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Pauls KAM, Nurmi P, Ala-Salomäki H, Renvall H, Kujala J, Liljeström M. Human sensorimotor resting state beta events and aperiodic activity show good test-retest reliability. Clin Neurophysiol 2024; 163:244-254. [PMID: 38820994 DOI: 10.1016/j.clinph.2024.03.021] [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: 09/22/2023] [Revised: 03/04/2024] [Accepted: 03/20/2024] [Indexed: 06/02/2024]
Abstract
OBJECTIVE Diseases affecting sensorimotor function impair physical independence. Reliable functional clinical biomarkers allowing early diagnosis or targeting treatment and rehabilitation could reduce this burden. Magnetoencephalography (MEG) non-invasively measures brain rhythms such as the somatomotor 'rolandic' rhythm which shows intermittent high-amplitude beta (14-30 Hz) 'events' that predict behavior across tasks and species and are altered by sensorimotor neurological diseases. METHODS We assessed test-retest stability, a prerequisite for biomarkers, of spontaneous sensorimotor aperiodic (1/f) signal and beta events in 50 healthy human controls across two MEG sessions using the intraclass correlation coefficient (ICC). Beta events were determined using an amplitude-thresholding approach on a narrow-band filtered amplitude envelope obtained using Morlet wavelet decomposition. RESULTS Resting sensorimotor characteristics showed good to excellent test-retest stability. Aperiodic component (ICC 0.77-0.88) and beta event amplitude (ICC 0.74-0.82) were very stable, whereas beta event duration was more variable (ICC 0.55-0.7). 2-3 minute recordings were sufficient to obtain stable results. Analysis automatization was successful in 86%. CONCLUSIONS Sensorimotor beta phenotype is a stable feature of an individual's resting brain activity even for short recordings easily measured in patients. SIGNIFICANCE Spontaneous sensorimotor beta phenotype has potential as a clinical biomarker of sensorimotor system integrity.
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Affiliation(s)
- K Amande M Pauls
- BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, 00290 Helsinki, Finland; Department of Neurology, Helsinki University Hospital and Department of Clinical Neurosciences (Neurology), University of Helsinki, 00029 Helsinki, Finland.
| | - Pietari Nurmi
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, 02150 Espoo, Finland
| | - Heidi Ala-Salomäki
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, 02150 Espoo, Finland
| | - Hanna Renvall
- BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, 00290 Helsinki, Finland; Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, 02150 Espoo, Finland
| | - Jan Kujala
- Department of Psychology, University of Jyväskylä, 40014 Jyväskylä, Finland
| | - Mia Liljeström
- BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, 00290 Helsinki, Finland; Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, 02150 Espoo, Finland; Aalto NeuroImaging, Aalto University, 00076 Aalto, Finland
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Rier L, Rhodes N, Pakenham DO, Boto E, Holmes N, Hill RM, Reina Rivero G, Shah V, Doyle C, Osborne J, Bowtell RW, Taylor M, Brookes MJ. Tracking the neurodevelopmental trajectory of beta band oscillations with optically pumped magnetometer-based magnetoencephalography. eLife 2024; 13:RP94561. [PMID: 38831699 PMCID: PMC11149934 DOI: 10.7554/elife.94561] [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] [Indexed: 06/05/2024] Open
Abstract
Neural oscillations mediate the coordination of activity within and between brain networks, supporting cognition and behaviour. How these processes develop throughout childhood is not only an important neuroscientific question but could also shed light on the mechanisms underlying neurological and psychiatric disorders. However, measuring the neurodevelopmental trajectory of oscillations has been hampered by confounds from instrumentation. In this paper, we investigate the suitability of a disruptive new imaging platform - optically pumped magnetometer-based magnetoencephalography (OPM-MEG) - to study oscillations during brain development. We show how a unique 192-channel OPM-MEG device, which is adaptable to head size and robust to participant movement, can be used to collect high-fidelity electrophysiological data in individuals aged between 2 and 34 years. Data were collected during a somatosensory task, and we measured both stimulus-induced modulation of beta oscillations in sensory cortex, and whole-brain connectivity, showing that both modulate significantly with age. Moreover, we show that pan-spectral bursts of electrophysiological activity drive task-induced beta modulation, and that their probability of occurrence and spectral content change with age. Our results offer new insights into the developmental trajectory of beta oscillations and provide clear evidence that OPM-MEG is an ideal platform for studying electrophysiology in neurodevelopment.
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Affiliation(s)
- Lukas Rier
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Natalie Rhodes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
- Diagnostic Imaging, The Hospital for Sick Children, Toronto, Canada
| | - Daisie O Pakenham
- Clinical Neurophysiology, Nottingham University Hospitals NHS Trust, Queens Medical Centre, Nottingham, United States
| | - Elena Boto
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
- Cerca Magnetics Limited, 7-8 Castlebridge Office Village, Kirtley Drive, Nottingham, United Kingdom
| | - Niall Holmes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
- Cerca Magnetics Limited, 7-8 Castlebridge Office Village, Kirtley Drive, Nottingham, United Kingdom
| | - Ryan M Hill
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
- Cerca Magnetics Limited, 7-8 Castlebridge Office Village, Kirtley Drive, Nottingham, United Kingdom
| | - Gonzalo Reina Rivero
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | | | | | | | - Richard W Bowtell
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Margot Taylor
- Diagnostic Imaging, The Hospital for Sick Children, Toronto, Canada
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
- Cerca Magnetics Limited, 7-8 Castlebridge Office Village, Kirtley Drive, Nottingham, United Kingdom
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3
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Thunberg C, Wiker T, Bundt C, Huster RJ. On the (un)reliability of common behavioral and electrophysiological measures from the stop signal task: Measures of inhibition lack stability over time. Cortex 2024; 175:81-105. [PMID: 38508968 DOI: 10.1016/j.cortex.2024.02.008] [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: 09/22/2023] [Revised: 10/31/2023] [Accepted: 02/12/2024] [Indexed: 03/22/2024]
Abstract
Response inhibition, the intentional stopping of planned or initiated actions, is often considered a key facet of control, impulsivity, and self-regulation. The stop signal task is argued to be the purest inhibition task we have, and it is thus central to much work investigating the role of inhibition in areas like development and psychopathology. Most of this work quantifies stopping behavior by calculating the stop signal reaction time as a measure of individual stopping latency. Individual difference studies aiming to investigate why and how stopping latencies differ between people often do this under the assumption that the stop signal reaction time indexes a stable, dispositional trait. However, empirical support for this assumption is lacking, as common measures of inhibition and control tend to show low test-retest reliability and thus appear unstable over time. The reasons for this could be methodological, where low stability is driven by measurement noise, or substantive, where low stability is driven by a larger influence of state-like and situational factors. To investigate this, we characterized the split-half and test-retest reliability of a range of common behavioral and electrophysiological measures derived from the stop signal task. Across three independent studies, different measurement modalities, and a systematic review of the literature, we found a pattern of low temporal stability for inhibition measures and higher stability for measures of manifest behavior and non-inhibitory processing. This pattern could not be explained by measurement noise and low internal consistency. Consequently, response inhibition appears to have mostly state-like and situational determinants, and there is little support for the validity of conceptualizing common inhibition measures as reflecting stable traits.
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Affiliation(s)
- Christina Thunberg
- Multimodal Imaging and Cognitive Control Lab, Department of Psychology, University of Oslo, Oslo, Norway; Cognitive and Translational Neuroscience Cluster, Department of Psychology, University of Oslo, Oslo, Norway.
| | - Thea Wiker
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Research Center for Developmental Processes and Gradients in Mental Health, Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Carsten Bundt
- Multimodal Imaging and Cognitive Control Lab, Department of Psychology, University of Oslo, Oslo, Norway; Cognitive and Translational Neuroscience Cluster, Department of Psychology, University of Oslo, Oslo, Norway
| | - René J Huster
- Multimodal Imaging and Cognitive Control Lab, Department of Psychology, University of Oslo, Oslo, Norway; Cognitive and Translational Neuroscience Cluster, Department of Psychology, University of Oslo, Oslo, Norway
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4
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Kavanaugh BC, Vigne MM, Tirrell E, Luke Acuff W, Fukuda AM, Thorpe R, Sherman A, Jones SR, Carpenter LL, Tyrka AR. Frontoparietal beta event characteristics are associated with early life stress and psychiatric symptoms in adults. Brain Cogn 2024; 177:106164. [PMID: 38670050 PMCID: PMC11193540 DOI: 10.1016/j.bandc.2024.106164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 04/16/2024] [Accepted: 04/18/2024] [Indexed: 04/28/2024]
Abstract
Recent work has found that the presence of transient, oscillatory burst-like events, particularly within the beta band (15-29 Hz), is more closely tied to disease state and behavior across species than traditional electroencephalography (EEG) power metrics. This study sought to examine whether features of beta events over frontoparietal electrodes were associated with early life stress (ELS) and the related clinical presentation. Eighteen adults with documented ELS (n = 18; ELS + ) and eighteen adults without documented ELS (n = 18; ELS-) completed eyes-closed resting state EEG as part of their participation in a larger childhood stress study. The rate, power, duration, and frequency span of transient oscillatory events were calculated within the beta band at five frontoparietal electrodes. ELS variables were positively associated with beta event rate at Fp2 and beta event duration at Pz, in that greater ELS was associated with higher resting rates and longer durations. These beta event characteristics were used to successfully distinguish between ELS + and ELS- groups. In an independent clinical dataset (n = 25), beta event power at Pz was positively correlated with ELS. Beta events deserve ongoing investigation as a potential disease marker of ELS and subsequent psychiatric treatment outcomes.
