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Donoghue T, Hammonds R, Lybrand E, Washcke L, Gao R, Voytek B. Evaluating and Comparing Measures of Aperiodic Neural Activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.15.613114. [PMID: 39314334 PMCID: PMC11419150 DOI: 10.1101/2024.09.15.613114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
Neuro-electrophysiological recordings contain prominent aperiodic activity - meaning irregular activity, with no characteristic frequency - which has variously been referred to as 1/f (or 1/f-like activity), fractal, or 'scale-free' activity. Previous work has established that aperiodic features of neural activity is dynamic and variable, relating (between subjects) to healthy aging and to clinical diagnoses, and also (within subjects) tracking conscious states and behavioral performance. There are, however, a wide variety of conceptual frameworks and associated methods for the analyses and interpretation of aperiodic activity - for example, time domain measures such as the autocorrelation, fractal measures, and/or various complexity and entropy measures, as well as measures of the aperiodic exponent in the frequency domain. There is a lack of clear understanding of how these different measures relate to each other and to what extent they reflect the same or different properties of the data, which makes it difficult to synthesize results across approaches and complicates our overall understanding of the properties, biological significance, and demographic, clinical, and behavioral correlates of aperiodic neural activity. To address this problem, in this project we systematically survey the different approaches for measuring aperiodic neural activity, starting with an automated literature analysis to curate a collection of the most common methods. We then evaluate and compare these methods, using statistically representative time series simulations. In doing so, we establish consistent relationships between the measures, showing that much of what they capture reflects shared variance - though with some notable idiosyncrasies. Broadly, frequency domain methods are more specific to aperiodic features of the data, whereas time domain measures are more impacted by oscillatory activity. We extend this analysis by applying the measures to a series of empirical EEG and iEEG datasets, replicating the simulation results. We conclude by summarizing the relationships between the multiple methods, emphasizing opportunities for reexamining previous findings and for future work.
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Affiliation(s)
- Thomas Donoghue
- Department of Cognitive Science, University of California, San Diego
| | - Ryan Hammonds
- Department of Cognitive Science, University of California, San Diego
| | - Eric Lybrand
- Department of Mathematics, University of California, San Diego
| | - Leonhard Washcke
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, Germany
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Germany
| | - Richard Gao
- Department of Cognitive Science, University of California, San Diego
| | - Bradley Voytek
- Department of Cognitive Science, University of California, San Diego
- Neurosciences Graduate Program, University of California, San Diego
- Halıcıoğlu Data Science Institute
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2
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Wilson LE, Castanheira JDS, Kinder BL, Baillet S. Model selection for spectral parameterization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.01.606216. [PMID: 39149403 PMCID: PMC11326208 DOI: 10.1101/2024.08.01.606216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Neurophysiological brain activity comprises rhythmic (periodic) and arrhythmic (aperiodic) signal elements, which are increasingly studied in relation to behavioral traits and clinical symptoms. Current methods for spectral parameterization of neural recordings rely on user-dependent parameter selection, which challenges the replicability and robustness of findings. Here, we introduce a principled approach to model selection, relying on Bayesian information criterion, for static and time-resolved spectral parameterization of neurophysiological data. We present extensive tests of the approach with ground-truth and empirical magnetoencephalography recordings. Data-driven model selection enhances both the specificity and sensitivity of spectral and spectrogram decompositions, even in non-stationary contexts. Overall, the proposed spectral decomposition with data-driven model selection minimizes the reliance on user expertise and subjective choices, enabling more robust, reproducible, and interpretable research findings.
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Affiliation(s)
| | | | | | - Sylvain Baillet
- Montreal Neurological Institute, McGill University, Montreal QC, Canada
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3
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Lundqvist M, Miller EK, Nordmark J, Liljefors J, Herman P. Beta: bursts of cognition. Trends Cogn Sci 2024; 28:662-676. [PMID: 38658218 DOI: 10.1016/j.tics.2024.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 03/11/2024] [Accepted: 03/20/2024] [Indexed: 04/26/2024]
Abstract
Beta oscillations are linked to the control of goal-directed processing of sensory information and the timing of motor output. Recent evidence demonstrates they are not sustained but organized into intermittent high-power bursts mediating timely functional inhibition. This implies there is a considerable moment-to-moment variation in the neural dynamics supporting cognition. Beta bursts thus offer new opportunities for studying how sensory inputs are selectively processed, reshaped by inhibitory cognitive operations and ultimately result in motor actions. Recent method advances reveal diversity in beta bursts that provide deeper insights into their function and the underlying neural circuit activity motifs. We propose that brain-wide, spatiotemporal patterns of beta bursting reflect various cognitive operations and that their dynamics reveal nonlinear aspects of cortical processing.
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Affiliation(s)
- Mikael Lundqvist
- Division of Psychology, Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden; The Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Earl K Miller
- The Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jonatan Nordmark
- Division of Psychology, Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden
| | - Johan Liljefors
- Division of Psychology, Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden
| | - Pawel Herman
- School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden; Digital Futures, KTH Royal Institute of Technology, Stockholm, Sweden
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4
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Rodriguez-Larios J, Foong Wong K, Lim J. Assessing the effects of an 8-week mindfulness training program on neural oscillations and self-reports during meditation practice. PLoS One 2024; 19:e0299275. [PMID: 38843236 PMCID: PMC11156404 DOI: 10.1371/journal.pone.0299275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 05/21/2024] [Indexed: 06/09/2024] Open
Abstract
Previous literature suggests that mindfulness meditation can have positive effects on mental health, however, its mechanisms of action are still unclear. In this pre-registered study, we investigate the effects of mindfulness training on lapses of attention (and their associated neural correlates) during meditation practice. For this purpose, we recorded Electroencephalogram (EEG) during meditation practice before and after 8 weeks of mindfulness training (or waitlist) in 41 participants (21 treatment and 20 controls). In order to detect lapses of attention and characterize their EEG correlates, we interrupted participants during meditation to report their level of focus and drowsiness. First, we show that self-reported lapses of attention during meditation practice were associated to an increased occurrence of theta oscillations (3-6 Hz), which were slower in frequency and more spatially widespread than theta oscillations occurring during focused attention states. Then, we show that mindfulness training did not reduce the occurrence of lapses of attention nor their associated EEG correlate (i.e. theta oscillations) during meditation. Instead, we find that mindfulness training was associated with a significant slowing of alpha oscillations in frontal electrodes during meditation. Crucially, frontal alpha slowing during meditation practice has been reported in experienced meditators and is thought to reflect relative decreases in arousal levels. Together, our findings provide insights into the EEG correlates of mindfulness meditation, which could have important implications for the identification of its mechanisms of action and/or the development of neuromodulation protocols aimed at facilitating meditation practice.
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Affiliation(s)
| | - Kian Foong Wong
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Julian Lim
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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5
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Forbes E, Hassien A, Tan RJ, Wang D, Lega B. Modulation of hippocampal theta oscillations via deep brain stimulation of the parietal cortex depends on cognitive state. Cortex 2024; 175:28-40. [PMID: 38691923 PMCID: PMC11221570 DOI: 10.1016/j.cortex.2024.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 12/07/2023] [Accepted: 03/24/2024] [Indexed: 05/03/2024]
Abstract
The angular gyrus (AG) and posterior cingulate cortex (PCC) demonstrate extensive structural and functional connectivity with the hippocampus and other core recollection network regions. Consequently, recent studies have explored neuromodulation targeting these and other regions as a potential strategy for restoring function in memory disorders such as Alzheimer's Disease. However, determining the optimal approach for neuromodulatory devices requires understanding how parameters like selected stimulation site, cognitive state during modulation, and stimulation duration influence the effects of deep brain stimulation (DBS) on electrophysiological features relevant to episodic memory. We report experimental data examining the effects of high-frequency stimulation delivered to the AG or PCC on hippocampal theta oscillations during the memory encoding (study) or retrieval (test) phases of an episodic memory task. Results showed selective enhancement of anterior hippocampal slow theta oscillations with stimulation of the AG preferentially during memory retrieval. Conversely, stimulation of the PCC attenuated slow theta oscillations. We did not observe significant behavioral effects in this (open-loop) stimulation experiment, suggesting that neuromodulation strategies targeting episodic memory performance may require more temporally precise stimulation approaches.
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Affiliation(s)
- Eugenio Forbes
- The University of Texas Southwestern Medical Center, Dallas, TX, United States.
| | - Alexa Hassien
- The University of Texas Southwestern Medical Center, Dallas, TX, United States.
| | - Ryan Joseph Tan
- The University of Texas Southwestern Medical Center, Dallas, TX, United States.
| | - David Wang
- The University of Texas Southwestern Medical Center, Dallas, TX, United States.
| | - Bradley Lega
- The University of Texas Southwestern Medical Center, Dallas, TX, United States.
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6
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Hebron H, Lugli B, Dimitrova R, Jaramillo V, Yeh LR, Rhodes E, Grossman N, Dijk DJ, Violante IR. A closed-loop auditory stimulation approach selectively modulates alpha oscillations and sleep onset dynamics in humans. PLoS Biol 2024; 22:e3002651. [PMID: 38889194 PMCID: PMC11185466 DOI: 10.1371/journal.pbio.3002651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 05/01/2024] [Indexed: 06/20/2024] Open
Abstract
Alpha oscillations play a vital role in managing the brain's resources, inhibiting neural activity as a function of their phase and amplitude, and are changed in many brain disorders. Developing minimally invasive tools to modulate alpha activity and identifying the parameters that determine its response to exogenous modulators is essential for the implementation of focussed interventions. We introduce Alpha Closed-Loop Auditory Stimulation (αCLAS) as an EEG-based method to modulate and investigate these brain rhythms in humans with specificity and selectivity, using targeted auditory stimulation. Across a series of independent experiments, we demonstrate that αCLAS alters alpha power, frequency, and connectivity in a phase, amplitude, and topography-dependent manner. Using single-pulse-αCLAS, we show that the effects of auditory stimuli on alpha oscillations can be explained within the theoretical framework of oscillator theory and a phase-reset mechanism. Finally, we demonstrate the functional relevance of our approach by showing that αCLAS can interfere with sleep onset dynamics in a phase-dependent manner.
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Affiliation(s)
- Henry Hebron
- School of Psychology, University of Surrey, Guildford, United Kingdom
- Surrey Sleep Research Centre, University of Surrey, Guildford, United Kingdom
- UK Dementia Research Institute Care Research and Technology Centre, Imperial College London and the University of Surrey, Guildford, United Kingdom
| | - Beatrice Lugli
- School of Psychology, University of Surrey, Guildford, United Kingdom
| | - Radost Dimitrova
- School of Psychology, University of Surrey, Guildford, United Kingdom
| | - Valeria Jaramillo
- School of Psychology, University of Surrey, Guildford, United Kingdom
- Surrey Sleep Research Centre, University of Surrey, Guildford, United Kingdom
- UK Dementia Research Institute Care Research and Technology Centre, Imperial College London and the University of Surrey, Guildford, United Kingdom
| | - Lisa R. Yeh
- School of Psychology, University of Surrey, Guildford, United Kingdom
| | - Edward Rhodes
- Department of Brain Sciences, Imperial College London, London, United Kingdom
- UK Dementia Research Institute Imperial College London, United Kingdom
| | - Nir Grossman
- Department of Brain Sciences, Imperial College London, London, United Kingdom
- UK Dementia Research Institute Imperial College London, United Kingdom
| | - Derk-Jan Dijk
- Surrey Sleep Research Centre, University of Surrey, Guildford, United Kingdom
- UK Dementia Research Institute Care Research and Technology Centre, Imperial College London and the University of Surrey, Guildford, United Kingdom
| | - Ines R. Violante
- School of Psychology, University of Surrey, Guildford, United Kingdom
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7
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Auer T, Goldthorpe R, Peach R, Hebron H, Violante IR. Functionally annotated electrophysiological neuromarkers of healthy ageing and memory function. Hum Brain Mapp 2024; 45:e26687. [PMID: 38651629 PMCID: PMC11036379 DOI: 10.1002/hbm.26687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 02/22/2024] [Accepted: 04/05/2024] [Indexed: 04/25/2024] Open
Abstract
The unprecedented increase in life expectancy presents a unique opportunity and the necessity to explore both healthy and pathological aspects of ageing. Electroencephalography (EEG) has been widely used to identify neuromarkers of cognitive ageing due to its affordability and richness in information. However, despite the growing volume of data and methodological advancements, the abundance of contradictory and non-reproducible findings has hindered clinical translation. To address these challenges, our study introduces a comprehensive workflow expanding on previous EEG studies and investigates various static and dynamic power and connectivity estimates as potential neuromarkers of cognitive ageing in a large dataset. We also assess the robustness of our findings by testing their susceptibility to band specification. Finally, we characterise our findings using functionally annotated brain networks to improve their interpretability and multi-modal integration. Our analysis demonstrates the effect of methodological choices on findings and that dynamic rather than static neuromarkers are not only more sensitive but also more robust. Consequently, they emerge as strong candidates for cognitive ageing neuromarkers. Moreover, we were able to replicate the most established EEG findings in cognitive ageing, such as alpha oscillation slowing, increased beta power, reduced reactivity across multiple bands, and decreased delta connectivity. Additionally, when considering individual variations in the alpha band, we clarified that alpha power is characteristic of memory performance rather than ageing, highlighting its potential as a neuromarker for cognitive ageing. Finally, our approach using functionally annotated source reconstruction allowed us to provide insights into domain-specific electrophysiological mechanisms underlying memory performance and ageing. HIGHLIGHTS: We provide an open and reproducible pipeline with a comprehensive workflow to investigate static and dynamic EEG neuromarkers. Neuromarkers related to neural dynamics are sensitive and robust. Individualised alpha power characterises cognitive performance rather than ageing. Functional annotation allows cross-modal interpretation of EEG findings.
