings from primate cortex. We provide a customizable Python package implementing our framework with different generative models suitable for diverse applications.
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Affiliation(s)
- Roxana Zeraati
- International Max Planck Research School for the Mechanisms of Mental Function and Dysfunction, University of Tübingen, Germany
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | | | - Anna Levina
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Germany
- Bernstein Center for Computational Neuroscience Tübingen, Tübingen, Germany
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568
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EEG spectral exponent as a synthetic index for the longitudinal assessment of stroke recovery. Clin Neurophysiol 2022; 137:92-101. [PMID: 35303540 PMCID: PMC9038588 DOI: 10.1016/j.clinph.2022.02.022] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 01/02/2022] [Accepted: 02/22/2022] [Indexed: 12/20/2022]
Abstract
The Spectral Exponent (SE) indexes power-law features of the resting EEG in stroke patients. SE is consistently steeper in the affected hemisphere of patients after middle cerebral artery stroke. SE is linked to clinical status and seems to be a good predictor of clinical outcome.
Objective Quantitative Electroencephalography (qEEG) can capture changes in brain activity following stroke. qEEG metrics traditionally focus on oscillatory activity, however recent findings highlight the importance of aperiodic (power-law) structure in characterizing pathological brain states. We assessed neurophysiological alterations and recovery after mono-hemispheric stroke by means of the Spectral Exponent (SE), a metric that reflects EEG slowing and quantifies the power-law decay of the EEG Power Spectral Density (PSD). Methods Eighteen patients (n = 18) with mild to moderate mono-hemispheric Middle Cerebral Artery (MCA) ischaemic stroke were retrospectively enrolled for this study. Patients underwent EEG recording in the sub-acute phase (T0) and after 2 months of physical rehabilitation (T1). Sixteen healthy controls (HC; n = 16) matched by age and sex were enrolled as a normative group. SE values and narrow-band PSD were estimated for each recording. We compared SE and band-power between patients and HC, and between the affected (AH) and unaffected hemisphere (UH) at T0 and T1 in patients. Results At T0, stroke patients showed significantly more negative SE values than HC (p = 0.003), reflecting broad-band EEG slowing. Most important, in patients SE over the AH was consistently more negative compared to the UH and showed a renormalization at T1. This SE renormalization significantly correlated with National Institute of Health Stroke Scale (NIHSS) improvement (R = 0.63, p = 0.005). Conclusions SE is a reliable readout of the neurophysiological and clinical alterations occurring after an ischaemic cortical lesion. Significance SE promise to be a robust method to monitor and predict patients’ functional outcome.
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569
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Fucci E, Poublan-Couzardot A, Abdoun O, Lutz A. No effect of focused attention and open monitoring meditation on EEG auditory mismatch negativity in expert and novice practitioners. Int J Psychophysiol 2022; 176:62-72. [DOI: 10.1016/j.ijpsycho.2022.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 03/11/2022] [Accepted: 03/23/2022] [Indexed: 10/18/2022]
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570
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Sonkusare S, Ding Q, Zhang Y, Wang L, Gong H, Mandali A, Manssuer L, Zhao YJ, Pan Y, Zhang C, Li D, Sun B, Voon V. Power signatures of habenular neuronal signals in patients with bipolar or unipolar depressive disorders correlate with their disease severity. Transl Psychiatry 2022; 12:72. [PMID: 35194027 PMCID: PMC8863838 DOI: 10.1038/s41398-022-01830-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 01/17/2022] [Accepted: 01/28/2022] [Indexed: 01/03/2023] Open
Abstract
The habenula is an epithalamic structure implicated in negative reward mechanisms and plays a downstream modulatory role in regulation of dopaminergic and serotonergic functions. Human and animal studies show its hyperactivity in depression which is curtailed by the antidepressant response of ketamine. Deep brain stimulation of habenula (DBS) for major depression have also shown promising results. However, direct neuronal activity of habenula in human studies have rarely been reported. Here, in a cross-sectional design, we acquired both spontaneous resting state and emotional task-induced neuronal recordings from habenula from treatment resistant depressed patients undergoing DBS surgery. We first characterise the aperiodic component (1/f slope) of the power spectrum, interpreted to signify excitation-inhibition balance, in resting and task state. This aperiodicity for left habenula correlated between rest and task and which was significantly positively correlated with depression severity. Time-frequency responses to the emotional picture viewing task show condition differences in beta and gamma frequencies for left habenula and alpha for right habenula. Notably, alpha activity for right habenula was negatively correlated with depression severity. Overall, from direct habenular recordings, we thus show findings convergent with depression models of aberrant excitatory glutamatergic output of the habenula driving inhibition of monoaminergic systems.
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Affiliation(s)
- Saurabh Sonkusare
- grid.5335.00000000121885934Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom ,grid.16821.3c0000 0004 0368 8293Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China ,grid.8547.e0000 0001 0125 2443Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Qiong Ding
- grid.16821.3c0000 0004 0368 8293Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yingying Zhang
- grid.16821.3c0000 0004 0368 8293Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Linbin Wang
- grid.16821.3c0000 0004 0368 8293Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hengfen Gong
- grid.24516.340000000123704535Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai, China
| | - Alekhya Mandali
- grid.5335.00000000121885934Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Luis Manssuer
- grid.5335.00000000121885934Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom ,grid.16821.3c0000 0004 0368 8293Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China ,grid.8547.e0000 0001 0125 2443Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Yi-Jie Zhao
- grid.8547.e0000 0001 0125 2443Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China ,grid.8547.e0000 0001 0125 2443Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Yixin Pan
- grid.16821.3c0000 0004 0368 8293Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chencheng Zhang
- grid.16821.3c0000 0004 0368 8293Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dianyou Li
- grid.16821.3c0000 0004 0368 8293Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bomin Sun
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Valerie Voon
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom. .,Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. .,Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
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571
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Parker JL, Appleton SL, Melaku YA, D'Rozario AL, Wittert GA, Martin SA, Toson B, Catcheside PG, Lechat B, Teare AJ, Adams RJ, Vakulin A. The association between sleep microarchitecture and cognitive function in middle-aged and older men: a community-based cohort study. J Clin Sleep Med 2022; 18:1593-1608. [PMID: 35171095 DOI: 10.5664/jcsm.9934] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
STUDY OBJECTIVES Sleep microarchitecture parameters determined by quantitative power spectral analysis (PSA) of electroencephalograms (EEGs) have been proposed as potential brain-specific markers of cognitive dysfunction. However, data from community samples remains limited. This study examined cross-sectional associations between sleep microarchitecture and cognitive dysfunction in community-dwelling men. METHODS Florey Adelaide Male Ageing Study participants (n=477) underwent home-based polysomnography (PSG) (2010-2011). All-night EEG recordings were processed using PSA following artefact exclusion. Cognitive testing (2007-2010) included the inspection time task, trail-making tests A (TMT-A) and B (TMT-B), and Fuld object memory evaluation. Complete case cognition, PSG, and covariate data were available in 366 men. Multivariable linear regression models controlling for demographic, biomedical, and behavioral confounders determined cross-sectional associations between sleep microarchitecture and cognitive dysfunction overall and by age-stratified subgroups. RESULTS In the overall sample, worse TMT-A performance was associated with higher NREM theta and REM theta and alpha but lower delta power (all p<0.05). In men ≥65 years, worse TMT-A performance was associated with lower NREM delta but higher NREM and REM theta and alpha power (all p<0.05). Furthermore, in men ≥65 years, worse TMT-B performance was associated with lower REM delta but higher theta and alpha power (all p<0.05). CONCLUSIONS Sleep microarchitecture parameters may represent important brain-specific markers of cognitive dysfunction, particularly in older community-dwelling men. Therefore, this study extends the emerging community-based cohort literature on a potentially important link between sleep microarchitecture and cognitive dysfunction. Utility of sleep microarchitecture for predicting prospective cognitive dysfunction and decline warrants further investigation.