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Affiliation(s)
- Brian C Kavanaugh
- E.P. Bradley Hospital, Riverside RI, USA, Brown University; Department of Psychiatry & Human Behavior, Alpert Medical School of Brown University, Providence RI, USA.
| | - Megan M Vigne
- Department of Psychiatry & Human Behavior, Alpert Medical School of Brown University, Providence RI, USA; Butler Hospital COBRE Center for Neuromodulation, Providence RI, USA
| | - Eric Tirrell
- Department of Psychiatry & Human Behavior, Alpert Medical School of Brown University, Providence RI, USA; Butler Hospital COBRE Center for Neuromodulation, Providence RI, USA
| | - W Luke Acuff
- Department of Psychiatry & Human Behavior, Alpert Medical School of Brown University, Providence RI, USA; Butler Hospital COBRE Center for Neuromodulation, Providence RI, USA
| | - Andrew M Fukuda
- Department of Psychiatry & Human Behavior, Alpert Medical School of Brown University, Providence RI, USA; Butler Hospital COBRE Center for Neuromodulation, Providence RI, USA
| | - Ryan Thorpe
- Brown University, Department of Neuroscience, Providence RI, USA , Providence Veteran's Association Medical Center
| | - Anna Sherman
- Department of Psychiatry & Human Behavior, Alpert Medical School of Brown University, Providence RI, USA; Butler Hospital COBRE Center for Neuromodulation, Providence RI, USA
| | - Stephanie R Jones
- Brown University, Department of Neuroscience, Providence RI, USA , Providence Veteran's Association Medical Center; Center for Neurorestoration and Neurotechnology, Providence RI, USA
| | - Linda L Carpenter
- Department of Psychiatry & Human Behavior, Alpert Medical School of Brown University, Providence RI, USA; Butler Hospital COBRE Center for Neuromodulation, Providence RI, USA
| | - Audrey R Tyrka
- Department of Psychiatry & Human Behavior, Alpert Medical School of Brown University, Providence RI, USA; Butler Hospital COBRE Center for Neuromodulation, Providence RI, USA
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5
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Coleman SC, Seedat ZA, Pakenham DO, Quinn AJ, Brookes MJ, Woolrich MW, Mullinger KJ. Post-task responses following working memory and movement are driven by transient spectral bursts with similar characteristics. Hum Brain Mapp 2024; 45:e26700. [PMID: 38726799 PMCID: PMC11082833 DOI: 10.1002/hbm.26700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 03/09/2024] [Accepted: 04/14/2024] [Indexed: 05/13/2024] Open
Abstract
The post-movement beta rebound has been studied extensively using magnetoencephalography (MEG) and is reliably modulated by various task parameters as well as illness. Our recent study showed that rebounds, which we generalise as "post-task responses" (PTRs), are a ubiquitous phenomenon in the brain, occurring across the cortex in theta, alpha, and beta bands. Currently, it is unknown whether PTRs following working memory are driven by transient bursts, which are moments of short-lived high amplitude activity, similar to those that drive the post-movement beta rebound. Here, we use three-state univariate hidden Markov models (HMMs), which can identify bursts without a priori knowledge of frequency content or response timings, to compare bursts that drive PTRs in working memory and visuomotor MEG datasets. Our results show that PTRs across working memory and visuomotor tasks are driven by pan-spectral transient bursts. These bursts have very similar spectral content variation over the cortex, correlating strongly between the two tasks in the alpha (R2 = .89) and beta (R2 = .53) bands. Bursts also have similar variation in duration over the cortex (e.g., long duration bursts occur in the motor cortex for both tasks), strongly correlating over cortical regions between tasks (R2 = .56), with a mean over all regions of around 300 ms in both datasets. Finally, we demonstrate the ability of HMMs to isolate signals of interest in MEG data, such that the HMM probability timecourse correlates more strongly with reaction times than frequency filtered power envelopes from the same brain regions. Overall, we show that induced PTRs across different tasks are driven by bursts with similar characteristics, which can be identified using HMMs. Given the similarity between bursts across tasks, we suggest that PTRs across the cortex may be driven by a common underlying neural phenomenon.
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Affiliation(s)
- Sebastian C. Coleman
- Sir Peter Mansfield Imaging Centre, School of Physics and AstronomyUniversity of NottinghamNottinghamUK
| | - Zelekha A. Seedat
- Sir Peter Mansfield Imaging Centre, School of Physics and AstronomyUniversity of NottinghamNottinghamUK
- Young EpilepsyLingfieldUK
| | - Daisie O. Pakenham
- Sir Peter Mansfield Imaging Centre, School of Physics and AstronomyUniversity of NottinghamNottinghamUK
- Clinical NeurophysiologyQueen's Medical Centre, Nottingham University Hospitals NHS TrustNottinghamUK
| | - Andrew J. Quinn
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of PsychiatryUniversity of OxfordOxfordUK
- Centre for Human Brain Health, School of PsychologyUniversity of BirminghamBirminghamUK
| | - Matthew J. Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and AstronomyUniversity of NottinghamNottinghamUK
| | - Mark W. Woolrich
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of PsychiatryUniversity of OxfordOxfordUK
| | - Karen J. Mullinger
- Sir Peter Mansfield Imaging Centre, School of Physics and AstronomyUniversity of NottinghamNottinghamUK
- Centre for Human Brain Health, School of PsychologyUniversity of BirminghamBirminghamUK
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6
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Yu Y, Oh Y, Kounios J, Beeman M. Electroencephalography Spectral-power Volatility Predicts Problem-solving Outcomes. J Cogn Neurosci 2024; 36:901-915. [PMID: 38437171 DOI: 10.1162/jocn_a_02136] [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] [Indexed: 03/06/2024]
Abstract
Temporal variability is a fundamental property of brain processes and is functionally important to human cognition. This study examined how fluctuations in neural oscillatory activity are related to problem-solving performance as one example of how temporal variability affects high-level cognition. We used volatility to assess step-by-step fluctuations of EEG spectral power while individuals attempted to solve word-association puzzles. Inspired by recent results with hidden-state modeling, we tested the hypothesis that spectral-power volatility is directly associated with problem-solving outcomes. As predicted, volatility was lower during trials solved with insight compared with those solved analytically. Moreover, volatility during prestimulus preparation for problem-solving predicted solving outcomes, including solving success and solving time. These novel findings were replicated in a separate data set from an anagram-solving task, suggesting that less-rapid transitions between neural oscillatory synchronization and desynchronization predict better solving performance and are conducive to solving with insight for these types of problems. Thus, volatility can be a valuable index of cognition-related brain dynamics.
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Affiliation(s)
- Yuhua Yu
- Northwestern University, Evanston, IL
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7
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Lundqvist M, Miller EK, Nordmark J, Liljefors J, Herman P. Beta: bursts of cognition. Trends Cogn Sci 2024:S1364-6613(24)00077-9. [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] [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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8
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Mirdamadi JL, Ting LH, Borich MR. Distinct Cortical Correlates of Perception and Motor Function in Balance Control. J Neurosci 2024; 44:e1520232024. [PMID: 38413231 PMCID: PMC11007305 DOI: 10.1523/jneurosci.1520-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 02/07/2024] [Accepted: 02/14/2024] [Indexed: 02/29/2024] Open
Abstract
Fluctuations in brain activity alter how we perceive our body and generate movements but have not been investigated in functional whole-body behaviors. During reactive balance, we recently showed that evoked brain activity is associated with the balance ability in young individuals. Furthermore, in PD, impaired whole-body motion perception in reactive balance is associated with impaired balance. Here, we investigated the brain activity during the whole-body motion perception in reactive balance in young adults (9 female, 10 male). We hypothesized that both ongoing and evoked cortical activity influences the efficiency of information processing for successful perception and movement during whole-body behaviors. We characterized two cortical signals using electroencephalography localized to the SMA: (1) the "N1," a perturbation-evoked potential that decreases in amplitude with expectancy and is larger in individuals with lower balance function, and (2) preperturbation β power, a transient rhythm that favors maintenance of the current sensorimotor state and is inversely associated with tactile perception. In a two-alternative forced choice task, participants judged whether pairs of backward support surface perturbations during standing were in the "same" or "different" direction. As expected, lower whole-body perception was associated with lower balance ability. Within a perturbation pair, N1 attenuation was larger on correctly perceived trials and associated with better balance, but not perception. In contrast, preperturbation β power was higher on incorrectly perceived trials and associated with poorer perception, but not balance. Together, ongoing and evoked cortical activity have unique roles in information processing that give rise to distinct associations with perceptual and balance ability.
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Affiliation(s)
- Jasmine L Mirdamadi
- Division of Physical Therapy, Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, Georgia 30322
| | - Lena H Ting
- Division of Physical Therapy, Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, Georgia 30322
- The Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia 30322
| | - Michael R Borich
- Division of Physical Therapy, Department of Rehabilitation Medicine, Emory University School of Medicine, Atlanta, Georgia 30322
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9
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Jackson AD, Cohen JL, Phensy AJ, Chang EF, Dawes HE, Sohal VS. Amygdala-hippocampus somatostatin interneuron beta-synchrony underlies a cross-species biomarker of emotional state. Neuron 2024; 112:1182-1195.e5. [PMID: 38266646 PMCID: PMC10994747 DOI: 10.1016/j.neuron.2023.12.017] [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: 07/28/2022] [Revised: 03/20/2023] [Accepted: 12/22/2023] [Indexed: 01/26/2024]
Abstract
Emotional responses arise from limbic circuits including the hippocampus and amygdala. In the human brain, beta-frequency communication between these structures correlates with self-reported mood and anxiety. However, both the mechanism and significance of this biomarker as a readout vs. driver of emotional state remain unknown. Here, we show that beta-frequency communication between ventral hippocampus and basolateral amygdala also predicts anxiety-related behavior in mice, both on long timescales (∼30 min) and immediately preceding behavioral choices. Genetically encoded voltage indicators reveal that this biomarker reflects synchronization between somatostatin interneurons across both structures. Indeed, synchrony between these neurons dynamically predicts approach-avoidance decisions, and optogenetically shifting the phase of synchronization by just 25 ms is sufficient to bidirectionally modulate anxiety-related behaviors. Thus, back-translation establishes a human biomarker as a causal determinant (not just predictor) of emotional state, revealing a novel mechanism whereby interregional synchronization that is frequency, phase, and cell type specific controls emotional processing.
