601
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Wise T, Liu Y, Chowdhury F, Dolan RJ. Model-based aversive learning in humans is supported by preferential task state reactivation. SCIENCE ADVANCES 2021; 7:eabf9616. [PMID: 34321205 PMCID: PMC8318377 DOI: 10.1126/sciadv.abf9616] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 06/10/2021] [Indexed: 06/13/2023]
Abstract
Harm avoidance is critical for survival, yet little is known regarding the neural mechanisms supporting avoidance in the absence of trial-and-error experience. Flexible avoidance may be supported by a mental model (i.e., model-based), a process for which neural reactivation and sequential replay have emerged as candidate mechanisms. During an aversive learning task, combined with magnetoencephalography, we show prospective and retrospective reactivation during planning and learning, respectively, coupled to evidence for sequential replay. Specifically, when individuals plan in an aversive context, we find preferential reactivation of subsequently chosen goal states. Stronger reactivation is associated with greater hippocampal theta power. At outcome receipt, unchosen goal states are reactivated regardless of outcome valence. Replay of paths leading to goal states was modulated by outcome valence, with aversive outcomes associated with stronger reverse replay than safe outcomes. Our findings are suggestive of avoidance involving simulation of unexperienced states through hippocampally mediated reactivation and replay.
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Affiliation(s)
- Toby Wise
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK.
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Yunzhe Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Fatima Chowdhury
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, UK
| | - Raymond J Dolan
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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602
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Adaptive circuit dynamics across human cortex during evidence accumulation in changing environments. Nat Neurosci 2021; 24:987-997. [PMID: 33903770 DOI: 10.1038/s41593-021-00839-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2020] [Accepted: 03/12/2021] [Indexed: 02/02/2023]
Abstract
Many decisions under uncertainty entail the temporal accumulation of evidence that informs about the state of the environment. When environments are subject to hidden changes in their state, maximizing accuracy and reward requires non-linear accumulation of evidence. How this adaptive, non-linear computation is realized in the brain is unknown. We analyzed human behavior and cortical population activity (measured with magnetoencephalography) recorded during visual evidence accumulation in a changing environment. Behavior and decision-related activity in cortical regions involved in action planning exhibited hallmarks of adaptive evidence accumulation, which could also be implemented by a recurrent cortical microcircuit. Decision dynamics in action-encoding parietal and frontal regions were mirrored in a frequency-specific modulation of the state of the visual cortex that depended on pupil-linked arousal and the expected probability of change. These findings link normative decision computations to recurrent cortical circuit dynamics and highlight the adaptive nature of decision-related feedback to the sensory cortex.
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603
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Gordon PC, Dörre S, Belardinelli P, Stenroos M, Zrenner B, Ziemann U, Zrenner C. Prefrontal Theta-Phase Synchronized Brain Stimulation With Real-Time EEG-Triggered TMS. Front Hum Neurosci 2021; 15:691821. [PMID: 34234662 PMCID: PMC8255809 DOI: 10.3389/fnhum.2021.691821] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 05/27/2021] [Indexed: 11/30/2022] Open
Abstract
Background Theta-band neuronal oscillations in the prefrontal cortex are associated with several cognitive functions. Oscillatory phase is an important correlate of excitability and phase synchrony mediates information transfer between neuronal populations oscillating at that frequency. The ability to extract and exploit the prefrontal theta rhythm in real time in humans would facilitate insight into neurophysiological mechanisms of cognitive processes involving the prefrontal cortex, and development of brain-state-dependent stimulation for therapeutic applications. Objectives We investigate individual source-space beamforming-based estimation of the prefrontal theta oscillation as a method to target specific phases of the ongoing theta oscillations in the human dorsomedial prefrontal cortex (DMPFC) with real-time EEG-triggered transcranial magnetic stimulation (TMS). Different spatial filters for extracting the prefrontal theta oscillation from EEG signals are compared and additional signal quality criteria are assessed to take into account the dynamics of this cortical oscillation. Methods Twenty two healthy participants were recruited for anatomical MRI scans and EEG recordings with 18 composing the final analysis. We calculated individual spatial filters based on EEG beamforming in source space. The extracted EEG signal was then used to simulate real-time phase-detection and quantify the accuracy as compared to post-hoc phase estimates. Different spatial filters and triggering parameters were compared. Finally, we validated the feasibility of this approach by actual real-time triggering of TMS pulses at different phases of the prefrontal theta oscillation. Results Higher phase-detection accuracy was achieved using individualized source-based spatial filters, as compared to an average or standard Laplacian filter, and also by detecting and avoiding periods of low theta amplitude and periods containing a phase reset. Using optimized parameters, prefrontal theta-phase synchronized TMS of DMPFC was achieved with an accuracy of ±55°. Conclusion This study demonstrates the feasibility of triggering TMS pulses during different phases of the ongoing prefrontal theta oscillation in real time. This method is relevant for brain state-dependent stimulation in human studies of cognition. It will also enable new personalized therapeutic repetitive TMS protocols for more effective treatment of neuropsychiatric disorders.
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Affiliation(s)
- Pedro Caldana Gordon
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany.,Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Sara Dörre
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany.,Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Paolo Belardinelli
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany.,Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,CIMeC, Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy
| | - Matti Stenroos
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Brigitte Zrenner
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany.,Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Ulf Ziemann
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany.,Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Christoph Zrenner
- Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany.,Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
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604
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Tort AB, Hammer M, Zhang J, Brankačk J, Draguhn A. Temporal Relations between Cortical Network Oscillations and Breathing Frequency during REM Sleep. J Neurosci 2021; 41:5229-5242. [PMID: 33963051 PMCID: PMC8211551 DOI: 10.1523/jneurosci.3067-20.2021] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 03/29/2021] [Accepted: 04/27/2021] [Indexed: 11/21/2022] Open
Abstract
Nasal breathing generates a rhythmic signal which entrains cortical network oscillations in widespread brain regions on a cycle-to-cycle time scale. It is unknown, however, how respiration and neuronal network activity interact on a larger time scale: are breathing frequency and typical neuronal oscillation patterns correlated? Is there any directionality or temporal relationship? To address these questions, we recorded field potentials from the posterior parietal cortex of mice together with respiration during REM sleep. In this state, the parietal cortex exhibits prominent θ and γ oscillations while behavioral activity is minimal, reducing confounding signals. We found that the instantaneous breathing frequency strongly correlates with the instantaneous frequency and amplitude of both θ and γ oscillations. Cross-correlograms and Granger causality revealed specific directionalities for different rhythms: changes in θ activity precede and Granger-cause changes in breathing frequency, suggesting control by the functional state of the brain. On the other hand, the instantaneous breathing frequency Granger causes changes in γ frequency, suggesting that γ is influenced by a peripheral reafference signal. These findings show that changes in breathing frequency temporally relate to changes in different patterns of rhythmic brain activity. We hypothesize that such temporal relations are mediated by a common central drive likely to be located in the brainstem.
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Affiliation(s)
- Adriano B.L. Tort
- Brain Institute, Federal University of Rio Grande do Norte, Natal, RN 59056-450, Brazil
| | - Maximilian Hammer
- Institute for Physiology and Pathophysiology, Heidelberg University, Heidelberg, 69120, Germany
| | - Jiaojiao Zhang
- Institute for Physiology and Pathophysiology, Heidelberg University, Heidelberg, 69120, Germany
| | - Jurij Brankačk
- Institute for Physiology and Pathophysiology, Heidelberg University, Heidelberg, 69120, Germany
| | - Andreas Draguhn
- Institute for Physiology and Pathophysiology, Heidelberg University, Heidelberg, 69120, Germany
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605
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Immink MA, Cross ZR, Chatburn A, Baumeister J, Schlesewsky M, Bornkessel-Schlesewsky I. Resting-state aperiodic neural dynamics predict individual differences in visuomotor performance and learning. Hum Mov Sci 2021; 78:102829. [PMID: 34139391 DOI: 10.1016/j.humov.2021.102829] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 05/03/2021] [Accepted: 06/03/2021] [Indexed: 11/29/2022]
Abstract
An emerging body of work has demonstrated that resting-state non-oscillatory, or aperiodic, 1/f neural activity is a functional and behaviorally relevant marker of cognitive function capacity. In the motor domain, previous work has only applied 1/f analyses to investigations of motor coordination and performance measures. The value of aperiodic resting-state neural dynamics as a marker of individual visuomotor performance capacity remains unknown. Accordingly, the aim of this work was to investigate if individual 1/f intercept and slope parameters of aperiodic resting-state neural activity predict reaction time and perceptual sensitivity in an immersive virtual reality marksmanship task. The marksmanship task required speeded selection of target stimuli and avoidance of selecting non-target stimuli. Motor and perceptual demands were incrementally increased across task blocks and participants performed the task across three training sessions spanning one week. When motor demands were high, steeper individual 1/f slope predicted shorter reaction time. This relationship did not change with practice. Increased 1/f intercept and a steeper 1/f slope were associated with higher perceptual sensitivity, measured as d'. However, this association was only observed under the highest levels of perceptual demand and only in the initial exposure to these conditions. Individuals with a lower 1/f intercept and a shallower 1/f slope demonstrated the greatest gains in perceptual sensitivity from task practice. These findings demonstrate that individual differences in motor and perceptual performance can be accounted for with resting-state aperiodic neural dynamics. The 1/f aperiodic parameters are most informative in predicting visuomotor performance under complex and demanding task conditions. In addition to predicting capacity for high visuomotor performance with a novel task, 1/f aperiodic parameters might also be useful in predicting which individuals might derive the most improvements from practice.
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Affiliation(s)
- Maarten A Immink
- Sport, Health, Activity, Performance and Exercise (SHAPE) Research Centre, Flinders University, Adelaide, Australia; Cognitive and Systems Neuroscience Research Hub, University of South Australia, Adelaide, Australia.
| | - Zachariah R Cross
- Cognitive and Systems Neuroscience Research Hub, University of South Australia, Adelaide, Australia
| | - Alex Chatburn
- Cognitive and Systems Neuroscience Research Hub, University of South Australia, Adelaide, Australia
| | - James Baumeister
- Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, Australia
| | - Matthias Schlesewsky
- Cognitive and Systems Neuroscience Research Hub, University of South Australia, Adelaide, Australia
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606
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van der Werf OJ, Ten Oever S, Schuhmann T, Sack AT. No evidence of rhythmic visuospatial attention at cued locations in a spatial cuing paradigm, regardless of their behavioural relevance. Eur J Neurosci 2021; 55:3100-3116. [PMID: 34131983 PMCID: PMC9542203 DOI: 10.1111/ejn.15353] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Revised: 06/04/2021] [Accepted: 06/09/2021] [Indexed: 12/01/2022]
Abstract
Recent evidence suggests that visuospatial attentional performance is not stable over time but fluctuates in a rhythmic fashion. These attentional rhythms allow for sampling of different visuospatial locations in each cycle of this rhythm. However, it is still unclear in which paradigmatic circumstances rhythmic attention becomes evident. First, it is unclear at what spatial locations rhythmic attention occurs. Second, it is unclear how the behavioural relevance of each spatial location determines the rhythmic sampling patterns. Here, we aim to elucidate these two issues. Firstly, we aim to find evidence of rhythmic attention at the predicted (i.e. cued) location under moderately informative predictor value, replicating earlier studies. Secondly, we hypothesise that rhythmic attentional sampling behaviour will be affected by the behavioural relevance of the sampled location, ranging from non-informative to fully informative. To these aims, we used a modified Egly-Driver task with three conditions: a fully informative cue, a moderately informative cue (replication condition), and a non-informative cue. We did not find evidence of rhythmic sampling at cued locations, failing to replicate earlier studies. Nor did we find differences in rhythmic sampling under different predictive values of the cue. The current data does not allow for robust conclusions regarding the non-cued locations due to the absence of a priori hypotheses. Post-hoc explorative data analyses, however, clearly indicate that attention samples non-cued locations in a theta-rhythmic manner, specifically when the cued location bears higher behavioural relevance than the non-cued locations.