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Affiliation(s)
- Tibor Auer
- School of PsychologyUniversity of SurreyGuildfordUK
| | | | | | - Henry Hebron
- School of PsychologyUniversity of SurreyGuildfordUK
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8
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McKeown DJ, Finley AJ, Kelley NJ, Cavanagh JF, Keage HAD, Baumann O, Schinazi VR, Moustafa AA, Angus DJ. Test-retest reliability of spectral parameterization by 1/f characterization using SpecParam. Cereb Cortex 2024; 34:bhad482. [PMID: 38100367 PMCID: PMC10793580 DOI: 10.1093/cercor/bhad482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 11/26/2023] [Accepted: 11/27/2023] [Indexed: 12/17/2023] Open
Abstract
SpecParam (formally known as FOOOF) allows for the refined measurements of electroencephalography periodic and aperiodic activity, and potentially provides a non-invasive measurement of excitation: inhibition balance. However, little is known about the psychometric properties of this technique. This is integral for understanding the usefulness of SpecParam as a tool to determine differences in measurements of cognitive function, and electroencephalography activity. We used intraclass correlation coefficients to examine the test-retest reliability of parameterized activity across three sessions (90 minutes apart and 30 days later) in 49 healthy young adults at rest with eyes open, eyes closed, and during three eyes closed cognitive tasks including subtraction (Math), music recall (Music), and episodic memory (Memory). Intraclass correlation coefficients were good for the aperiodic exponent and offset (intraclass correlation coefficients > 0.70) and parameterized periodic activity (intraclass correlation coefficients > 0.66 for alpha and beta power, central frequency, and bandwidth) across conditions. Across all three sessions, SpecParam performed poorly in eyes open (40% of participants had poor fits over non-central sites) and had poor test-retest reliability for parameterized periodic activity. SpecParam mostly provides reliable metrics of individual differences in parameterized neural activity. More work is needed to understand the suitability of eyes open resting data for parameterization using SpecParam.
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Affiliation(s)
- Daniel J McKeown
- The Mind Space Laboratory, Department of Psychology, Faculty of Society and Design, Bond University, Gold Coast, QLD 4229, Australia
| | - Anna J Finley
- Institute on Aging, University of Wisconsin-Madison, Madison, WI 53706, United States
| | - Nicholas J Kelley
- School of Psychology, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - James F Cavanagh
- Department of Psychology, University of New Mexico, Albuquerque, NM 87106, United States
| | - Hannah A D Keage
- School of Psychology, University of South Australia, Adelaide, SA 5001, Australia
| | - Oliver Baumann
- The Mind Space Laboratory, Department of Psychology, Faculty of Society and Design, Bond University, Gold Coast, QLD 4229, Australia
| | - Victor R Schinazi
- The Mind Space Laboratory, Department of Psychology, Faculty of Society and Design, Bond University, Gold Coast, QLD 4229, Australia
| | - Ahmed A Moustafa
- The Mind Space Laboratory, Department of Psychology, Faculty of Society and Design, Bond University, Gold Coast, QLD 4229, Australia
| | - Douglas J Angus
- The Mind Space Laboratory, Department of Psychology, Faculty of Society and Design, Bond University, Gold Coast, QLD 4229, Australia
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9
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Zhang B, Tian H, Yu Y, Zhen X, Zhang L, Yuan Y, Wang L. A localized pallidal physiomarker in Meige syndrome. Front Neurol 2023; 14:1286634. [PMID: 38178893 PMCID: PMC10764606 DOI: 10.3389/fneur.2023.1286634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 12/06/2023] [Indexed: 01/06/2024] Open
Abstract
Objectives Oscillatory patterns in local field potentials (LFPs) have been recognized as disease-specific physiomarkers, particularly in the context of Parkinson's disease and cervical dystonia. This characteristic oscillatory feature is currently employed in adaptive deep brain stimulation (aDBS). However, for other types of dystonia, especially Meige syndrome, a distinct physiomarker of this nature is yet to be identified. Methods Local field potentials were recorded during microelectrode-guided deep brain stimulation surgery from 28 patients with primary Meige syndrome. Before surgery, the severity of patients' motor syndrome were assessed using the Burke-Fahn-Marsden Dystonia Rating Scale-Motor (BFMDRS-M). An instantaneous oscillation detection method was employed to identify true narrowband oscillations. Subsequently, a linear mixed effects model was utilized to examine the relationship between oscillatory activities (including power amplitude and burst duration) and symptom severity. Results The focal peaks of "oscillatory activities" detected were predominantly concentrated in the narrow theta band (4-8 Hz), constituting 81.5% of the total detected oscillations in all recording sites near active DBS contacts in the globus pallidus internus (GPi). The linear mixed effects model revealed a positive correlation between the theta burst duration and the severity of preoperative motor impairment, but no correlation with postoperative motor scores. Additionally, there was no significant lateralization effect observed between the left and right GPi. Conclusion Our findings suggest that the exaggerated narrowband theta activity (mainly the burst duration) in the GPi is predictive of dystonia symptom severity and may be used as a physiomarker for optimized DBS target during surgery and adaptive DBS for the treatment of Meige syndrome.
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Affiliation(s)
- Bo Zhang
- Key Laboratory of Brain Science, Zunyi Medical University, Zunyi, China
- Guizhou Key Laboratory of Anesthesia and Organ Protection, Zunyi Medical University, Zunyi, China
| | - Hong Tian
- Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, China
| | - Yanbing Yu
- Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, China
| | - Xueke Zhen
- Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, China
| | - Li Zhang
- Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, China
| | - Yue Yuan
- Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, China
| | - Liang Wang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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10
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Rayson H, Szul MJ, El-Khoueiry P, Debnath R, Gautier-Martins M, Ferrari PF, Fox N, Bonaiuto JJ. Bursting with Potential: How Sensorimotor Beta Bursts Develop from Infancy to Adulthood. J Neurosci 2023; 43:8487-8503. [PMID: 37833066 PMCID: PMC10711718 DOI: 10.1523/jneurosci.0886-23.2023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/15/2023] [Accepted: 07/20/2023] [Indexed: 10/15/2023] Open
Abstract
Beta activity is thought to play a critical role in sensorimotor processes. However, little is known about how activity in this frequency band develops. Here, we investigated the developmental trajectory of sensorimotor beta activity from infancy to adulthood. We recorded EEG from 9-month-old, 12-month-old, and adult humans (male and female) while they observed and executed grasping movements. We analyzed "beta burst" activity using a novel method that combines time-frequency decomposition and principal component analysis. We then examined the changes in burst rate and waveform motifs along the selected principal components. Our results reveal systematic changes in beta activity during action execution across development. We found a decrease in beta burst rate during movement execution in all age groups, with the greatest decrease observed in adults. Additionally, we identified three principal components that defined waveform motifs that systematically changed throughout the trial. We found that bursts with waveform shapes closer to the median waveform were not rate-modulated, whereas those with waveform shapes further from the median were differentially rate-modulated. Interestingly, the decrease in the rate of certain burst motifs occurred earlier during movement and was more lateralized in adults than in infants, suggesting that the rate modulation of specific types of beta bursts becomes increasingly refined with age.SIGNIFICANCE STATEMENT We demonstrate that, like in adults, sensorimotor beta activity in infants during reaching and grasping movements occurs in bursts, not oscillations like thought traditionally. Furthermore, different beta waveform shapes were differentially modulated with age, including more lateralization in adults. Aberrant beta activity characterizes various developmental disorders and motor difficulties linked to early brain injury, so looking at burst waveform shape could provide more sensitivity for early identification and treatment of affected individuals before any behavioral symptoms emerge. More generally, comparison of beta burst activity in typical versus atypical motor development will also be instrumental in teasing apart the mechanistic functional roles of different types of beta bursts.
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Affiliation(s)
- Holly Rayson
- Institut des Sciences, Cognitives Marc Jeannerod, Centre National de la Recherche Scientifique Unité Mixte de Recherche 5229, Bron, 69500, France
- Université de Lyon, Université Claude Bernard Lyon 1, Lyon, 69100, France
- Inovarion, Paris, 75005, France
| | - Maciej J Szul
- Institut des Sciences, Cognitives Marc Jeannerod, Centre National de la Recherche Scientifique Unité Mixte de Recherche 5229, Bron, 69500, France
- Université de Lyon, Université Claude Bernard Lyon 1, Lyon, 69100, France
| | - Perla El-Khoueiry
- Institut des Sciences, Cognitives Marc Jeannerod, Centre National de la Recherche Scientifique Unité Mixte de Recherche 5229, Bron, 69500, France
- Université de Lyon, Université Claude Bernard Lyon 1, Lyon, 69100, France
| | - Ranjan Debnath
- Center for Psychiatry and Psychotherapy, Justus-Liebig University, Giessen, 35394, Germany
| | - Marine Gautier-Martins
- Institut des Sciences, Cognitives Marc Jeannerod, Centre National de la Recherche Scientifique Unité Mixte de Recherche 5229, Bron, 69500, France
- Université de Lyon, Université Claude Bernard Lyon 1, Lyon, 69100, France
| | - Pier F Ferrari
- Institut des Sciences, Cognitives Marc Jeannerod, Centre National de la Recherche Scientifique Unité Mixte de Recherche 5229, Bron, 69500, France
- Université de Lyon, Université Claude Bernard Lyon 1, Lyon, 69100, France
| | - Nathan Fox
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, Maryland, 20742
| | - James J Bonaiuto
- Institut des Sciences, Cognitives Marc Jeannerod, Centre National de la Recherche Scientifique Unité Mixte de Recherche 5229, Bron, 69500, France
- Université de Lyon, Université Claude Bernard Lyon 1, Lyon, 69100, France
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11
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Schmidt R, Rose J, Muralidharan V. Transient oscillations as computations for cognition: Analysis, modeling and function. Curr Opin Neurobiol 2023; 83:102796. [PMID: 37804772 DOI: 10.1016/j.conb.2023.102796] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 08/31/2023] [Accepted: 09/06/2023] [Indexed: 10/09/2023]
Abstract
Our view of neural oscillations is currently changing. The dominant picture of sustained oscillations is now often replaced by transient oscillations occurring in bursts. This phenomenon seems to be quite comprehensive, as it has been reported for different oscillation frequencies, including the theta, beta, and gamma bands, as well as cortical and subcortical regions in a variety of cognitive tasks and species. Here we review recent developments in their analysis, computational modeling, and functional roles. For the analysis of transient oscillations methods using lagged coherence and Hidden Markov Models have been developed and applied in recent studies to ascertain their transient nature and study their contribution to cognitive functions. Furthermore, computational models have been developed that account for their stochastic nature, which poses interesting functional constraints. Finally, as transient oscillations have been observed across many species, they are likely of functional significance and we consider challenges in characterizing their function.
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Affiliation(s)
- Robert Schmidt
- Institute for Neural Computation, Faculty of Computer Science, Ruhr-University Bochum, Germany.
| | - Jonas Rose
- Neural Basis of Learning, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Germany
| | - Vignesh Muralidharan
- Center for Brain Science and Application, School of AI and Data Science, Indian Institute of Technology Jodhpur, India. https://twitter.com/vigmdhrn
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12
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Codispoti M, De Cesarei A, Ferrari V. Alpha-band oscillations and emotion: A review of studies on picture perception. Psychophysiology 2023; 60:e14438. [PMID: 37724827 DOI: 10.1111/psyp.14438] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 08/04/2023] [Accepted: 08/24/2023] [Indexed: 09/21/2023]
Abstract
Although alpha-band activity has long been a focus of psychophysiological research, its modulation by emotional value during picture perception has only recently been studied systematically. Here, we review these studies and report that the most consistent alpha oscillatory pattern indexing emotional processing is an enhanced desynchronization (ERD) over posterior sensors when viewing emotional compared with neutral pictures. This enhanced alpha ERD is not specific to unpleasant picture content, as previously proposed for other measures of affective response, but has also been observed for pleasant stimuli. Evidence suggests that this effect is not confined to the alpha band but that it also involves a desynchronization of the lower beta frequencies (8-20 Hz). The emotional modulation of alpha ERD occurs even after massive stimulus repetition and when emotional cues serve as task-irrelevant distractors, consistent with the hypothesis that evaluative processes are mandatory in emotional picture processing. A similar enhanced ERD has been observed for other significant cues (e.g., conditioned aversive stimuli, or in anticipation of a potential threat), suggesting that it reflects cortical excitability associated with the engagement of the motivational systems.