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Affiliation(s)
- Adam D Jackson
- Department of Psychiatry and Behavioral Sciences, Center for Integrative Neuroscience and Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94143-0444, USA; Weill Institute for Neurosciences, Center for Integrative Neuroscience and Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94143-0444, USA
| | - Joshua L Cohen
- Department of Psychiatry and Behavioral Sciences, Center for Integrative Neuroscience and Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94143-0444, USA; Weill Institute for Neurosciences, Center for Integrative Neuroscience and Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94143-0444, USA
| | - Aarron J Phensy
- Department of Psychiatry and Behavioral Sciences, Center for Integrative Neuroscience and Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94143-0444, USA; Weill Institute for Neurosciences, Center for Integrative Neuroscience and Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94143-0444, USA
| | - Edward F Chang
- Department of Neurological Surgery, Center for Integrative Neuroscience and Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94143-0444, USA; Weill Institute for Neurosciences, Center for Integrative Neuroscience and Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94143-0444, USA
| | - Heather E Dawes
- Department of Neurological Surgery, Center for Integrative Neuroscience and Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94143-0444, USA; Weill Institute for Neurosciences, Center for Integrative Neuroscience and Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94143-0444, USA
| | - Vikaas S Sohal
- Department of Psychiatry and Behavioral Sciences, Center for Integrative Neuroscience and Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94143-0444, USA; Weill Institute for Neurosciences, Center for Integrative Neuroscience and Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA 94143-0444, USA.
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10
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Karvat G, Ofir N, Landau AN. Sensory Drive Modifies Brain Dynamics and the Temporal Integration Window. J Cogn Neurosci 2024; 36:614-631. [PMID: 38010294 DOI: 10.1162/jocn_a_02088] [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] [Indexed: 11/29/2023]
Abstract
Perception is suggested to occur in discrete temporal windows, clocked by cycles of neural oscillations. An important testable prediction of this theory is that individuals' peak frequencies of oscillations should correlate with their ability to segregate the appearance of two successive stimuli. An influential study tested this prediction and showed that individual peak frequency of spontaneously occurring alpha (8-12 Hz) correlated with the temporal segregation threshold between two successive flashes of light [Samaha, J., & Postle, B. R. The speed of alpha-band oscillations predicts the temporal resolution of visual perception. Current Biology, 25, 2985-2990, 2015]. However, these findings were recently challenged [Buergers, S., & Noppeney, U. The role of alpha oscillations in temporal binding within and across the senses. Nature Human Behaviour, 6, 732-742, 2022]. To advance our understanding of the link between oscillations and temporal segregation, we devised a novel experimental approach. Rather than relying entirely on spontaneous brain dynamics, we presented a visual grating before the flash stimuli that is known to induce continuous oscillations in the gamma band (45-65 Hz). By manipulating the contrast of the grating, we found that high contrast induces a stronger gamma response and a shorter temporal segregation threshold, compared to low-contrast trials. In addition, we used a novel tool to characterize sustained oscillations and found that, for half of the participants, both the low- and high-contrast gratings were accompanied by a sustained and phase-locked alpha oscillation. These participants tended to have longer temporal segregation thresholds. Our results suggest that visual stimulus drive, reflected by oscillations in specific bands, is related to the temporal resolution of visual perception.
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11
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Ubeda Matzilevich E, Daniel PL, Little S. Towards therapeutic electrophysiological neurofeedback in Parkinson's disease. Parkinsonism Relat Disord 2024; 121:106010. [PMID: 38245382 DOI: 10.1016/j.parkreldis.2024.106010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 01/10/2024] [Accepted: 01/12/2024] [Indexed: 01/22/2024]
Abstract
Neurofeedback (NF) techniques support individuals to self-regulate specific features of brain activity, which has been shown to impact behavior and potentially ameliorate clinical symptoms. Electrophysiological NF (epNF) may be particularly impactful for patients with Parkinson's disease (PD), as evidence mounts to suggest a central role of pathological neural oscillations underlying symptoms in PD. Exaggerated beta oscillations (12-30 Hz) in the basal ganglia-cortical network are linked to motor symptoms (e.g., bradykinesia, rigidity), and beta is reduced by successful therapy with dopaminergic medication and Deep Brain Stimulation (DBS). PD patients also experience non-motor symptoms related to sleep, mood, motivation, and cognitive control. Although less is known about the mechanisms of non-motor symptoms in PD and how to successfully treat them, low frequency neural oscillations (1-12 Hz) in the basal ganglia-cortical network are particularly implicated in non-motor symptoms. Here, we review how cortical and subcortical epNF could be used to target motor and non-motor specific oscillations, and potentially serve as an adjunct therapy that enables PD patients to endogenously control their own pathological neural activities. Recent studies have demonstrated that epNF protocols can successfully support volitional control of cortical and subcortical beta rhythms. Importantly, this endogenous control of beta has been linked to changes in motor behavior. epNF for PD, as a casual intervention on neural signals, has the potential to increase understanding of the neurophysiology of movement, mood, and cognition and to identify new therapeutic approaches for motor and non-motor symptoms.
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Affiliation(s)
- Elena Ubeda Matzilevich
- Movement Disorders and Neuromodulation Division, Department of Neurology, University of California San Francisco, CA, USA
| | - Pria Lauren Daniel
- Movement Disorders and Neuromodulation Division, Department of Neurology, University of California San Francisco, CA, USA; Department of Psychology, University of California San Diego, CA, USA.
| | - Simon Little
- Movement Disorders and Neuromodulation Division, Department of Neurology, University of California San Francisco, CA, USA
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12
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Power L, Friedman A, Bardouille T. Atypical paroxysmal slow cortical activity in healthy adults: Relationship to age and cognitive performance. Neurobiol Aging 2024; 136:44-57. [PMID: 38309051 DOI: 10.1016/j.neurobiolaging.2024.01.009] [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: 02/21/2023] [Revised: 01/17/2024] [Accepted: 01/19/2024] [Indexed: 02/05/2024]
Abstract
Paroxysmal patterns of slow cortical activity have been detected in EEG recordings from individuals with age-related neuropathology and have been shown to be correlated with cognitive dysfunction and blood-brain barrier disruption in these participants. The prevalence of these events in healthy participants, however, has not been studied. In this work, we inspect MEG recordings from 623 healthy participants from the Cam-CAN dataset for the presence of paroxysmal slow wave events (PSWEs). PSWEs were detected in approximately 20% of healthy participants in the dataset, and participants with PSWEs tended to be older and have lower cognitive performance than those without PSWEs. In addition, event features changed linearly with age and cognitive performance, resulting in longer and slower events in older adults, and more widespread events in those with low cognitive performance. These findings provide the first evidence of PSWEs in a subset of purportedly healthy adults. Going forward, these events may have utility as a diagnostic biomarker for atypical brain activity in older adults.
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Affiliation(s)
- Lindsey Power
- School of Biomedical Engineering, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Alon Friedman
- Department of Medical Neuroscience, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Timothy Bardouille
- Department of Physics & Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada.
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13
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Rier L, Rhodes N, Pakenham D, Boto E, Holmes N, Hill RM, Rivero GR, Shah V, Doyle C, Osborne J, Bowtell R, Taylor MJ, Brookes MJ. The neurodevelopmental trajectory of beta band oscillations: an OPM-MEG study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.04.573933. [PMID: 38260246 PMCID: PMC10802362 DOI: 10.1101/2024.01.04.573933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Neural oscillations mediate the coordination of activity within and between brain networks, supporting cognition and behaviour. How these processes develop throughout childhood is not only an important neuroscientific question but could also shed light on the mechanisms underlying neurological and psychiatric disorders. However, measuring the neurodevelopmental trajectory of oscillations has been hampered by confounds from instrumentation. In this paper, we investigate the suitability of a disruptive new imaging platform - Optically Pumped Magnetometer-based magnetoencephalography (OPM-MEG) - to study oscillations during brain development. We show how a unique 192-channel OPM-MEG device, which is adaptable to head size and robust to participant movement, can be used to collect high-fidelity electrophysiological data in individuals aged between 2 and 34 years. Data were collected during a somatosensory task, and we measured both stimulus-induced modulation of beta oscillations in sensory cortex, and whole-brain connectivity, showing that both modulate significantly with age. Moreover, we show that pan-spectral bursts of electrophysiological activity drive task-induced beta modulation, and that their probability of occurrence and spectral content change with age. Our results offer new insights into the developmental trajectory of beta oscillations and provide clear evidence that OPM-MEG is an ideal platform for studying electrophysiology in neurodevelopment.