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Affiliation(s)
- Olof J van der Werf
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.,Maastricht Brain Imaging Centre (MBIC), Maastricht University, Maastricht, The Netherlands
| | - Sanne Ten Oever
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.,Language and Computation in Neural Systems group, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.,Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands
| | - Teresa Schuhmann
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.,Maastricht Brain Imaging Centre (MBIC), Maastricht University, Maastricht, The Netherlands
| | - Alexander T Sack
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.,Maastricht Brain Imaging Centre (MBIC), Maastricht University, Maastricht, The Netherlands.,Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNs), Brain and Nerve Centre, Maastricht University Medical Centre+ (MUMC+), Maastricht, The Netherlands
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607
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Wainio-Theberge S, Wolff A, Northoff G. Dynamic relationships between spontaneous and evoked electrophysiological activity. Commun Biol 2021; 4:741. [PMID: 34131279 PMCID: PMC8206204 DOI: 10.1038/s42003-021-02240-9] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 05/14/2021] [Indexed: 02/06/2023] Open
Abstract
Spontaneous neural activity fluctuations have been shown to influence trial-by-trial variation in perceptual, cognitive, and behavioral outcomes. However, the complex electrophysiological mechanisms by which these fluctuations shape stimulus-evoked neural activity remain largely to be explored. Employing a large-scale magnetoencephalographic dataset and an electroencephalographic replication dataset, we investigate the relationship between spontaneous and evoked neural activity across a range of electrophysiological variables. We observe that for high-frequency activity, high pre-stimulus amplitudes lead to greater evoked desynchronization, while for low frequencies, high pre-stimulus amplitudes induce larger degrees of event-related synchronization. We further decompose electrophysiological power into oscillatory and scale-free components, demonstrating different patterns of spontaneous-evoked correlation for each component. Finally, we find correlations between spontaneous and evoked time-domain electrophysiological signals. Overall, we demonstrate that the dynamics of multiple electrophysiological variables exhibit distinct relationships between their spontaneous and evoked activity, a result which carries implications for experimental design and analysis in non-invasive electrophysiology.
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Affiliation(s)
- Soren Wainio-Theberge
- Mind, Brain Imaging and Neuroethics, Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada.,Integrated Program in Neuroscience, McGill University, Montréal, QC, Canada
| | - Annemarie Wolff
- Mind, Brain Imaging and Neuroethics, Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada
| | - Georg Northoff
- Mind, Brain Imaging and Neuroethics, Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada. .,Mental Health Centre, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
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608
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Aperiodic sleep networks promote memory consolidation. Trends Cogn Sci 2021; 25:648-659. [PMID: 34127388 DOI: 10.1016/j.tics.2021.04.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 04/17/2021] [Accepted: 04/20/2021] [Indexed: 11/22/2022]
Abstract
Hierarchical synchronization of sleep oscillations establishes communication pathways to support memory reactivation, transfer, and consolidation. From an information-theoretical perspective, oscillations constitute highly structured network states that provide limited information-coding capacity. Recent findings indicate that sleep oscillations occur in transient bursts that are interleaved with aperiodic network states, which were previously considered to be random noise. We argue that aperiodic activity exhibits unique and variable spatiotemporal patterns, providing an ideal information-rich neurophysiological substrate for imprinting new mnemonic patterns onto existing circuits. We discuss novel avenues in conceptualizing and quantifying aperiodic network states during sleep to further understand their relevance and interplay with sleep oscillations in support of memory consolidation.
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609
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Qasim SE, Fried I, Jacobs J. Phase precession in the human hippocampus and entorhinal cortex. Cell 2021; 184:3242-3255.e10. [PMID: 33979655 PMCID: PMC8195854 DOI: 10.1016/j.cell.2021.04.017] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 02/18/2021] [Accepted: 04/09/2021] [Indexed: 12/11/2022]
Abstract
Knowing where we are, where we have been, and where we are going is critical to many behaviors, including navigation and memory. One potential neuronal mechanism underlying this ability is phase precession, in which spatially tuned neurons represent sequences of positions by activating at progressively earlier phases of local network theta oscillations. Based on studies in rodents, researchers have hypothesized that phase precession may be a general neural pattern for representing sequential events for learning and memory. By recording human single-neuron activity during spatial navigation, we show that spatially tuned neurons in the human hippocampus and entorhinal cortex exhibit phase precession. Furthermore, beyond the neural representation of locations, we show evidence for phase precession related to specific goal states. Our findings thus extend theta phase precession to humans and suggest that this phenomenon has a broad functional role for the neural representation of both spatial and non-spatial information.
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Affiliation(s)
- Salman E Qasim
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Itzhak Fried
- Department of Neurological Surgery, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Joshua Jacobs
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA.
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610
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Kragel JE, Schuele S, VanHaerents S, Rosenow JM, Voss JL. Rapid coordination of effective learning by the human hippocampus. SCIENCE ADVANCES 2021; 7:7/25/eabf7144. [PMID: 34144985 PMCID: PMC8213228 DOI: 10.1126/sciadv.abf7144] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Accepted: 05/06/2021] [Indexed: 06/12/2023]
Abstract
Although the human hippocampus is necessary for long-term memory, controversial findings suggest that it may also support short-term memory in the service of guiding effective behaviors during learning. We tested the counterintuitive theory that the hippocampus contributes to long-term memory through remarkably short-term processing, as reflected in eye movements during scene encoding. While viewing scenes for the first time, short-term retrieval operative within the episode over only hundreds of milliseconds was indicated by a specific eye-movement pattern, which was effective in that it enhanced spatiotemporal memory formation. This viewing pattern was predicted by hippocampal theta oscillations recorded from depth electrodes and by shifts toward top-down influence of hippocampal theta on activity within visual perception and attention networks. The hippocampus thus supports short-term memory processing that coordinates behavior in the service of effective spatiotemporal learning.
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Affiliation(s)
- James E Kragel
- Interdepartmental Neuroscience Program, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Stephan Schuele
- Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Stephen VanHaerents
- Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Joshua M Rosenow
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Joel L Voss
- Interdepartmental Neuroscience Program, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
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611
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Wairagkar M, Hayashi Y, Nasuto SJ. Dynamics of Long-Range Temporal Correlations in Broadband EEG During Different Motor Execution and Imagery Tasks. Front Neurosci 2021; 15:660032. [PMID: 34121989 PMCID: PMC8193084 DOI: 10.3389/fnins.2021.660032] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 03/22/2021] [Indexed: 11/13/2022] Open
Abstract
Brain activity is composed of oscillatory and broadband arrhythmic components; however, there is more focus on oscillatory sensorimotor rhythms to study movement, but temporal dynamics of broadband arrhythmic electroencephalography (EEG) remain unexplored. We have previously demonstrated that broadband arrhythmic EEG contains both short- and long-range temporal correlations that change significantly during movement. In this study, we build upon our previous work to gain a deeper understanding of these changes in the long-range temporal correlation (LRTC) in broadband EEG and contrast them with the well-known LRTC in alpha oscillation amplitude typically found in the literature. We investigate and validate changes in LRTCs during five different types of movements and motor imagery tasks using two independent EEG datasets recorded with two different paradigms-our finger tapping dataset with single self-initiated asynchronous finger taps and publicly available EEG dataset containing cued continuous movement and motor imagery of fists and feet. We quantified instantaneous changes in broadband LRTCs by detrended fluctuation analysis on single trial 2 s EEG sliding windows. The broadband LRTC increased significantly (p < 0.05) during all motor tasks as compared to the resting state. In contrast, the alpha oscillation LRTC, which had to be computed on longer stitched EEG segments, decreased significantly (p < 0.05) consistently with the literature. This suggests the complementarity of underlying fast and slow neuronal scale-free dynamics during movement and motor imagery. The single trial broadband LRTC gave high average binary classification accuracy in the range of 70.54±10.03% to 76.07±6.40% for all motor execution and imagery tasks and hence can be used in brain-computer interface (BCI). Thus, we demonstrate generalizability, robustness, and reproducibility of novel motor neural correlate, the single trial broadband LRTC, during different motor execution and imagery tasks in single asynchronous and cued continuous motor-BCI paradigms and its contrasting behavior with LRTC in alpha oscillation amplitude.
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Affiliation(s)
- Maitreyee Wairagkar
- Brain Embodiment Laboratory, Biomedical Engineering, School of Biological Sciences, University of Reading, Reading, United Kingdom
- Biomechatronics Laboratory, Department of Mechanical Engineering, Imperial College London, London, United Kingdom
- Care Research and Technology Centre, The UK Dementia Research Institute (UK DRI), London, United Kingdom
| | - Yoshikatsu Hayashi
- Brain Embodiment Laboratory, Biomedical Engineering, School of Biological Sciences, University of Reading, Reading, United Kingdom
| | - Slawomir J. Nasuto
- Brain Embodiment Laboratory, Biomedical Engineering, School of Biological Sciences, University of Reading, Reading, United Kingdom
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612
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Huang WA, Stitt IM, Negahbani E, Passey DJ, Ahn S, Davey M, Dannhauer M, Doan TT, Hoover AC, Peterchev AV, Radtke-Schuller S, Fröhlich F. Transcranial alternating current stimulation entrains alpha oscillations by preferential phase synchronization of fast-spiking cortical neurons to stimulation waveform. Nat Commun 2021; 12:3151. [PMID: 34035240 PMCID: PMC8149416 DOI: 10.1038/s41467-021-23021-2] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Accepted: 03/24/2021] [Indexed: 12/12/2022] Open
Abstract
Computational modeling and human studies suggest that transcranial alternating current stimulation (tACS) modulates alpha oscillations by entrainment. Yet, a direct examination of how tACS interacts with neuronal spiking activity that gives rise to the alpha oscillation in the thalamo-cortical system has been lacking. Here, we demonstrate how tACS entrains endogenous alpha oscillations in head-fixed awake ferrets. We first show that endogenous alpha oscillations in the posterior parietal cortex drive the primary visual cortex and the higher-order visual thalamus. Spike-field coherence is largest for the alpha frequency band, and presumed fast-spiking inhibitory interneurons exhibit strongest coupling to this oscillation. We then apply alpha-tACS that results in a field strength comparable to what is commonly used in humans (<0.5 mV/mm). Both in these ferret experiments and in a computational model of the thalamo-cortical system, tACS entrains alpha oscillations by following the theoretically predicted Arnold tongue. Intriguingly, the fast-spiking inhibitory interneurons exhibit a stronger entrainment response to tACS in both the ferret experiments and the computational model, likely due to their stronger endogenous coupling to the alpha oscillation. Our findings demonstrate the in vivo mechanism of action for the modulation of the alpha oscillation by tACS.