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Affiliation(s)
| | | | - Vera Ferrari
- Department of Medicine and Surgery, University of Parma, Parma, Italy
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Joechner AK, Hahn MA, Gruber G, Hoedlmoser K, Werkle-Bergner M. Sleep spindle maturity promotes slow oscillation-spindle coupling across child and adolescent development. eLife 2023; 12:e83565. [PMID: 37999945 PMCID: PMC10672804 DOI: 10.7554/elife.83565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 10/18/2023] [Indexed: 11/25/2023] Open
Abstract
The synchronization of canonical fast sleep spindle activity (12.5-16 Hz, adult-like) precisely during the slow oscillation (0.5-1 Hz) up peak is considered an essential feature of adult non-rapid eye movement sleep. However, there is little knowledge on how this well-known coalescence between slow oscillations and sleep spindles develops. Leveraging individualized detection of single events, we first provide a detailed cross-sectional characterization of age-specific patterns of slow and fast sleep spindles, slow oscillations, and their coupling in children and adolescents aged 5-6, 8-11, and 14-18 years, and an adult sample of 20- to 26-year-olds. Critically, based on this, we then investigated how spindle and slow oscillation maturity substantiate age-related differences in their precise orchestration. While the predominant type of fast spindles was development-specific in that it was still nested in a frequency range below the canonical fast spindle range for the majority of children, the well-known slow oscillation-spindle coupling pattern was evident for sleep spindles in the adult-like canonical fast spindle range in all four age groups-but notably less precise in children. To corroborate these findings, we linked personalized measures of fast spindle maturity, which indicate the similarity between the prevailing development-specific and adult-like canonical fast spindles, and slow oscillation maturity, which reflects the extent to which slow oscillations show frontal dominance, with individual slow oscillation-spindle coupling patterns. Importantly, we found that fast spindle maturity was uniquely associated with enhanced slow oscillation-spindle coupling strength and temporal precision across the four age groups. Taken together, our results suggest that the increasing ability to generate adult-like canonical fast sleep spindles actuates precise slow oscillation-spindle coupling patterns from childhood through adolescence and into young adulthood.
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Affiliation(s)
- Ann-Kathrin Joechner
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Michael A Hahn
- Department of Psychology, Laboratory for Sleep, Cognition and Consciousness Research, University of Salzburg, Salzburg, Austria
- Centre for Cognitive Neuroscience Salzburg (CCNS), University of Salzburg, Salzburg, Austria
- Hertie-Institute for Clinical Brain Research, University Medical Center Tuebingen, Tuebingen, Germany
| | - Georg Gruber
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- The Siesta Group, Vienna, Austria
| | - Kerstin Hoedlmoser
- Department of Psychology, Laboratory for Sleep, Cognition and Consciousness Research, University of Salzburg, Salzburg, Austria
- Centre for Cognitive Neuroscience Salzburg (CCNS), University of Salzburg, Salzburg, Austria
| | - Markus Werkle-Bergner
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
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14
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Kim B, Erickson BA, Fernandez-Nunez G, Rich R, Mentzelopoulos G, Vitale F, Medaglia JD. EEG Phase Can Be Predicted with Similar Accuracy across Cognitive States after Accounting for Power and Signal-to-Noise Ratio. eNeuro 2023; 10:ENEURO.0050-23.2023. [PMID: 37558464 PMCID: PMC10481640 DOI: 10.1523/eneuro.0050-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/25/2023] [Accepted: 06/15/2023] [Indexed: 08/11/2023] Open
Abstract
EEG phase is increasingly used in cognitive neuroscience, brain-computer interfaces, and closed-loop stimulation devices. However, it is unknown how accurate EEG phase prediction is across cognitive states. We determined the EEG phase prediction accuracy of parieto-occipital alpha waves across rest and task states in 484 participants over 11 public datasets. We were able to track EEG phase accurately across various cognitive conditions and datasets, especially during periods of high instantaneous alpha power and signal-to-noise ratio (SNR). Although resting states generally have higher accuracies than task states, absolute accuracy differences were small, with most of these differences attributable to EEG power and SNR. These results suggest that experiments and technologies using EEG phase should focus more on minimizing external noise and waiting for periods of high power rather than inducing a particular cognitive state.
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Affiliation(s)
- Brian Kim
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania 19104
| | - Brian A Erickson
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania 19104
| | | | - Ryan Rich
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania 19104
| | - Georgios Mentzelopoulos
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania 19104
| | - Flavia Vitale
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania 19104
- Departments of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Physical Medicine and Rehabilitation, University of Pennsylvania, Philadelphia, Pennsylvania 19146
| | - John D Medaglia
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania 19104
- Departments of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Department of Neurology, Drexel University, Philadelphia, Pennsylvania 19104
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15
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Davis MC, Hill AT, Fitzgerald PB, Bailey NW, Stout JC, Hoy KE. Neurophysiological correlates of non-motor symptoms in late premanifest and early-stage manifest huntington's disease. Clin Neurophysiol 2023; 153:166-176. [PMID: 37506604 DOI: 10.1016/j.clinph.2023.06.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 05/22/2023] [Accepted: 06/18/2023] [Indexed: 07/30/2023]
Abstract
OBJECTIVE To find sensitive neurophysiological correlates of non-motor symptoms in Huntington's disease (HD), which are essential for the development and assessment of novel treatments. METHODS We used resting state EEG to examine differences in oscillatory activity (analysing the isolated periodic as well as the complete EEG signal) and functional connectivity in 22 late premanifest and early stage people with HD and 20 neurotypical controls. We then assessed the correlations between these neurophysiological markers and clinical measures of apathy and processing speed. RESULTS Significantly lower theta and greater delta resting state power was seen in the HD group, as well as significantly greater delta connectivity. There was a significant positive correlation between theta power and processing speed, however there were no associations between the neurophysiological and apathy measures. CONCLUSIONS We speculate that these changes in oscillatory power and connectivity reflect ongoing, frontally concentrated degenerative and compensatory processes associated with HD. SIGNIFICANCE Our findings support the potential utility of quantitative EEG as a proximate marker of processing speed, but not apathy in HD.
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Affiliation(s)
- Marie-Claire Davis
- Central Clinical School, Department of Psychiatry, Monash University, Victoria, Australia; Statewide Progressive Neurological Disease Service, Calvary Health Care Bethlehem, Victoria, Australia.
| | - Aron T Hill
- Central Clinical School, Department of Psychiatry, Monash University, Victoria, Australia; Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Victoria, Australia.
| | - Paul B Fitzgerald
- Central Clinical School, Department of Psychiatry, Monash University, Victoria, Australia; School of Medicine and Psychology, Australian National University, Canberra, ACT, Australia.
| | - Neil W Bailey
- Central Clinical School, Department of Psychiatry, Monash University, Victoria, Australia; School of Medicine and Psychology, Australian National University, Canberra, ACT, Australia; Monarch Research Institute Monarch Mental Health Group, 225 Clarence Street, Sydney, NSW 2000, Australia.
| | - Julie C Stout
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, 18 Innovation Walk, Clayton Campus, Wellington Road, Clayton, VIC 3800, Australia.
| | - Kate E Hoy
- Central Clinical School, Department of Psychiatry, Monash University, Victoria, Australia; The Bionics Institute of Australia, 384-388 Albert St, East Melbourne, VIC 3002, Australia.
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16
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Szul MJ, Papadopoulos S, Alavizadeh S, Daligaut S, Schwartz D, Mattout J, Bonaiuto JJ. Diverse beta burst waveform motifs characterize movement-related cortical dynamics. Prog Neurobiol 2023; 228:102490. [PMID: 37391061 DOI: 10.1016/j.pneurobio.2023.102490] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 05/03/2023] [Accepted: 06/21/2023] [Indexed: 07/02/2023]
Abstract
Classical analyses of induced, frequency-specific neural activity typically average band-limited power over trials. More recently, it has become widely appreciated that in individual trials, beta band activity occurs as transient bursts rather than amplitude-modulated oscillations. Most studies of beta bursts treat them as unitary, and having a stereotyped waveform. However, we show there is a wide diversity of burst shapes. Using a biophysical model of burst generation, we demonstrate that waveform variability is predicted by variability in the synaptic drives that generate beta bursts. We then use a novel, adaptive burst detection algorithm to identify bursts from human MEG sensor data recorded during a joystick-based reaching task, and apply principal component analysis to burst waveforms to define a set of dimensions, or motifs, that best explain waveform variance. Finally, we show that bursts with a particular range of waveform motifs, ones not fully accounted for by the biophysical model, differentially contribute to movement-related beta dynamics. Sensorimotor beta bursts are therefore not homogeneous events and likely reflect distinct computational processes.
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Affiliation(s)
- Maciej J Szul
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Lyon, France; Université Claude Bernard Lyon 1, Université de Lyon, France.
| | - Sotirios Papadopoulos
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Lyon, France; Université Claude Bernard Lyon 1, Université de Lyon, France; Lyon Neuroscience Research Center, CRNL, INSERM, U1028, CNRS, UMR 5292, Lyon, France
| | - Sanaz Alavizadeh
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Lyon, France; Université Claude Bernard Lyon 1, Université de Lyon, France
| | | | - Denis Schwartz
- CERMEP - Imagerie du Vivant, MEG Departement, Lyon, France
| | - Jérémie Mattout
- Université Claude Bernard Lyon 1, Université de Lyon, France; Lyon Neuroscience Research Center, CRNL, INSERM, U1028, CNRS, UMR 5292, Lyon, France
| | - James J Bonaiuto
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Lyon, France; Université Claude Bernard Lyon 1, Université de Lyon, France
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17
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Vigué-Guix I, Soto-Faraco S. Using occipital ⍺-bursts to modulate behavior in real-time. Cereb Cortex 2023; 33:9465-9477. [PMID: 37365814 DOI: 10.1093/cercor/bhad217] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 05/26/2023] [Accepted: 05/27/2023] [Indexed: 06/28/2023] Open
Abstract
Pre-stimulus endogenous neural activity can influence the processing of upcoming sensory input and subsequent behavioral reactions. Despite it is known that spontaneous oscillatory activity mostly appears in stochastic bursts, typical approaches based on trial averaging fail to capture this. We aimed at relating spontaneous oscillatory bursts in the alpha band (8-13 Hz) to visual detection behavior, via an electroencephalography-based brain-computer interface (BCI) that allowed for burst-triggered stimulus presentation in real-time. According to alpha theories, we hypothesized that visual targets presented during alpha-bursts should lead to slower responses and higher miss rates, whereas targets presented in the absence of bursts (low alpha activity) should lead to faster responses and higher false alarm rates. Our findings support the role of bursts of alpha oscillations in visual perception and exemplify how real-time BCI systems can be used as a test bench for brain-behavioral theories.
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Affiliation(s)
- Irene Vigué-Guix
- Center for Brain and Cognition, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona 08005, Spain
| | - Salvador Soto-Faraco
- Center for Brain and Cognition, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona 08005, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona 08010, Spain
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18
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Cho S, Choi JH. A guide towards optimal detection of transient oscillatory bursts with unknown parameters. J Neural Eng 2023; 20:046007. [PMID: 37339619 DOI: 10.1088/1741-2552/acdffd] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 06/20/2023] [Indexed: 06/22/2023]
Abstract
Objectives. Recent event-based analyses of transient neural activities have characterized the oscillatory bursts as a neural signature that bridges dynamic neural states to cognition and behaviors. Following this insight, our study aimed to (1) compare the efficacy of common burst detection algorithms under varying signal-to-noise ratios and event durations using synthetic signals and (2) establish a strategic guideline for selecting the optimal algorithm for real datasets with undefined properties.Approach.We tested the robustness of burst detection algorithms using a simulation dataset comprising bursts of multiple frequencies. To systematically assess their performance, we used a metric called 'detection confidence', quantifying classification accuracy and temporal precision in a balanced manner. Given that burst properties in empirical data are often unknown in advance, we then proposed a selection rule to identify an optimal algorithm for a given dataset and validated its application on local field potentials of basolateral amygdala recorded from male mice (n=8) exposed to a natural threat.Main Results.Our simulation-based evaluation demonstrated that burst detection is contingent upon event duration, whereas accurately pinpointing burst onsets is more susceptible to noise level. For real data, the algorithm chosen based on the selection rule exhibited superior detection and temporal accuracy, although its statistical significance differed across frequency bands. Notably, the algorithm chosen by human visual screening differed from the one recommended by the rule, implying a potential misalignment between human priors and mathematical assumptions of the algorithms.Significance.Therefore, our findings underscore that the precise detection of transient bursts is fundamentally influenced by the chosen algorithm. The proposed algorithm-selection rule suggests a potentially viable solution, while also emphasizing the inherent limitations originating from algorithmic design and volatile performances across datasets. Consequently, this study cautions against relying solely on heuristic-based approaches, advocating for a careful algorithm selection in burst detection studies.