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Affiliation(s)
- Lukas Rier
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Natalie Rhodes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
- Diagnostic Imaging,The Hospital for Sick Children, 555 University Avenue, Toronto, M5G 1X8, Canada
| | - Daisie Pakenham
- Clinical Neurophysiology, Nottingham University Hospitals NHS Trust, Queens Medical Centre, Derby Rd, Lenton, Nottingham NG7 2UH, UK
| | - Elena Boto
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
- Cerca Magnetics Limited, 7-8 Castlebridge Office Village, Kirtley Drive, Nottingham, NG7 1LD, Nottingham, UK
| | - Niall Holmes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
- Cerca Magnetics Limited, 7-8 Castlebridge Office Village, Kirtley Drive, Nottingham, NG7 1LD, Nottingham, UK
| | - Ryan M. Hill
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
- Cerca Magnetics Limited, 7-8 Castlebridge Office Village, Kirtley Drive, Nottingham, NG7 1LD, Nottingham, UK
| | - Gonzalo Reina Rivero
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Vishal Shah
- QuSpin Inc. 331 South 104th Street, Suite 130, Louisville, Colorado, 80027, USA
| | - Cody Doyle
- QuSpin Inc. 331 South 104th Street, Suite 130, Louisville, Colorado, 80027, USA
| | - James Osborne
- QuSpin Inc. 331 South 104th Street, Suite 130, Louisville, Colorado, 80027, USA
| | - Richard Bowtell
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Margot J. Taylor
- Diagnostic Imaging,The Hospital for Sick Children, 555 University Avenue, Toronto, M5G 1X8, Canada
| | - Matthew J. Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
- Cerca Magnetics Limited, 7-8 Castlebridge Office Village, Kirtley Drive, Nottingham, NG7 1LD, Nottingham, UK
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14
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Vinding MC, Waldthaler J, Eriksson A, Manting CL, Ferreira D, Ingvar M, Svenningsson P, Lundqvist D. Oscillatory and non-oscillatory features of the magnetoencephalic sensorimotor rhythm in Parkinson's disease. NPJ Parkinsons Dis 2024; 10:51. [PMID: 38443402 PMCID: PMC10915140 DOI: 10.1038/s41531-024-00669-3] [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: 05/27/2022] [Accepted: 02/26/2024] [Indexed: 03/07/2024] Open
Abstract
Parkinson's disease (PD) is associated with changes in neural activity in the sensorimotor alpha and beta bands. Using magnetoencephalography (MEG), we investigated the role of spontaneous neuronal activity within the somatosensory cortex in a large cohort of early- to mid-stage PD patients (N = 78) on Parkinsonian medication and age- and sex-matched healthy controls (N = 60) using source reconstructed resting-state MEG. We quantified features of the time series data in terms of oscillatory alpha power and central alpha frequency, beta power and central beta frequency, and 1/f broadband characteristics using power spectral density. Furthermore, we characterised transient oscillatory burst events in the mu-beta band time-domain signals. We examined the relationship between these signal features and the patients' disease state, symptom severity, age, sex, and cortical thickness. PD patients and healthy controls differed on PSD broadband characteristics, with PD patients showing a steeper 1/f exponential slope and higher 1/f offset. PD patients further showed a steeper age-related decrease in the burst rate. Out of all the signal features of the sensorimotor activity, the burst rate was associated with increased severity of bradykinesia, whereas the burst duration was associated with axial symptoms. Our study shows that general non-oscillatory features (broadband 1/f exponent and offset) of the sensorimotor signals are related to disease state and oscillatory burst rate scales with symptom severity in PD.
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Affiliation(s)
- Mikkel C Vinding
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark.
| | - Josefine Waldthaler
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Section of Neurology, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neurology, University Hospital Marburg, Marburg, Germany
| | - Allison Eriksson
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Cassia Low Manting
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Cognitive Neuroimaging Centre, Lee Kong Chien School of Medicine, Nanyang Technological University, Singapore, Singapore
- McGovern Institute of Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Daniel Ferreira
- Division of Clinical Geriatrics, Center for Alzheimer's Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
- Facultad de Ciencias de la Salud, Universidad Fernando Pessoa Canarias, Las Palmas de Gran, Canaria, España
| | - Martin Ingvar
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Per Svenningsson
- Section of Neurology, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Daniel Lundqvist
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
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15
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Tolley N, Rodrigues PLC, Gramfort A, Jones SR. Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference. PLoS Comput Biol 2024; 20:e1011108. [PMID: 38408099 PMCID: PMC10919875 DOI: 10.1371/journal.pcbi.1011108] [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: 04/16/2023] [Revised: 03/07/2024] [Accepted: 02/10/2024] [Indexed: 02/28/2024] Open
Abstract
Biophysically detailed neural models are a powerful technique to study neural dynamics in health and disease with a growing number of established and openly available models. A major challenge in the use of such models is that parameter inference is an inherently difficult and unsolved problem. Identifying unique parameter distributions that can account for observed neural dynamics, and differences across experimental conditions, is essential to their meaningful use. Recently, simulation based inference (SBI) has been proposed as an approach to perform Bayesian inference to estimate parameters in detailed neural models. SBI overcomes the challenge of not having access to a likelihood function, which has severely limited inference methods in such models, by leveraging advances in deep learning to perform density estimation. While the substantial methodological advancements offered by SBI are promising, their use in large scale biophysically detailed models is challenging and methods for doing so have not been established, particularly when inferring parameters that can account for time series waveforms. We provide guidelines and considerations on how SBI can be applied to estimate time series waveforms in biophysically detailed neural models starting with a simplified example and extending to specific applications to common MEG/EEG waveforms using the the large scale neural modeling framework of the Human Neocortical Neurosolver. Specifically, we describe how to estimate and compare results from example oscillatory and event related potential simulations. We also describe how diagnostics can be used to assess the quality and uniqueness of the posterior estimates. The methods described provide a principled foundation to guide future applications of SBI in a wide variety of applications that use detailed models to study neural dynamics.
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Affiliation(s)
- Nicholas Tolley
- Department of Neuroscience, Brown University, Providence, Rhode Island, United States of America
| | | | | | - Stephanie R. Jones
- Department of Neuroscience, Brown University, Providence, Rhode Island, United States of America
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16
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Marzetti L, Makkinayeri S, Pieramico G, Guidotti R, D'Andrea A, Roine T, Mutanen TP, Souza VH, Kičić D, Baldassarre A, Ermolova M, Pankka H, Ilmoniemi RJ, Ziemann U, Luca Romani G, Pizzella V. Towards real-time identification of large-scale brain states for improved brain state-dependent stimulation. Clin Neurophysiol 2024; 158:196-203. [PMID: 37827877 DOI: 10.1016/j.clinph.2023.09.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 08/04/2023] [Accepted: 09/13/2023] [Indexed: 10/14/2023]
Affiliation(s)
- Laura Marzetti
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy; Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Abruzzo, Italy.
| | - Saeed Makkinayeri
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Giulia Pieramico
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Roberto Guidotti
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Antea D'Andrea
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Timo Roine
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; Turku Brain and Mind Center, University of Turku, Turku, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
| | - Tuomas P Mutanen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Victor H Souza
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
| | - Dubravko Kičić
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University and Helsinki University Hospital, Helsinki, Finland
| | - Antonello Baldassarre
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Maria Ermolova
- Hertie-Institute for Clinical Brain Research, Tübingen, Baden-Württemberg, Germany; Department of Neurology & Stroke, University of Tübingen, Tübingen, Baden-Württemberg, Germany
| | - Hanna Pankka
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Ulf Ziemann
- Hertie-Institute for Clinical Brain Research, Tübingen, Baden-Württemberg, Germany; Department of Neurology & Stroke, University of Tübingen, Tübingen, Baden-Württemberg, Germany
| | - Gian Luca Romani
- Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Vittorio Pizzella
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy; Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Abruzzo, Italy
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17
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Pauls KAM, Salmela E, Korsun O, Kujala J, Salmelin R, Renvall H. Human Sensorimotor Beta Event Characteristics and Aperiodic Signal Are Highly Heritable. J Neurosci 2024; 44:e0265232023. [PMID: 37973377 PMCID: PMC10860623 DOI: 10.1523/jneurosci.0265-23.2023] [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: 02/13/2023] [Revised: 10/24/2023] [Accepted: 11/03/2023] [Indexed: 11/19/2023] Open
Abstract
Individuals' phenotypes, including the brain's structure and function, are largely determined by genes and their interplay. The resting brain generates salient rhythmic patterns that can be characterized noninvasively using functional neuroimaging such as magnetoencephalography (MEG). One of these rhythms, the somatomotor (rolandic) beta rhythm, shows intermittent high amplitude "events" that predict behavior across tasks and species. Beta rhythm is altered in neurological disease. The aperiodic (1/f) signal present in electrophysiological recordings is also modulated by some neurological conditions and aging. Both sensorimotor beta and aperiodic signal could thus serve as biomarkers of sensorimotor function. Knowledge about the extent to which these brain functional measures are heritable could shed light on the mechanisms underlying their generation. We investigated the heritability and variability of human spontaneous sensorimotor beta rhythm events and aperiodic activity in 210 healthy male and female adult siblings' spontaneous MEG activity. The most heritable trait was the aperiodic 1/f signal, with a heritability of 0.87 in the right hemisphere. Time-resolved beta event amplitude parameters were also highly heritable, whereas the heritabilities for overall beta power, peak frequency, and measures of event duration remained nonsignificant. Human sensorimotor neural activity can thus be dissected into different components with variable heritability. We postulate that these differences partially reflect different underlying signal-generating mechanisms. The 1/f signal and beta event amplitude measures may depend more on fixed, anatomical parameters, whereas beta event duration and its modulation reflect dynamic characteristics, guiding their use as potential disease biomarkers.