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Affiliation(s)
- Jesse L Parker
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia, Australia
| | - Sarah L Appleton
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia, Australia.,South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| | - Yohannes Adama Melaku
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia, Australia
| | - Angela L D'Rozario
- CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, University of Sydney, Sydney, New South Wales, Australia.,The University of Sydney, Faculty of Science, School of Psychology, Sydney, New South Wales, Australia
| | - Gary A Wittert
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia.,Freemasons Centre for Male Health and Wellbeing, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
| | - Sean A Martin
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia.,Freemasons Centre for Male Health and Wellbeing, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
| | - Barbara Toson
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
| | - Peter G Catcheside
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia, Australia
| | - Bastien Lechat
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia, Australia
| | - Alison J Teare
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia, Australia
| | - Robert J Adams
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia, Australia.,South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia.,Respiratory and Sleep Services, Southern Adelaide Local Health Network, Bedford Park, Adelaide, South Australia, Australia
| | - Andrew Vakulin
- Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia, Australia.,CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, University of Sydney, Sydney, New South Wales, Australia
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572
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Toker D, Pappas I, Lendner JD, Frohlich J, Mateos DM, Muthukumaraswamy S, Carhart-Harris R, Paff M, Vespa PM, Monti MM, Sommer FT, Knight RT, D'Esposito M. Consciousness is supported by near-critical slow cortical electrodynamics. Proc Natl Acad Sci U S A 2022; 119:e2024455119. [PMID: 35145021 PMCID: PMC8851554 DOI: 10.1073/pnas.2024455119] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 12/20/2021] [Indexed: 12/21/2022] Open
Abstract
Mounting evidence suggests that during conscious states, the electrodynamics of the cortex are poised near a critical point or phase transition and that this near-critical behavior supports the vast flow of information through cortical networks during conscious states. Here, we empirically identify a mathematically specific critical point near which waking cortical oscillatory dynamics operate, which is known as the edge-of-chaos critical point, or the boundary between stability and chaos. We do so by applying the recently developed modified 0-1 chaos test to electrocorticography (ECoG) and magnetoencephalography (MEG) recordings from the cortices of humans and macaques across normal waking, generalized seizure, anesthesia, and psychedelic states. Our evidence suggests that cortical information processing is disrupted during unconscious states because of a transition of low-frequency cortical electric oscillations away from this critical point; conversely, we show that psychedelics may increase the information richness of cortical activity by tuning low-frequency cortical oscillations closer to this critical point. Finally, we analyze clinical electroencephalography (EEG) recordings from patients with disorders of consciousness (DOC) and show that assessing the proximity of slow cortical oscillatory electrodynamics to the edge-of-chaos critical point may be useful as an index of consciousness in the clinical setting.
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Affiliation(s)
- Daniel Toker
- Department of Psychology, University of California, Los Angeles, CA 90095;
| | - Ioannis Pappas
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94704
- Department of Psychology, University of California, Berkeley, CA 94704
- Laboratory of Neuro Imaging, Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Janna D Lendner
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94704
- Department of Anesthesiology and Intensive Care, University Medical Center, 72076 Tübingen, Germany
| | - Joel Frohlich
- Department of Psychology, University of California, Los Angeles, CA 90095
| | - Diego M Mateos
- Consejo Nacional de Investigaciones Científicas y Técnicas de Argentina, C1425 Buenos Aires, Argentina
- Facultad de Ciencia y Tecnología, Universidad Autónoma de Entre Ríos, E3202 Paraná, Entre Ríos, Argentina
- Grupo de Análisis de Neuroimágenes, Instituo de Matemática Aplicada del Litoral, S3000 Santa Fe, Argentina
| | - Suresh Muthukumaraswamy
- School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, 1010 Auckland, New Zealand
| | - Robin Carhart-Harris
- Neuropsychopharmacology Unit, Centre for Psychiatry, Imperial College London, London SW7 2AZ, United Kingdom
- Centre for Psychedelic Research, Department of Psychiatry, Imperial College London, London SW7 2AZ, United Kingdom
| | - Michelle Paff
- Department of Neurological Surgery, University of California, Irvine, CA 92697
| | - Paul M Vespa
- Brain Injury Research Center, Department of Neurosurgery, University of California, Los Angeles, CA 90095
| | - Martin M Monti
- Department of Psychology, University of California, Los Angeles, CA 90095
- Brain Injury Research Center, Department of Neurosurgery, University of California, Los Angeles, CA 90095
| | - Friedrich T Sommer
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94704
- Redwood Center for Theoretical Neuroscience, University of California, Berkeley, CA 94704
| | - Robert T Knight
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94704
- Department of Psychology, University of California, Berkeley, CA 94704
| | - Mark D'Esposito
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94704
- Department of Psychology, University of California, Berkeley, CA 94704
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573
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Krishnakumaran R, Raees M, Ray S. Shape analysis of gamma rhythm supports a superlinear inhibitory regime in an inhibition-stabilized network. PLoS Comput Biol 2022; 18:e1009886. [PMID: 35157699 PMCID: PMC8880865 DOI: 10.1371/journal.pcbi.1009886] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 02/25/2022] [Accepted: 01/31/2022] [Indexed: 12/02/2022] Open
Abstract
Visual inspection of stimulus-induced gamma oscillations (30–70 Hz) often reveals a non-sinusoidal shape. Such distortions are a hallmark of non-linear systems and are also observed in mean-field models of gamma oscillations. A thorough characterization of the shape of the gamma cycle can therefore provide additional constraints on the operating regime of such models. However, the gamma waveform has not been quantitatively characterized, partially because the first harmonic of gamma, which arises because of the non-sinusoidal nature of the signal, is typically weak and gets masked due to a broadband increase in power related to spiking. To address this, we recorded local field potential (LFP) from the primary visual cortex (V1) of two awake female macaques while presenting full-field gratings or iso-luminant chromatic hues that produced huge gamma oscillations with prominent peaks at harmonic frequencies in the power spectra. We found that gamma and its first harmonic always maintained a specific phase relationship, resulting in a distinctive shape with a sharp trough and a shallow peak. Interestingly, a Wilson-Cowan (WC) model operating in an inhibition stabilized mode could replicate this shape, but only when the inhibitory population operated in the super-linear regime, as predicted recently. However, another recently developed model of gamma that operates in a linear regime driven by stochastic noise failed to produce salient harmonics or the observed shape. Our results impose additional constraints on models that generate gamma oscillations and their operating regimes. Gamma rhythm is not sinusoidal. Understanding these distortions could provide clues about the cortical network that generates the rhythm. Here, we use harmonic phase analysis to describe these waveforms quantitatively and show that gamma rhythm recorded from the primary visual cortex of macaques has a signature arch shaped waveform, with a sharp trough and a shallow peak, when visual stimuli such as full-screen plain hues and achromatic gratings are presented. This arch shaped waveform is observed over a wide range of stimuli, despite the variation in power and frequency of the rhythm. We then compare two population rate models that have been used to accurately describe the stimulus dependencies of gamma rhythm and show that this arch shaped waveform is obtained only in one of those models. Further, the waveform shape is dependent on the operating domain of the system. Therefore, shape analysis provides additional constraints on cortical models and their operating regimes.
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Affiliation(s)
- R Krishnakumaran
- IISc Mathematics Initiative, Department of Mathematics, Indian Institute of Science, Bangalore, India
| | - Mohammed Raees
- Centre for Neuroscience, Indian Institute of Science, Bangalore, India
| | - Supratim Ray
- IISc Mathematics Initiative, Department of Mathematics, Indian Institute of Science, Bangalore, India
- Centre for Neuroscience, Indian Institute of Science, Bangalore, India
- * E-mail:
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574
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Johnson EL, Yin Q, O'Hara NB, Tang L, Jeong JW, Asano E, Ofen N. Dissociable oscillatory theta signatures of memory formation in the developing brain. Curr Biol 2022; 32:1457-1469.e4. [PMID: 35172128 PMCID: PMC9007830 DOI: 10.1016/j.cub.2022.01.053] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 12/15/2021] [Accepted: 01/19/2022] [Indexed: 11/16/2022]
Abstract
Understanding complex human brain functions is critically informed by studying such functions during development. Here, we addressed a major gap in models of human memory by leveraging rare direct electrophysiological recordings from children and adolescents. Specifically, memory relies on interactions between the medial temporal lobe (MTL) and prefrontal cortex (PFC), and the maturation of these interactions is posited to play a key role in supporting memory development. To understand the nature of MTL-PFC interactions, we examined subdural recordings from MTL and PFC in 21 neurosurgical patients aged 5.9-20.5 years as they performed an established scene memory task. We determined signatures of memory formation by comparing the study of subsequently recognized to forgotten scenes in single trials. Results establish that MTL and PFC interact via two distinct theta mechanisms, an ∼3-Hz oscillation that supports amplitude coupling and slows down with age and an ∼7-Hz oscillation that supports phase coupling and speeds up with age. Slow and fast theta interactions immediately preceding scene onset further explained age-related differences in recognition performance. Last, with additional diffusion imaging data, we linked both functional mechanisms to the structural maturation of the cingulum tract. Our findings establish system-level dynamics of memory formation and suggest that MTL and PFC interact via increasingly dissociable mechanisms as memory improves across development.
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Affiliation(s)
- Elizabeth L Johnson
- Life-Span Cognitive Neuroscience Program, Institute of Gerontology and Merrill Palmer Skillman Institute, Wayne State University, Detroit, MI 48202, USA; Departments of Medical Social Sciences and Pediatrics, Northwestern University, Chicago, IL 60611, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA.
| | - Qin Yin
- Life-Span Cognitive Neuroscience Program, Institute of Gerontology and Merrill Palmer Skillman Institute, Wayne State University, Detroit, MI 48202, USA; Department of Psychology, Wayne State University, Detroit, MI 48202, USA
| | - Nolan B O'Hara
- Translational Neuroscience Program, Wayne State University, Detroit, MI 48201, USA
| | - Lingfei Tang
- Life-Span Cognitive Neuroscience Program, Institute of Gerontology and Merrill Palmer Skillman Institute, Wayne State University, Detroit, MI 48202, USA; Department of Psychology, Wayne State University, Detroit, MI 48202, USA
| | - Jeong-Won Jeong
- Translational Neuroscience Program, Wayne State University, Detroit, MI 48201, USA; Departments of Pediatrics and Neurology, Children's Hospital of Michigan, Wayne State University, Detroit, MI 48201, USA
| | - Eishi Asano
- Translational Neuroscience Program, Wayne State University, Detroit, MI 48201, USA; Departments of Pediatrics and Neurology, Children's Hospital of Michigan, Wayne State University, Detroit, MI 48201, USA
| | - Noa Ofen
- Life-Span Cognitive Neuroscience Program, Institute of Gerontology and Merrill Palmer Skillman Institute, Wayne State University, Detroit, MI 48202, USA; Department of Psychology, Wayne State University, Detroit, MI 48202, USA; Translational Neuroscience Program, Wayne State University, Detroit, MI 48201, USA.