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Affiliation(s)
- SungJun Cho
- Center for Neuroscience, Korea Institute of Science and Technology, Hwarang-ro 14-gil 5, Seongbuk-gu, Seoul 02792, Republic of Korea
- Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford OX3 7JX, United Kingdom
| | - Jee Hyun Choi
- Center for Neuroscience, Korea Institute of Science and Technology, Hwarang-ro 14-gil 5, Seongbuk-gu, Seoul 02792, Republic of Korea
- Department of Neural Sciences, University of Science and Technology, 217, Gajeong-ro, Yuseong-gu, Daejeon 34113, Republic of Korea
- Department of Physics and Center for Theoretical Physics, Seoul National University, Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
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19
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Ibitoye RT, Castro P, Ellmers TJ, Kaski DN, Bronstein AM. Vestibular loss disrupts visual reactivity in the alpha EEG rhythm. Neuroimage Clin 2023; 39:103469. [PMID: 37459699 PMCID: PMC10368920 DOI: 10.1016/j.nicl.2023.103469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 06/11/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023]
Abstract
The alpha rhythm is a dominant electroencephalographic oscillation relevant to sensory-motor and cognitive function. Alpha oscillations are reactive, being for example enhanced by eye closure, and suppressed following eye opening. The determinants of inter-individual variability in reactivity in the alpha rhythm (e.g. changes with amplitude following eye closure) are not fully understood despite the physiological and clinical applicability of this phenomenon, as indicated by the fact that ageing and neurodegeneration reduce reactivity. Strong interactions between visual and vestibular systems raise the theoretical possibility that the vestibular system plays a role in alpha reactivity. To test this hypothesis, we applied electroencephalography in sitting and standing postures in 15 participants with reduced vestibular function (bilateral vestibulopathy, median age = 70 years, interquartile range = 51-77 years) and 15 age-matched controls. We found participants with reduced vestibular function showed less enhancement of alpha electroencephalography power on eye closure in frontoparietal areas, compared to controls. In participants with reduced vestibular function, video head impulse test gain - as a measure of residual vestibulo-ocular reflex function - correlated with reactivity in alpha power across most of the head. Greater reliance on visual input for spatial orientation ('visual dependence', measured with the rod-and-disc test) correlated with less alpha enhancement on eye closure only in participants with reduced vestibular function, and this was partially moderated by video head impulse test gain. Our results demonstrate for the first time that vestibular function influences alpha reactivity. The results are partly explained by the lack of ascending peripheral vestibular input but also by central reorganisation of processing relevant to visuo-vestibular judgements.
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Affiliation(s)
- Richard T Ibitoye
- Centre for Vestibular Neurology, Department of Brain Sciences, Imperial College London, London W6 8RP, United Kingdom; Department of Neurology, Gloucestershire Hospital NHS Foundation Trust, Gloucester GL1 3NN, United Kingdom; Department of Clinical and Motor Neurosciences, Centre for Vestibular and Behavioural Neurosciences, University College London, London WC1N 3BG, United Kingdom
| | - Patricia Castro
- Centre for Vestibular Neurology, Department of Brain Sciences, Imperial College London, London W6 8RP, United Kingdom; Universidad del Desarrollo, Escuela de Fonoaudiología, Facultad de Medicina Clínica Alemana, Santiago, Chile
| | - Toby J Ellmers
- Centre for Vestibular Neurology, Department of Brain Sciences, Imperial College London, London W6 8RP, United Kingdom
| | - Diego N Kaski
- Universidad del Desarrollo, Escuela de Fonoaudiología, Facultad de Medicina Clínica Alemana, Santiago, Chile
| | - Adolfo M Bronstein
- Centre for Vestibular Neurology, Department of Brain Sciences, Imperial College London, London W6 8RP, United Kingdom.
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20
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Gunasekaran H, Azizi L, van Wassenhove V, Herbst SK. Characterizing endogenous delta oscillations in human MEG. Sci Rep 2023; 13:11031. [PMID: 37419933 PMCID: PMC10328979 DOI: 10.1038/s41598-023-37514-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 06/22/2023] [Indexed: 07/09/2023] Open
Abstract
Rhythmic activity in the delta frequency range (0.5-3 Hz) is a prominent feature of brain dynamics. Here, we examined whether spontaneous delta oscillations, as found in invasive recordings in awake animals, can be observed in non-invasive recordings performed in humans with magnetoencephalography (MEG). In humans, delta activity is commonly reported when processing rhythmic sensory inputs, with direct relationships to behaviour. However, rhythmic brain dynamics observed during rhythmic sensory stimulation cannot be interpreted as an endogenous oscillation. To test for endogenous delta oscillations we analysed human MEG data during rest. For comparison, we additionally analysed two conditions in which participants engaged in spontaneous finger tapping and silent counting, arguing that internally rhythmic behaviours could incite an otherwise silent neural oscillator. A novel set of analysis steps allowed us to show narrow spectral peaks in the delta frequency range in rest, and during overt and covert rhythmic activity. Additional analyses in the time domain revealed that only the resting state condition warranted an interpretation of these peaks as endogenously periodic neural dynamics. In sum, this work shows that using advanced signal processing techniques, it is possible to observe endogenous delta oscillations in non-invasive recordings of human brain dynamics.
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Affiliation(s)
- Harish Gunasekaran
- Cognitive Neuroimaging Unit, NeuroSpin, CEA, INSERM, CNRS, Université Paris-Saclay, 91191, Gif/Yvette, France
| | - Leila Azizi
- Cognitive Neuroimaging Unit, NeuroSpin, CEA, INSERM, CNRS, Université Paris-Saclay, 91191, Gif/Yvette, France
| | - Virginie van Wassenhove
- Cognitive Neuroimaging Unit, NeuroSpin, CEA, INSERM, CNRS, Université Paris-Saclay, 91191, Gif/Yvette, France
| | - Sophie K Herbst
- Cognitive Neuroimaging Unit, NeuroSpin, CEA, INSERM, CNRS, Université Paris-Saclay, 91191, Gif/Yvette, France.
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21
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Perera MPN, Mallawaarachchi S, Bailey NW, Murphy OW, Fitzgerald PB. Obsessive-compulsive disorder (OCD) is associated with increased electroencephalographic (EEG) delta and theta oscillatory power but reduced delta connectivity. J Psychiatr Res 2023; 163:310-317. [PMID: 37245318 DOI: 10.1016/j.jpsychires.2023.05.026] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 03/07/2023] [Accepted: 05/01/2023] [Indexed: 05/30/2023]
Abstract
Obsessive-Compulsive Disorder (OCD) is a mental health condition causing significant decline in the quality of life of sufferers and the limited knowledge on the pathophysiology hinders successful treatment. The aim of the current study was to examine electroencephalographic (EEG) findings of OCD to broaden our understanding of the disease. Resting-state eyes-closed EEG data was recorded from 25 individuals with OCD and 27 healthy controls (HC). The 1/f arrhythmic activity was removed prior to computing oscillatory powers of all frequency bands (delta, theta, alpha, beta, gamma). Cluster-based permutation was used for between-group statistical analyses, and comparisons were performed for the 1/f slope and intercept parameters. Functional connectivity (FC) was measured using coherence and debiased weighted phase lag index (d-wPLI), and statistically analyzed using the Network Based Statistic method. Compared to HC, the OCD group showed increased oscillatory power in the delta and theta bands in the fronto-temporal and parietal brain regions. However, there were no significant between-group findings in other bands or 1/f parameters. The coherence measure showed significantly reduced FC in the delta band in OCD compared to HC but the d-wPLI analysis showed no significant differences. OCD is associated with raised oscillatory power in slow frequency bands in the fronto-temporal brain regions, which agrees with the previous literature and therefore is a potential biomarker. Although delta coherence was found to be lower in OCD, due to inconsistencies found between measures and the previous literature, further research is required to ascertain definitive conclusions.
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Affiliation(s)
- M Prabhavi N Perera
- Central Clinical School, Monash University, Wellington Road, Clayton, Victoria, 3800, Australia.
| | - Sudaraka Mallawaarachchi
- Melbourne Integrative Genomics, School of Mathematics & Statistics, University of Melbourne, Parkville, Victoria, 3052, Australia
| | - Neil W Bailey
- Central Clinical School, Monash University, Wellington Road, Clayton, Victoria, 3800, Australia
| | - Oscar W Murphy
- Central Clinical School, Monash University, Wellington Road, Clayton, Victoria, 3800, Australia; Bionics Institute, East Melbourne, Victoria, 3002, Australia
| | - Paul B Fitzgerald
- Central Clinical School, Monash University, Wellington Road, Clayton, Victoria, 3800, Australia; School of Medicine and Psychology, Australian National University, Canberra, ACT, 2600, Australia
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22
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Amin HU, Ullah R, Reza MF, Malik AS. Single-trial extraction of event-related potentials (ERPs) and classification of visual stimuli by ensemble use of discrete wavelet transform with Huffman coding and machine learning techniques. J Neuroeng Rehabil 2023; 20:70. [PMID: 37269019 DOI: 10.1186/s12984-023-01179-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 04/19/2023] [Indexed: 06/04/2023] Open
Abstract
BACKGROUND Presentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple trials for individual participant analysis as well as for groups or conditions analysis of multiple participants. This study proposes a new method based on the discrete wavelet transform with Huffman coding and machine learning for single-trial analysis of evenal (ERPs) and classification of different visual events in the visual object detection task. METHODS EEG single trials are decomposed with discrete wavelet transform (DWT) up to the [Formula: see text] level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects. RESULTS The proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60[Formula: see text], sensitivities 93.55[Formula: see text], specificities 94.85[Formula: see text], precisions 92.50[Formula: see text], and area under the curve (AUC) 0.93[Formula: see text] using SVM and k-NN machine learning classifiers. CONCLUSION The proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in single-trial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired to smoothly operate a machine with minds.
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Affiliation(s)
- Hafeez Ullah Amin
- School of Computer Science, Faculty of Science and Engineering, University of Nottingham, Jalan Broga, 43500, Semenyih, Malaysia
| | - Rafi Ullah
- Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Malaysia
| | - Mohammed Faruque Reza
- Department of Neurosciences, School of Medical Sciences, Hospital Universiti Sains Malaysia, Kubang Kerian, 16150, Kota Bharu, Malaysia
| | - Aamir Saeed Malik
- Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic.
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23
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Rodriguez-Larios J, Haegens S. Genuine beta bursts in human working memory: controlling for the influence of lower-frequency rhythms. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.26.542448. [PMID: 37292960 PMCID: PMC10245977 DOI: 10.1101/2023.05.26.542448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Human working memory is associated with significant modulations in oscillatory brain activity. However, the functional role of brain rhythms at different frequencies is still debated. Modulations in the beta frequency range (15-40 Hz) are especially difficult to interpret because they could be artifactually produced by (more prominent) oscillations in lower frequencies that show non-sinusoidal properties. In this study, we investigate beta oscillations during working memory while controlling for the possible influence of lower frequency rhythms. We collected electroencephalography (EEG) data in 31 participants who performed a spatial working-memory task with two levels of cognitive load. In order to rule out the possibility that observed beta activity was affected by non-sinusoidalities of lower frequency rhythms, we developed an algorithm that detects transient beta oscillations that do not coincide with more prominent lower frequency rhythms in time and space. Using this algorithm, we show that the amplitude and duration of beta bursts decrease with memory load and during memory manipulation, while their peak frequency and rate increase. In addition, interindividual differences in performance were significantly associated with beta burst rates. Together, our results show that beta rhythms are functionally modulated during working memory and that these changes cannot be attributed to lower frequency rhythms with non-sinusoidal properties.