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Affiliation(s)
- K Amande M Pauls
- Department of Neurology, Helsinki University Hospital, and Department of Clinical Neurosciences, University of Helsinki, 00029 Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, 00290 Helsinki, Finland
| | - Elina Salmela
- Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, 00014 Helsinki, Finland
- Department of Biology, University of Turku, 20014 Turku, Finland
| | - Olesia Korsun
- BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, 00290 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, 02150 Espoo, Finland
| | - Jan Kujala
- Department of Psychology, University of Jyväskylä, 40014 Jyväskylä, Finland
| | - Riitta Salmelin
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, 02150 Espoo, Finland
| | - Hanna Renvall
- BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, 00290 Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, 02150 Espoo, Finland
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18
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Vinding MC, Eriksson A, Comarovschii I, Waldthaler J, Manting CL, Oostenveld R, Ingvar M, Svenningsson P, Lundqvist D. The Swedish National Facility for Magnetoencephalography Parkinson's disease dataset. Sci Data 2024; 11:150. [PMID: 38296972 PMCID: PMC10830455 DOI: 10.1038/s41597-024-02987-w] [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: 01/23/2023] [Accepted: 01/18/2024] [Indexed: 02/02/2024] Open
Abstract
Parkinson's disease (PD) is characterised by a loss of dopamine and dopaminergic cells. The consequences hereof are widespread network disturbances in brain function. It is an ongoing topic of investigation how the disease-related changes in brain function manifest in PD relate to clinical symptoms. We present The Swedish National Facility for Magnetoencephalography Parkinson's Disease Dataset (NatMEG-PD) as an Open Science contribution to identify the functional neural signatures of Parkinson's disease and contribute to diagnosis and treatment. The dataset contains whole-head magnetoencephalographic (MEG) recordings from 66 well-characterised PD patients on their regular dose of dopamine replacement therapy and 68 age- and sex-matched healthy controls. NatMEG-PD contains three-minute eyes-closed resting-state MEG, MEG during an active movement task, and MEG during passive movements. The data includes anonymised MRI for source analysis and clinical scores. MEG data is rich in nature and can be used to explore numerous functional features. By sharing these data, we hope other researchers will contribute to advancing our understanding of the relationship between brain activity and disease state or symptoms.
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Affiliation(s)
- Mikkel C Vinding
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark.
| | - Allison Eriksson
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Igori Comarovschii
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Josefine Waldthaler
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neurology, University Hospital Marburg, Marburg, Germany
| | - Cassia Low Manting
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- McGovern Institute of Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Robert Oostenveld
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Martin Ingvar
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Per Svenningsson
- Section of Neurology, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Daniel Lundqvist
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
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19
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Gohil C, Huang R, Roberts E, van Es MWJ, Quinn AJ, Vidaurre D, Woolrich MW. osl-dynamics, a toolbox for modeling fast dynamic brain activity. eLife 2024; 12:RP91949. [PMID: 38285016 PMCID: PMC10945565 DOI: 10.7554/elife.91949] [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] [Indexed: 01/30/2024] Open
Abstract
Neural activity contains rich spatiotemporal structure that corresponds to cognition. This includes oscillatory bursting and dynamic activity that span across networks of brain regions, all of which can occur on timescales of tens of milliseconds. While these processes can be accessed through brain recordings and imaging, modeling them presents methodological challenges due to their fast and transient nature. Furthermore, the exact timing and duration of interesting cognitive events are often a priori unknown. Here, we present the OHBA Software Library Dynamics Toolbox (osl-dynamics), a Python-based package that can identify and describe recurrent dynamics in functional neuroimaging data on timescales as fast as tens of milliseconds. At its core are machine learning generative models that are able to adapt to the data and learn the timing, as well as the spatial and spectral characteristics, of brain activity with few assumptions. osl-dynamics incorporates state-of-the-art approaches that can be, and have been, used to elucidate brain dynamics in a wide range of data types, including magneto/electroencephalography, functional magnetic resonance imaging, invasive local field potential recordings, and electrocorticography. It also provides novel summary measures of brain dynamics that can be used to inform our understanding of cognition, behavior, and disease. We hope osl-dynamics will further our understanding of brain function, through its ability to enhance the modeling of fast dynamic processes.
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Affiliation(s)
- Chetan Gohil
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
| | - Rukuang Huang
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
| | - Evan Roberts
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
| | - Mats WJ van Es
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
| | - Andrew J Quinn
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
- Centre for Human Brain Health, School of Psychology, University of BirminghamBirminghamUnited Kingdom
| | - Diego Vidaurre
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
- Center for Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus UniversityAarhusDenmark
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
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20
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Papadopoulos S, Szul MJ, Congedo M, Bonaiuto JJ, Mattout J. Beta bursts question the ruling power for brain-computer interfaces. J Neural Eng 2024; 21:016010. [PMID: 38167234 DOI: 10.1088/1741-2552/ad19ea] [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: 09/15/2023] [Accepted: 01/02/2024] [Indexed: 01/05/2024]
Abstract
Objective: Current efforts to build reliable brain-computer interfaces (BCI) span multiple axes from hardware, to software, to more sophisticated experimental protocols, and personalized approaches. However, despite these abundant efforts, there is still room for significant improvement. We argue that a rather overlooked direction lies in linking BCI protocols with recent advances in fundamental neuroscience.Approach: In light of these advances, and particularly the characterization of the burst-like nature of beta frequency band activity and the diversity of beta bursts, we revisit the role of beta activity in 'left vs. right hand' motor imagery (MI) tasks. Current decoding approaches for such tasks take advantage of the fact that MI generates time-locked changes in induced power in the sensorimotor cortex and rely on band-passed power changes in single or multiple channels. Although little is known about the dynamics of beta burst activity during MI, we hypothesized that beta bursts should be modulated in a way analogous to their activity during performance of real upper limb movements.Main results and Significance: We show that classification features based on patterns of beta burst modulations yield decoding results that are equivalent to or better than typically used beta power across multiple open electroencephalography datasets, thus providing insights into the specificity of these bio-markers.
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Affiliation(s)
- Sotirios Papadopoulos
- University Lyon 1, Lyon, France
- Lyon Neuroscience Research Center, CRNL, INSERM U1028, CNRS, UMR5292, Lyon, France
- Institut de Sciences Cognitives Marc Jeannerod, CNRS, UMR5229, Lyon, France
| | - Maciej J Szul
- University Lyon 1, Lyon, France
- Institut de Sciences Cognitives Marc Jeannerod, CNRS, UMR5229, Lyon, France
| | - Marco Congedo
- GIPSA-lab, University Grenoble Alpes, CNRS, Grenoble-INP, Grenoble, France
| | - James J Bonaiuto
- University Lyon 1, Lyon, France
- Institut de Sciences Cognitives Marc Jeannerod, CNRS, UMR5229, Lyon, France
| | - Jérémie Mattout
- University Lyon 1, Lyon, France
- Lyon Neuroscience Research Center, CRNL, INSERM U1028, CNRS, UMR5292, Lyon, France
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21
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Iidaka T, Maesawa S, Kanayama N, Miyakoshi M, Ishizaki T, Saito R. Hemodynamic and electrophysiological responses of the human amygdala during face imitation-a study using functional MRI and intracranial EEG. Cereb Cortex 2024; 34:bhad488. [PMID: 38112625 DOI: 10.1093/cercor/bhad488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 11/27/2023] [Accepted: 11/29/2023] [Indexed: 12/21/2023] Open
Abstract
The involvement of the human amygdala in facial mimicry remains a matter of debate. We investigated neural activity in the human amygdala during a task in which an imitation task was separated in time from an observation task involving facial expressions. Neural activity in the amygdala was measured using functional magnetic resonance imaging in 18 healthy individuals and using intracranial electroencephalogram in six medically refractory patients with epilepsy. The results of functional magnetic resonance imaging experiment showed that mimicry of negative and positive expressions activated the amygdala more than mimicry of non-emotional facial movements. In intracranial electroencephalogram experiment and time-frequency analysis, emotion-related activity of the amygdala during mimicry was observed as a significant neural oscillation in the high gamma band range. Furthermore, spectral event analysis of individual trial intracranial electroencephalogram data revealed that sustained oscillation of gamma band activity originated from an increased number and longer duration of neural events in the amygdala. Based on these findings, we conclude that during facial mimicry, visual information of expressions and feedback from facial movements are combined in the amygdalar nuclei. Considering the time difference of information approaching the amygdala, responses to facial movements are likely to modulate rather than initiate affective processing in human participants.
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Affiliation(s)
- Tetsuya Iidaka
- Brain & Mind Research Center, Nagoya University, Nagoya 461-8673, Japan
| | - Satoshi Maesawa
- Brain & Mind Research Center, Nagoya University, Nagoya 461-8673, Japan
- Department of Neurosurgery, Graduate School of Medicine, Nagoya University, Nagoya 466-8550, Japan
| | - Noriaki Kanayama
- Human Informatics Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, 305-8566, Japan
| | - Makoto Miyakoshi
- Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229-3026, United States
- Department of Psychiatry, University of Cincinnati College of Medicine, Cincinati, OH 45627-0555, United States
| | - Tomotaka Ishizaki
- Department of Neurosurgery, Graduate School of Medicine, Nagoya University, Nagoya 466-8550 , Japan
| | - Ryuta Saito
- Brain & Mind Research Center, Nagoya University, Nagoya 461-8673, Japan
- Department of Neurosurgery, Graduate School of Medicine, Nagoya University, Nagoya 466-8550 , Japan
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22
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Ten Oever S, Martin AE. Interdependence of "What" and "When" in the Brain. J Cogn Neurosci 2024; 36:167-186. [PMID: 37847823 DOI: 10.1162/jocn_a_02067] [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] [Indexed: 10/19/2023]
Abstract
From a brain's-eye-view, when a stimulus occurs and what it is are interrelated aspects of interpreting the perceptual world. Yet in practice, the putative perceptual inferences about sensory content and timing are often dichotomized and not investigated as an integrated process. We here argue that neural temporal dynamics can influence what is perceived, and in turn, stimulus content can influence the time at which perception is achieved. This computational principle results from the highly interdependent relationship of what and when in the environment. Both brain processes and perceptual events display strong temporal variability that is not always modeled; we argue that understanding-and, minimally, modeling-this temporal variability is key for theories of how the brain generates unified and consistent neural representations and that we ignore temporal variability in our analysis practice at the peril of both data interpretation and theory-building. Here, we review what and when interactions in the brain, demonstrate via simulations how temporal variability can result in misguided interpretations and conclusions, and outline how to integrate and synthesize what and when in theories and models of brain computation.