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Affiliation(s)
- Wei A Huang
- 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
| | - Iain M Stitt
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
- Carolina Center for Neurostimulation, University of North Carolina, Chapel Hill, NC, USA
| | - Ehsan Negahbani
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
- Carolina Center for Neurostimulation, University of North Carolina, Chapel Hill, NC, USA
| | - D J Passey
- Carolina Center for Neurostimulation, University of North Carolina, Chapel Hill, NC, USA
- Department of Mathematics, University of North Carolina, Chapel Hill, NC, USA
| | - Sangtae Ahn
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
- Carolina Center for Neurostimulation, University of North Carolina, Chapel Hill, NC, USA
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, South Korea
| | - Marshall Davey
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
- Neuroscience Center, University of North Carolina, Chapel Hill, NC, USA
| | - Moritz Dannhauer
- Department of Psychiatry and Behavioral Science, Duke University, Durham, NC, USA
| | - Thien T Doan
- Department of Psychiatry and Behavioral Science, Duke University, Durham, NC, USA
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Anna C Hoover
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Angel V Peterchev
- Department of Psychiatry and Behavioral Science, Duke University, Durham, NC, USA
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
- Department of Neurosurgery, Duke University, Durham, NC, USA
| | - Susanne Radtke-Schuller
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
- Carolina Center for Neurostimulation, University of North Carolina, Chapel Hill, NC, USA
| | - Flavio Fröhlich
- 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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613
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Gyurkovics M, Clements GM, Low KA, Fabiani M, Gratton G. The impact of 1/f activity and baseline correction on the results and interpretation of time-frequency analyses of EEG/MEG data: A cautionary tale. Neuroimage 2021; 237:118192. [PMID: 34048899 PMCID: PMC8354524 DOI: 10.1016/j.neuroimage.2021.118192] [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: 03/18/2021] [Accepted: 05/19/2021] [Indexed: 11/30/2022] Open
Abstract
Typically, time-frequency analysis (TFA) of electrophysiological data is aimed at isolating narrowband signals (oscillatory activity) from broadband non-oscillatory (1/f) activity, so that changes in oscillatory activity resulting from experimental manipulations can be assessed. A widely used method to do this is to convert the data to the decibel (dB) scale through baseline division and log transformation. This procedure assumes that, for each frequency, sources of power (i.e., oscillations and 1/f activity) scale by the same factor relative to the baseline (multiplicative model). This assumption may be incorrect when signal and noise are independent contributors to the power spectrum (additive model). Using resting-state EEG data from 80 participants, we found that the level of 1/f activity and alpha power are not positively correlated within participants, in line with the additive but not the multiplicative model. Then, to assess the effects of dB conversion on data that violate the multiplicativity assumption, we simulated a mixed design study with one between-subject (noise level, i.e., level of 1/f activity) and one within-subject (signal amplitude, i.e., amplitude of oscillatory activity added onto the background 1/f activity) factor. The effect size of the noise level × signal amplitude interaction was examined as a function of noise difference between groups, following dB conversion. Findings revealed that dB conversion led to the over- or under-estimation of the true interaction effect when groups differing in 1/f levels were compared, and it also led to the emergence of illusory interactions when none were present. This is because signal amplitude was systematically underestimated in the noisier compared to the less noisy group. Hence, we recommend testing whether the level of 1/f activity differs across groups or conditions and using multiple baseline correction strategies to validate results if it does. Such a situation may be particularly common in aging, developmental, or clinical studies.
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Affiliation(s)
- Máté Gyurkovics
- Beckman Institute for Advanced Sciences and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.
| | - Grace M Clements
- Beckman Institute for Advanced Sciences and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States; Department of Psychology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Kathy A Low
- Beckman Institute for Advanced Sciences and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Monica Fabiani
- Beckman Institute for Advanced Sciences and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States; Department of Psychology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Gabriele Gratton
- Beckman Institute for Advanced Sciences and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States; Department of Psychology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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614
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Kraus B, Salvador CE, Kamikubo A, Hsiao NC, Hu JF, Karasawa M, Kitayama S. Oscillatory alpha power at rest reveals an independent self: A cross-cultural investigation. Biol Psychol 2021; 163:108118. [PMID: 34019966 DOI: 10.1016/j.biopsycho.2021.108118] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 05/03/2021] [Accepted: 05/14/2021] [Indexed: 11/19/2022]
Abstract
In the current cultural psychology literature, it is commonly assumed that the personal self is cognitively more salient for those with an independent (vs. interdependent) self-construal (SC). So far, however, this assumption remains largely untested. Here, we drew on evidence that resting state alpha power (RSAP) reflects mental processes constituting the personal self, and tested whether RSAP is positively correlated with independent (vs. interdependent) SC. Study 1 tested European Americans and Taiwanese, whereas Study 2 tested European Americans and Japanese (total N = 164). A meta-analysis performed on the combined data confirmed a reliable association between independent (vs. interdependent) SC and RSAP. However, this association was only reliable when participants had their eyes closed. Even though European Americans were consistently more independent than East Asians, RSAP was no greater for European Americans than for East Asians. Our data helps explore a missing link in the theorizing of contemporary cultural psychology.
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Affiliation(s)
- Brian Kraus
- Northwestern University, Department of Psychology, United States.
| | | | - Aya Kamikubo
- Tokyo Woman's Christian University, Graduate School of Humanities and Sciences, Japan
| | - Nai-Ching Hsiao
- National Cheng Kung University, Department of Psychology, Taiwan
| | - Jon-Fan Hu
- National Cheng Kung University, Department of Psychology, Taiwan
| | - Mayumi Karasawa
- Tokyo Woman's Christian University, Department of Communication, Japan
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615
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Belova EM, Semenova U, Gamaleya AA, Tomskiy AA, Sedov A. Is there a single beta oscillation band interfering with movement in Parkinson's disease? Eur J Neurosci 2021; 54:4381-4391. [PMID: 33905150 DOI: 10.1111/ejn.15257] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 04/16/2021] [Accepted: 04/16/2021] [Indexed: 11/29/2022]
Abstract
Beta oscillations in basal ganglia are considered to contribute to motor dysfunction in Parkinson's disease (PD). However, there is a high variety in frequency borders for beta oscillations between studies, which complicates the comparison and interpretation of results. Here we aimed to study the homogeneity of oscillations in the broad "beta" range (8-30 Hz) and their implication to motor functioning in PD. For this purpose, we recorded local field potentials (LFP) in the subthalamic nucleus (STN) during 34 deep brain stimulation surgeries. We identified spectral features of LFP recordings in the range 8-30 Hz to search for candidate sub-regions of stable oscillations and assessed their association with clinical scores on the contralateral side of the body and sensitivity to motor tests. Lower frequency oscillations (8-16 Hz) had a significant positive association with bradykinesia score. During voluntary movements, we observed a significant increase in LFP power in the 12-16 Hz range and a decrease in the 18-26 Hz range. We may conclude that the 8-30 Hz oscillation range includes oscillations with different functional features-sensitivity and responsiveness to movement, and clinical symptoms, which should be taken into account in further studies of beta oscillations association with PD pathophysiology. These data assume the coexistence of several frequency domains within beta range that are modulated in different ways under dopaminergic regulation and motor processing in human STN.
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Affiliation(s)
- Elena M Belova
- Laboratory of Human Cell Neurophysiology, Semenov Institute of Chemical Physics RAS, Moscow, Russia
| | - Ulia Semenova
- Laboratory of Human Cell Neurophysiology, Semenov Institute of Chemical Physics RAS, Moscow, Russia
| | - Anna A Gamaleya
- Scientific Advisory Department, Federal State Autonomous Institution, N. N. Burdenko National Medical Research Center of Neurosurgery, Moscow, Russia
| | - Alexey A Tomskiy
- Group of functional neurosurgery, Federal State Autonomous Institution, N. N. Burdenko National Medical Research Center of Neurosurgery, Moscow, Russia
| | - Alexey Sedov
- Laboratory of Human Cell Neurophysiology, Semenov Institute of Chemical Physics RAS, Moscow Institute of Physics and Technology, Moscow, Russia
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616
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Singh SH, Peterson SM, Rao RPN, Brunton BW. Mining naturalistic human behaviors in long-term video and neural recordings. J Neurosci Methods 2021; 358:109199. [PMID: 33910024 DOI: 10.1016/j.jneumeth.2021.109199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 04/07/2021] [Accepted: 04/19/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND Recent technological advances in brain recording and machine learning algorithms are enabling the study of neural activity underlying spontaneous human behaviors, beyond the confines of cued, repeated trials. However, analyzing such unstructured data lacking a priori experimental design remains a significant challenge, especially when the data is multi-modal and long-term. NEW METHOD Here we describe an automated, behavior-first approach for analyzing simultaneously recorded long-term, naturalistic electrocorticography (ECoG) and behavior video data. We identify and characterize spontaneous human upper-limb movements by combining computer vision, discrete latent-variable modeling, and string pattern-matching on the video. RESULTS Our pipeline discovers and annotates over 40,000 instances of naturalistic arm movements in long term (7-9 day) behavioral videos, across 12 subjects. Analysis of the simultaneously recorded brain data reveals neural signatures of movement that corroborate previous findings. Our pipeline produces large training datasets for brain-computer interfacing applications, and we show decoding results from a movement initiation detection task. COMPARISON WITH EXISTING METHODS Spontaneous movements capture real-world neural and behavior variability that is missing from traditional cued tasks. Building beyond window-based movement detection metrics, our unsupervised discretization scheme produces a queryable pose representation, allowing localization of movements with finer temporal resolution. CONCLUSIONS Our work addresses the unique analytic challenges of studying naturalistic human behaviors and contributes methods that may generalize to other neural recording modalities beyond ECoG. We publish our curated dataset and believe that it will be a valuable resource for future studies of naturalistic movements.
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Affiliation(s)
- Satpreet H Singh
- Department of Electrical and Computer Engineering, University of Washington, Seattle, USA
| | - Steven M Peterson
- Department of Biology, University of Washington, Seattle, USA; eScience Institute, University of Washington, Seattle, USA
| | - Rajesh P N Rao
- Department of Electrical and Computer Engineering, University of Washington, Seattle, USA; Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA; Center for Neurotechnology, University of Washington, Seattle, USA; University of Washington Institute for Neuroengineering, Seattle, USA
| | - Bingni W Brunton
- Department of Biology, University of Washington, Seattle, USA; eScience Institute, University of Washington, Seattle, USA; University of Washington Institute for Neuroengineering, Seattle, USA.
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617
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Zhou Y, Sheremet A, Kennedy JP, DiCola NM, Maciel CB, Burke SN, Maurer AP. Spectrum Degradation of Hippocampal LFP During Euthanasia. Front Syst Neurosci 2021; 15:647011. [PMID: 33967707 PMCID: PMC8102791 DOI: 10.3389/fnsys.2021.647011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 03/23/2021] [Indexed: 11/13/2022] Open
Abstract
The hippocampal local field potential (LFP) exhibits a strong correlation with behavior. During rest, the theta rhythm is not prominent, but during active behavior, there are strong rhythms in the theta, theta harmonics, and gamma ranges. With increasing running velocity, theta, theta harmonics and gamma increase in power and in cross-frequency coupling, suggesting that neural entrainment is a direct consequence of the total excitatory input. While it is common to study the parametric range between the LFP and its complementing power spectra between deep rest and epochs of high running velocity, it is also possible to explore how the spectra degrades as the energy is completely quenched from the system. Specifically, it is unknown whether the 1/f slope is preserved as synaptic activity becomes diminished, as low frequencies are generated by large pools of neurons while higher frequencies comprise the activity of more local neuronal populations. To test this hypothesis, we examined rat LFPs recorded from the hippocampus and entorhinal cortex during barbiturate overdose euthanasia. Within the hippocampus, the initial stage entailed a quasi-stationary LFP state with a power-law feature in the power spectral density. In the second stage, there was a successive erosion of power from high- to low-frequencies in the second stage that continued until the only dominant remaining power was <20 Hz. This stage was followed by a rapid collapse of power spectrum toward the absolute electrothermal noise background. As the collapse of activity occurred later in hippocampus compared with medial entorhinal cortex, it suggests that the ability of a neural network to maintain the 1/f slope with decreasing energy is a function of general connectivity. Broadly, these data support the energy cascade theory where there is a cascade of energy from large cortical populations into smaller loops, such as those that supports the higher frequency gamma rhythm. As energy is pulled from the system, neural entrainment at gamma frequency (and higher) decline first. The larger loops, comprising a larger population, are fault-tolerant to a point capable of maintaining their activity before a final collapse.