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Affiliation(s)
- Julio Rodriguez-Larios
- Dept. of Psychiatry, Columbia University, New York, USA, NY 10032
- Div. of Systems Neuroscience, New York State Psychiatric Institute, New York, USA, NY 10032
| | - Saskia Haegens
- Dept. of Psychiatry, Columbia University, New York, USA, NY 10032
- Div. of Systems Neuroscience, New York State Psychiatric Institute, New York, USA, NY 10032
- Donders Institute for Brain, Cognition & Behavior, Radboud University, Nijmegen, The Netherlands, 6525 EN
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Davis MC, Fitzgerald PB, Bailey NW, Sullivan C, Stout JC, Hill AT, Hoy KE. Effects of medial prefrontal transcranial alternating current stimulation on neural activity and connectivity in people with Huntington's disease and neurotypical controls. Brain Res 2023; 1811:148379. [PMID: 37121424 DOI: 10.1016/j.brainres.2023.148379] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 03/30/2023] [Accepted: 04/25/2023] [Indexed: 05/02/2023]
Abstract
We investigated the effects of transcranial alternating current stimulation (tACS) targeted to the medial prefrontal cortex (mPFC) on resting electroencephalographic (EEG) indices of oscillatory power, aperiodic exponent and offset, and functional connectivity in 22 late premanifest and early manifest stage individuals with HD and 20 neurotypical controls. Participants underwent three 20-minute sessions of tACS at least 72 hours apart; one session at alpha frequency (either each participant's Individualised Alpha Frequency (IAF), or 10Hz when an IAF was not detected); one session at delta frequency (2Hz); and a session of sham tACS. Session order was randomised and counterbalanced across participants. EEG recordings revealed a reduction of the spectral exponent ('flattening' of the 1/f slope) of the eyes-open aperiodic signal in participants with HD following alpha-tACS, suggestive of an enhancement in excitatory tone. Contrary to expectation, there were no changes in oscillatory power or functional connectivity in response to any of the tACS conditions in the participants with HD. By contrast, alpha-tACS increased delta power in neurotypical controls, who further demonstrated significant increases in theta power and theta functional connectivity in response to delta-tACS. This study contributes to the rapidly growing literature on the potential experimental and therapeutic applications of tACS by examining neurophysiological outcome measures in people with HD as well as neurotypical controls.
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Affiliation(s)
- Marie-Claire Davis
- Central Clinical School, Department of Psychiatry, Monash University, Victoria, Australia; Statewide Progressive Neurological Disease Service, Calvary Health Care Bethlehem, Victoria Australia.
| | - Paul B Fitzgerald
- School of Medicine and Psychology, Australian National University, Canberra, ACT, Australia
| | - Neil W Bailey
- Central Clinical School, Department of Psychiatry, Monash University, Victoria, Australia; School of Medicine and Psychology, Australian National University, Canberra, ACT, Australia; Monarch Research Institute Monarch Mental Health Group, Sydney, NSW, Australia
| | - Caley Sullivan
- Central Clinical School, Department of Psychiatry, Monash University, Victoria, Australia
| | - Julie C Stout
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, Australia
| | - Aron T Hill
- Central Clinical School, Department of Psychiatry, Monash University, Victoria, Australia; Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, Australia
| | - Kate E Hoy
- Central Clinical School, Department of Psychiatry, Monash University, Victoria, Australia; The Bionics Institute of Australia, 384-388 Albert St, East Melbourne, VIC, 3002, Australia
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Morris AT, Temereanca S, Zandvakili A, Thorpe R, Sliva DD, Greenberg BD, Carpenter LL, Philip NS, Jones SR. Fronto-central resting-state 15-29 Hz transient beta events change with therapeutic transcranial magnetic stimulation for posttraumatic stress disorder and major depressive disorder. Sci Rep 2023; 13:6366. [PMID: 37076496 PMCID: PMC10115889 DOI: 10.1038/s41598-023-32801-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 04/03/2023] [Indexed: 04/21/2023] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is an established treatment for major depressive disorder (MDD) and shows promise for posttraumatic stress disorder (PTSD), yet effectiveness varies. Electroencephalography (EEG) can identify rTMS-associated brain changes. EEG oscillations are often examined using averaging approaches that mask finer time-scale dynamics. Recent advances show some brain oscillations emerge as transient increases in power, a phenomenon termed "Spectral Events," and that event characteristics correspond with cognitive functions. We applied Spectral Event analyses to identify potential EEG biomarkers of effective rTMS treatment. Resting 8-electrode EEG was collected from 23 patients with MDD and PTSD before and after 5 Hz rTMS targeting the left dorsolateral prefrontal cortex. Using an open-source toolbox ( https://github.com/jonescompneurolab/SpectralEvents ), we quantified event features and tested for treatment associated changes. Spectral Events in delta/theta (1-6 Hz), alpha (7-14 Hz), and beta (15-29 Hz) bands occurred in all patients. rTMS-induced improvement in comorbid MDD PTSD were associated with pre- to post-treatment changes in fronto-central electrode beta event features, including frontal beta event frequency spans and durations, and central beta event maxima power. Furthermore, frontal pre-treatment beta event duration correlated negatively with MDD symptom improvement. Beta events may provide new biomarkers of clinical response and advance the understanding of rTMS.
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Affiliation(s)
- Alexander T Morris
- VA RR&D Center for Neurorestoration and Neurotechnology, VA Providence, Providence, RI, USA
| | - Simona Temereanca
- VA RR&D Center for Neurorestoration and Neurotechnology, VA Providence, Providence, RI, USA.
- Department of Neuroscience, Brown University, Providence, RI, USA.
- Carney Institute for Brain Science, Brown University, Providence, RI, USA.
| | - Amin Zandvakili
- VA RR&D Center for Neurorestoration and Neurotechnology, VA Providence, Providence, RI, USA
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
| | - Ryan Thorpe
- Department of Neuroscience, Brown University, Providence, RI, USA
- Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - Danielle D Sliva
- Department of Neuroscience, Brown University, Providence, RI, USA
- Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - Benjamin D Greenberg
- VA RR&D Center for Neurorestoration and Neurotechnology, VA Providence, Providence, RI, USA
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
- COBRE Center for Neuromodulation, Butler Hospital, Providence, RI, USA
| | - Linda L Carpenter
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
- COBRE Center for Neuromodulation, Butler Hospital, Providence, RI, USA
- Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - Noah S Philip
- VA RR&D Center for Neurorestoration and Neurotechnology, VA Providence, Providence, RI, USA
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
- COBRE Center for Neuromodulation, Butler Hospital, Providence, RI, USA
| | - Stephanie R Jones
- VA RR&D Center for Neurorestoration and Neurotechnology, VA Providence, Providence, RI, USA.
- Department of Neuroscience, Brown University, Providence, RI, USA.
- Carney Institute for Brain Science, Brown University, Providence, RI, USA.
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26
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Morris AT, Temereanca S, Zandvakili A, Thorpe R, Sliva DD, Greenberg BD, Carpenter LL, Philip NS, Jones SR. Fronto-central resting-state 15-29Hz transient beta events change with therapeutic transcranial magnetic stimulation for posttraumatic stress disorder and major depressive disorder. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.11.23286902. [PMID: 36993547 PMCID: PMC10055566 DOI: 10.1101/2023.03.11.23286902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/28/2023]
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is an established treatment for major depressive disorder (MDD) and shows promise for posttraumatic stress disorder (PTSD), yet effectiveness varies. Electroencephalography (EEG) can identify rTMS-associated brain changes. EEG oscillations are often examined using averaging approaches that mask finer time-scale dynamics. Recent advances show some brain oscillations emerge as transient increases in power, a phenomenon termed "Spectral Events," and that event characteristics correspond with cognitive functions. We applied Spectral Event analyses to identify potential EEG biomarkers of effective rTMS treatment. Resting 8-electrode EEG was collected from 23 patients with MDD and PTSD before and after 5Hz rTMS targeting the left dorsolateral prefrontal cortex. Using an open-source toolbox ( https://github.com/jonescompneurolab/SpectralEvents ), we quantified event features and tested for treatment associated changes. Spectral Events in delta/theta (1-6 Hz), alpha (7-14 Hz), and beta (15-29 Hz) bands occurred in all patients. rTMS-induced improvement in comorbid MDD PTSD were associated with pre-to post-treatment changes in fronto-central electrode beta event features, including frontal beta event frequency spans and durations, and central beta event maxima power. Furthermore, frontal pre-treatment beta event duration correlated negatively with MDD symptom improvement. Beta events may provide new biomarkers of clinical response and advance the understanding of rTMS.
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27
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Bierwirth P, Antov MI, Stockhorst U. Oscillatory and non-oscillatory brain activity reflects fear expression in an immediate and delayed fear extinction task. Psychophysiology 2023:e14283. [PMID: 36906880 DOI: 10.1111/psyp.14283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 01/23/2023] [Accepted: 02/07/2023] [Indexed: 03/13/2023]
Abstract
Fear extinction is pivotal for inhibiting fear responding to former threat-predictive stimuli. In rodents, short intervals between fear acquisition and extinction impair extinction recall compared to long intervals. This is called Immediate Extinction Deficit (IED). Importantly, human studies of the IED are sparse and its neurophysiological correlates have not been examined in humans. We, therefore, investigated the IED by recording electroencephalography (EEG), skin conductance responses (SCRs), an electrocardiogram (ECG), and subjective ratings of valence and arousal. Forty male participants were randomly assigned to extinction learning either 10 min after fear acquisition (immediate extinction) or 24 h afterward (delayed extinction). Fear and extinction recall were assessed 24 h after extinction learning. We observed evidence for an IED in SCR responses, but not in the ECG, subjective ratings, or in any assessed neurophysiological marker of fear expression. Irrespective of extinction timing (immediate vs. delayed), fear conditioning caused a tilt of the non-oscillatory background spectrum with decreased low-frequency power (<30 Hz) for threat-predictive stimuli. When controlling for this tilt, we observed a suppression of theta and alpha oscillations to threat-predictive stimuli, especially pronounced during fear acquisition. In sum, our data show that delayed extinction might be partially advantageous over immediate extinction in reducing sympathetic arousal (as assessed via SCR) to former threat-predictive stimuli. However, this effect was limited to SCR responses since all other fear measures were not affected by extinction timing. Additionally, we demonstrate that oscillatory and non-oscillatory activity is sensitive to fear conditioning, which has important implications for fear conditioning studies examining neural oscillations.
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Affiliation(s)
- Philipp Bierwirth
- Institute of Psychology, Experimental Psychology II and Biological Psychology, University of Osnabrück, Osnabrück, Germany
| | - Martin I Antov
- Institute of Psychology, Experimental Psychology II and Biological Psychology, University of Osnabrück, Osnabrück, Germany
| | - Ursula Stockhorst
- Institute of Psychology, Experimental Psychology II and Biological Psychology, University of Osnabrück, Osnabrück, Germany
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Lendner JD, Harler U, Daume J, Engel AK, Zöllner C, Schneider TR, Fischer M. Oscillatory and aperiodic neuronal activity in working memory following anesthesia. Clin Neurophysiol 2023; 150:79-88. [PMID: 37028144 DOI: 10.1016/j.clinph.2023.03.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 02/07/2023] [Accepted: 03/02/2023] [Indexed: 03/28/2023]
Abstract
OBJECTIVE Anesthesia and surgery are associated with cognitive impairment, particularly memory deficits. So far, electroencephalography markers of perioperative memory function remain scarce. METHODS We included male patients >60 years scheduled for prostatectomy under general anesthesia. We obtained neuropsychological assessments and a visual match-to-sample working memory task with simultaneous 62-channel scalp electroencephalography 1 day before and 2 to 3 days after surgery. RESULTS Twenty-six patients completed both pre- and postoperative sessions. Compared with preoperative performance, verbal learning deteriorated after anesthesia (California Verbal Learning Test total recall; t25 = -3.25, p = 0.015, d = -0.902), while visual working memory performance showed a dissociation between match and mismatch accuracy (match*session F1,25 = 3.866, p = 0.060). Better verbal learning was associated with an increase of aperiodic brain activity (total recall r = 0.66, p = 0.029, learning slope r = 0.66, p = 0.015), whereas visual working memory accuracy was tracked by oscillatory theta/alpha (7 - 9 Hz), low beta (14 - 18 Hz) and high beta/gamma (34 - 38 Hz) activity (matches: p < 0.001, mismatches: p = 0.022). CONCLUSIONS Oscillatory and aperiodic brain activity in scalp electroencephalography track distinct features of perioperative memory function. SIGNIFICANCE Aperiodic activity provides a potential electroencephalographic biomarker to identify patients at risk for postoperative cognitive impairments.
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29
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Tröndle M, Popov T, Pedroni A, Pfeiffer C, Barańczuk-Turska Z, Langer N. Decomposing age effects in EEG alpha power. Cortex 2023; 161:116-144. [PMID: 36933455 DOI: 10.1016/j.cortex.2023.02.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 12/09/2022] [Accepted: 02/03/2023] [Indexed: 02/24/2023]
Abstract
Increasing life expectancy is prompting the need to understand how the brain changes during healthy aging. Research utilizing electroencephalography (EEG) has found that the power of alpha oscillations decrease from adulthood on. However, non-oscillatory (aperiodic) components in the data may confound results and thus require re-investigation of these findings. Thus, the present report analyzed a pilot and two additional independent samples (total N = 533) of resting-state EEG from healthy young and elderly individuals. A newly developed algorithm was utilized that allows the decomposition of the measured signal into periodic and aperiodic signal components. By using multivariate sequential Bayesian updating of the age effect in each signal component, evidence across the datasets was accumulated. It was hypothesized that previously reported age-related alpha power differences will largely diminish when total power is adjusted for the aperiodic signal component. First, the age-related decrease in total alpha power was replicated. Concurrently, decreases of the intercept and slope (i.e. exponent) of the aperiodic signal component were observed. Findings on aperiodic-adjusted alpha power indicated that this general shift of the power spectrum leads to an overestimation of the true age effects in conventional analyses of total alpha power. Thus, the importance of separating neural power spectra into periodic and aperiodic signal components is highlighted. However, also after accounting for these confounding factors, the sequential Bayesian updating analysis provided robust evidence that aging is associated with decreased aperiodic-adjusted alpha power. While the relation of the aperiodic component and aperiodic-adjusted alpha power to cognitive decline demands further investigation, the consistent findings on age effects across independent datasets and high test-retest reliabilities support that these newly emerging measures are reliable markers of the aging brain. Hence, previous interpretations of age-related decreases in alpha power are reevaluated, incorporating changes in the aperiodic signal.