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Affiliation(s)
- Sanne Ten Oever
- Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
- Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands
- Maastricht University, The Netherlands
| | - Andrea E Martin
- Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
- Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands
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23
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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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24
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Kavanaugh BC, Fukuda AM, Gemelli ZT, Thorpe R, Tirrell E, Vigne M, Jones SR, Carpenter LL. Pre-treatment frontal beta events are associated with executive dysfunction improvement after repetitive transcranial magnetic stimulation for depression: A preliminary report. J Psychiatr Res 2023; 168:71-81. [PMID: 37897839 DOI: 10.1016/j.jpsychires.2023.10.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 08/31/2023] [Accepted: 10/14/2023] [Indexed: 10/30/2023]
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is an established clinical treatment for major depressive disorder (MDD) that has also been found to improve aspects of executive functioning. The objective of this study was to examine whether oscillatory burst-like events within the beta band (15-29 Hz) prior to treatment could predict subsequent change in self-reported executive dysfunction (EDF) across a clinical course of rTMS for MDD. Twenty-eight adults (64% female) with MDD completed the self-report Frontal Systems Behavior Scale (FrSBe) and provided eyes-closed resting-state electroencephalography (EEG) before and after a clinical course of rTMS therapy for primary MDD. The rate, power, duration, and frequency span of transient EEG measured oscillatory beta events were calculated. Events within delta/theta and alpha bands were examined to assess for beta specificity. After controlling for improvement in primary depressive symptoms, a lower rate of beta events at F3, Fz, F4, and Cz prior to rTMS treatment was associated with a larger improvement in EDF after rTMS treatment. In addition, a decrease in beta event rate at Fz pre-to-post treatment was associated with a larger improvement in EDF after treatment. Results were largely specific to the beta band. In this study, the rate of frontrocentral beta events prior to treatment significantly predicted the likelihood of subsequent improvement in EDF symptoms following a clinical course of rTMS for MDD. These preliminary findings suggest the potential utility of EEG measured beta events and rTMS for targeting EDF across an array of neuropsychiatric disorders.
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Affiliation(s)
- Brian C Kavanaugh
- E.P. Bradley Hospital, United States; Brown University, Department of Psychiatry & Human Behavior, United States.
| | - Andrew M Fukuda
- Brown University, Department of Psychiatry & Human Behavior, United States; Butler Hospital, United States
| | - Zachary T Gemelli
- Brown University, Department of Psychiatry & Human Behavior, United States; Rhode Island Hospital, United States
| | - Ryan Thorpe
- Brown University, Department of Neuroscience, United States
| | - Eric Tirrell
- Brown University, Department of Psychiatry & Human Behavior, United States; Butler Hospital, United States
| | - Megan Vigne
- Brown University, Department of Psychiatry & Human Behavior, United States; Butler Hospital, United States
| | - Stephanie R Jones
- Brown University, Department of Neuroscience, United States; Providence Veteran's Association Medical Center, Center for Neurorestoration and Neurotechnology, United States
| | - Linda L Carpenter
- Brown University, Department of Psychiatry & Human Behavior, United States; Butler Hospital, United States
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25
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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: 0] [Impact Index Per Article: 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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26
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Azizi L, Polti I, van Wassenhove V. Spontaneous α Brain Dynamics Track the Episodic "When". J Neurosci 2023; 43:7186-7197. [PMID: 37704373 PMCID: PMC10601376 DOI: 10.1523/jneurosci.0816-23.2023] [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: 04/27/2023] [Revised: 07/14/2023] [Accepted: 08/06/2023] [Indexed: 09/15/2023] Open
Abstract
Across species, neurons track time over the course of seconds to minutes, which may feed the sense of time passing. Here, we asked whether neural signatures of time-tracking could be found in humans. Participants stayed quietly awake for a few minutes while being recorded with magnetoencephalography (MEG). They were unaware they would be asked how long the recording lasted (retrospective time) or instructed beforehand to estimate how long it will last (prospective timing). At rest, rhythmic brain activity is nonstationary and displays bursts of activity in the alpha range (α: 7-14 Hz). When participants were not instructed to attend to time, the relative duration of α bursts linearly predicted individuals' retrospective estimates of how long their quiet wakefulness lasted. The relative duration of α bursts was a better predictor than α power or burst amplitude. No other rhythmic or arrhythmic activity predicted retrospective duration. However, when participants timed prospectively, the relative duration of α bursts failed to predict their duration estimates. Consistent with this, the amount of α bursts was discriminant between prospective and retrospective timing. Last, with a control experiment, we demonstrate that the relation between α bursts and retrospective time is preserved even when participants are engaged in a visual counting task. Thus, at the time scale of minutes, we report that the relative time of spontaneous α burstiness predicts conscious retrospective time. We conclude that in the absence of overt attention to time, α bursts embody discrete states of awareness constitutive of episodic timing.SIGNIFICANCE STATEMENT The feeling that time passes is a core component of consciousness and episodic memory. A century ago, brain rhythms called "α" were hypothesized to embody an internal clock. However, rhythmic brain activity is nonstationary and displays on-and-off oscillatory bursts, which would serve irregular ticks to the hypothetical clock. Here, we discovered that in a given lapse of time, the relative bursting time of α rhythms is a good indicator of how much time an individual will report to have elapsed. Remarkably, this relation only holds true when the individual does not attend to time and vanishes when attending to it. Our observations suggest that at the scale of minutes, α brain activity tracks episodic time.
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Affiliation(s)
- Leila Azizi
- Cognitive Neuroimaging Unit, NeuroSpin, Commissariat à l'énergie atomique et aux énergies alternatives, Institut National de la Santé et de la Recherche Médicale, Université Paris-Saclay, Gif/Yvette 91191, France
| | - Ignacio Polti
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway 7030
- Department of Psychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany D-04103
| | - Virginie van Wassenhove
- Cognitive Neuroimaging Unit, NeuroSpin, Commissariat à l'énergie atomique et aux énergies alternatives, Institut National de la Santé et de la Recherche Médicale, Université Paris-Saclay, Gif/Yvette 91191, France
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27
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Su F, Wang H, Zu L, Chen Y. Closed-loop modulation of model parkinsonian beta oscillations based on CAR-fuzzy control algorithm. Cogn Neurodyn 2023; 17:1185-1199. [PMID: 37786652 PMCID: PMC10542090 DOI: 10.1007/s11571-022-09820-3] [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: 10/19/2021] [Revised: 04/20/2022] [Accepted: 04/28/2022] [Indexed: 12/01/2022] Open
Abstract
Closed-loop deep brain stimulation (DBS) can apply on-demand stimulation based on the feedback signal (e.g. beta band oscillation), which is deemed to lower side effects of clinically used open-loop DBS. To facilitate the application of model-based closed-loop DBS in clinical, studies must consider state variations, e.g., variation of desired signal with different movement conditions and variation of model parameters with time. This paper proposes to use the controlled autoregressive (CAR)-fuzzy control algorithm to modulate the pathological beta band (13-35 Hz) oscillation of a basal ganglia-cortex-thalamus model. The CAR model is used to identify the relationship between DBS frequency parameter and beta oscillation power. Then the error between the one-step-ahead predicted beta power of CAR model and the desired value is innovatively used as the input of fuzzy controller to calculate the next step stimulation frequency. Compared with 130 Hz open-loop DBS, the proposed closed-loop DBS method could lower the mean stimulation frequency to 74.04 Hz with similar beta oscillation suppression performance. The Mamdani fuzzy controller is selected because which could establish fuzzy controller rules according to human operation experience. Adding prediction module to closed-loop control improves the accuracy of fuzzy control, compared with proportional-integral control and fuzzy control, the proposed CAR-fuzzy control algorithm has higher tracking reliability, response speed and robustness.
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Affiliation(s)
- Fei Su
- School of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, 271018 China
| | - Hong Wang
- School of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, 271018 China
| | - Linlu Zu
- School of Mechanical and Electronic Engineering, Shandong Agricultural University, Taian, 271018 China
| | - Yan Chen
- Department of Neurology, Shanghai Jiahui International Hospital, Shanghai, 200233 China
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28
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Cardone P, Bodart O, Kirsch M, Sanfilippo J, Virgillito A, Martial C, Simon J, Wannez S, Sanders RD, Laureys S, Massimini M, Vandewalle G, Bonhomme V, Gosseries O. Depth of sedation with dexmedetomidine increases transcranial magnetic stimulation-evoked potential amplitude non-linearly. Br J Anaesth 2023; 131:715-725. [PMID: 37596183 DOI: 10.1016/j.bja.2023.05.030] [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: 06/24/2021] [Revised: 05/09/2023] [Accepted: 05/30/2023] [Indexed: 08/20/2023] Open
Abstract
BACKGROUND Cortical excitability is higher in unconsciousness than in wakefulness, but it is unclear how this relates to anaesthesia. We investigated cortical excitability in response to dexmedetomidine, the effects of which are not fully known. METHODS We recorded transcranial magnetic stimulation (TMS) and EEG in frontal and parietal cortex of 20 healthy subjects undergoing dexmedetomidine sedation in four conditions (baseline, light sedation, deep sedation, recovery). We used the first component (0-30 ms) of the TMS-evoked potential (TEP) to measure cortical excitability (amplitude), slope, and positive and negative peak latencies (collectively, TEP indices). We used generalised linear mixed models to test the effect of condition, brain region, and responsiveness on TEP indices. RESULTS Compared with baseline, amplitude in the frontal cortex increased by 6.52 μV (P<0.001) in light sedation, 4.55 μV (P=0.003) in deep sedation, and 5.03 μV (P<0.001) in recovery. Amplitude did not change in the parietal cortex. Compared with baseline, slope increased in all conditions (P<0.02) in the frontal but not parietal cortex. The frontal cortex showed 5.73 μV higher amplitude (P<0.001), 0.63 μV ms-1 higher slope (P<0.001), and 2.2 ms shorter negative peak latency (P=0.001) than parietal areas. Interactions between dexmedetomidine and region had effects over amplitude (P=0.004) and slope (P=0.009), with both being higher in light sedation, deep sedation, and recovery compared with baseline. CONCLUSIONS Transcranial magnetic stimulation-evoked potential amplitude changes non-linearly as a function of depth of sedation by dexmedetomidine, with a region-specific paradoxical increase. Future research should investigate other anaesthetics to elucidate the link between cortical excitability and depth of sedation.