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Affiliation(s)
- Yuchen Zhou
- Engineering School of Sustainable Infrastructure and Environment, University of Florida, Gainesville, FL, United States
| | - Alex Sheremet
- Engineering School of Sustainable Infrastructure and Environment, University of Florida, Gainesville, FL, United States.,Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, United States
| | - Jack P Kennedy
- Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, United States
| | - Nicholas M DiCola
- Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, United States
| | - Carolina B Maciel
- Division of Neurocritical Care, Department of Neurology, University of Florida, Gainesville, FL, United States
| | - Sara N Burke
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Andrew P Maurer
- Engineering School of Sustainable Infrastructure and Environment, University of Florida, Gainesville, FL, United States.,Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, United States.,Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
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618
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Jarzebowski P, Tang CS, Paulsen O, Hay YA. Impaired spatial learning and suppression of sharp wave ripples by cholinergic activation at the goal location. eLife 2021; 10:65998. [PMID: 33821790 PMCID: PMC8064750 DOI: 10.7554/elife.65998] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 04/05/2021] [Indexed: 02/06/2023] Open
Abstract
The hippocampus plays a central role in long-term memory formation, and different hippocampal network states are thought to have different functions in this process. These network states are controlled by neuromodulatory inputs, including the cholinergic input from the medial septum. Here, we used optogenetic stimulation of septal cholinergic neurons to understand how cholinergic activity affects different stages of spatial memory formation in a reward-based navigation task in mice. We found that optogenetic stimulation of septal cholinergic neurons (1) impaired memory formation when activated at goal location but not during navigation, (2) reduced sharp wave ripple (SWR) incidence at goal location, and (3) reduced SWR incidence and enhanced theta-gamma oscillations during sleep. These results underscore the importance of appropriate timing of cholinergic input in long-term memory formation, which might help explain the limited success of cholinesterase inhibitor drugs in treating memory impairment in Alzheimer’s disease.
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Affiliation(s)
- Przemyslaw Jarzebowski
- Department of Physiology, Development and Neuroscience, Physiological Laboratory, Cambridge, United Kingdom
| | - Clara S Tang
- Department of Physiology, Development and Neuroscience, Physiological Laboratory, Cambridge, United Kingdom
| | - Ole Paulsen
- Department of Physiology, Development and Neuroscience, Physiological Laboratory, Cambridge, United Kingdom
| | - Y Audrey Hay
- Department of Physiology, Development and Neuroscience, Physiological Laboratory, Cambridge, United Kingdom
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619
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Carter Leno V, Pickles A, van Noordt S, Huberty S, Desjardins J, Webb SJ, Elsabbagh M. 12-Month peak alpha frequency is a correlate but not a longitudinal predictor of non-verbal cognitive abilities in infants at low and high risk for autism spectrum disorder. Dev Cogn Neurosci 2021; 48:100938. [PMID: 33714056 PMCID: PMC7966984 DOI: 10.1016/j.dcn.2021.100938] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 12/10/2020] [Accepted: 03/01/2021] [Indexed: 10/26/2022] Open
Abstract
Although studies of PAF in individuals with autism spectrum disorder (ASD) report group differences and associations with non-verbal cognitive ability, it is not known how PAF relates to familial risk for ASD, and whether similar associations with cognition in are present in infancy. Using a large multi-site prospective longitudinal dataset of infants with low and high familial risk for ASD, metrics of PAF at 12 months were extracted and growth curves estimated for cognitive development between 12-36 months. Analyses tested whether PAF 1) differs between low and high risk infants, 2) is associated with concurrent non-verbal/verbal cognitive ability and 3) predicts developmental change in non-verbal/verbal ability. Moderation of associations between PAF and cognitive ability by familial risk status was also tested. No differences in 12-month PAF were found between low and high risk infants. PAF was associated with concurrent non-verbal cognitive ability, but did not predict change in non-verbal cognitive over development. No associations were found between PAF and verbal ability, along with no evidence of moderation. PAF is not related to familial risk for ASD, and is a neural marker of concurrent non-verbal cognitive ability, but not verbal ability, in young infants at low and high risk for ASD.
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Affiliation(s)
| | - Andrew Pickles
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Stefon van Noordt
- Montreal Neurological Institute, Azrieli Centre for Autism Research, McGill University, Montréal, Canada
| | - Scott Huberty
- Montreal Neurological Institute, Azrieli Centre for Autism Research, McGill University, Montréal, Canada
| | | | - Sara Jane Webb
- Center on Child Health, Behavior and Development, Seattle Children's Research Institute, Seattle, WA, USA
| | - Mayada Elsabbagh
- Montreal Neurological Institute, Azrieli Centre for Autism Research, McGill University, Montréal, Canada
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620
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Yang W, Chini M, Pöpplau JA, Formozov A, Dieter A, Piechocinski P, Rais C, Morellini F, Sporns O, Hanganu-Opatz IL, Wiegert JS. Anesthetics fragment hippocampal network activity, alter spine dynamics, and affect memory consolidation. PLoS Biol 2021; 19:e3001146. [PMID: 33793545 PMCID: PMC8016109 DOI: 10.1371/journal.pbio.3001146] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 02/15/2021] [Indexed: 02/07/2023] Open
Abstract
General anesthesia is characterized by reversible loss of consciousness accompanied by transient amnesia. Yet, long-term memory impairment is an undesirable side effect. How different types of general anesthetics (GAs) affect the hippocampus, a brain region central to memory formation and consolidation, is poorly understood. Using extracellular recordings, chronic 2-photon imaging, and behavioral analysis, we monitor the effects of isoflurane (Iso), medetomidine/midazolam/fentanyl (MMF), and ketamine/xylazine (Keta/Xyl) on network activity and structural spine dynamics in the hippocampal CA1 area of adult mice. GAs robustly reduced spiking activity, decorrelated cellular ensembles, albeit with distinct activity signatures, and altered spine dynamics. CA1 network activity under all 3 anesthetics was different to natural sleep. Iso anesthesia most closely resembled unperturbed activity during wakefulness and sleep, and network alterations recovered more readily than with Keta/Xyl and MMF. Correspondingly, memory consolidation was impaired after exposure to Keta/Xyl and MMF, but not Iso. Thus, different anesthetics distinctly alter hippocampal network dynamics, synaptic connectivity, and memory consolidation, with implications for GA strategy appraisal in animal research and clinical settings.
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Affiliation(s)
- Wei Yang
- Research Group Synaptic Wiring and Information Processing, Center for Molecular Neurobiology Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Mattia Chini
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jastyn A. Pöpplau
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Andrey Formozov
- Research Group Synaptic Wiring and Information Processing, Center for Molecular Neurobiology Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alexander Dieter
- Research Group Synaptic Wiring and Information Processing, Center for Molecular Neurobiology Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Patrick Piechocinski
- Research Group Synaptic Wiring and Information Processing, Center for Molecular Neurobiology Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Cynthia Rais
- Research Group Synaptic Wiring and Information Processing, Center for Molecular Neurobiology Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Fabio Morellini
- Research Group Behavioral Biology, Center for Molecular Neurobiology Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, United States of America
- Indiana University Network Science Institute, Indiana University, Bloomington, Indiana, United States of America
| | - Ileana L. Hanganu-Opatz
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - J. Simon Wiegert
- Research Group Synaptic Wiring and Information Processing, Center for Molecular Neurobiology Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- * E-mail:
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621
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Ostlund BD, Alperin BR, Drew T, Karalunas SL. Behavioral and cognitive correlates of the aperiodic (1/f-like) exponent of the EEG power spectrum in adolescents with and without ADHD. Dev Cogn Neurosci 2021; 48:100931. [PMID: 33535138 PMCID: PMC7856425 DOI: 10.1016/j.dcn.2021.100931] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 01/15/2021] [Accepted: 01/27/2021] [Indexed: 02/07/2023] Open
Abstract
Efficient information processing facilitates cognition and may be disrupted in a number of neurodevelopmental conditions. And yet, the role of inefficient information processing and its neural underpinnings remains poorly understood. In the current study, we examined the cognitive and behavioral correlates of the aperiodic exponent of the electroencephalogram (EEG) power spectrum, a putative marker of disrupted, inefficient neural communication, in a sample of adolescents with and without ADHD (n = 184 nADHD = 87; Mage = 13.95 years, SD = 1.36). Exponents were calculated via FOOOF (Donoghue et al., 2020a) from EEG data recorded during an 8-minute baseline episode. Reaction time speed and variability, as well as drift diffusion parameters (including the drift rate parameter, a cognitive parameter directly related to inefficient information processing) were calculated. Adolescents with ADHD had smaller aperiodic exponents (a "flattened" EEG power spectrum) relative to their typically-developing peers. After controlling for ADHD, aperiodic exponents were related to reaction time variability and the drift rate parameter, but not in the expected direction. Our findings lend support for the aperiodic exponent as a neural correlate of disrupted information processing, and provide insight into the role of cortical excitation/inhibition imbalance in the pathophysiology of ADHD.
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Affiliation(s)
- Brendan D Ostlund
- Department of Psychology, The Pennsylvania State University, United States.
| | | | - Trafton Drew
- Department of Psychology, University of Utah, United States
| | - Sarah L Karalunas
- Department of Psychological Sciences, Purdue University, United States
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622
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Martínez-Cañada P, Ness TV, Einevoll GT, Fellin T, Panzeri S. Computation of the electroencephalogram (EEG) from network models of point neurons. PLoS Comput Biol 2021; 17:e1008893. [PMID: 33798190 PMCID: PMC8046357 DOI: 10.1371/journal.pcbi.1008893] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 04/14/2021] [Accepted: 03/18/2021] [Indexed: 12/28/2022] Open
Abstract
The electroencephalogram (EEG) is a major tool for non-invasively studying brain function and dysfunction. Comparing experimentally recorded EEGs with neural network models is important to better interpret EEGs in terms of neural mechanisms. Most current neural network models use networks of simple point neurons. They capture important properties of cortical dynamics, and are numerically or analytically tractable. However, point neurons cannot generate an EEG, as EEG generation requires spatially separated transmembrane currents. Here, we explored how to compute an accurate approximation of a rodent's EEG with quantities defined in point-neuron network models. We constructed different approximations (or proxies) of the EEG signal that can be computed from networks of leaky integrate-and-fire (LIF) point neurons, such as firing rates, membrane potentials, and combinations of synaptic currents. We then evaluated how well each proxy reconstructed a ground-truth EEG obtained when the synaptic currents of the LIF model network were fed into a three-dimensional network model of multicompartmental neurons with realistic morphologies. Proxies based on linear combinations of AMPA and GABA currents performed better than proxies based on firing rates or membrane potentials. A new class of proxies, based on an optimized linear combination of time-shifted AMPA and GABA currents, provided the most accurate estimate of the EEG over a wide range of network states. The new linear proxies explained 85-95% of the variance of the ground-truth EEG for a wide range of network configurations including different cell morphologies, distributions of presynaptic inputs, positions of the recording electrode, and spatial extensions of the network. Non-linear EEG proxies using a convolutional neural network (CNN) on synaptic currents increased proxy performance by a further 2-8%. Our proxies can be used to easily calculate a biologically realistic EEG signal directly from point-neuron simulations thus facilitating a quantitative comparison between computational models and experimental EEG recordings.
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Affiliation(s)
- Pablo Martínez-Cañada
- Neural Coding Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
- Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
| | - Torbjørn V. Ness
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Gaute T. Einevoll
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
- Department of Physics, University of Oslo, Oslo, Norway
| | - Tommaso Fellin
- Neural Coding Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
- Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
| | - Stefano Panzeri
- Neural Coding Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
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623
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Berto S, Fontenot MR, Seger S, Ayhan F, Caglayan E, Kulkarni A, Douglas C, Tamminga CA, Lega BC, Konopka G. Gene-expression correlates of the oscillatory signatures supporting human episodic memory encoding. Nat Neurosci 2021; 24:554-564. [PMID: 33686299 PMCID: PMC8016736 DOI: 10.1038/s41593-021-00803-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 01/19/2021] [Indexed: 12/13/2022]
Abstract
In humans, brain oscillations support critical features of memory formation. However, understanding the molecular mechanisms underlying this activity remains a major challenge. Here, we measured memory-sensitive oscillations using intracranial electroencephalography recordings from the temporal cortex of patients performing an episodic memory task. When these patients subsequently underwent resection, we employed transcriptomics on the temporal cortex to link gene expression with brain oscillations and identified genes correlated with oscillatory signatures of memory formation across six frequency bands. A co-expression analysis isolated oscillatory signature-specific modules associated with neuropsychiatric disorders and ion channel activity, with highly correlated genes exhibiting strong connectivity within these modules. Using single-nucleus transcriptomics, we further revealed that these modules are enriched for specific classes of both excitatory and inhibitory neurons, and immunohistochemistry confirmed expression of highly correlated genes. This unprecedented dataset of patient-specific brain oscillations coupled to genomics unlocks new insights into the genetic mechanisms that support memory encoding.