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Affiliation(s)
- Marius Tröndle
- Department of Psychology, University of Zurich, Methods of Plasticity Research, Zurich, Switzerland; University Research Priority Program (URPP) Dynamics of Healthy Aging, Zurich, Switzerland; Neuroscience Center Zurich (ZNZ), Zurich, Switzerland.
| | - Tzvetan Popov
- Department of Psychology, University of Zurich, Methods of Plasticity Research, Zurich, Switzerland; University Research Priority Program (URPP) Dynamics of Healthy Aging, Zurich, Switzerland
| | - Andreas Pedroni
- Department of Psychology, University of Zurich, Methods of Plasticity Research, Zurich, Switzerland; University Research Priority Program (URPP) Dynamics of Healthy Aging, Zurich, Switzerland
| | - Christian Pfeiffer
- Department of Psychology, University of Zurich, Methods of Plasticity Research, Zurich, Switzerland
| | - Zofia Barańczuk-Turska
- Department of Psychology, University of Zurich, Methods of Plasticity Research, Zurich, Switzerland; University Research Priority Program (URPP) Dynamics of Healthy Aging, Zurich, Switzerland; Institute of Mathematics, University of Zurich, Switzerland
| | - Nicolas Langer
- Department of Psychology, University of Zurich, Methods of Plasticity Research, Zurich, Switzerland; University Research Priority Program (URPP) Dynamics of Healthy Aging, Zurich, Switzerland; Neuroscience Center Zurich (ZNZ), Zurich, Switzerland
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30
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Power L, Allain C, Moreau T, Gramfort A, Bardouille T. Using convolutional dictionary learning to detect task-related neuromagnetic transients and ageing trends in a large open-access dataset. Neuroimage 2023; 267:119809. [PMID: 36584759 DOI: 10.1016/j.neuroimage.2022.119809] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 12/05/2022] [Accepted: 12/12/2022] [Indexed: 12/28/2022] Open
Abstract
Human neuromagnetic activity is characterised by a complex combination of transient bursts with varying spatial and temporal characteristics. The characteristics of these transient bursts change during task performance and normal ageing in ways that can inform about underlying cortical sources. Many methods have been proposed to detect transient bursts, with the most successful ones being those that employ multi-channel, data-driven approaches to minimize bias in the detection procedure. There has been little research, however, into the application of these data-driven methods to large datasets for group-level analyses. In the current work, we apply a data-driven convolutional dictionary learning (CDL) approach to detect neuromagnetic transient bursts in a large group of healthy participants from the Cam-CAN dataset. CDL was used to extract repeating spatiotemporal motifs in 538 participants between the ages of 18-88 during a sensorimotor task. Motifs were then clustered across participants based on similarity, and relevant task-related clusters were analysed for age-related trends in their spatiotemporal characteristics. Seven task-related motifs resembling known transient burst types were identified through this analysis, including beta, mu, and alpha type bursts. All burst types showed positive trends in their activation levels with age that could be explained by increasing burst rate with age. This work validated the data-driven CDL approach for transient burst detection on a large dataset and identified robust information about the complex characteristics of human brain signals and how they change with age.
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Affiliation(s)
- Lindsey Power
- School of Biomedical Engineering, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Cédric Allain
- Inria, Mind team, Université Paris-Saclay, Saclay, France
| | - Thomas Moreau
- Inria, Mind team, Université Paris-Saclay, Saclay, France
| | | | - Timothy Bardouille
- Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada.
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31
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Nikolaev AR, Bramão I, Johansson R, Johansson M. Episodic memory formation in unrestricted viewing. Neuroimage 2023; 266:119821. [PMID: 36535321 DOI: 10.1016/j.neuroimage.2022.119821] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 11/16/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022] Open
Abstract
The brain systems of episodic memory and oculomotor control are tightly linked, suggesting a crucial role of eye movements in memory. But little is known about the neural mechanisms of memory formation across eye movements in unrestricted viewing behavior. Here, we leverage simultaneous eye tracking and EEG recording to examine episodic memory formation in free viewing. Participants memorized multi-element events while their EEG and eye movements were concurrently recorded. Each event comprised elements from three categories (face, object, place), with two exemplars from each category, in different locations on the screen. A subsequent associative memory test assessed participants' memory for the between-category associations that specified each event. We used a deconvolution approach to overcome the problem of overlapping EEG responses to sequential saccades in free viewing. Brain activity was time-locked to the fixation onsets, and we examined EEG power in the theta and alpha frequency bands, the putative oscillatory correlates of episodic encoding mechanisms. Three modulations of fixation-related EEG predicted high subsequent memory performance: (1) theta increase at fixations after between-category gaze transitions, (2) theta and alpha increase at fixations after within-element gaze transitions, (3) alpha decrease at fixations after between-exemplar gaze transitions. Thus, event encoding with unrestricted viewing behavior was characterized by three neural mechanisms, manifested in fixation-locked theta and alpha EEG activity that rapidly turned on and off during the unfolding eye movement sequences. These three distinct neural mechanisms may be the essential building blocks that subserve the buildup of coherent episodic memories during unrestricted viewing behavior.
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Affiliation(s)
- Andrey R Nikolaev
- Department of Psychology, Lund Memory Lab, Lund University, Lund, Sweden; Brain and Cognition Research Unit, KU Leuven, Leuven, Belgium.
| | - Inês Bramão
- Department of Psychology, Lund Memory Lab, Lund University, Lund, Sweden
| | - Roger Johansson
- Department of Psychology, Lund Memory Lab, Lund University, Lund, Sweden
| | - Mikael Johansson
- Department of Psychology, Lund Memory Lab, Lund University, Lund, Sweden
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32
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Seymour RA, Alexander N, Maguire EA. Robust estimation of 1/f activity improves oscillatory burst detection. Eur J Neurosci 2022; 56:5836-5852. [PMID: 36161675 PMCID: PMC9828710 DOI: 10.1111/ejn.15829] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 09/13/2022] [Indexed: 02/06/2023]
Abstract
Neural oscillations often occur as transient bursts with variable amplitude and frequency dynamics. Quantifying these effects is important for understanding brain-behaviour relationships, especially in continuous datasets. To robustly measure bursts, rhythmical periods of oscillatory activity must be separated from arrhythmical background 1/f activity, which is ubiquitous in electrophysiological recordings. The Better OSCillation (BOSC) framework achieves this by defining a power threshold above the estimated background 1/f activity, combined with a duration threshold. Here we introduce a modification to this approach called fBOSC, which uses a spectral parametrisation tool to accurately model background 1/f activity in neural data. fBOSC (which is openly available as a MATLAB toolbox) is robust to power spectra with oscillatory peaks and can also model non-linear spectra. Through a series of simulations, we show that fBOSC more accurately models the 1/f power spectrum compared with existing methods. fBOSC was especially beneficial where power spectra contained a 'knee' below ~.5-10 Hz, which is typical in neural data. We also found that, unlike other methods, fBOSC was unaffected by oscillatory peaks in the neural power spectrum. Moreover, by robustly modelling background 1/f activity, the sensitivity for detecting oscillatory bursts was standardised across frequencies (e.g., theta- and alpha-bands). Finally, using openly available resting state magnetoencephalography and intracranial electrophysiology datasets, we demonstrate the application of fBOSC for oscillatory burst detection in the theta-band. These simulations and empirical analyses highlight the value of fBOSC in detecting oscillatory bursts, including in datasets that are long and continuous with no distinct experimental trials.
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Affiliation(s)
- Robert A. Seymour
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Nicholas Alexander
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Eleanor A. Maguire
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
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33
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Mercier MR, Dubarry AS, Tadel F, Avanzini P, Axmacher N, Cellier D, Vecchio MD, Hamilton LS, Hermes D, Kahana MJ, Knight RT, Llorens A, Megevand P, Melloni L, Miller KJ, Piai V, Puce A, Ramsey NF, Schwiedrzik CM, Smith SE, Stolk A, Swann NC, Vansteensel MJ, Voytek B, Wang L, Lachaux JP, Oostenveld R. Advances in human intracranial electroencephalography research, guidelines and good practices. Neuroimage 2022; 260:119438. [PMID: 35792291 PMCID: PMC10190110 DOI: 10.1016/j.neuroimage.2022.119438] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/23/2022] [Accepted: 06/30/2022] [Indexed: 12/11/2022] Open
Abstract
Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.
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Affiliation(s)
- Manuel R Mercier
- INSERM, INS, Institut de Neurosciences des Systèmes, Aix-Marseille University, Marseille, France.
| | | | - François Tadel
- Signal & Image Processing Institute, University of Southern California, Los Angeles, CA United States of America
| | - Pietro Avanzini
- Institute of Neuroscience, National Research Council of Italy, Parma, Italy
| | - Nikolai Axmacher
- Department of Neuropsychology, Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr University Bochum, Universitätsstraße 150, Bochum 44801, Germany; State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, 19 Xinjiekou Outer St, Beijing 100875, China
| | - Dillan Cellier
- Department of Cognitive Science, University of California, La Jolla, San Diego, United States of America
| | - Maria Del Vecchio
- Institute of Neuroscience, National Research Council of Italy, Parma, Italy
| | - Liberty S Hamilton
- Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX, United States of America; Institute for Neuroscience, The University of Texas at Austin, Austin, TX, United States of America; Department of Speech, Language, and Hearing Sciences, Moody College of Communication, The University of Texas at Austin, Austin, TX, United States of America
| | - Dora Hermes
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States of America
| | - Michael J Kahana
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Robert T Knight
- Department of Psychology and the Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, United States of America
| | - Anais Llorens
- Helen Wills Neuroscience Institute, University of California, Berkeley, United States of America
| | - Pierre Megevand
- Department of Clinical neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Lucia Melloni
- Department of Neuroscience, Max Planck Institute for Empirical Aesthetics, Grüneburgweg 14, Frankfurt am Main 60322, Germany; Department of Neurology, NYU Grossman School of Medicine, 145 East 32nd Street, Room 828, New York, NY 10016, United States of America
| | - Kai J Miller
- Department of Neurosurgery, Mayo Clinic, Rochester, MN 55905, USA
| | - Vitória Piai
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands; Department of Medical Psychology, Radboudumc, Donders Centre for Medical Neuroscience, Nijmegen, the Netherlands
| | - Aina Puce
- Department of Psychological & Brain Sciences, Programs in Neuroscience, Cognitive Science, Indiana University, Bloomington, IN, United States of America
| | - Nick F Ramsey
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, UMC Utrecht, the Netherlands
| | - Caspar M Schwiedrzik
- Neural Circuits and Cognition Lab, European Neuroscience Institute Göttingen - A Joint Initiative of the University Medical Center Göttingen and the Max Planck Society, Göttingen, Germany; Perception and Plasticity Group, German Primate Center, Leibniz Institute for Primate Research, Göttingen, Germany
| | - Sydney E Smith
- Neurosciences Graduate Program, University of California, La Jolla, San Diego, United States of America
| | - Arjen Stolk
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands; Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States of America
| | - Nicole C Swann
- University of Oregon in the Department of Human Physiology, United States of America
| | - Mariska J Vansteensel
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, UMC Utrecht, the Netherlands
| | - Bradley Voytek
- Department of Cognitive Science, University of California, La Jolla, San Diego, United States of America; Neurosciences Graduate Program, University of California, La Jolla, San Diego, United States of America; Halıcıoğlu Data Science Institute, University of California, La Jolla, San Diego, United States of America; Kavli Institute for Brain and Mind, University of California, La Jolla, San Diego, United States of America
| | - Liang Wang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Jean-Philippe Lachaux
- Lyon Neuroscience Research Center, EDUWELL Team, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon F-69000, France
| | - Robert Oostenveld
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands; NatMEG, Karolinska Institutet, Stockholm, Sweden
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Skwara AC, King BG, Zanesco AP, Saron CD. Shifting Baselines: Longitudinal Reductions in EEG Beta Band Power Characterize Resting Brain Activity with Intensive Meditation. Mindfulness (N Y) 2022; 13:2488-2506. [PMID: 36258902 PMCID: PMC9568471 DOI: 10.1007/s12671-022-01974-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/02/2022] [Indexed: 11/18/2022]
Abstract
Objectives A core assumption of meditation training is that cognitive capacities developed during formal practice will transfer to other contexts or activities as expertise develops over time. This implies that meditation training might influence domain-general neurocognitive systems, the spontaneous activity of which should be reflected in the dynamics of the resting brain. Previous research has demonstrated that 3 months of meditation training led to reductions in EEG beta band power during mindfulness of breathing practice. The current study extends these findings to ask whether concomitant shifts in power are observed during 2 min of eyes closed rest, when participants are not explicitly engaged in formal meditation. Methods Experienced meditation practitioners were randomly assigned to practice 3 months of focused attention meditation in a residential retreat, or to serve as waitlist controls. The waitlist controls later completed their own 3-month retreat. Permutation-based cluster analysis of 88-channel resting EEG data was used to test for spectral changes in spontaneous brain activity over the course of the retreats. Results Longitudinal reductions in EEG power in the beta frequency range were identified and replicated across the two independent training periods. Less robust reductions were also observed in the high alpha frequency range, and in individual peak alpha frequency. These changes closely mirror those previously observed during formal mindfulness of breathing meditation practice. Conclusions These findings suggest that the neurocognitive effects of meditation training can extend beyond the bounds of formal practice, influencing the spontaneous activity of the resting brain. Rather than serving as an invariant baseline, resting states might carry meaningful training-related effects, blurring the line between state and trait change. Supplementary Information The online version contains supplementary material available at 10.1007/s12671-022-01974-9.