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575
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Pfeffer T, Keitel C, Kluger DS, Keitel A, Russmann A, Thut G, Donner TH, Gross J. Coupling of pupil- and neuronal population dynamics reveals diverse influences of arousal on cortical processing. eLife 2022; 11:e71890. [PMID: 35133276 PMCID: PMC8853659 DOI: 10.7554/elife.71890] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 02/04/2022] [Indexed: 11/13/2022] Open
Abstract
Fluctuations in arousal, controlled by subcortical neuromodulatory systems, continuously shape cortical state, with profound consequences for information processing. Yet, how arousal signals influence cortical population activity in detail has so far only been characterized for a few selected brain regions. Traditional accounts conceptualize arousal as a homogeneous modulator of neural population activity across the cerebral cortex. Recent insights, however, point to a higher specificity of arousal effects on different components of neural activity and across cortical regions. Here, we provide a comprehensive account of the relationships between fluctuations in arousal and neuronal population activity across the human brain. Exploiting the established link between pupil size and central arousal systems, we performed concurrent magnetoencephalographic (MEG) and pupillographic recordings in a large number of participants, pooled across three laboratories. We found a cascade of effects relative to the peak timing of spontaneous pupil dilations: Decreases in low-frequency (2-8 Hz) activity in temporal and lateral frontal cortex, followed by increased high-frequency (>64 Hz) activity in mid-frontal regions, followed by monotonic and inverted U relationships with intermediate frequency-range activity (8-32 Hz) in occipito-parietal regions. Pupil-linked arousal also coincided with widespread changes in the structure of the aperiodic component of cortical population activity, indicative of changes in the excitation-inhibition balance in underlying microcircuits. Our results provide a novel basis for studying the arousal modulation of cognitive computations in cortical circuits.
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Affiliation(s)
- Thomas Pfeffer
- Universitat Pompeu Fabra, Center for Brain and Cognition, Computational Neuroscience GroupBarcelonaSpain
- University Medical Center Hamburg-Eppendorf, Department of Neurophysiology and PathophysiologyHamburgGermany
| | - Christian Keitel
- University of Stirling, PsychologyStirlingUnited Kingdom
- Centre for Cognitive Neuroimaging, Institute of Neuroscience and Psychology, University of GlasgowGlasgowUnited Kingdom
| | - Daniel S Kluger
- Institute for Biomagnetism and Biosignal Analysis, University of Münster, MalmedywegMuensterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of MünsterMuensterGermany
| | - Anne Keitel
- University of Dundee, PsychologyDundeeUnited Kingdom
| | - Alena Russmann
- University Medical Center Hamburg-Eppendorf, Department of Neurophysiology and PathophysiologyHamburgGermany
| | - Gregor Thut
- Centre for Cognitive Neuroimaging, Institute of Neuroscience and Psychology, University of GlasgowGlasgowUnited Kingdom
| | - Tobias H Donner
- University Medical Center Hamburg-Eppendorf, Department of Neurophysiology and PathophysiologyHamburgGermany
| | - Joachim Gross
- Centre for Cognitive Neuroimaging, Institute of Neuroscience and Psychology, University of GlasgowGlasgowUnited Kingdom
- Institute for Biomagnetism and Biosignal Analysis, University of Münster, MalmedywegMuensterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of MünsterMuensterGermany
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576
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Katerman BS, Li Y, Pazdera JK, Keane C, Kahana MJ. EEG biomarkers of free recall. Neuroimage 2022; 246:118748. [PMID: 34863960 PMCID: PMC9070361 DOI: 10.1016/j.neuroimage.2021.118748] [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: 07/07/2021] [Revised: 10/28/2021] [Accepted: 11/20/2021] [Indexed: 11/29/2022] Open
Abstract
Brain activity in the moments leading up to spontaneous verbal recall provide a window into the cognitive processes underlying memory retrieval. But these same recordings also subsume neural signals unrelated to mnemonic retrieval, such as response-related motor activity. Here we examined spectral EEG biomarkers of memory retrieval under an extreme manipulation of mnemonic demands: subjects either recalled items after a few seconds or after several days. This manipulation helped to isolate EEG components specifically related to long-term memory retrieval. In the moments immediately preceding recall we observed increased theta (4-8 Hz) power (+T), decreased alpha (8-20 Hz) power (-A), and increased gamma (40-128 Hz) power (+G), with this spectral pattern (+T-A + G) distinguishing the long-delay and immediate recall conditions. As subjects vocalized the same set of studied words in both conditions, we interpret the spectral +T-A + G as a biomarker of episodic memory retrieval.
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Affiliation(s)
| | - Y Li
- University of Pennsylvania, United States
| | | | - C Keane
- University of Pennsylvania, United States
| | - M J Kahana
- University of Pennsylvania, United States.
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577
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Brady B, Bardouille T. Periodic/Aperiodic Parameterization of Transient Oscillations (PAPTO): Implications for Healthy Ageing. Neuroimage 2022; 251:118974. [DOI: 10.1016/j.neuroimage.2022.118974] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 01/28/2022] [Accepted: 02/03/2022] [Indexed: 12/11/2022] Open
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578
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Karekal A, Miocinovic S, Swann NC. Novel approaches for quantifying beta synchrony in Parkinson's disease. Exp Brain Res 2022; 240:991-1004. [PMID: 35099592 DOI: 10.1007/s00221-022-06308-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 01/12/2022] [Indexed: 11/25/2022]
Abstract
Despite the clinical and financial burden of Parkinson's disease (PD), there is no standardized, reliable biomarker to diagnose and track PD progression. Instead, PD is primarily assessed using subjective clinical rating scales and patient self-report. Such approaches can be imprecise, hindering diagnosis and disease monitoring. An objective biomarker would be beneficial for clinical care, refining diagnosis, and treatment. Due to widespread electrophysiological abnormalities both within and between brain structures in PD, development of electrophysiologic biomarkers may be feasible. Basal ganglia recordings acquired with neurosurgical approaches have revealed elevated power in the beta frequency range (13-30 Hz) in PD, suggesting that beta power could be a putative PD biomarker. However, there are limitations to the use of beta power as a biomarker. Recent advances in analytic approaches have led to novel methods to quantify oscillatory synchrony in the beta frequency range. Here we describe some of these novel approaches in the context of PD and explore how they may serve as electrophysiological biomarkers. These novel signatures include (1) interactions between beta phase and broadband (> 50 Hz, "gamma") amplitude (i.e., phase amplitude coupling, PAC), (2) asymmetries in waveform shape, (3) beta coherence, and (4) beta "bursts." Development of a robust, reliable, and readily accessible electrophysiologic biomarker would represent a major step towards more precise and personalized care in PD.
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Affiliation(s)
- Apoorva Karekal
- Department of Human Physiology, University of Oregon, Eugene, OR, USA
| | | | - Nicole C Swann
- Department of Human Physiology, University of Oregon, Eugene, OR, USA.
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579
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Frohlich J, Crone JS, Johnson MA, Lutkenhoff ES, Spivak NM, Dell'Italia J, Hipp JF, Shrestha V, Ruiz Tejeda JE, Real C, Vespa PM, Monti MM. Neural oscillations track recovery of consciousness in acute traumatic brain injury patients. Hum Brain Mapp 2022; 43:1804-1820. [PMID: 35076993 PMCID: PMC8933330 DOI: 10.1002/hbm.25725] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 10/26/2021] [Accepted: 11/07/2021] [Indexed: 11/25/2022] Open
Abstract
Electroencephalography (EEG), easily deployed at the bedside, is an attractive modality for deriving quantitative biomarkers of prognosis and differential diagnosis in severe brain injury and disorders of consciousness (DOC). Prior work by Schiff has identified four dynamic regimes of progressive recovery of consciousness defined by the presence or absence of thalamically‐driven EEG oscillations. These four predefined categories (ABCD model) relate, on a theoretical level, to thalamocortical integrity and, on an empirical level, to behavioral outcome in patients with cardiac arrest coma etiologies. However, whether this theory‐based stratification of patients might be useful as a diagnostic biomarker in DOC and measurably linked to thalamocortical dysfunction remains unknown. In this work, we relate the reemergence of thalamically‐driven EEG oscillations to behavioral recovery from traumatic brain injury (TBI) in a cohort of N = 38 acute patients with moderate‐to‐severe TBI and an average of 1 week of EEG recorded per patient. We analyzed an average of 3.4 hr of EEG per patient, sampled to coincide with 30‐min periods of maximal behavioral arousal. Our work tests and supports the ABCD model, showing that it outperforms a data‐driven clustering approach and may perform equally well compared to a more parsimonious categorization. Additionally, in a subset of patients (N = 11), we correlated EEG findings with functional magnetic resonance imaging (fMRI) connectivity between nodes in the mesocircuit—which has been theoretically implicated by Schiff in DOC—and report a trend‐level relationship that warrants further investigation in larger studies.