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Affiliation(s)
- Paolo Cardone
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau, University of Liège, Liège, Belgium
| | - Olivier Bodart
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau, University of Liège, Liège, Belgium; Department of Neurology, University of Liège, Liège, Belgium
| | - Murielle Kirsch
- Anesthesia and Perioperative Neuroscience Laboratory, GIGA-Consciousness, University of Liège, Liège, Belgium; Department of Anaesthesia and Intensive Care Medicine, University of Liège, Liège, Belgium
| | - Julien Sanfilippo
- Anesthesia and Perioperative Neuroscience Laboratory, GIGA-Consciousness, University of Liège, Liège, Belgium
| | | | - Charlotte Martial
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau, University of Liège, Liège, Belgium
| | - Jessica Simon
- Psychology and Neuroscience of Cognition, University of Liège, Liège, Belgium
| | - Sarah Wannez
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium
| | - Robert D Sanders
- Specialty of Anaesthetics, University of Sydney, Camperdown, Australia; Department of Anaesthetics & Institute of Academic Surgery, Royal Prince Alfred Hospital, Camperdown, Australia
| | - Steven Laureys
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau, University of Liège, Liège, Belgium; Joint International Research Unit on Consciousness, CERVO Brain Research Centre, CIUSS, University Laval, Québec City, QC, Canada
| | - Marcello Massimini
- Department of Biomedical and Clinical Sciences "L. Sacco", Università degli Studi di Milano, Milan, Italy; IRCCS Fondazione Don Gnocchi, Milan, Italy
| | - Gilles Vandewalle
- Sleep and Chronobiology Lab, GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Vincent Bonhomme
- Anesthesia and Perioperative Neuroscience Laboratory, GIGA-Consciousness, University of Liège, Liège, Belgium; Department of Anaesthesia and Intensive Care Medicine, University of Liège, Liège, Belgium; University Department of Anaesthesia and Intensive Care Medicine, Centre Hospitalier Régional de la Citadelle (CHR Citadelle), Liège, Belgium.
| | - Olivia Gosseries
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau, University of Liège, Liège, Belgium.
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29
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Semenkov I, Fedosov N, Makarov I, Ossadtchi A. Real-time low latency estimation of brain rhythms with deep neural networks. J Neural Eng 2023; 20:056008. [PMID: 37683653 DOI: 10.1088/1741-2552/acf7f3] [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: 08/08/2023] [Accepted: 09/08/2023] [Indexed: 09/10/2023]
Abstract
Objective.Neurofeedback and brain-computer interfacing technology open the exciting opportunity for establishing interactive closed-loop real-time communication with the human brain. This requires interpreting brain's rhythmic activity and generating timely feedback to the brain. Lower delay between neuronal events and the appropriate feedback increases the efficacy of such interaction. Novel more efficient approaches capable of tracking brain rhythm's phase and envelope are needed for scenarios that entail instantaneous interaction with the brain circuits.Approach.Isolating narrow-band signals incurs fundamental delays. To some extent they can be compensated using forecasting models. Given the high quality of modern time series forecasting neural networks we explored their utility for low-latency extraction of brain rhythm parameters. We tested five neural networks with conceptually distinct architectures in forecasting synthetic EEG rhythms. The strongest architecture was then trained to simultaneously filter and forecast EEG data. We compared it against the state-of-the-art techniques using synthetic and real data from 25 subjects.Main results.The temporal convolutional network (TCN) remained the strongest forecasting model that achieved in the majority of testing scenarios>90% rhythm's envelope correlation with<10 ms effective delay and<20∘circular standard deviation of phase estimates. It also remained stable enough to noise level perturbations. Trained to filter and predict the TCN outperformed the cFIR, the Kalman filter based state-space estimation technique and remained on par with the larger Conv-TasNet architecture.Significance.Here we have for the first time demonstrated the utility of the neural network approach for low-latency narrow-band filtering of brain activity signals. Our proposed approach coupled with efficient implementation enhances the effectiveness of brain-state dependent paradigms across various applications. Moreover, our framework for forecasting EEG signals holds promise for investigating the predictability of brain activity, providing valuable insights into the fundamental questions surrounding the functional organization and hierarchical information processing properties of the brain.
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Affiliation(s)
- Ilia Semenkov
- Artificial Intelligence Research Institute (AIRI), Moscow 105064, Russia
- HSE University, Moscow 109028, Russia
| | - Nikita Fedosov
- Artificial Intelligence Research Institute (AIRI), Moscow 105064, Russia
- HSE University, Moscow 109028, Russia
| | - Ilya Makarov
- Artificial Intelligence Research Institute (AIRI), Moscow 105064, Russia
| | - Alexei Ossadtchi
- Artificial Intelligence Research Institute (AIRI), Moscow 105064, Russia
- HSE University, Moscow 109028, Russia
- LLC 'Life Improvement by Future Technologies Center', Moscow, Russia
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30
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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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Mirdamadi JL, Ting LH, Borich MR. Distinct cortical correlates of perception and motor function in balance control. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.22.554282. [PMID: 37662247 PMCID: PMC10473579 DOI: 10.1101/2023.08.22.554282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Fluctuations in brain state alter how we perceive our body and generate movements but have not been investigated in functional whole-body behaviors. During reactive balance control, we recently showed that evoked brain activity is associated with balance ability in healthy young individuals. Further, in individuals with Parkinson's disease, impairments in whole-body motion perception in reactive balance are associated with clinical balance impairment. Here we investigated brain activity during whole-body motion perception in reactive balance in healthy young adults. We hypothesized that flexibility in brain states underlies successful perception and movement during whole-body movement. We characterized two cortical sensorimotor signals using electroencephalography localized to the supplementary motor area: 1) the "N1 response", a perturbation-evoked potential that decreases in amplitude with expectancy and is larger in individuals with lower balance function; and 2) pre-perturbation beta oscillatory activity, a rhythm that favors maintenance of the current sensorimotor state and is inversely associated with perception in seated somatosensory perceptual tasks. In a two-alternative forced choice task, participants judged whether pairs of backward support-surface perturbations during standing were in the "same" or "different" direction. As expected, lower whole-body perception was associated with lower balance ability. Within a perturbation pair, N1 attenuation was larger on correctly perceived trials and associated with better balance, but not perception. In contrast, pre-perturbation beta power was higher on incorrectly perceived trials and associated with poorer perception, but not balance. Taken together, flexibility in different cortical processes influences perceptual accuracy but have distinct associations with balance and perceptual ability.
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Affiliation(s)
- Jasmine L. Mirdamadi
- Department of Rehabilitation Medicine, Division of Physical Therapy, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Lena H. Ting
- Department of Rehabilitation Medicine, Division of Physical Therapy, Emory University School of Medicine, Atlanta, GA 30322, USA
- The Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA 30322, USA
| | - Michael R. Borich
- Department of Rehabilitation Medicine, Division of Physical Therapy, Emory University School of Medicine, Atlanta, GA 30322, USA
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Wadsley CG, Cirillo J, Nieuwenhuys A, Byblow WD. A global pause generates nonselective response inhibition during selective stopping. Cereb Cortex 2023; 33:9729-9740. [PMID: 37395336 PMCID: PMC10472494 DOI: 10.1093/cercor/bhad239] [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: 04/04/2023] [Revised: 06/07/2023] [Accepted: 06/09/2023] [Indexed: 07/04/2023] Open
Abstract
Selective response inhibition may be required when stopping a part of a multicomponent action. A persistent response delay (stopping-interference effect) indicates nonselective response inhibition during selective stopping. This study aimed to elucidate whether nonselective response inhibition is the consequence of a global pause process during attentional capture or specific to a nonselective cancel process during selective stopping. Twenty healthy human participants performed a bimanual anticipatory response inhibition paradigm with selective stop and ignore signals. Frontocentral and sensorimotor beta-bursts were recorded with electroencephalography. Corticomotor excitability and short-interval intracortical inhibition in primary motor cortex were recorded with transcranial magnetic stimulation. Behaviorally, responses in the non-signaled hand were delayed during selective ignore and stop trials. The response delay was largest during selective stop trials and indicated that stopping-interference could not be attributed entirely to attentional capture. A stimulus-nonselective increase in frontocentral beta-bursts occurred during stop and ignore trials. Sensorimotor response inhibition was reflected in maintenance of beta-bursts and short-interval intracortical inhibition relative to disinhibition observed during go trials. Response inhibition signatures were not associated with the magnitude of stopping-interference. Therefore, nonselective response inhibition during selective stopping results primarily from a nonselective pause process but does not entirely account for the stopping-interference effect.