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Affiliation(s)
- Stefano Berto
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, USA
| | - Miles R Fontenot
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, USA
| | - Sarah Seger
- Department of Neurosurgery, UT Southwestern Medical Center, Dallas, TX, USA
| | - Fatma Ayhan
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, USA
| | - Emre Caglayan
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, USA
| | | | - Connor Douglas
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, USA
| | - Carol A Tamminga
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA
| | - Bradley C Lega
- Department of Neurosurgery, UT Southwestern Medical Center, Dallas, TX, USA.
| | - Genevieve Konopka
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, USA.
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624
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Köhler MHA, Demarchi G, Weisz N. Cochlear activity in silent cue-target intervals shows a theta-rhythmic pattern and is correlated to attentional alpha and theta modulations. BMC Biol 2021; 19:48. [PMID: 33726746 PMCID: PMC7968255 DOI: 10.1186/s12915-021-00992-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 02/24/2021] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND A long-standing debate concerns where in the processing hierarchy of the central nervous system (CNS) selective attention takes effect. In the auditory system, cochlear processes can be influenced via direct and mediated (by the inferior colliculus) projections from the auditory cortex to the superior olivary complex (SOC). Studies illustrating attentional modulations of cochlear responses have so far been limited to sound-evoked responses. The aim of the present study is to investigate intermodal (audiovisual) selective attention in humans simultaneously at the cortical and cochlear level during a stimulus-free cue-target interval. RESULTS We found that cochlear activity in the silent cue-target intervals was modulated by a theta-rhythmic pattern (~ 6 Hz). While this pattern was present independently of attentional focus, cochlear theta activity was clearly enhanced when attending to the upcoming auditory input. On a cortical level, classical posterior alpha and beta power enhancements were found during auditory selective attention. Interestingly, participants with a stronger release of inhibition in auditory brain regions show a stronger attentional modulation of cochlear theta activity. CONCLUSIONS These results hint at a putative theta-rhythmic sampling of auditory input at the cochlear level. Furthermore, our results point to an interindividual variable engagement of efferent pathways in an attentional context that are linked to processes within and beyond processes in auditory cortical regions.
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Affiliation(s)
- Moritz Herbert Albrecht Köhler
- Centre for Cognitive Neuroscience, University of Salzburg, Hellbrunner Straße 34, 5020, Salzburg, Austria.
- Department of Psychology, University of Salzburg, Hellbrunner Straße 34, 5020, Salzburg, Austria.
| | - Gianpaolo Demarchi
- Centre for Cognitive Neuroscience, University of Salzburg, Hellbrunner Straße 34, 5020, Salzburg, Austria
- Department of Psychology, University of Salzburg, Hellbrunner Straße 34, 5020, Salzburg, Austria
| | - Nathan Weisz
- Centre for Cognitive Neuroscience, University of Salzburg, Hellbrunner Straße 34, 5020, Salzburg, Austria
- Department of Psychology, University of Salzburg, Hellbrunner Straße 34, 5020, Salzburg, Austria
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625
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van Heumen S, Moreau JT, Simard-Tremblay E, Albrecht S, Dudley RWR, Baillet S. Case Report: Aperiodic Fluctuations of Neural Activity in the Ictal MEG of a Child With Drug-Resistant Fronto-Temporal Epilepsy. Front Hum Neurosci 2021; 15:646426. [PMID: 33746727 PMCID: PMC7969518 DOI: 10.3389/fnhum.2021.646426] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Accepted: 02/12/2021] [Indexed: 11/24/2022] Open
Abstract
Successful surgical treatment of patients with focal drug-resistant epilepsy remains challenging, especially in cases for which it is difficult to define the area of cortex from which seizures originate, the seizure onset zone (SOZ). Various diagnostic methods are needed to select surgical candidates and determine the extent of resection. Interictal magnetoencephalography (MEG) with source imaging has proven to be useful for presurgical evaluation, but the use of ictal MEG data remains limited. The purpose of the present study was to determine whether pre-ictal variations of spectral properties of neural activity from ictal MEG recordings are predictive of SOZ location.We performed a 4 h overnight MEG recording in an 8-year-old child with drug-resistant focal epilepsy of suspected right fronto-temporal origin and captured one ~45-s seizure. The patient underwent a right temporal resection from the anterior temporal neocortex and amygdala to the mid-posterior temporal neocortex, sparing the hippocampus proper. She remains seizure-free 21 months postoperatively. The histopathological assessment confirmed frank focal cortical dysplasia (FCD) type IIa in the MEG-defined SOZ, which was based on source imaging of averaged ictal spikes at seizure onset. We investigated temporal changes (inter-ictal, pre-ictal, and ictal periods) together with spatial differences (SOZ vs. control regions) in spectral parameters of background brain activity, namely the aperiodic broadband offset and slope, and assessed how they confounded the interpretation of apparent variations of signal power in typical electrophysiological bands. Our data show that the SOZ was associated with a higher aperiodic offset and exponent during the seizure compared to control regions. Both parameters increased in all regions from 2 min before the seizure onwards. Regions anatomically closer to the SOZ also expressed higher values compared to contralateral regions, potentially indicating ictal spread. We also show that narrow-band power changes were caused by these fluctuations in the aperiodic component of ongoing brain activity. Our results indicate that the broadband aperiodic component of ongoing brain activity cannot be reduced to background noise of no physiological interest, and rather may be indicative of the neuropathophysiology of the SOZ. We believe these findings will inspire future studies of ictal MEG cases and confirm their significance.
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Affiliation(s)
- Saskia van Heumen
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Jeremy T. Moreau
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
- Department of Pediatric Surgery, Division of Neurosurgery, Montreal Children’s Hospital, Montreal, QC, Canada
- Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Elisabeth Simard-Tremblay
- Department of Pediatrics, Division of Pediatric Neurology, Montreal Children’s Hospital, McGill University, Montreal, QC, Canada
| | - Steffen Albrecht
- Department of Pathology, Montreal Children’s Hospital, McGill University, Montreal, QC, Canada
| | - Roy WR. Dudley
- Department of Pediatric Surgery, Division of Neurosurgery, Montreal Children’s Hospital, Montreal, QC, Canada
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
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626
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Wilkinson CL, Nelson CA. Increased aperiodic gamma power in young boys with Fragile X Syndrome is associated with better language ability. Mol Autism 2021; 12:17. [PMID: 33632320 PMCID: PMC7908768 DOI: 10.1186/s13229-021-00425-x] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 02/16/2021] [Indexed: 02/17/2023] Open
Abstract
Background The lack of robust and reliable clinical biomarkers in Fragile X Syndrome (FXS), the most common inherited form of intellectual disability, has limited the successful translation of bench-to-bedside therapeutics. While numerous drugs have shown promise in reversing synaptic and behavioral phenotypes in mouse models of FXS, none have demonstrated clinical efficacy in humans. Electroencephalographic (EEG) measures have been identified as candidate biomarkers as EEG recordings of both adults with FXS and mouse models of FXS consistently exhibit alterations in resting state and task-related activity. However, the developmental timing of these EEG differences is not known as thus far EEG studies have not focused on young children with FXS. Further, understanding how EEG differences are associated with core symptoms of FXS is crucial to successful use of EEG as a biomarker, and may improve our understanding of the disorder. Methods Resting-state EEG was collected from FXS boys with full mutation of Fmr1 (2.5–7 years old, n = 11) and compared with both age-matched (n = 12) and cognitive-matched (n = 12) typically developing boys. Power spectra (including aperiodic and periodic components) were compared using non-parametric cluster-based permutation testing. Associations between 30 and 50 Hz gamma power and cognitive, language, and behavioral measures were evaluated using Pearson correlation and linear regression with age as a covariate. Results FXS participants showed increased power in the beta/gamma range (~ 25–50 Hz) across multiple brain regions. Both a reduction in the aperiodic (1/f) slope and increase in beta/gamma periodic activity contributed to the significant increase in high-frequency power. Increased gamma power, driven by the aperiodic component, was associated with better language ability in the FXS group. No association was observed between gamma power and parent report measures of behavioral challenges, sensory hypersensitivities, or adaptive behaviors. Limitations The study sample size was small, although comparable to other human studies in rare-genetic disorders. Findings are also limited to males in the age range studied. Conclusions Resting-state EEG measures from this study in young boys with FXS identified similar increases in gamma power previously reported in adults and mouse models. The observed positive association between resting state aperiodic gamma power and language development supports hypotheses that alterations in some EEG measures may reflect ongoing compensatory mechanisms. Supplementary Information The online version contains supplementary material available at 10.1186/s13229-021-00425-x.
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Affiliation(s)
- Carol L Wilkinson
- Division of Developmental Medicine, Boston Children's Hospital, 1 Autumn Street, 6th Floor, Boston, MA, 02115, USA.
| | - Charles A Nelson
- Division of Developmental Medicine, Boston Children's Hospital, 1 Autumn Street, 6th Floor, Boston, MA, 02115, USA
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627
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Waschke L, Kloosterman NA, Obleser J, Garrett DD. Behavior needs neural variability. Neuron 2021; 109:751-766. [PMID: 33596406 DOI: 10.1016/j.neuron.2021.01.023] [Citation(s) in RCA: 109] [Impact Index Per Article: 36.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 11/16/2020] [Accepted: 01/22/2021] [Indexed: 01/26/2023]
Abstract
Human and non-human animal behavior is highly malleable and adapts successfully to internal and external demands. Such behavioral success stands in striking contrast to the apparent instability in neural activity (i.e., variability) from which it arises. Here, we summon the considerable evidence across scales, species, and imaging modalities that neural variability represents a key, undervalued dimension for understanding brain-behavior relationships at inter- and intra-individual levels. We believe that only by incorporating a specific focus on variability will the neural foundation of behavior be comprehensively understood.
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Affiliation(s)
- Leonhard Waschke
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, 14195 Berlin, Germany; Center for Lifespan Psychology, Max Planck Institute for Human Development, 14195 Berlin, Germany.
| | - Niels A Kloosterman
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, 14195 Berlin, Germany; Center for Lifespan Psychology, Max Planck Institute for Human Development, 14195 Berlin, Germany
| | - Jonas Obleser
- Department of Psychology, University of Lübeck, 23562 Lübeck, Germany; Center of Brain, Behavior, and Metabolism, University of Lübeck, 23562 Lübeck, Germany
| | - Douglas D Garrett
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, 14195 Berlin, Germany; Center for Lifespan Psychology, Max Planck Institute for Human Development, 14195 Berlin, Germany
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628
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Parto Dezfouli M, Davoudi S, Knight RT, Daliri MR, Johnson EL. Prefrontal lesions disrupt oscillatory signatures of spatiotemporal integration in working memory. Cortex 2021; 138:113-126. [PMID: 33684625 DOI: 10.1016/j.cortex.2021.01.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 10/22/2020] [Accepted: 01/28/2021] [Indexed: 12/24/2022]
Abstract
How does the human brain integrate spatial and temporal information into unified mnemonic representations? Building on classic theories of feature binding, we first define the oscillatory signatures of integrating 'where' and 'when' information in working memory (WM) and then investigate the role of prefrontal cortex (PFC) in spatiotemporal integration. Fourteen individuals with lateral PFC damage and 20 healthy controls completed a visuospatial WM task while electroencephalography (EEG) was recorded. On each trial, two shapes were presented sequentially in a top/bottom spatial orientation. We defined EEG signatures of spatiotemporal integration by comparing the maintenance of two possible where-when configurations: the first shape presented on top and the reverse. Frontal delta-theta (δθ; 2-7 Hz) activity, frontal-posterior δθ functional connectivity, lateral posterior event-related potentials, and mesial posterior alpha phase-to-gamma amplitude coupling dissociated the two configurations in controls. WM performance and frontal and mesial posterior signatures of spatiotemporal integration were diminished in PFC lesion patients, whereas lateral posterior signatures were intact. These findings reveal both PFC-dependent and independent substrates of spatiotemporal integration and link optimal performance to PFC.