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Affiliation(s)
- Joel Frohlich
- Department of Psychology University of California Los Angeles Los Angeles California USA
| | - Julia S. Crone
- Department of Psychology University of California Los Angeles Los Angeles California USA
- Vienna Cognitive Science Hub University of Vienna Vienna Austria
| | - Micah A. Johnson
- Department of Psychology University of California Los Angeles Los Angeles California USA
| | - Evan S. Lutkenhoff
- Department of Psychology University of California Los Angeles Los Angeles California USA
| | - Norman M. Spivak
- Department of Neurosurgery UCLA Brain Injury Research Center, David Geffen School of Medicine, University of California Los Angeles Los Angeles California USA
| | - John Dell'Italia
- Department of Psychology University of California Los Angeles Los Angeles California USA
| | - Joerg F. Hipp
- Roche Pharma Research and Early Development Roche Innovation Center Basel Basel Switzerland
| | - Vikesh Shrestha
- Department of Neurosurgery UCLA Brain Injury Research Center, David Geffen School of Medicine, University of California Los Angeles Los Angeles California USA
| | - Jesús E. Ruiz Tejeda
- Department of Neurosurgery UCLA Brain Injury Research Center, David Geffen School of Medicine, University of California Los Angeles Los Angeles California USA
| | - Courtney Real
- Department of Neurosurgery UCLA Brain Injury Research Center, David Geffen School of Medicine, University of California Los Angeles Los Angeles California USA
| | - Paul M. Vespa
- Department of Neurosurgery UCLA Brain Injury Research Center, David Geffen School of Medicine, University of California Los Angeles Los Angeles California USA
| | - Martin M. Monti
- Department of Psychology University of California Los Angeles Los Angeles California USA
- Department of Neurosurgery UCLA Brain Injury Research Center, David Geffen School of Medicine, University of California Los Angeles Los Angeles California USA
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580
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Cao Y, Oostenveld R, Alday PM, Piai V. Are alpha and beta oscillations spatially dissociated over the cortex in context-driven spoken-word production? Psychophysiology 2022; 59:e13999. [PMID: 35066874 PMCID: PMC9285923 DOI: 10.1111/psyp.13999] [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: 05/06/2021] [Revised: 10/19/2021] [Accepted: 12/06/2021] [Indexed: 11/28/2022]
Abstract
Decreases in oscillatory alpha‐ and beta‐band power have been consistently found in spoken‐word production. These have been linked to both motor preparation and conceptual‐lexical retrieval processes. However, the observed power decreases have a broad frequency range that spans two “classic” (sensorimotor) bands: alpha and beta. It remains unclear whether alpha‐ and beta‐band power decreases contribute independently when a spoken word is planned. Using a re‐analysis of existing magnetoencephalography data, we probed whether the effects in alpha and beta bands are spatially distinct. Participants read a sentence that was either constraining or non‐constraining toward the final word, which was presented as a picture. In separate blocks participants had to name the picture or score its predictability via button press. Irregular‐resampling auto‐spectral analysis (IRASA) was used to isolate the oscillatory activity in the alpha and beta bands from the background 1‐over‐f spectrum. The sources of alpha‐ and beta‐band oscillations were localized based on the participants’ individualized peak frequencies. For both tasks, alpha‐ and beta‐power decreases overlapped in left posterior temporal and inferior parietal cortex, regions that have previously been associated with conceptual and lexical processes. The spatial distributions of the alpha and beta power effects were spatially similar in these regions to the extent we could assess it. By contrast, for left frontal regions, the spatial distributions differed between alpha and beta effects. Our results suggest that for conceptual‐lexical retrieval, alpha and beta oscillations do not dissociate spatially and, thus, are distinct from the classical sensorimotor alpha and beta oscillations. It remains unclear whether the consistently found alpha‐ and beta‐band power decreases in spoken‐word production support a single operation or contribute independently. Using novel methodology, we probed whether the alpha and beta bands are distinct from an anatomical perspective. We found anatomical overlap in the left posterior temporal and inferior parietal cortex, whereas for the left frontal region, the spatial overlap was limited. Our results suggest that for conceptual‐lexical retrieval, alpha and beta oscillations do not dissociate and, thus, are distinct from the classical sensorimotor alpha and beta.
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Affiliation(s)
- Yang Cao
- Donders Centre for Cognition, Radboud University, Nijmegen, The Netherlands
| | - Robert Oostenveld
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands.,NatMEG, Karolinska Institutet, Stockholm, Sweden
| | - Phillip M Alday
- Max-Planck-Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Vitória Piai
- Donders Centre for Cognition, Radboud University, Nijmegen, The Netherlands.,Donders Centre for Medical Neuroscience, Department of Medical Psychology, Radboud University Medical Center, Nijmegen, The Netherlands
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581
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Samaha J, Cohen MX. Power spectrum slope confounds estimation of instantaneous oscillatory frequency. Neuroimage 2022; 250:118929. [PMID: 35077852 DOI: 10.1016/j.neuroimage.2022.118929] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 01/20/2022] [Accepted: 01/21/2022] [Indexed: 01/12/2023] Open
Abstract
Oscillatory neural dynamics are highly non-stationary and require methods capable of quantifying time-resolved changes in oscillatory activity in order to understand neural function. Recently, a method termed 'frequency sliding' was introduced to estimate the instantaneous frequency of oscillatory activity, providing a means of tracking temporal changes in the dominant frequency within a sub-band of field potential recordings. Here, the ability of frequency sliding to recover ground-truth oscillatory frequency in simulated data is tested while the exponent (slope) of the 1/fx component of the signal power spectrum is systematically varied, mimicking real electrophysiological data. The results show that 1) in the presence of 1/f activity, frequency sliding systematically underestimates the true frequency of the signal, 2) the magnitude of underestimation is correlated with the steepness of the slope, suggesting that, if unaccounted for, slope changes could be misinterpreted as frequency changes, 3) the impact of slope on frequency estimates interacts with oscillation amplitude, indicating that changes in oscillation amplitude alone may also influence instantaneous frequency estimates in the presence of strong 1/f activity; and 4) analysis parameters such as filter bandwidth and location also mediate the influence of slope on estimated frequency, indicating that these settings should be considered when interpreting estimates obtained via frequency sliding. The origin of these biases resides in the output of the filtering step of frequency sliding, whose energy is biased towards lower frequencies precisely because of the 1/f structure of the data. We discuss several strategies to mitigate these biases and provide a proof-of-principle for a 1/f normalization strategy.
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Affiliation(s)
- Jason Samaha
- Psychology Department, University of California, Santa Cruz.
| | - Michael X Cohen
- Donders Centre for Medical Neuroscience, Radboud University Medical Centre
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582
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Zsido RG, Molloy EN, Cesnaite E, Zheleva G, Beinhölzl N, Scharrer U, Piecha FA, Regenthal R, Villringer A, Nikulin VV, Sacher J. One‐week escitalopram intake alters the excitation–inhibition balance in the healthy female brain. Hum Brain Mapp 2022; 43:1868-1881. [PMID: 35064716 PMCID: PMC8933318 DOI: 10.1002/hbm.25760] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 12/08/2021] [Accepted: 12/09/2021] [Indexed: 11/11/2022] Open
Affiliation(s)
- Rachel G. Zsido
- Emotion Neuroimaging Lab Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
- International Max Planck Research School NeuroCom Leipzig Germany
- Max Planck School of Cognition Leipzig Germany
- Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
| | - Eóin N. Molloy
- Emotion Neuroimaging Lab Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
- International Max Planck Research School NeuroCom Leipzig Germany
- Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
- University Clinic for Radiology and Nuclear Medicine, Otto von Guericke University Magdeburg Magdeburg Germany
| | - Elena Cesnaite
- Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
| | - Gergana Zheleva
- Emotion Neuroimaging Lab Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
- Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
| | - Nathalie Beinhölzl
- Emotion Neuroimaging Lab Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
- Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
| | - Ulrike Scharrer
- Emotion Neuroimaging Lab Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
- Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
- Clinic for Cognitive Neurology Leipzig University Leipzig Germany
| | - Fabian A. Piecha
- Emotion Neuroimaging Lab Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
- Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
| | - Ralf Regenthal
- Division of Clinical Pharmacology, Rudolf Boehm Institute of Pharmacology and Toxicology Leipzig University Leipzig Germany
| | - Arno Villringer
- International Max Planck Research School NeuroCom Leipzig Germany
- Max Planck School of Cognition Leipzig Germany
- Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
- Clinic for Cognitive Neurology Leipzig University Leipzig Germany
- Berlin School of Mind and Brain Berlin Germany
| | - Vadim V. Nikulin
- International Max Planck Research School NeuroCom Leipzig Germany
- Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
| | - Julia Sacher
- Emotion Neuroimaging Lab Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
- International Max Planck Research School NeuroCom Leipzig Germany
- Max Planck School of Cognition Leipzig Germany
- Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
- Clinic for Cognitive Neurology Leipzig University Leipzig Germany
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583
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Houtman SJ, Lammertse HCA, van Berkel AA, Balagura G, Gardella E, Ramautar JR, Reale C, Møller RS, Zara F, Striano P, Misra-Isrie M, van Haelst MM, Engelen M, van Zuijen TL, Mansvelder HD, Verhage M, Bruining H, Linkenkaer-Hansen K. STXBP1 Syndrome Is Characterized by Inhibition-Dominated Dynamics of Resting-State EEG. Front Physiol 2022; 12:775172. [PMID: 35002760 PMCID: PMC8733612 DOI: 10.3389/fphys.2021.775172] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 11/23/2021] [Indexed: 11/19/2022] Open
Abstract
STXBP1 syndrome is a rare neurodevelopmental disorder caused by heterozygous variants in the STXBP1 gene and is characterized by psychomotor delay, early-onset developmental delay, and epileptic encephalopathy. Pathogenic STXBP1 variants are thought to alter excitation-inhibition (E/I) balance at the synaptic level, which could impact neuronal network dynamics; however, this has not been investigated yet. Here, we present the first EEG study of patients with STXBP1 syndrome to quantify the impact of the synaptic E/I dysregulation on ongoing brain activity. We used high-frequency-resolution analyses of classical and recently developed methods known to be sensitive to E/I balance. EEG was recorded during eyes-open rest in children with STXBP1 syndrome (n = 14) and age-matched typically developing children (n = 50). Brain-wide abnormalities were observed in each of the four resting-state measures assessed here: (i) slowing of activity and increased low-frequency power in the range 1.75–4.63 Hz, (ii) increased long-range temporal correlations in the 11–18 Hz range, (iii) a decrease of our recently introduced measure of functional E/I ratio in a similar frequency range (12–24 Hz), and (iv) a larger exponent of the 1/f-like aperiodic component of the power spectrum. Overall, these findings indicate that large-scale brain activity in STXBP1 syndrome exhibits inhibition-dominated dynamics, which may be compensatory to counteract local circuitry imbalances expected to shift E/I balance toward excitation, as observed in preclinical models. We argue that quantitative EEG investigations in STXBP1 and other neurodevelopmental disorders are a crucial step to understand large-scale functional consequences of synaptic E/I perturbations.