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Maria Pani S, Saba L, Fraschini M. Clinical applications of EEG power spectra aperiodic component analysis: a mini-review. Clin Neurophysiol 2022; 143:1-13. [DOI: 10.1016/j.clinph.2022.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 08/09/2022] [Accepted: 08/11/2022] [Indexed: 11/03/2022]
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36
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Donoghue T, Schaworonkow N, Voytek B. Methodological considerations for studying neural oscillations. Eur J Neurosci 2022; 55:3502-3527. [PMID: 34268825 PMCID: PMC8761223 DOI: 10.1111/ejn.15361] [Citation(s) in RCA: 92] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 05/25/2021] [Accepted: 06/16/2021] [Indexed: 12/29/2022]
Abstract
Neural oscillations are ubiquitous across recording methodologies and species, broadly associated with cognitive tasks, and amenable to computational modelling that investigates neural circuit generating mechanisms and neural population dynamics. Because of this, neural oscillations offer an exciting potential opportunity for linking theory, physiology and mechanisms of cognition. However, despite their prevalence, there are many concerns-new and old-about how our analysis assumptions are violated by known properties of field potential data. For investigations of neural oscillations to be properly interpreted, and ultimately developed into mechanistic theories, it is necessary to carefully consider the underlying assumptions of the methods we employ. Here, we discuss seven methodological considerations for analysing neural oscillations. The considerations are to (1) verify the presence of oscillations, as they may be absent; (2) validate oscillation band definitions, to address variable peak frequencies; (3) account for concurrent non-oscillatory aperiodic activity, which might otherwise confound measures; measure and account for (4) temporal variability and (5) waveform shape of neural oscillations, which are often bursty and/or nonsinusoidal, potentially leading to spurious results; (6) separate spatially overlapping rhythms, which may interfere with each other; and (7) consider the required signal-to-noise ratio for obtaining reliable estimates. For each topic, we provide relevant examples, demonstrate potential errors of interpretation, and offer suggestions to address these issues. We primarily focus on univariate measures, such as power and phase estimates, though we discuss how these issues can propagate to multivariate measures. These considerations and recommendations offer a helpful guide for measuring and interpreting neural oscillations.
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Affiliation(s)
- Thomas Donoghue
- Department of Cognitive Science, University of California, San Diego
| | | | - Bradley Voytek
- Department of Cognitive Science, University of California, San Diego
- Neurosciences Graduate Program, University of California, San Diego
- Halıcıoğlu Data Science Institute, University of California, San Diego
- Kavli Institute for Brain and Mind, University of California, San Diego
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37
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Cross ZR, Corcoran AW, Schlesewsky M, Kohler MJ, Bornkessel-Schlesewsky I. Oscillatory and Aperiodic Neural Activity Jointly Predict Language Learning. J Cogn Neurosci 2022; 34:1630-1649. [PMID: 35640095 DOI: 10.1162/jocn_a_01878] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Memory formation involves the synchronous firing of neurons in task-relevant networks, with recent models postulating that a decrease in low-frequency oscillatory activity underlies successful memory encoding and retrieval. However, to date, this relationship has been investigated primarily with face and image stimuli; considerably less is known about the oscillatory correlates of complex rule learning, as in language. Furthermore, recent work has shown that nonoscillatory (1/ƒ) activity is functionally relevant to cognition, yet its interaction with oscillatory activity during complex rule learning remains unknown. Using spectral decomposition and power-law exponent estimation of human EEG data (17 females, 18 males), we show for the first time that 1/ƒ and oscillatory activity jointly influence the learning of word order rules of a miniature artificial language system. Flexible word-order rules were associated with a steeper 1/ƒ slope, whereas fixed word-order rules were associated with a shallower slope. We also show that increased theta and alpha power predicts fixed relative to flexible word-order rule learning and behavioral performance. Together, these results suggest that 1/ƒ activity plays an important role in higher-order cognition, including language processing, and that grammar learning is modulated by different word-order permutations, which manifest in distinct oscillatory profiles.
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38
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Wainio-Theberge S, Wolff A, Gomez-Pilar J, Zhang J, Northoff G. Variability and task-responsiveness of electrophysiological dynamics: scale-free stability and oscillatory flexibility. Neuroimage 2022; 256:119245. [PMID: 35477021 DOI: 10.1016/j.neuroimage.2022.119245] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 04/17/2022] [Accepted: 04/22/2022] [Indexed: 11/18/2022] Open
Abstract
Cortical oscillations and scale-free neural activity are thought to influence a variety of cognitive functions, but their differential relationships to neural stability and flexibility has never been investigated. Based on the existing literature, we hypothesize that scale-free and oscillatory processes in the brain exhibit different trade-offs between stability and flexibility; specifically, cortical oscillations may reflect variable, task-responsive aspects of brain activity, while scale-free activity is proposed to reflect a more stable and task-unresponsive aspect. We test this hypothesis using data from two large-scale MEG studies (HCP: n = 89; CamCAN: n = 195), operationalizing stability and flexibility by task-responsiveness and spontaneous intra-subject variability in resting state. We demonstrate that the power-law exponent of scale-free activity is a highly stable parameter, which responds little to external cognitive demands and shows minimal spontaneous fluctuations over time. In contrast, oscillatory power, particularly in the alpha range (8-13 Hz), responds strongly to tasks and exhibits comparatively large spontaneous fluctuations over time. In sum, our data support differential roles for oscillatory and scale-free activity in the brain with respect to neural stability and flexibility. This result carries implications for criticality-based theories of scale-free activity, state-trait models of variability, and homeostatic views of the brain with regulated variables vs. effectors.
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Affiliation(s)
- Soren Wainio-Theberge
- Mind, Brain Imaging, and Neuroethics Unit, Institute of Mental Health Research, Royal Ottawa Mental Health Centre, University of Ottawa, 1145 Carling Avenue, Rm. 6435, Ottawa, ON K1Z 7K4, Canada; Integrated Program in Neuroscience, McGill University, Montréal, QC, Canada.
| | - Annemarie Wolff
- Mind, Brain Imaging, and Neuroethics Unit, Institute of Mental Health Research, Royal Ottawa Mental Health Centre, University of Ottawa, 1145 Carling Avenue, Rm. 6435, Ottawa, ON K1Z 7K4, Canada
| | - Javier Gomez-Pilar
- Biomedical Engineering Group, University of Valladolid, Paseo de Belén, 15, Valladolid 47011, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Valladolid, Spain
| | - Jianfeng Zhang
- Mental Health Centre/7th Hospital, Zhejiang University School of Medicine, Tianmu Road 305, Hangzhou, Zhejiang 310013, China; College of Biomedical Engineering and Instrument Sciences, Zhejiang University, Hangzhou, China
| | - Georg Northoff
- Mind, Brain Imaging, and Neuroethics Unit, Institute of Mental Health Research, Royal Ottawa Mental Health Centre, University of Ottawa, 1145 Carling Avenue, Rm. 6435, Ottawa, ON K1Z 7K4, Canada; Mental Health Centre/7th Hospital, Zhejiang University School of Medicine, Tianmu Road 305, Hangzhou, Zhejiang 310013, China; Centre for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China.
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39
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Theta oscillations shift towards optimal frequency for cognitive control. Nat Hum Behav 2022; 6:1000-1013. [PMID: 35449299 DOI: 10.1038/s41562-022-01335-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 03/10/2022] [Indexed: 12/19/2022]
Abstract
Cognitive control allows to flexibly guide behaviour in a complex and ever-changing environment. It is supported by theta band (4-7 Hz) neural oscillations that coordinate distant neural populations. However, little is known about the precise neural mechanisms permitting such flexible control. Most research has focused on theta amplitude, showing that it increases when control is needed, but a second essential aspect of theta oscillations, their peak frequency, has mostly been overlooked. Here, using computational modelling and behavioural and electrophysiological recordings, in three independent datasets, we show that theta oscillations adaptively shift towards optimal frequency depending on task demands. We provide evidence that theta frequency balances reliable set-up of task representation and gating of task-relevant sensory and motor information and that this frequency shift predicts behavioural performance. Our study presents a mechanism supporting flexible control and calls for a reevaluation of the mechanistic role of theta oscillations in adaptive behaviour.
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40
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Ostlund B, Donoghue T, Anaya B, Gunther KE, Karalunas SL, Voytek B, Pérez-Edgar KE. Spectral parameterization for studying neurodevelopment: How and why. Dev Cogn Neurosci 2022; 54:101073. [PMID: 35074579 PMCID: PMC8792072 DOI: 10.1016/j.dcn.2022.101073] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 12/07/2021] [Accepted: 01/15/2022] [Indexed: 12/12/2022] Open
Abstract
A growing body of literature suggests that the explicit parameterization of neural power spectra is important for the appropriate physiological interpretation of periodic and aperiodic electroencephalogram (EEG) activity. In this paper, we discuss why parameterization is an imperative step for developmental cognitive neuroscientists interested in cognition and behavior across the lifespan, as well as how parameterization can be readily accomplished with an automated spectral parameterization ("specparam") algorithm (Donoghue et al., 2020a). We provide annotated code for power spectral parameterization, via specparam, in Jupyter Notebook and R Studio. We then apply this algorithm to EEG data in childhood (N = 60; Mage = 9.97, SD = 0.95) to illustrate its utility for developmental cognitive neuroscientists. Ultimately, the explicit parameterization of EEG power spectra may help us refine our understanding of how dynamic neural communication contributes to normative and aberrant cognition across the lifespan. Data and annotated analysis code for this manuscript are available on GitHub as a supplement to the open-access specparam toolbox.
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Affiliation(s)
- Brendan Ostlund
- Department of Psychology, The Pennsylvania State University, USA.
| | - Thomas Donoghue
- Department of Cognitive Science, University of California, San Diego, USA
| | - Berenice Anaya
- Department of Psychology, The Pennsylvania State University, USA
| | - Kelley E Gunther
- Department of Psychology, The Pennsylvania State University, USA
| | | | - Bradley Voytek
- Department of Cognitive Science, University of California, San Diego, USA; Halıcıoğlu Data Science Institute, University of California, San Diego, USA; Neurosciences Graduate Program, University of California, San Diego, USA
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41
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Brady B, Bardouille T. Periodic/Aperiodic Parameterization of Transient Oscillations (PAPTO): Implications for Healthy Ageing. Neuroimage 2022; 251:118974. [DOI: 10.1016/j.neuroimage.2022.118974] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 01/28/2022] [Accepted: 02/03/2022] [Indexed: 12/11/2022] Open
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42
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Senoussi M, Verbeke P, Verguts T. Time-Based Binding as a Solution to and a Limitation for Flexible Cognition. Front Psychol 2022; 12:798061. [PMID: 35140662 PMCID: PMC8818715 DOI: 10.3389/fpsyg.2021.798061] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 12/15/2021] [Indexed: 02/03/2023] Open
Abstract
Why can't we keep as many items as we want in working memory? It has long been debated whether this resource limitation is a bug (a downside of our fallible biological system) or instead a feature (an optimal response to a computational problem). We propose that the resource limitation is a consequence of a useful feature. Specifically, we propose that flexible cognition requires time-based binding, and time-based binding necessarily limits the number of (bound) memoranda that can be stored simultaneously. Time-based binding is most naturally instantiated via neural oscillations, for which there exists ample experimental evidence. We report simulations that illustrate this theory and that relate it to empirical data. We also compare the theory to several other (feature and bug) resource theories.