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Affiliation(s)
- Mohsen Parto Dezfouli
- School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Tehran, Iran; Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.
| | - Saeideh Davoudi
- Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran
| | - Robert T Knight
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA; Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
| | - Mohammad Reza Daliri
- School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Tehran, Iran; Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.
| | - Elizabeth L Johnson
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA; Life-Span Cognitive Neuroscience Program, Institute of Gerontology, Wayne State University, Detroit, MI, USA.
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629
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Theta Synchrony Is Increased near Neural Populations That Are Active When Initiating Instructed Movement. eNeuro 2021; 8:ENEURO.0252-20.2020. [PMID: 33355232 PMCID: PMC7901148 DOI: 10.1523/eneuro.0252-20.2020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 11/29/2020] [Accepted: 12/05/2020] [Indexed: 12/21/2022] Open
Abstract
Theta oscillations (3–8 Hz) in the human brain have been linked to perception, cognitive control, and spatial memory, but their relation to the motor system is less clear. We tested the hypothesis that theta oscillations coordinate distributed behaviorally relevant neural representations during movement using intracranial electroencephalography (iEEG) recordings from nine patients (n = 490 electrodes) as they performed a simple instructed movement task. Using high frequency activity (HFA; 70–200 Hz) as a marker of local spiking activity, we identified electrodes that were positioned near neural populations that showed increased activity during instruction and movement. We found that theta synchrony was widespread throughout the brain but was increased near regions that showed movement-related increases in neural activity. These results support the view that theta oscillations represent a general property of brain activity that may also play a specific role in coordinating widespread neural activity when initiating voluntary movement.
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630
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Barry RJ, De Blasio FM. Characterizing pink and white noise in the human electroencephalogram. J Neural Eng 2021; 18. [PMID: 33545698 DOI: 10.1088/1741-2552/abe399] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 02/05/2021] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The power spectrum of the human electroencephalogram (EEG) as a function of frequency is a mix of brain oscillations (e.g. alpha activity around 10 Hz) and non-oscillations or noise of uncertain origin. "White noise" is uniformly distributed over frequency, while "pink noise" has an inverse power-frequency relation (power ∝ 1/f). Interest in EEG pink noise has been growing, but previous human estimates appear methodologically flawed. We propose a new approach to extract separate valid estimates of pink and white noise from an EEG power spectrum. APPROACH We use simulated data to demonstrate its effectiveness compared with established procedures, and provide an illustrative example from a new resting eyes-open (EO) and eyes-closed (EC) dataset. The topographic characteristics of the obtained pink and white noise estimates are examined, as is the alpha power in this sample. MAIN RESULTS Valid pink and white noise estimates were successfully obtained for each of our 5400 individual spectra (60 participants × 30 electrodes × 3 conditions/blocks [EO1, EC, EO2]). The 1/f noise had a distinct central scalp topography, and white noise was occipital in distribution, both differing from the parietal topography of the alpha oscillation. These differences point to their separate neural origins. EC pink and white noise powers were globally greater than in EO. SIGNIFICANCE This valid estimation of pink and white noise in the human EEG holds promise for more accurate assessment of oscillatory neural activity in both typical and clinical groups, such as those with attention deficits. Further, outside the human EEG, the new methodology can be generalized to remove noise from spectra in many fields of science and technology.
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Affiliation(s)
- Robert J Barry
- School of Psychology, University of Wollongong, Northfields Ave, Wollongong, Wollongong, New South Wales, 2522, AUSTRALIA
| | - Frances M De Blasio
- School of Psychology, University of Wollongong, Northfields Ave, Wollongong, New South Wales, 2522, AUSTRALIA
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631
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van Bree S, Sohoglu E, Davis MH, Zoefel B. Sustained neural rhythms reveal endogenous oscillations supporting speech perception. PLoS Biol 2021; 19:e3001142. [PMID: 33635855 PMCID: PMC7946281 DOI: 10.1371/journal.pbio.3001142] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 03/10/2021] [Accepted: 02/08/2021] [Indexed: 12/23/2022] Open
Abstract
Rhythmic sensory or electrical stimulation will produce rhythmic brain responses. These rhythmic responses are often interpreted as endogenous neural oscillations aligned (or "entrained") to the stimulus rhythm. However, stimulus-aligned brain responses can also be explained as a sequence of evoked responses, which only appear regular due to the rhythmicity of the stimulus, without necessarily involving underlying neural oscillations. To distinguish evoked responses from true oscillatory activity, we tested whether rhythmic stimulation produces oscillatory responses which continue after the end of the stimulus. Such sustained effects provide evidence for true involvement of neural oscillations. In Experiment 1, we found that rhythmic intelligible, but not unintelligible speech produces oscillatory responses in magnetoencephalography (MEG) which outlast the stimulus at parietal sensors. In Experiment 2, we found that transcranial alternating current stimulation (tACS) leads to rhythmic fluctuations in speech perception outcomes after the end of electrical stimulation. We further report that the phase relation between electroencephalography (EEG) responses and rhythmic intelligible speech can predict the tACS phase that leads to most accurate speech perception. Together, we provide fundamental results for several lines of research-including neural entrainment and tACS-and reveal endogenous neural oscillations as a key underlying principle for speech perception.
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Affiliation(s)
- Sander van Bree
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
- Centre for Cognitive Neuroimaging, University of Glasgow, Glasgow, United Kingdom
- School of Psychology and Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
| | - Ediz Sohoglu
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
- School of Psychology, University of Sussex, Brighton, United Kingdom
| | - Matthew H. Davis
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Benedikt Zoefel
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
- Centre de Recherche Cerveau et Cognition, CNRS, Toulouse, France
- Université Toulouse III Paul Sabatier, Toulouse, France
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632
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Yoo HJ, Ham J, Duc NT, Lee B. Quantification of stroke lesion volume using epidural EEG in a cerebral ischaemic rat model. Sci Rep 2021; 11:2308. [PMID: 33504903 PMCID: PMC7841185 DOI: 10.1038/s41598-021-81912-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 01/08/2021] [Indexed: 01/01/2023] Open
Abstract
Precise monitoring of the brain after a stroke is essential for clinical decision making. Due to the non-invasive nature and high temporal resolution of electroencephalography (EEG), it is widely used to evaluate real-time cortical activity. In this study, we investigated the stroke-related EEG biomarkers and developed a predictive model for quantifying the structural brain damage in a focal cerebral ischaemic rat model. We enrolled 31 male Sprague-Dawley rats and randomly assigned them to mild stroke, moderate stroke, severe stroke, and control groups. We induced photothrombotic stroke targeting the right auditory cortex. We then acquired EEG signal responses to sound stimuli (frequency linearly increasing from 8 to 12 kHz with 750 ms duration). Power spectral analysis revealed a significant correlation of the relative powers of alpha, theta, delta, delta/alpha ratio, and (delta + theta)/(alpha + beta) ratio with the stroke lesion volume. The auditory evoked potential analysis revealed a significant association of amplitude and latency with stroke lesion volume. Finally, we developed a multiple regression model combining EEG predictors for quantifying the ischaemic lesion (R2 = 0.938, p value < 0.001). These findings demonstrate the potential application of EEG as a valid modality for monitoring the brain after a stroke.
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Affiliation(s)
- Hyun-Joon Yoo
- Department of Physical Medicine and Rehabilitation, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Korea
| | - Jinsil Ham
- Department of Biomedical Science and Engineering (BMSE), Institute Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), 123 Cheomdan-gwagiro, Buk-gu, Gwangju, 61005, Korea
| | - Nguyen Thanh Duc
- Department of Biomedical Science and Engineering (BMSE), Institute Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), 123 Cheomdan-gwagiro, Buk-gu, Gwangju, 61005, Korea
| | - Boreom Lee
- Department of Biomedical Science and Engineering (BMSE), Institute Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), 123 Cheomdan-gwagiro, Buk-gu, Gwangju, 61005, Korea.
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633
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Bódizs R, Szalárdy O, Horváth C, Ujma PP, Gombos F, Simor P, Pótári A, Zeising M, Steiger A, Dresler M. A set of composite, non-redundant EEG measures of NREM sleep based on the power law scaling of the Fourier spectrum. Sci Rep 2021; 11:2041. [PMID: 33479280 PMCID: PMC7820008 DOI: 10.1038/s41598-021-81230-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 12/28/2020] [Indexed: 01/09/2023] Open
Abstract
Features of sleep were shown to reflect aging, typical sex differences and cognitive abilities of humans. However, these measures are characterized by redundancy and arbitrariness. Our present approach relies on the assumptions that the spontaneous human brain activity as reflected by the scalp-derived electroencephalogram (EEG) during non-rapid eye movement (NREM) sleep is characterized by arrhythmic, scale-free properties and is based on the power law scaling of the Fourier spectra with the additional consideration of the rhythmic, oscillatory waves at specific frequencies, including sleep spindles. Measures derived are the spectral intercept and slope, as well as the maximal spectral peak amplitude and frequency in the sleep spindle range, effectively reducing 191 spectral measures to 4, which were efficient in characterizing known age-effects, sex-differences and cognitive correlates of sleep EEG. Future clinical and basic studies are supposed to be significantly empowered by the efficient data reduction provided by our approach.
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Affiliation(s)
- Róbert Bódizs
- Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary. .,Epilepsy Center, National Institute of Clinical Neurosciences, Budapest, Hungary.
| | - Orsolya Szalárdy
- Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary.,Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
| | - Csenge Horváth
- Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary
| | - Péter P Ujma
- Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary.,Epilepsy Center, National Institute of Clinical Neurosciences, Budapest, Hungary
| | - Ferenc Gombos
- Department of General Psychology, Pázmány Péter Catholic University, Budapest, Hungary.,MTA-PPKE Adolescent Development Research Group, Budapest, Hungary
| | - Péter Simor
- Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary.,Institute of Psychology, ELTE, Eötvös Loránd University, Budapest, Hungary.,UR2NF, Neuropsychology and Functional Neuroimaging Research Unit At CRCN - Center for Research in Cognition and Neurosciences and UNI - ULB Neurosciences Institute, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Adrián Pótári
- MTA-PPKE Adolescent Development Research Group, Budapest, Hungary.,Doctoral School of Psychology (Cognitive Science), Budapest University of Technology and Economics, Budapest, Hungary
| | - Marcel Zeising
- Max Planck Institute of Psychiatry, Research Group Sleep Endocrinology, Munich, Germany.,Centre of Mental Health, Klinikum Ingolstadt, Ingolstadt, Germany
| | - Axel Steiger
- Max Planck Institute of Psychiatry, Research Group Sleep Endocrinology, Munich, Germany
| | - Martin Dresler
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
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634
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Peterson SM, Steine-Hanson Z, Davis N, Rao RPN, Brunton BW. Generalized neural decoders for transfer learning across participants and recording modalities. J Neural Eng 2021; 18. [PMID: 33418552 DOI: 10.1088/1741-2552/abda0b] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 01/08/2021] [Indexed: 02/07/2023]
Abstract
OBJECTIVE Advances in neural decoding have enabled brain-computer interfaces to perform increasingly complex and clinically-relevant tasks. However, such decoders are often tailored to specific participants, days, and recording sites, limiting their practical long-term usage. Therefore, a fundamental challenge is to develop neural decoders that can robustly train on pooled, multi-participant data and generalize to new participants. APPROACH We introduce a new decoder, HTNet, which uses a convolutional neural network with two innovations: (1) a Hilbert transform that computes spectral power at data-driven frequencies and (2) a layer that projects electrode-level data onto predefined brain regions. The projection layer critically enables applications with intracranial electrocorticography (ECoG), where electrode locations are not standardized and vary widely across participants. We trained HTNet to decode arm movements using pooled ECoG data from 11 of 12 participants and tested performance on unseen ECoG or electroencephalography (EEG) participants; these pretrained models were also subsequently fine-tuned to each test participant. MAIN RESULTS HTNet outperformed state-of-the-art decoders when tested on unseen participants, even when a different recording modality was used. By fine-tuning these generalized HTNet decoders, we achieved performance approaching the best tailored decoders with as few as 50 ECoG or 20 EEG events. We were also able to interpret HTNet's trained weights and demonstrate its ability to extract physiologically-relevant features. SIGNIFICANCE By generalizing to new participants and recording modalities, robustly handling variations in electrode placement, and allowing participant-specific fine-tuning with minimal data, HTNet is applicable across a broader range of neural decoding applications compared to current state-of-the-art decoders.