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Affiliation(s)
- Simon J Houtman
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
| | - Hanna C A Lammertse
- Department of Functional Genomics, Center for Neurogenomics and Cognitive Research, (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands.,Department of Human Genetics, Amsterdam UMC, Amsterdam, Netherlands
| | - Annemiek A van Berkel
- Department of Functional Genomics, Center for Neurogenomics and Cognitive Research, (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands.,Department of Human Genetics, Amsterdam UMC, Amsterdam, Netherlands
| | - Ganna Balagura
- Department of Functional Genomics, Center for Neurogenomics and Cognitive Research, (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands.,IRCCS Istituto Giannina Gaslini, Genova, Italy.,Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy
| | - Elena Gardella
- Department of Epilepsy Genetics and Personalized Treatment, Danish Epilepsy Centre, Dianalund, Denmark.,Department of Regional Health Research, University of Southern Denmark, Odense, Denmark.,Member of the ERN EpiCARE
| | - Jennifer R Ramautar
- Child and Adolescent Psychiatry and Psychosocial Care, Emma Children's Hospital, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Chiara Reale
- Department of Epilepsy Genetics and Personalized Treatment, Danish Epilepsy Centre, Dianalund, Denmark.,Department of Clinical and Experimental Medicine, Epilepsy Center, University Hospital of Messina, Messina, Italy
| | - Rikke S Møller
- Department of Epilepsy Genetics and Personalized Treatment, Danish Epilepsy Centre, Dianalund, Denmark.,Department of Regional Health Research, University of Southern Denmark, Odense, Denmark.,Member of the ERN EpiCARE
| | - Federico Zara
- IRCCS Istituto Giannina Gaslini, Genova, Italy.,Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy
| | - Pasquale Striano
- IRCCS Istituto Giannina Gaslini, Genova, Italy.,Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy
| | - Mala Misra-Isrie
- Department of Human Genetics, Amsterdam UMC, Amsterdam, Netherlands
| | | | - Marc Engelen
- Department of Pediatric Neurology, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Titia L van Zuijen
- Research Institute of Child Development and Education, University of Amsterdam, Amsterdam, Netherlands
| | - Huibert D Mansvelder
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
| | - Matthijs Verhage
- Department of Functional Genomics, Center for Neurogenomics and Cognitive Research, (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands.,Department of Human Genetics, Amsterdam UMC, Amsterdam, Netherlands
| | - Hilgo Bruining
- Child and Adolescent Psychiatry and Psychosocial Care, Emma Children's Hospital, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,N=You Neurodevelopmental Precision Center, Amsterdam Neuroscience, Amsterdam Reproduction and Development, Amsterdam UMC, Amsterdam, Netherlands.,Levvel, Center for Child and Adolescent Psychiatry, Amsterdam, Netherlands
| | - Klaus Linkenkaer-Hansen
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, (CNCR), Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
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584
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Pathania A, Euler MJ, Clark M, Cowan R, Duff K, Lohse KR. Resting EEG spectral slopes are associated with age-related differences in information processing speed. Biol Psychol 2022; 168:108261. [PMID: 34999166 DOI: 10.1016/j.biopsycho.2022.108261] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 01/03/2022] [Accepted: 01/05/2022] [Indexed: 12/22/2022]
Abstract
BACKGROUND Previous research has shown the slope of the EEG power spectrum differentiates between older and younger adults in various experimental cognitive tasks. We extend that work, assessing the relation between the EEG power spectrum and performance on the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). METHODS Twenty-one younger and twenty-three older adults completed the RBANS with EEG data collected at rest. Using spectral parameterization, we tested the mediating effect of the spectral slope on differences in subsequent cognitive task performance. RESULTS Older adults performed reliably worse on the RBANS overall, and on the Attention and Delayed Memory domains specifically. However, evidence of mediation was only found for the Coding subtest. CONCLUSIONS The slope of the resting EEG power spectrum mediated age-related differences in cognition, but only in a task requiring speeded processing. Mediation was not statistically significant for delayed memory, even though age-related differences were present.
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Affiliation(s)
- A Pathania
- Department of Health and Kinesiology, University of Utah
| | - M J Euler
- Department of Psychology, University of Utah
| | - M Clark
- Department of Health and Kinesiology, University of Utah
| | - R Cowan
- Department of Health and Kinesiology, University of Utah
| | - K Duff
- Department of Neurology, University of Utah
| | - K R Lohse
- Department of Health and Kinesiology, University of Utah; Program in Physical Therapy and Department of Neurology, Washington University School of Medicine in Saint Louis
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585
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Putative rhythms in attentional switching can be explained by aperiodic temporal structure. Nat Hum Behav 2022; 6:1280-1291. [PMID: 35680992 PMCID: PMC9489532 DOI: 10.1038/s41562-022-01364-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 04/25/2022] [Indexed: 02/02/2023]
Abstract
The neural and perceptual effects of attention were traditionally assumed to be sustained over time, but recent work suggests that covert attention rhythmically switches between objects at 3-8 Hz. Here I use simulations to demonstrate that the analysis approaches commonly used to test for rhythmic oscillations generate false positives in the presence of aperiodic temporal structure. I then propose two alternative analyses that are better able to discriminate between periodic and aperiodic structure in time series. Finally, I apply these alternative analyses to published datasets and find no evidence for behavioural rhythms in attentional switching after accounting for aperiodic temporal structure. The techniques presented here will help clarify the periodic and aperiodic dynamics of perception and of cognition more broadly.
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586
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Arnett AB, Rutter TM, Stein MA. Neural Markers of Methylphenidate Response in Children With Attention Deficit Hyperactivity Disorder. Front Behav Neurosci 2022; 16:887622. [PMID: 35600991 PMCID: PMC9121006 DOI: 10.3389/fnbeh.2022.887622] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 04/05/2022] [Indexed: 01/09/2023] Open
Abstract
Background Despite widespread use of stimulants to treat ADHD, individual responses vary considerably and few predictors of response have been identified. The identification of reliable and clinically feasible biomarkers would facilitate a precision medicine approach to pharmacological treatment of ADHD. We test the hypothesis that two electroencephalography (EEG) based neural signatures of ADHD, resting aperiodic slope exponent and novelty P3 amplitude, are markers of methylphenidate response in children. We hypothesize that positive response to methylphenidate treatment will be associated with greater abnormality of both neural markers. Methods Twenty-nine 7-11 year-old children with ADHD and a history of methylphenidate treatment, and 30 controls completed resting EEG and visual oddball event related potential (ERP) paradigms. ADHD participants were characterized as methylphenidate responders (n = 16) or non-responders (n = 13) using the clinical global improvement (CGI-I) scale during blinded retrospective interview. All participants abstained from prescribed medications for at least 48 hours prior to the EEG. Results As expected, methylphenidate responders (CGI-I rating < 3) demonstrated attenuated P3 amplitude relative to controls. Unexpectedly, methylphenidate non-responders showed atypically flat aperiodic spectral slope relative to controls, while responders did not differ on this measure. Conclusion ADHD symptoms associated with atypical patterns of intrinsic neural activity may be less responsive to methylphenidate. In contrast, ADHD symptoms associated with abnormal frontal-striatal neural network excitation may be correctable with methylphenidate. Altogether, EEG is a feasible and promising candidate methodology for identifying biomarkers of stimulant response.