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43
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Samaha J, Cohen MX. Power spectrum slope confounds estimation of instantaneous oscillatory frequency. Neuroimage 2022; 250:118929. [PMID: 35077852 DOI: 10.1016/j.neuroimage.2022.118929] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 01/20/2022] [Accepted: 01/21/2022] [Indexed: 01/12/2023] Open
Abstract
Oscillatory neural dynamics are highly non-stationary and require methods capable of quantifying time-resolved changes in oscillatory activity in order to understand neural function. Recently, a method termed 'frequency sliding' was introduced to estimate the instantaneous frequency of oscillatory activity, providing a means of tracking temporal changes in the dominant frequency within a sub-band of field potential recordings. Here, the ability of frequency sliding to recover ground-truth oscillatory frequency in simulated data is tested while the exponent (slope) of the 1/fx component of the signal power spectrum is systematically varied, mimicking real electrophysiological data. The results show that 1) in the presence of 1/f activity, frequency sliding systematically underestimates the true frequency of the signal, 2) the magnitude of underestimation is correlated with the steepness of the slope, suggesting that, if unaccounted for, slope changes could be misinterpreted as frequency changes, 3) the impact of slope on frequency estimates interacts with oscillation amplitude, indicating that changes in oscillation amplitude alone may also influence instantaneous frequency estimates in the presence of strong 1/f activity; and 4) analysis parameters such as filter bandwidth and location also mediate the influence of slope on estimated frequency, indicating that these settings should be considered when interpreting estimates obtained via frequency sliding. The origin of these biases resides in the output of the filtering step of frequency sliding, whose energy is biased towards lower frequencies precisely because of the 1/f structure of the data. We discuss several strategies to mitigate these biases and provide a proof-of-principle for a 1/f normalization strategy.
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Affiliation(s)
- Jason Samaha
- Psychology Department, University of California, Santa Cruz.
| | - Michael X Cohen
- Donders Centre for Medical Neuroscience, Radboud University Medical Centre
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44
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Separating Neural Oscillations from Aperiodic 1/f Activity: Challenges and Recommendations. Neuroinformatics 2022; 20:991-1012. [PMID: 35389160 PMCID: PMC9588478 DOI: 10.1007/s12021-022-09581-8] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/25/2022] [Indexed: 12/31/2022]
Abstract
Electrophysiological power spectra typically consist of two components: An aperiodic part usually following an 1/f power law [Formula: see text] and periodic components appearing as spectral peaks. While the investigation of the periodic parts, commonly referred to as neural oscillations, has received considerable attention, the study of the aperiodic part has only recently gained more interest. The periodic part is usually quantified by center frequencies, powers, and bandwidths, while the aperiodic part is parameterized by the y-intercept and the 1/f exponent [Formula: see text]. For investigation of either part, however, it is essential to separate the two components. In this article, we scrutinize two frequently used methods, FOOOF (Fitting Oscillations & One-Over-F) and IRASA (Irregular Resampling Auto-Spectral Analysis), that are commonly used to separate the periodic from the aperiodic component. We evaluate these methods using diverse spectra obtained with electroencephalography (EEG), magnetoencephalography (MEG), and local field potential (LFP) recordings relating to three independent research datasets. Each method and each dataset poses distinct challenges for the extraction of both spectral parts. The specific spectral features hindering the periodic and aperiodic separation are highlighted by simulations of power spectra emphasizing these features. Through comparison with the simulation parameters defined a priori, the parameterization error of each method is quantified. Based on the real and simulated power spectra, we evaluate the advantages of both methods, discuss common challenges, note which spectral features impede the separation, assess the computational costs, and propose recommendations on how to use them.
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45
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Ongoing neural oscillations influence behavior and sensory representations by suppressing neuronal excitability. Neuroimage 2021; 247:118746. [PMID: 34875382 DOI: 10.1016/j.neuroimage.2021.118746] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 10/21/2021] [Accepted: 11/19/2021] [Indexed: 12/28/2022] Open
Abstract
The ability to process and respond to external input is critical for adaptive behavior. Why, then, do neural and behavioral responses vary across repeated presentations of the same sensory input? Ongoing fluctuations of neuronal excitability are currently hypothesized to underlie the trial-by-trial variability in sensory processing. To test this, we capitalized on intracranial electrophysiology in neurosurgical patients performing an auditory discrimination task with visual cues: specifically, we examined the interaction between prestimulus alpha oscillations, excitability, task performance, and decoded neural stimulus representations. We found that strong prestimulus oscillations in the alpha+ band (i.e., alpha and neighboring frequencies), rather than the aperiodic signal, correlated with a low excitability state, indexed by reduced broadband high-frequency activity. This state was related to slower reaction times and reduced neural stimulus encoding strength. We propose that the alpha+ rhythm modulates excitability, thereby resulting in variability in behavior and sensory representations despite identical input.
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46
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Bonaiuto JJ, Little S, Neymotin SA, Jones SR, Barnes GR, Bestmann S. Laminar dynamics of high amplitude beta bursts in human motor cortex. Neuroimage 2021; 242:118479. [PMID: 34407440 PMCID: PMC8463839 DOI: 10.1016/j.neuroimage.2021.118479] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 08/12/2021] [Accepted: 08/14/2021] [Indexed: 12/28/2022] Open
Abstract
Motor cortical activity in the beta frequency range is one of the strongest and most studied movement-related neural signals. At the single trial level, beta band activity is often characterized by transient, high amplitude, bursting events rather than slowly modulating oscillations. The timing of these bursting events is tightly linked to behavior, suggesting a more dynamic functional role for beta activity than previously believed. However, the neural mechanisms underlying beta bursts in sensorimotor circuits are poorly understood. To address this, we here leverage and extend recent developments in high precision MEG for temporally resolved laminar analysis of burst activity, combined with a neocortical circuit model that simulates the biophysical generators of the electrical currents which drive beta bursts. This approach pinpoints the generation of beta bursts in human motor cortex to distinct excitatory synaptic inputs to deep and superficial cortical layers, which drive current flow in opposite directions. These laminar dynamics of beta bursts in motor cortex align with prior invasive animal recordings within the somatosensory cortex, and suggest a conserved mechanism for somatosensory and motor cortical beta bursts. More generally, we demonstrate the ability for uncovering the laminar dynamics of event-related neural signals in human non-invasive recordings. This provides important constraints to theories about the functional role of burst activity for movement control in health and disease, and crucial links between macro-scale phenomena measured in humans and micro-circuit activity recorded from animal models.
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Affiliation(s)
- James J Bonaiuto
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Bron, France; Université Claude Bernard Lyon 1, Université de Lyon, France; Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK; Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK.
| | - Simon Little
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK; Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Samuel A Neymotin
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department of Neuroscience, Brown University, Providence, RI, USA; Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
| | - Stephanie R Jones
- Department of Neuroscience, Brown University, Providence, RI, USA; Center for Neurorestoration and Neurotechnology, Providence VAMC, Providence, RI, USA
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK
| | - Sven Bestmann
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK; Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK
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47
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The EEG spectral properties of meditation and mind wandering differ between experienced meditators and novices. Neuroimage 2021; 245:118669. [PMID: 34688899 DOI: 10.1016/j.neuroimage.2021.118669] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 10/15/2021] [Accepted: 10/18/2021] [Indexed: 01/26/2023] Open
Abstract
Previous literature suggests that individuals with meditation training become less distracted during meditation practice. In this study, we assess whether putative differences in the subjective experience of meditation between meditators and non-meditators are reflected in EEG spectral modulations. For this purpose, we recorded electroencephalography (EEG) during rest and two breath focus meditations (with and without experience sampling) in a group of 29 adult participants with more than 3 years of meditation experience and a control group of 29 participants without any meditation experience. Experience sampling in one of the meditation conditions allowed us to disentangle periods of breath focus from mind wandering (i.e. moments of distraction driven by task-irrelevant thoughts) during meditation practice. Overall, meditators reported a greater level of focus and reduced mind wandering during meditation practice than controls. In line with these reports, EEG spectral modulations associated with meditation and mind wandering also differed significantly between meditators and controls. While meditators (but not controls) showed a significant decrease in individual alpha frequency / amplitude and a steeper 1/f slope during meditation relative to rest, controls (but not meditators) showed a relative increase in individual alpha amplitude during mind wandering relative to breath focus periods. Together, our results show that the subjective experience of meditation and mind wandering differs between meditators and novices and that this is reflected in oscillatory and non-oscillatory properties of EEG.
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Chen S, Tan Z, Xia W, Gomes CA, Zhang X, Zhou W, Liang S, Axmacher N, Wang L. Theta oscillations synchronize human medial prefrontal cortex and amygdala during fear learning. SCIENCE ADVANCES 2021; 7:7/34/eabf4198. [PMID: 34407939 PMCID: PMC8373137 DOI: 10.1126/sciadv.abf4198] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 06/29/2021] [Indexed: 05/20/2023]
Abstract
Numerous animal studies have demonstrated that fear acquisition and expression rely on the coordinated activity of medial prefrontal cortex (mPFC) and amygdala and that theta oscillations support interregional communication within the fear network. However, it remains unclear whether these results can be generalized to fear learning in humans. We addressed this question using intracranial electroencephalography recordings in 13 patients with epilepsy during a fear conditioning paradigm. We observed increased power and inter-regional synchronization of amygdala and mPFC in theta (4 to 8 hertz) oscillations for conditioned stimulus (CS+) versus CS-. Analysis of information flow revealed that the dorsal mPFC (dmPFC) led amygdala activity in theta oscillations. Last, a computational model showed that trial-by-trial changes in amygdala theta oscillations predicted the model-based associability (i.e., learning rate). This study provides compelling evidence that theta oscillations within and between amygdala, ventral mPFC, and dmPFC constitute a general mechanism of fear learning across species.
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Affiliation(s)
- Si Chen
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Zheng Tan
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Wenran Xia
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Carlos Alexandre Gomes
- Department of Neuropsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Xilei Zhang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
| | - Wenjing Zhou
- Epilepsy Center, Tsinghua University Yuquan Hospital, Beijing, China
| | - Shuli Liang
- Functional Neurosurgery Department, Beijing Children's Hospital, Capital Medical University, Beijing, China
| | - Nikolai Axmacher
- Department of Neuropsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Xinjiekouwai Street 19, Beijing 100875, China
| | - Liang Wang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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Helfrich RF, Lendner JD, Knight RT. Aperiodic sleep networks promote memory consolidation. Trends Cogn Sci 2021; 25:648-659. [PMID: 34127388 PMCID: PMC9017392 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] [MESH Headings] [Grants] [Track Full Text] [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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Affiliation(s)
- Randolph F Helfrich
- Hertie Institute for Clinical Brain Research, Center for Neurology, University Medical Center Tübingen, Hoppe-Seyler-Strasse 3, 72076 Tübingen, Germany.
| | - Janna D Lendner
- Department of Anesthesiology and Intensive Care Medicine, University Medical Center Tübingen, Hoppe-Seyler-Strasse 3, 72076 Tübingen, Germany
| | - Robert T Knight
- Helen Wills Neuroscience Institute, University of California Berkeley, 132 Barker Hall, Berkeley, CA 94720, USA; Department of Psychology, University of California Berkeley, Tolman Hall, Berkeley, CA 94720, USA
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Joechner AK, Wehmeier S, Werkle-Bergner M. Electrophysiological indicators of sleep-associated memory consolidation in 5- to 6-year-old children. Psychophysiology 2021; 58:e13829. [PMID: 33951193 DOI: 10.1111/psyp.13829] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 03/01/2021] [Accepted: 03/17/2021] [Indexed: 12/21/2022]
Abstract
In adults, the synchronized interplay of sleep spindles (SP) and slow oscillations (SO) supports memory consolidation. Given tremendous developmental changes in SP and SO morphology, it remains elusive whether across childhood the same mechanisms as identified in adults are functional. Based on topography and frequency, we characterize slow and fast SPs and their temporal coupling to SOs in 24 pre-school children. Further, we ask whether slow and fast SPs and their modulation during SOs are associated with behavioral indicators of declarative memory consolidation as suggested by the literature on adults. Employing an individually tailored approach, we reliably identify an inherent, development-specific fast centro-parietal SP type, nested in the adult-like slow SP frequency range, along with a dominant slow frontal SP type. Further, we provide evidence that the modulation of fast centro-parietal SPs during SOs is already present in pre-school children. However, the temporal coordination between fast centro-parietal SPs and SOs is weaker and less precise than expected from research on adults. While we do not find evidence for a critical contribution of SP-SO coupling for memory consolidation, crucially, slow frontal and fast centro-parietal SPs are each differentially related to sleep-associated consolidation of items of varying quality. Whereas a higher number of slow frontal SPs is associated with stronger maintenance of medium-quality memories, a higher number of fast centro-parietal SPs is linked to a greater gain of low-quality items. Our results demonstrate two functionally relevant inherent SP types in pre-school children although SP-SO coupling is not yet fully mature.
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Affiliation(s)
- Ann-Kathrin Joechner
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Sarah Wehmeier
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Markus Werkle-Bergner
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
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