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Affiliation(s)
- Steven M Peterson
- Biology, University of Washington, 4000 15th Ave NE, Seattle, Washington, 98195, UNITED STATES
| | - Zoe Steine-Hanson
- Computer Science and Engineering, University of Washington, 4000 15th Ave NE, Seattle, Washington, 98195, UNITED STATES
| | - Nathan Davis
- Computer Science and Engineering, University of Washington, 4000 15th Ave NE, Seattle, Washington, 98195, UNITED STATES
| | - Rajesh P N Rao
- Computer Science and Engineering, University of Washington, 185 E Stevens Way NE, Seattle, Washington, 98195, UNITED STATES
| | - Bingni W Brunton
- Biology, University of Washington, 4000 15th Ave NE, Seattle, Washington, 98195, UNITED STATES
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635
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Colzato LS, Zhang W, Brandt MD, Stock AK, Beste C. Cognitive profile in Restless Legs Syndrome: A signal-to-noise ratio account. CURRENT RESEARCH IN NEUROBIOLOGY 2021; 2:100021. [PMID: 36246509 PMCID: PMC9559071 DOI: 10.1016/j.crneur.2021.100021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 07/05/2021] [Accepted: 07/29/2021] [Indexed: 11/19/2022] Open
Abstract
Restless legs syndrome (RLS) is a common neurological disorder characterized by a sensorimotor condition, where patients feel an uncontrollable urge to move the lower limbs in the evening and/or during the night. RLS does not only have a profound impact on quality of life due to the disturbed night-time sleep, but there is growing evidence that untreated or insufficiently managed RLS might also cause cognitive changes in patients affected by this syndrome. It has been proposed that RLS is caused by alterations in the signal-to-noise ratio (SNR) and in dopamine (DA) neurotransmission in the nervous system. Based on this evidence, we propose the “SNR-DA hypothesis” as an explanation of how RLS could affect cognitive performance. According to this hypothesis, variations/reductions in the SNR underlie RLS-associated cognitive deficits, which follow an inverted U-shaped function: In unmedicated patients, low dopamine levels worsen the SNR, which eventually impairs cognition. Pharmacological treatment enhances DA levels in medicated patients, which likely improves/normalizes the SNR in case of optimal doses, thus restoring cognition to a normal level. However, overmedication might push patients past the optimal point on the inverted U-shaped curve, where an exaggerated SNR potentially impairs cognitive performance relying on cortical noise such as cognitive flexibility. Based on these assumptions of SNR alterations, we propose to directly measure neural noise via “1/f noise” and related metrics to use transcranial random noise stimulation (tRNS), a noninvasive brain stimulation method which manipulates the SNR, as a research tool and potential treatment option for RLS. Restless legs syndrome (RLS) is a common neurological disorder. RLS is caused by alterations in the SNR ratio and in DA neurotransmission. The SNR- DA hypothesis how RLS affects cognitive performance is presented.
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Affiliation(s)
- Lorenza S. Colzato
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Germany
- Cognitive Psychology, Faculty of Psychology, Shandong Normal University, Jinan, China
- University Neuropsychology Center, Faculty of Medicine, TU Dresden, Germany
| | - Wenxin Zhang
- Cognitive Psychology, Faculty of Psychology, Shandong Normal University, Jinan, China
| | - Moritz D. Brandt
- Department of Neurology, University Hospital, Technische Universität Dresden, Dresden, Germany
- German Center for Neurodegenerative Diseases (DZNE) Dresden, Dresden, Germany
| | - Ann-Kathrin Stock
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Germany
- University Neuropsychology Center, Faculty of Medicine, TU Dresden, Germany
- Biopsychology, Faculty of Psychology, TU Dresden, Dresden, Germany
| | - Christian Beste
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Germany
- Cognitive Psychology, Faculty of Psychology, Shandong Normal University, Jinan, China
- University Neuropsychology Center, Faculty of Medicine, TU Dresden, Germany
- Corresponding author. Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine of the TU Dresden, Germany Schubertstrasse 42, D-01309, Dresden, Germany.
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636
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Martínez-Cañada P, Panzeri S. Spectral Properties of Local Field Potentials and Electroencephalograms as Indices for Changes in Neural Circuit Parameters. Brain Inform 2021. [DOI: 10.1007/978-3-030-86993-9_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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637
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Catrambone V, Talebi A, Barbieri R, Valenza G. Time-resolved Brain-to-Heart Probabilistic Information Transfer Estimation Using Inhomogeneous Point-Process Models. IEEE Trans Biomed Eng 2021; 68:3366-3374. [DOI: 10.1109/tbme.2021.3071348] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Vincenzo Catrambone
- Research Center E. Piaggio, Information Engineering, University of Pisa, 9310 Pisa, Toscana, Italy, (e-mail: )
| | - Alireza Talebi
- Research Center E. Piaggio, Information Engineering, University of Pisa, 9310 Pisa, Toscana, Italy, (e-mail: )
| | | | - Gaetano Valenza
- Research Center E. Piaggio, Information Engineering, University of Pisa, 9310 Pisa, Toscana, Italy, (e-mail: )
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638
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Electrophysiological Frequency Band Ratio Measures Conflate Periodic and Aperiodic Neural Activity. eNeuro 2020; 7:ENEURO.0192-20.2020. [PMID: 32978216 PMCID: PMC7768281 DOI: 10.1523/eneuro.0192-20.2020] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 09/01/2020] [Accepted: 09/08/2020] [Indexed: 12/11/2022] Open
Abstract
Band ratio measures, computed as the ratio of power between two frequency bands, are a common analysis measure in neuroelectrophysiological recordings. Band ratio measures are typically interpreted as reflecting quantitative measures of periodic, or oscillatory, activity. This assumes that the measure reflects relative powers of distinct periodic components that are well captured by predefined frequency ranges. However, electrophysiological signals contain periodic components and a 1/f-like aperiodic component, the latter of which contributes power across all frequencies. Here, we investigate whether band ratio measures truly reflect oscillatory power differences, and/or to what extent ratios may instead reflect other periodic changes, such as in center frequency or bandwidth, and/or aperiodic activity. In simulation, we investigate how band ratio measures relate to changes in multiple spectral features, and show how multiple periodic and aperiodic features influence band ratio measures. We validate these findings in human electroencephalography (EEG) data, comparing band ratio measures to parameterizations of power spectral features and find that multiple disparate features influence ratio measures. For example, the commonly applied θ/β ratio is most reflective of differences in aperiodic activity, and not oscillatory θ or β power. Collectively, we show that periodic and aperiodic features can create the same observed changes in band ratio measures, and that this is inconsistent with their typical interpretations as measures of periodic power. We conclude that band ratio measures are a non-specific measure, conflating multiple possible underlying spectral changes, and recommend explicit parameterization of neural power spectra as a more specific approach.
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639
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Schaworonkow N, Voytek B. Longitudinal changes in aperiodic and periodic activity in electrophysiological recordings in the first seven months of life. Dev Cogn Neurosci 2020; 47:100895. [PMID: 33316695 PMCID: PMC7734223 DOI: 10.1016/j.dcn.2020.100895] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 10/30/2020] [Accepted: 12/05/2020] [Indexed: 12/26/2022] Open
Abstract
Neuronal oscillations emerge in early human development. These periodic oscillations are thought to rapidly change in infancy and stabilize during maturity. Given their numerous connections to physiological and cognitive processes, understanding the trajectory of oscillatory development is important for understanding healthy human brain development. This understanding is complicated by recent evidence that assessment of periodic neuronal oscillations is confounded by aperiodic neuronal activity, an inherent feature of electrophysiological recordings. Recent cross-sectional evidence shows that this aperiodic signal progressively shifts from childhood through early adulthood, and from early adulthood into later life. None of these studies, however, have been performed in infants, nor have they been examined longitudinally. Here, we analyzed longitudinal non-invasive EEG data from 22 typically developing infants, ranging between 38 and 203 days old. We show that the progressive flattening of the EEG power spectrum begins in very early development, continuing through the first months of life. These results highlight the importance of separating the periodic and aperiodic neuronal signals, because the aperiodic signal can bias measurement of neuronal oscillations. Given the infrequent, bursting nature of oscillations in infants, we recommend using quantitative time domain approaches that isolate bursts and uncover changes in waveform properties of oscillatory bursts. We assess oscillatory and aperiodic activity in longitudinal infant EEG recordings. Infant EEG activity is predominantly of aperiodic nature. The aperiodic exponent shows a strong decrease in the first half year of life. We confirm a developmental increase in alpha-frequency of infant oscillatory bursts.
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Affiliation(s)
- Natalie Schaworonkow
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA.
| | - Bradley Voytek
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA; Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA; Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, USA; Kavli Institute for Brain and Mind, University of California, San Diego, La Jolla, CA, USA
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640
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Watrous AJ, Buchanan RJ. The Oscillatory ReConstruction Algorithm adaptively identifies frequency bands to improve spectral decomposition in human and rodent neural recordings. J Neurophysiol 2020; 124:1914-1922. [DOI: 10.1152/jn.00292.2020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Neural oscillations show substantial variability within and across individuals and brain regions, yet most existing studies analyze oscillations using canonical, fixed-frequency bands. Thus, there is an ongoing need for tools that capture oscillatory variability in neural signals. Toward this end, Oscillatory ReConstruction Algorithm is a novel and adaptive analytic tool that allows researchers to measure neural oscillations with more precision and less researcher bias.
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Affiliation(s)
- Andrew J. Watrous
- Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, Texas
- Institute for Neuroscience, The University of Texas at Austin, Austin, Texas
- Center for Learning and Memory, The University of Texas at Austin, Austin, Texas
- Department of Psychology, The University of Texas at Austin, Austin, Texas
- Seton Brain and Spine Institute, Division of Neurosurgery, Austin, Texas
| | - Robert J. Buchanan
- Institute for Neuroscience, The University of Texas at Austin, Austin, Texas
- Department of Psychology, The University of Texas at Austin, Austin, Texas
- Seton Brain and Spine Institute, Division of Neurosurgery, Austin, Texas
- Department of Neurosurgery, Dell Medical School, The University of Texas at Austin, Austin, Texas
- Department of Psychiatry, Dell Medical School, The University of Texas at Austin, Austin, Texas
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641
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Özkurt TE, Akram H, Zrinzo L, Limousin P, Foltynie T, Oswal A, Litvak V. Identification of nonlinear features in cortical and subcortical signals of Parkinson's Disease patients via a novel efficient measure. Neuroimage 2020; 223:117356. [PMID: 32916287 PMCID: PMC8417768 DOI: 10.1016/j.neuroimage.2020.117356] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 07/31/2020] [Accepted: 09/04/2020] [Indexed: 11/25/2022] Open
Abstract
This study offers a novel and efficient measure based on a higher order version of autocorrelative signal memory that can identify nonlinearities in a single time series. The suggested method was applied to simultaneously recorded subthalamic nucleus (STN) local field potentials (LFP) and magnetoencephalography (MEG) from fourteen Parkinson's Disease (PD) patients who underwent surgery for deep brain stimulation. Recordings were obtained during rest for both OFF and ON dopaminergic medication states. We analyzed the bilateral LFP channels that had the maximum beta power in the OFF state and the cortical sources that had the maximum coherence with the selected LFP channels in the alpha band. Our findings revealed the inherent nonlinearity in the PD data as subcortical high beta (20-30 Hz) band and cortical alpha (8-12 Hz) band activities. While the former was discernible without medication (p=0.015), the latter was induced upon the dopaminergic medication (p<6.10-4). The degree of subthalamic nonlinearity was correlated with contralateral tremor severity (r=0.45, p=0.02). Conversely, for the cortical signals nonlinearity was present for the ON medication state with a peak in the alpha band and correlated with contralateral akinesia and rigidity (r=0.46, p=0.02). This correlation appeared to be independent from that of alpha power and the two measures combined explained 34 % of the variance in contralateral akinesia scores. Our findings suggest that particular frequency bands and brain regions display nonlinear features closely associated with distinct motor symptoms and functions.