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Affiliation(s)
- Anne B Arnett
- Division of Developmental Medicine, Boston Children's Hospital, Boston, MA, United States.,Department of Pediatrics, Harvard Medical School, Cambridge, MA, United States
| | - Tara M Rutter
- Department of Psychology, Seattle Pacific University, Seattle, WA, United States
| | - Mark A Stein
- Department of Psychiatry & Behavioral Medicine, Seattle Children's Hospital, Seattle, WA, United States.,Department of Psychiatry & Behavioral Sciences, University of Washington, Seattle, WA, United States
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587
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Separating Neural Oscillations from Aperiodic 1/f Activity: Challenges and Recommendations. Neuroinformatics 2022; 20:991-1012. [PMID: 35389160 PMCID: PMC9588478 DOI: 10.1007/s12021-022-09581-8] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/25/2022] [Indexed: 12/31/2022]
Abstract
Electrophysiological power spectra typically consist of two components: An aperiodic part usually following an 1/f power law [Formula: see text] and periodic components appearing as spectral peaks. While the investigation of the periodic parts, commonly referred to as neural oscillations, has received considerable attention, the study of the aperiodic part has only recently gained more interest. The periodic part is usually quantified by center frequencies, powers, and bandwidths, while the aperiodic part is parameterized by the y-intercept and the 1/f exponent [Formula: see text]. For investigation of either part, however, it is essential to separate the two components. In this article, we scrutinize two frequently used methods, FOOOF (Fitting Oscillations & One-Over-F) and IRASA (Irregular Resampling Auto-Spectral Analysis), that are commonly used to separate the periodic from the aperiodic component. We evaluate these methods using diverse spectra obtained with electroencephalography (EEG), magnetoencephalography (MEG), and local field potential (LFP) recordings relating to three independent research datasets. Each method and each dataset poses distinct challenges for the extraction of both spectral parts. The specific spectral features hindering the periodic and aperiodic separation are highlighted by simulations of power spectra emphasizing these features. Through comparison with the simulation parameters defined a priori, the parameterization error of each method is quantified. Based on the real and simulated power spectra, we evaluate the advantages of both methods, discuss common challenges, note which spectral features impede the separation, assess the computational costs, and propose recommendations on how to use them.
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588
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McLoughlin G, Gyurkovics M, Aydin Ü. What Has Been Learned from Using EEG Methods in Research of ADHD? Curr Top Behav Neurosci 2022; 57:415-444. [PMID: 35637406 DOI: 10.1007/7854_2022_344] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Electrophysiological recording methods, including electroencephalography (EEG) and magnetoencephalography (MEG), have an unparalleled capacity to provide insights into the timing and frequency (spectral) composition of rapidly changing neural activity associated with various cognitive processes. The current chapter provides an overview of EEG studies examining alterations in brain activity in ADHD, measured both at rest and during cognitive tasks. While EEG resting state studies of ADHD indicate no universal alterations in the disorder, event-related studies reveal consistent deficits in attentional and inhibitory control and consequently inform the proposed cognitive models of ADHD. Similar to other neuroimaging measures, EEG research indicates alterations in multiple neural circuits and cognitive functions. EEG methods - supported by the constant refinement of analytic strategies - have the potential to contribute to improved diagnostics and interventions for ADHD, underlining their clinical utility.
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Affiliation(s)
- Gráinne McLoughlin
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Máté Gyurkovics
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Ümit Aydin
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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589
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Zhang M, Frohlich F. Cell type-specific excitability probed by optogenetic stimulation depends on the phase of the alpha oscillation. Brain Stimul 2022; 15:472-482. [PMID: 35219922 PMCID: PMC8975618 DOI: 10.1016/j.brs.2022.02.014] [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: 11/08/2021] [Revised: 01/30/2022] [Accepted: 02/21/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Alpha oscillations have been proposed to provide phasic inhibition in the brain. Yet, pinging alpha oscillations with transcranial magnetic stimulation (TMS) to examine phase-dependent network excitability has resulted in conflicting findings. At the cellular level, such gating by the alpha oscillation remains poorly understood. OBJECTIVE We examine how the excitability of pyramidal cells and presumed fast-spiking inhibitory interneurons depends on the phase of the alpha oscillation. METHODS Optogenetic stimulation pulses were administered at random phases of the alpha oscillation in the posterior parietal cortex (PPC) of two adult ferrets that expressed channelrhodopsin in pyramidal cells. Post-stimulation firing probability was calculated as a function of the stimulation phase of the alpha oscillation for both verum and sham stimulation. RESULTS The excitability of pyramidal cells depended on the alpha phase, in anticorrelation with their intrinsic phase preference; pyramidal cells were more responsive to optogenetic stimulation at the alpha phase with intrinsically low firing rates. In contrast, presumed fast-spiking inhibitory interneurons did not show such a phase dependency despite their stronger intrinsic phase preference. CONCLUSIONS Alpha oscillations gate input to PPC in a phase-dependent manner such that low intrinsic activity was associated with higher responsiveness to input. This finding supports a model of cortical oscillation, in which internal processing and communication are limited to the depolarized half-cycle, whereas the other half-cycle serves as a signal detector for unexpected input. The functional role of different parts of the alpha cycle may vary across the cortex depending on local neuronal firing properties.
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Affiliation(s)
- Mengsen Zhang
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Flavio Frohlich
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA; Carolina Center for Neurostimulation, University of North Carolina, Chapel Hill, NC, USA; Neuroscience Center, University of North Carolina, Chapel Hill, NC, USA; Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC, USA; Department of Biomedical Engineering, University of North Carolina, Chapel Hill, NC, USA; Department of Neurology, University of North Carolina, Chapel Hill, NC, USA.
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590
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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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591
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Jacob MS, Roach BJ, Sargent KS, Mathalon DH, Ford JM. Aperiodic measures of neural excitability are associated with anticorrelated hemodynamic networks at rest: A combined EEG-fMRI study. Neuroimage 2021; 245:118705. [PMID: 34798229 DOI: 10.1016/j.neuroimage.2021.118705] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 10/11/2021] [Accepted: 11/01/2021] [Indexed: 11/24/2022] Open
Abstract
The hallmark of resting EEG spectra are distinct rhythms emerging from a broadband, aperiodic background. This aperiodic neural signature accounts for most of total EEG power, although its significance and relation to functional neuroanatomy remains obscure. We hypothesized that aperiodic EEG reflects a significant metabolic expenditure and therefore might be associated with the default mode network while at rest. During eyes-open, resting-state recordings of simultaneous EEG-fMRI, we find that aperiodic and periodic components of EEG power are only minimally associated with activity in the default mode network. However, a whole-brain analysis identifies increases in aperiodic power correlated with hemodynamic activity in an auditory-salience-cerebellar network, and decreases in aperiodic power are correlated with hemodynamic activity in prefrontal regions. Desynchronization in residual alpha and beta power is associated with visual and sensorimotor hemodynamic activity, respectively. These findings suggest that resting-state EEG signals acquired in an fMRI scanner reflect a balance of top-down and bottom-up stimulus processing, even in the absence of an explicit task.
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Affiliation(s)
- Michael S Jacob
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States; Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143 United States.
| | - Brian J Roach
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States.
| | - Kaia S Sargent
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States.
| | - Daniel H Mathalon
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States; Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143 United States.
| | - Judith M Ford
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States; Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143 United States.
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592
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Martínez-Cañada P, Noei S, Panzeri S. Methods for inferring neural circuit interactions and neuromodulation from local field potential and electroencephalogram measures. Brain Inform 2021; 8:27. [PMID: 34910260 PMCID: PMC8674171 DOI: 10.1186/s40708-021-00148-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 11/29/2021] [Indexed: 11/10/2022] Open
Abstract
Electrical recordings of neural mass activity, such as local field potentials (LFPs) and electroencephalograms (EEGs), have been instrumental in studying brain function. However, these aggregate signals lack cellular resolution and thus are not easy to be interpreted directly in terms of parameters of neural microcircuits. Developing tools for a reliable estimation of key neural parameters from these signals, such as the interaction between excitation and inhibition or the level of neuromodulation, is important for both neuroscientific and clinical applications. Over the years, we have developed tools based on neural network modeling and computational analysis of empirical data to estimate neural parameters from aggregate neural signals. This review article gives an overview of the main computational tools that we have developed and employed to invert LFPs and EEGs in terms of circuit-level neural phenomena, and outlines future challenges and directions for future research.
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Affiliation(s)
- Pablo Martínez-Cañada
- Neural Computation Laboratory, Istituto Italiano Di Tecnologia, Genova and Rovereto, Italy
- Optical Approaches To Brain Function Laboratory, Istituto Italiano Di Tecnologia, Genova, Italy
| | - Shahryar Noei
- Neural Computation Laboratory, Istituto Italiano Di Tecnologia, Genova and Rovereto, Italy
- CIMeC, University of Trento, Rovereto, Italy
| | - Stefano Panzeri
- Neural Computation Laboratory, Istituto Italiano Di Tecnologia, Genova and Rovereto, Italy.
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany.
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593
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Ibarra Chaoul A, Siegel M. Cortical correlation structure of aperiodic neuronal population activity. Neuroimage 2021; 245:118672. [PMID: 34715318 PMCID: PMC8752963 DOI: 10.1016/j.neuroimage.2021.118672] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 10/12/2021] [Accepted: 10/20/2021] [Indexed: 11/02/2022] Open
Abstract
Electrophysiological population signals contain oscillatory and non-oscillatory aperiodic (1/frequency-like) components. So far research has largely focused on oscillatory activity, and only recently, interest in aperiodic population activity has gained momentum. Accordingly, while the cortical correlation structure of oscillatory population activity has been characterized, little is known about the correlation of aperiodic neuronal activity. To address this, we investigated aperiodic neuronal population activity in the human brain using resting-state magnetoencephalography (MEG). We combined source-analysis, signal orthogonalization and irregular-resampling auto-spectral analysis (IRASA) to systematically characterize the cortical distribution and correlation of aperiodic neuronal activity. We found that aperiodic population activity is robustly correlated across the cortex and that this correlation is spatially well structured. Furthermore, we found that the cortical correlation structure of aperiodic activity is similar but distinct from the correlation structure of oscillatory neuronal activity. Anterior cortical regions showed the strongest differences between oscillatory and aperiodic correlation patterns. Our results suggest that correlations of aperiodic population activity serve as robust markers of cortical network interactions. Furthermore, our results show that aperiodic and oscillatory signal components provide non-redundant information about large-scale neuronal correlations. This may reflect at least partly distinct neuronal mechanisms underlying and reflected by oscillatory and aperiodic neuronal population activity.
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Affiliation(s)
- Andrea Ibarra Chaoul
- Department of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, Otfried-Müller-Str. 25, Tübingen 72076, Germany; Centre for Integrative Neuroscience, University of Tübingen, Otfried-Müller-Str. 25, Tübingen 72076, Germany; MEG Center, University of Tübingen, Otfried-Müller-Str. 47, Tübingen 72076, Germany; IMPRS for Cognitive and Systems Neuroscience, University of Tübingen, Otfried-Müller-Str. 27, Tübingen 72076, Germany.
| | - Markus Siegel
- Department of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, Otfried-Müller-Str. 25, Tübingen 72076, Germany; Centre for Integrative Neuroscience, University of Tübingen, Otfried-Müller-Str. 25, Tübingen 72076, Germany; MEG Center, University of Tübingen, Otfried-Müller-Str. 47, Tübingen 72076, Germany.
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594
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Metzner C, Schilling A, Traxdorf M, Schulze H, Krauss P. Sleep as a random walk: a super-statistical analysis of EEG data across sleep stages. Commun Biol 2021; 4:1385. [PMID: 34893700 PMCID: PMC8664947 DOI: 10.1038/s42003-021-02912-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 11/23/2021] [Indexed: 11/15/2022] Open
Abstract
In clinical practice, human sleep is classified into stages, each associated with different levels of muscular activity and marked by characteristic patterns in the EEG signals. It is however unclear whether this subdivision into discrete stages with sharply defined boundaries is truly reflecting the dynamics of human sleep. To address this question, we consider one-channel EEG signals as heterogeneous random walks: stochastic processes controlled by hyper-parameters that are themselves time-dependent. We first demonstrate the heterogeneity of the random process by showing that each sleep stage has a characteristic distribution and temporal correlation function of the raw EEG signals. Next, we perform a super-statistical analysis by computing hyper-parameters, such as the standard deviation, kurtosis, and skewness of the raw signal distributions, within subsequent 30-second epochs. It turns out that also the hyper-parameters have characteristic, sleep-stage-dependent distributions, which can be exploited for a simple Bayesian sleep stage detection. Moreover, we find that the hyper-parameters are not piece-wise constant, as the traditional hypnograms would suggest, but show rising or falling trends within and across sleep stages, pointing to an underlying continuous rather than sub-divided process that controls human sleep. Based on the hyper-parameters, we finally perform a pairwise similarity analysis between the different sleep stages, using a quantitative measure for the separability of data clusters in multi-dimensional spaces. To improve our understand of how EEG activity reflects the dynamics of human sleep, Metzner et al. use human EEG data and superstatistical analysis to demonstrate that each sleep stage has a characteristic distribution and temporal correlation function of raw EEG signals. They also show that the hyper-parameters controlling the EEG signals have characteristic, sleep-stage-dependent distributions, which can be exploited for a simple Bayesian sleep stage detection.
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Affiliation(s)
- Claus Metzner
- Neuroscience Lab, Experimental Otolaryngology, University Hospital Erlangen, Erlangen, Germany.
| | - Achim Schilling
- Neuroscience Lab, Experimental Otolaryngology, University Hospital Erlangen, Erlangen, Germany.,Laboratory of Sensory and Cognitive Neuroscience, Aix-Marseille University, Marseille, France.,Cognitive Computational Neuroscience Group, Friedrich-Alexander University Erlangen-Nuremberg, Nuremberg, Germany
| | - Maximilian Traxdorf
- Department of Otorhinolaryngology, Paracelsus Medical University, Nuremberg, Germany
| | - Holger Schulze
- Neuroscience Lab, Experimental Otolaryngology, University Hospital Erlangen, Erlangen, Germany
| | - Patrick Krauss
- Neuroscience Lab, Experimental Otolaryngology, University Hospital Erlangen, Erlangen, Germany.,Cognitive Computational Neuroscience Group, Friedrich-Alexander University Erlangen-Nuremberg, Nuremberg, Germany.,Pattern Recognition Lab, Friedrich-Alexander University Erlangen-Nuremberg, Nuremberg, Germany
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595
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Boord MS, Davis DHJ, Psaltis PJ, Coussens SW, Feuerriegel D, Garrido MI, Bourke A, Keage HAD. DelIrium VULnerability in GEriatrics (DIVULGE) study: a protocol for a prospective observational study of electroencephalogram associations with incident postoperative delirium. BMJ Neurol Open 2021; 3:e000199. [PMID: 34964043 PMCID: PMC8653776 DOI: 10.1136/bmjno-2021-000199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 11/07/2021] [Indexed: 11/04/2022] Open
Abstract
INTRODUCTION Delirium is a neurocognitive disorder common in older adults in acute care settings. Those who develop delirium are at an increased risk of dementia, cognitive decline and death. Electroencephalography (EEG) during delirium in older adults is characterised by slowing and reduced functional connectivity, but markers of vulnerability are poorly described. We aim to identify EEG spectral power and event-related potential (ERP) markers of incident delirium in older adults to understand neural mechanisms of delirium vulnerability. Characterising delirium vulnerability will provide substantial theoretical advances and outcomes have the potential to be translated into delirium risk assessment tools. METHODS AND ANALYSIS We will record EEG in 90 participants over 65 years of age prior to elective coronary artery bypass grafting (CABG) or transcatheter aortic valve implantation (TAVI). We will record 4-minutes of resting state (eyes open and eyes closed) and a 5-minute frequency auditory oddball paradigm. Outcome measures will include frequency band power, 1/f offset and slope, and ERP amplitude measures. Participants will undergo cognitive and EEG testing before their elective procedures and daily postoperative delirium assessments. Group allocation will be done retrospectively by linking preoperative EEG data according to postoperative delirium status (presence, severity, duration and subtype). ETHICS AND DISSEMINATION This study is approved by the Human Research Ethics Committee of the Royal Adelaide Hospital, Central Adelaide Local Health Network and the University of South Australia Human Ethics Committee. Findings will be disseminated through peer-reviewed journal articles and presentations at national and international conferences. TRIAL REGISTRATION NUMBER ACTRN12618001114235 and ACTRN12618000799257.
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Affiliation(s)
- Monique S Boord
- Cognitive Ageing and Impairment Neurosciences Laboratory, Justice and Society, University of South Australia, Adelaide, South Australia, Australia
| | | | - Peter J Psaltis
- Vascular Research Centre, Heart and Vascular Program, Lifelong Health Theme, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
- Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
- Department of Cardiology, Royal Adelaide Hospital, Central Adelaide Local Health Network, Adelaide, South Australia, Australia
| | - Scott W Coussens
- Cognitive Ageing and Impairment Neurosciences Laboratory, Justice and Society, University of South Australia, Adelaide, South Australia, Australia
| | - Daniel Feuerriegel
- Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Victoria, Australia
| | - Marta I Garrido
- Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Victoria, Australia
| | - Alice Bourke
- Aged Care, Rehabilitation and Palliative Care (Medical), Northern Adelaide Local Health Network, Adelaide, South Australia, Australia
| | - Hannah A D Keage
- Cognitive Ageing and Impairment Neurosciences Laboratory, Justice and Society, University of South Australia, Adelaide, South Australia, Australia
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596
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Ongoing neural oscillations influence behavior and sensory representations by suppressing neuronal excitability. Neuroimage 2021; 247:118746. [PMID: 34875382 DOI: 10.1016/j.neuroimage.2021.118746] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 10/21/2021] [Accepted: 11/19/2021] [Indexed: 12/28/2022] Open
Abstract
The ability to process and respond to external input is critical for adaptive behavior. Why, then, do neural and behavioral responses vary across repeated presentations of the same sensory input? Ongoing fluctuations of neuronal excitability are currently hypothesized to underlie the trial-by-trial variability in sensory processing. To test this, we capitalized on intracranial electrophysiology in neurosurgical patients performing an auditory discrimination task with visual cues: specifically, we examined the interaction between prestimulus alpha oscillations, excitability, task performance, and decoded neural stimulus representations. We found that strong prestimulus oscillations in the alpha+ band (i.e., alpha and neighboring frequencies), rather than the aperiodic signal, correlated with a low excitability state, indexed by reduced broadband high-frequency activity. This state was related to slower reaction times and reduced neural stimulus encoding strength. We propose that the alpha+ rhythm modulates excitability, thereby resulting in variability in behavior and sensory representations despite identical input.