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Affiliation(s)
- Tolga Esat Özkurt
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK; Middle East Technical University, Department of Health Informatics, Graduate School of Informatics, Ankara, Turkey.
| | - Harith Akram
- Department of Clinical and Movement Neurosciences, UCL Institute of Neurology and The National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Ludvic Zrinzo
- Department of Clinical and Movement Neurosciences, UCL Institute of Neurology and The National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Patricia Limousin
- Department of Clinical and Movement Neurosciences, UCL Institute of Neurology and The National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Tom Foltynie
- Department of Clinical and Movement Neurosciences, UCL Institute of Neurology and The National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Ashwini Oswal
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK; Department of Clinical Neurology, John Radcliffe Hospital, Oxford, UK
| | - Vladimir Litvak
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK
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642
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Gao R, van den Brink RL, Pfeffer T, Voytek B. Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture. eLife 2020; 9:e61277. [PMID: 33226336 PMCID: PMC7755395 DOI: 10.7554/elife.61277] [Citation(s) in RCA: 104] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 11/22/2020] [Indexed: 12/21/2022] Open
Abstract
Complex cognitive functions such as working memory and decision-making require information maintenance over seconds to years, from transient sensory stimuli to long-term contextual cues. While theoretical accounts predict the emergence of a corresponding hierarchy of neuronal timescales, direct electrophysiological evidence across the human cortex is lacking. Here, we infer neuronal timescales from invasive intracranial recordings. Timescales increase along the principal sensorimotor-to-association axis across the entire human cortex, and scale with single-unit timescales within macaques. Cortex-wide transcriptomic analysis shows direct alignment between timescales and expression of excitation- and inhibition-related genes, as well as genes specific to voltage-gated transmembrane ion transporters. Finally, neuronal timescales are functionally dynamic: prefrontal cortex timescales expand during working memory maintenance and predict individual performance, while cortex-wide timescales compress with aging. Thus, neuronal timescales follow cytoarchitectonic gradients across the human cortex and are relevant for cognition in both short and long terms, bridging microcircuit physiology with macroscale dynamics and behavior.
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Affiliation(s)
- Richard Gao
- Department of Cognitive Science, University of California, San DiegoLa JollaUnited States
| | - Ruud L van den Brink
- Section Computational Cognitive Neuroscience, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Thomas Pfeffer
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu FabraBarcelonaSpain
| | - Bradley Voytek
- Department of Cognitive Science, University of California, San DiegoLa JollaUnited States
- Halıcıoğlu Data Science Institute, University of California, San DiegoLa JollaUnited States
- Neurosciences Graduate Program, University of California, San DiegoLa JollaUnited States
- Kavli Institute for Brain and Mind, University of California, San DiegoLa JollaUnited States
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643
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Cox R, Rüber T, Staresina BP, Fell J. Sharp Wave-Ripples in Human Amygdala and Their Coordination with Hippocampus during NREM Sleep. Cereb Cortex Commun 2020; 1:tgaa051. [PMID: 33015623 PMCID: PMC7521160 DOI: 10.1093/texcom/tgaa051] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 08/05/2020] [Accepted: 08/07/2020] [Indexed: 12/21/2022] Open
Abstract
Cooperative interactions between the amygdala and hippocampus are widely regarded as critical for overnight emotional processing of waking experiences, but direct support from the human brain for such a dialog is absent. Using overnight intracranial recordings in 4 presurgical epilepsy patients (3 female), we discovered ripples within human amygdala during nonrapid eye movement (NREM) sleep, a brain state known to contribute to affective processing. Like hippocampal ripples, amygdala ripples are associated with sharp waves, linked to sleep spindles, and tend to co-occur with their hippocampal counterparts. Moreover, sharp waves and ripples are temporally linked across the 2 brain structures, with amygdala ripples occurring during hippocampal sharp waves and vice versa. Combined with further evidence of interregional sharp-wave and spindle synchronization, these findings offer a potential physiological substrate for the NREM-sleep-dependent consolidation and regulation of emotional experiences.
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Affiliation(s)
- Roy Cox
- Department of Epileptology, University of Bonn, Bonn 53127, Germany
| | - Theodor Rüber
- Department of Epileptology, University of Bonn, Bonn 53127, Germany
- Department of Neurology, Epilepsy Center Frankfurt Rhine-Main, Goethe University Frankfurt, Frankfurt am Main 60590, Germany
- Center for Personalized Translational Epilepsy Research (CePTER), Goethe University Frankfurt, Frankfurt am Main 60590, Germany
| | | | - Juergen Fell
- Department of Epileptology, University of Bonn, Bonn 53127, Germany
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644
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Furman AJ, Prokhorenko M, Keaser ML, Zhang J, Chen S, Mazaheri A, Seminowicz DA. Sensorimotor Peak Alpha Frequency Is a Reliable Biomarker of Prolonged Pain Sensitivity. Cereb Cortex 2020; 30:6069-6082. [PMID: 32591813 DOI: 10.1093/cercor/bhaa124] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 03/29/2020] [Accepted: 04/21/2020] [Indexed: 01/28/2023] Open
Abstract
Previous research has observed that the speed of alpha band oscillations (8-12 Hz range) recorded during resting electroencephalography is slowed in chronic pain patients. While this slowing may reflect pathological changes that occur during the chronification of pain, an alternative explanation is that healthy individuals with slower alpha oscillations are more sensitive to prolonged pain, and by extension, more susceptible to developing chronic pain. To test this hypothesis, we examined the relationship between the pain-free, resting alpha oscillation speed of healthy individuals and their sensitivity to two models of prolonged pain, Phasic Heat Pain and Capsaicin Heat Pain, at two visits separated by 8 weeks on average (n = 61 Visit 1, n = 46 Visit 2). We observed that the speed of an individual's pain-free alpha oscillations was negatively correlated with sensitivity to both models and that this relationship was reliable across short (minutes) and long (weeks) timescales. Furthermore, the speed of pain-free alpha oscillations can successfully identify the most pain sensitive individuals, which we validated on data from a separate, independent study. These results suggest that alpha oscillation speed is a reliable biomarker of prolonged pain sensitivity with potential for prospectively identifying pain sensitivity in the clinic.
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Affiliation(s)
- Andrew J Furman
- Program in Neuroscience, University of Maryland School of Medicine, Baltimore, MD 21201, USA.,Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore, MD 21201, USA.,Center to Advance Chronic Pain Research, University of Maryland Baltimore, Baltimore, MD 21201, USA
| | - Mariya Prokhorenko
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore, MD 21201, USA
| | - Michael L Keaser
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore, MD 21201, USA.,Center to Advance Chronic Pain Research, University of Maryland Baltimore, Baltimore, MD 21201, USA
| | - Jing Zhang
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore, MD 21201, USA
| | - Shuo Chen
- Department of Epidemiology and Public Health, University of Maryland Baltimore, Baltimore, MD 21201, USA
| | - Ali Mazaheri
- School of Psychology, University of Birmingham, Birmingham, B15 2TT, UK.,Centre for Human Brain Health, University of Birmingham, Birmingham, B15 2TT, UK
| | - David A Seminowicz
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore, MD 21201, USA.,Center to Advance Chronic Pain Research, University of Maryland Baltimore, Baltimore, MD 21201, USA
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645
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McAssey M, Dowsett J, Kirsch V, Brandt T, Dieterich M. Different EEG brain activity in right and left handers during visually induced self-motion perception. J Neurol 2020; 267:79-90. [PMID: 32462347 PMCID: PMC7718188 DOI: 10.1007/s00415-020-09915-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 05/11/2020] [Accepted: 05/13/2020] [Indexed: 01/12/2023]
Abstract
Visually induced self-motion perception (vection) relies on visual-vestibular interaction. Imaging studies using vestibular stimulation have revealed a vestibular thalamo-cortical dominance in the right hemisphere in right handers and the left hemisphere in left handers. We investigated if the behavioural characteristics and neural correlates of vection differ between healthy left and right-handed individuals. 64-channel EEG was recorded while 25 right handers and 25 left handers were exposed to vection-compatible roll motion (coherent motion) and a matched, control condition (incoherent motion). Behavioural characteristics, i.e. vection presence, onset latency, duration and subjective strength, were also recorded. The behavioural characteristics of vection did not differ between left and right handers (all p > 0.05). Fast Fourier Transform (FFT) analysis revealed significant decreases in alpha power during vection-compatible roll motion (p < 0.05). The topography of this decrease was handedness-dependent, with left handers showing a left lateralized centro-parietal decrease and right handers showing a bilateral midline centro-parietal decrease. Further time-frequency analysis, time locked to vection onset, revealed a comparable decrease in alpha power around vection onset and a relative increase in alpha power during ongoing vection, for left and right handers. No effects were observed in theta and beta bands. Left and right-handed individuals show vection-related alpha power decreases at different topographical regions, possibly related to the influence of handedness-dependent vestibular dominance in the visual-vestibular interaction that facilitates visual self-motion perception. Despite this difference in where vection-related activity is observed, left and right handers demonstrate comparable perception and underlying alpha band changes during vection.
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Affiliation(s)
- Michaela McAssey
- Department of Neurology, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistraße 15, 81377, Munich, Germany.
- German Center for Vertigo and Balance Disorders (DSGZ), University Hospital, Ludwig-Maximilians-Universität, Munich, Germany.
- Graduate School of Systemic Neuroscience (GSN), Ludwig-Maximilians-Universität, Munich, Germany.
- RTG 2175, Perception in Context and its Neural Basis, Ludwig-Maximilians-Universität, Munich, Germany.
| | - James Dowsett
- Department of Neurology, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistraße 15, 81377, Munich, Germany
- German Center for Vertigo and Balance Disorders (DSGZ), University Hospital, Ludwig-Maximilians-Universität, Munich, Germany
| | - Valerie Kirsch
- Department of Neurology, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistraße 15, 81377, Munich, Germany
- German Center for Vertigo and Balance Disorders (DSGZ), University Hospital, Ludwig-Maximilians-Universität, Munich, Germany
- Graduate School of Systemic Neuroscience (GSN), Ludwig-Maximilians-Universität, Munich, Germany
| | - Thomas Brandt
- German Center for Vertigo and Balance Disorders (DSGZ), University Hospital, Ludwig-Maximilians-Universität, Munich, Germany
- Graduate School of Systemic Neuroscience (GSN), Ludwig-Maximilians-Universität, Munich, Germany
- RTG 2175, Perception in Context and its Neural Basis, Ludwig-Maximilians-Universität, Munich, Germany
| | - Marianne Dieterich
- Department of Neurology, University Hospital, Ludwig-Maximilians-Universität München, Marchioninistraße 15, 81377, Munich, Germany
- German Center for Vertigo and Balance Disorders (DSGZ), University Hospital, Ludwig-Maximilians-Universität, Munich, Germany
- Graduate School of Systemic Neuroscience (GSN), Ludwig-Maximilians-Universität, Munich, Germany
- RTG 2175, Perception in Context and its Neural Basis, Ludwig-Maximilians-Universität, Munich, Germany
- SyNergy, Munich Cluster of Systems Neurology, Munich, Germany
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