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597
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Peterson SM, Ferris DP. Human electrocortical, electromyographical, ocular, and kinematic data during perturbed walking and standing. Data Brief 2021; 39:107635. [PMID: 34988270 PMCID: PMC8711048 DOI: 10.1016/j.dib.2021.107635] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 11/15/2021] [Accepted: 11/22/2021] [Indexed: 11/26/2022] Open
Abstract
Active balance control is critical for performing many of our everyday activities. Our nervous systems rely on multiple sensory inputs to inform cortical processing, leading to coordinated muscle actions that maintain balance. However, such cortical processing can be challenging to record during mobile balance tasks due to limitations in noninvasive neuroimaging and motion artifact contamination. Here, we present a synchronized, multi-modal dataset from 30 healthy, young human participants during standing and walking while undergoing brief sensorimotor perturbations. Our dataset includes 20 total hours of high-density electroencephalography (EEG) recorded from 128 scalp electrodes, along with surface electromyography (EMG) from 10 neck and leg electrodes, electrooculography (EOG) recorded from 3 electrodes, and 3D body position from 2 sensors. In addition, we include ∼18000 total balance perturbation events across participants. To facilitate data reuse, we share this dataset in the Brain Imaging Data Structure (BIDS) data standard and publicly release code that replicates our previous event-related findings.
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598
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Hirano Y, Uhlhaas PJ. Current findings and perspectives on aberrant neural oscillations in schizophrenia. Psychiatry Clin Neurosci 2021; 75:358-368. [PMID: 34558155 DOI: 10.1111/pcn.13300] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 08/20/2021] [Accepted: 09/09/2021] [Indexed: 12/11/2022]
Abstract
There is now consistent evidence that neural oscillation at low- and high-frequencies constitute an important aspect of the pathophysiology of schizophrenia. Specifically, impaired rhythmic activity may underlie the deficit to generate coherent cognition and behavior, leading to the characteristic symptoms of psychosis and cognitive deficits. Importantly, the generating mechanisms of neural oscillations are relatively well-understood and thus enable the targeted search for the underlying circuit impairments and novel treatment targets. In the following review, we will summarize and assess the evidence for aberrant rhythmic activity in schizophrenia through evaluating studies that have utilized Electro/Magnetoencephalography to examine neural oscillations during sensory and cognitive tasks as well as during resting-state measurements. These data will be linked to current evidence from post-mortem, neuroimaging, genetics, and animal models that have implicated deficits in GABAergic interneurons and glutamatergic neurotransmission in oscillatory deficits in schizophrenia. Finally, we will highlight methodological and analytical challenges as well as provide recommendations for future research.
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Affiliation(s)
- Yoji Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Peter J Uhlhaas
- Department of Child and Adolescent Psychiatry, Charité - Universitätsmedizin, Berlin, Germany
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK
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599
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Adelhöfer N, Paulus T, Mückschel M, Bäumer T, Bluschke A, Takacs A, Tóth-Fáber E, Tárnok Z, Roessner V, Weissbach A, Münchau A, Beste C. Increased scale-free and aperiodic neural activity during sensorimotor integration-a novel facet in Tourette syndrome. Brain Commun 2021; 3:fcab250. [PMID: 34805995 PMCID: PMC8599001 DOI: 10.1093/braincomms/fcab250] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 07/15/2021] [Accepted: 09/22/2021] [Indexed: 11/14/2022] Open
Abstract
Tourette syndrome is a common neurodevelopmental disorder defined by multiple motor and phonic tics. Tics in Tourette syndrome resemble spontaneously occurring movements in healthy controls and are therefore sometimes difficult to distinguish from these. Tics may in fact be mis-interpreted as a meaningful action, i.e. a signal with social content, whereas they lack such information and could be conceived a surplus of action or 'motor noise'. These and other considerations have led to a 'neural noise account' of Tourette syndrome suggesting that the processing of neural noise and adaptation of the signal-to-noise ratio during information processing is relevant for the understanding of Tourette syndrome. So far, there is no direct evidence for this. Here, we tested the 'neural noise account' examining 1/f noise, also called scale-free neural activity as well as aperiodic activity, in n = 74 children, adolescents and adults with Tourette syndrome and n = 74 healthy controls during task performance using EEG data recorded during a sensorimotor integration task. In keeping with results of a previous study in adults with Tourette syndrome, behavioural data confirmed that sensorimotor integration was also stronger in this larger Tourette syndrome cohort underscoring the relevance of perceptual-action processes in this disorder. More importantly, we show that 1/f noise and aperiodic activity during sensorimotor processing is increased in patients with Tourette syndrome supporting the 'neural noise account'. This implies that asynchronous/aperiodic neural activity during sensorimotor integration is stronger in patients with Tourette syndrome compared to healthy controls, which is probably related to abnormalities of GABAergic and dopaminergic transmission in these patients. Differences in 1/f noise and aperiodic activity between patients with Tourette syndrome and healthy controls were driven by high-frequency oscillations and not lower-frequency activity currently discussed to be important in the pathophysiology of tics. This and the fact that Bayesian statistics showed that there is evidence for the absence of a correlation between neural noise and clinical measures of tics, suggest that increased 1/f noise and aperiodic activity are not directly related to tics but rather represents a novel facet of Tourette syndrome.
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Affiliation(s)
- Nico Adelhöfer
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, 01069 Dresden, Germany
| | - Theresa Paulus
- Institute of Systems Motor Science, University of Lübeck, 23562 Lübeck, Germany.,Department of Neurology, University of Lübeck, 23538 Lübeck, Germany
| | - Moritz Mückschel
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, 01069 Dresden, Germany
| | - Tobias Bäumer
- Institute of Systems Motor Science, University of Lübeck, 23562 Lübeck, Germany
| | - Annet Bluschke
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, 01069 Dresden, Germany
| | - Adam Takacs
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, 01069 Dresden, Germany
| | - Eszter Tóth-Fáber
- Doctoral School of Psychology, ELTE Eötvös Loránd University, 1064 Budapest, Hungary.,Institute of Psychology, ELTE Eötvös Loránd University, 1053 Budapest, Hungary
| | - Zsanett Tárnok
- Vadaskert Child and Adolescent Psychiatry Hospital and Outpatient Clinic, 1021 Budapest, Hungary
| | - Veit Roessner
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, 01069 Dresden, Germany
| | - Anne Weissbach
- Institute of Systems Motor Science, University of Lübeck, 23562 Lübeck, Germany
| | - Alexander Münchau
- Institute of Systems Motor Science, University of Lübeck, 23562 Lübeck, Germany
| | - Christian Beste
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, 01069 Dresden, Germany.,Cognitive Psychology, Faculty of Psychology, Shandong Normal University, Qianfoshan Campus, No. 88 East Wenhua Road, Lixia District, Ji'nan, 250014, China
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600
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The Brain Electrophysiological recording & STimulation (BEST) toolbox. Brain Stimul 2021; 15:109-115. [PMID: 34826626 DOI: 10.1016/j.brs.2021.11.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 11/17/2021] [Accepted: 11/22/2021] [Indexed: 11/23/2022] Open
Abstract
Non-invasive brain stimulation (NIBS) experiments involve many recurring procedures that are not sufficiently standardized in the community. Given the diversity in experimental design and experience of the investigators, automated but yet flexible data collection and analysis tools are needed to increase objectivity, reliability, and reproducibility of NIBS experiments. The Brain Electrophysiological recording and STimulation (BEST) Toolbox is a MATLAB-based, open-source software with graphical user interface that allows users to design, run, and share freely configurable multi-protocol, multi-session NIBS studies, including transcranial magnetic, electric, and ultrasound stimulation (TMS, tES, TUS). Interfacing with a variety of recording and stimulation devices, the BEST toolbox analyzes EMG and EEG data, and configures stimulation parameters on-the-fly to facilitate closed-loop protocols and real-time applications. Its functionality is continuously expanded and includes e.g., TMS motor hotspot search, threshold estimation, motor evoked potential (MEP) and TMS-evoked EEG potential (TEP) measurements, dose-response curves, paired-pulse and dual-coil TMS, rTMS interventions.
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