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Affiliation(s)
- Corey G Wadsley
- Movement Neuroscience Laboratory, Department of Exercise Sciences, The University of Auckland, Auckland 1142, New Zealand
- Centre for Brain Research, The University of Auckland, Auckland 1142, New Zealand
| | - John Cirillo
- Movement Neuroscience Laboratory, Department of Exercise Sciences, The University of Auckland, Auckland 1142, New Zealand
- Centre for Brain Research, The University of Auckland, Auckland 1142, New Zealand
| | - Arne Nieuwenhuys
- Movement Neuroscience Laboratory, Department of Exercise Sciences, The University of Auckland, Auckland 1142, New Zealand
| | - Winston D Byblow
- Movement Neuroscience Laboratory, Department of Exercise Sciences, The University of Auckland, Auckland 1142, New Zealand
- Centre for Brain Research, The University of Auckland, Auckland 1142, New Zealand
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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: 0] [Impact Index Per Article: 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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Rangel BO, Novembre G, Wessel JR. Measuring the nonselective effects of motor inhibition using isometric force recordings. Behav Res Methods 2023:10.3758/s13428-023-02197-z. [PMID: 37550468 DOI: 10.3758/s13428-023-02197-z] [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] [Accepted: 07/10/2023] [Indexed: 08/09/2023]
Abstract
Inhibition is a key cognitive control mechanism humans use to enable goal-directed behavior. When rapidly exerted, inhibitory control has broad, nonselective motor effects, typically demonstrated using corticospinal excitability measurements (CSE) elicited by transcranial magnetic stimulation (TMS). For example, during rapid action-stopping, CSE is suppressed at both stopped and task-unrelated muscles. While such TMS-based CSE measurements have provided crucial insights into the fronto-basal ganglia circuitry underlying inhibitory control, they have several downsides. TMS is contraindicated in many populations (e.g., epilepsy or deep-brain stimulation patients), has limited temporal resolution, produces distracting auditory and haptic stimulation, is difficult to combine with other imaging methods, and necessitates expensive, immobile equipment. Here, we attempted to measure the nonselective motor effects of inhibitory control using a method unaffected by these shortcomings. Thirty male and female human participants exerted isometric force on a high-precision handheld force transducer while performing a foot-response stop-signal task. Indeed, when foot movements were successfully stopped, force output at the task-irrelevant hand was suppressed as well. Moreover, this nonselective reduction of isometric force was highly correlated with stop-signal performance and showed frequency dynamics similar to established inhibitory signatures typically found in neural and muscle recordings. Together, these findings demonstrate that isometric force recordings can reliably capture the nonselective effects of motor inhibition, opening the door to many applications that are hard or impossible to realize with TMS.
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Affiliation(s)
- Benjamin O Rangel
- Interdisciplinary Graduate Program in Neuroscience, University of Iowa, Iowa City, IA, 52245, USA.
- Cognitive Control Collaborative, University of Iowa, Iowa City, IA, 52245, USA.
- University of Iowa, 444 Medical Research Center, Iowa City, IA, 52242, USA.
| | - Giacomo Novembre
- Neuroscience of Perception & Action Laboratory, Italian Institute of Technology, Rome, Italy
| | - Jan R Wessel
- Cognitive Control Collaborative, University of Iowa, Iowa City, IA, 52245, USA
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, 52245, USA
- Department of Neurology, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242, USA
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35
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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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36
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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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37
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McKeon SD, Calabro F, Thorpe RV, de la Fuente A, Foran W, Parr AC, Jones SR, Luna B. Age-related differences in transient gamma band activity during working memory maintenance through adolescence. Neuroimage 2023; 274:120112. [PMID: 37105338 PMCID: PMC10214866 DOI: 10.1016/j.neuroimage.2023.120112] [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: 01/29/2023] [Revised: 04/06/2023] [Accepted: 04/13/2023] [Indexed: 04/29/2023] Open
Abstract
Adolescence is a stage of development characterized by neurodevelopmental specialization of cognitive processes. In particular, working memory continues to improve through adolescence, with increases in response accuracy and decreases in response latency continuing well into the twenties. Human electroencephalogram (EEG) studies indicate that gamma oscillations (35-65 Hz) during the working memory delay period support the maintenance of mnemonic information guiding subsequent goal-driven behavior, which decrease in power with development. Importantly, recent electrophysiological studies have shown that gamma events, more so than sustained activity, may underlie working memory maintenance during the delay period. However, developmental differences in gamma events during working memory have not been studied. Here, we used EEG in conjunction with a novel spectral event processing approach to investigate age-related differences in transient gamma band activity during a memory guided saccade (MGS) task in 164 10- to 30-year-olds. Total gamma power was found to significantly decrease through adolescence, replicating prior findings. Results from the spectral event pipeline showed age-related decreases in the mean power of gamma events and trial-by-trial power variability across both the delay period and fixation epochs of the MGS task. In addition, we found that while event number decreased with age during the fixation period, the developmental decrease during the delay period was more dramatic, resulting in an increase in event spiking from fixation to delay in adolescence but not adulthood. While average power of the transient gamma events was found to mediate age-related differences in total gamma power in the fixation and delay periods, the number of gamma events was related to total power in only the delay period, suggesting that the power of gamma events may underlie the sustained gamma activity seen in EEG literature while the number of events may directly support age-related improvements in working memory maintenance. Our findings provide compelling new evidence for mechanistic changes in neural processing characterized by refinements in neural function as behavior becomes optimized in adulthood.
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Affiliation(s)
- Shane D McKeon
- Department of Bioengineering, University of Pittsburgh, PA, 15213, United States; The Center for the Neural Basis of Cognition, University of Pittsburgh, PA, 15213, United States.
| | - Finnegan Calabro
- Department of Bioengineering, University of Pittsburgh, PA, 15213, United States; The Center for the Neural Basis of Cognition, University of Pittsburgh, PA, 15213, United States; Department of Psychiatry, University of Pittsburgh, PA, 15213, United States
| | - Ryan V Thorpe
- Department of Neuroscience, Brown University, Providence, RI, United States
| | - Alethia de la Fuente
- Department of Physics, University of Buenos Aires, Argentina; Institute of Cognitive and Translational Neuroscience, INECO Foundation, Favaloro University, Buenos Aires, Argentina; National Scientific and Technical Research Council, Buenos Aires, Argentina
| | - Will Foran
- Department of Psychiatry, University of Pittsburgh, PA, 15213, United States
| | - Ashley C Parr
- The Center for the Neural Basis of Cognition, University of Pittsburgh, PA, 15213, United States; Department of Psychiatry, University of Pittsburgh, PA, 15213, United States
| | - Stephanie R Jones
- Department of Neuroscience, Brown University, Providence, RI, United States
| | - Beatriz Luna
- The Center for the Neural Basis of Cognition, University of Pittsburgh, PA, 15213, United States; Department of Psychiatry, University of Pittsburgh, PA, 15213, United States.
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38
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Torrecillos F, He S, Kühn AA, Tan H. Average power and burst analysis revealed complementary information on drug-related changes of motor performance in Parkinson's disease. NPJ Parkinsons Dis 2023; 9:93. [PMID: 37328511 PMCID: PMC10275865 DOI: 10.1038/s41531-023-00540-x] [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: 09/16/2022] [Accepted: 06/05/2023] [Indexed: 06/18/2023] Open
Abstract
In patients with Parkinson's disease (PD), suppression of beta and increase in gamma oscillations in the subthalamic nucleus (STN) have been associated with both levodopa treatment and motor functions. Recent results suggest that modulation of the temporal dynamics of theses oscillations (bursting activity) might contain more information about pathological states and behaviour than their average power. Here we directly compared the information provided by power and burst analyses about the drug-related changes in STN activities and their impact on motor performance within PD patients. STN local field potential (LFP) signals were recorded from externalized patients performing self-paced movements ON and OFF levodopa. When normalised across medication states, both power and burst analyses showed an increase in low-beta oscillations in the dopamine-depleted state during rest. When normalised within-medication state, both analyses revealed that levodopa increased movement-related modulation in the alpha and low-gamma bands, with higher gamma activity around movement predicting faster reaches. Finally, burst analyses helped to reveal opposite drug-related changes in low- and high-beta frequency bands, and identified additional within-patient relationships between high-beta bursting and movement performance. Our findings suggest that although power and burst analyses share a lot in common they also provide complementary information on how STN-LFP activity is associated with motor performance, and how levodopa treatment may modify these relationships in a way that helps explain drug-related changes in motor performance. Different ways of normalisation in the power analysis can reveal different information. Similarly, the burst analysis is sensitive to how the threshold is defined - either for separate medication conditions separately, or across pooled conditions. In addition, the burst interpretation has far-reaching implications about the nature of neural oscillations - whether the oscillations happen as isolated burst-events or are they sustained phenomena with dynamic amplitude variations? This can be different for different frequency bands, and different for different medication states even for the same frequency band.
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Affiliation(s)
- Flavie Torrecillos
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Shenghong He
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Andrea A Kühn
- Department of Neurology, Charitè, Universitätsmedizin, Berlin, Germany
| | - Huiling Tan
- Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, UK.
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
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Rassi E, Lin WM, Zhang Y, Emmerzaal J, Haegens S. β Band Rhythms Influence Reaction Times. eNeuro 2023; 10:ENEURO.0473-22.2023. [PMID: 37364994 PMCID: PMC10312120 DOI: 10.1523/eneuro.0473-22.2023] [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: 11/21/2022] [Revised: 04/24/2023] [Accepted: 04/27/2023] [Indexed: 06/28/2023] Open
Abstract
Despite their involvement in many cognitive functions, β oscillations are among the least understood brain rhythms. Reports on whether the functional role of β is primarily inhibitory or excitatory have been contradictory. Our framework attempts to reconcile these findings and proposes that several β rhythms co-exist at different frequencies. β Frequency shifts and their potential influence on behavior have thus far received little attention. In this human magnetoencephalography (MEG) experiment, we asked whether changes in β power or frequency in auditory cortex and motor cortex influence behavior (reaction times) during an auditory sweep discrimination task. We found that in motor cortex, increased β power slowed down responses, while in auditory cortex, increased β frequency slowed down responses. We further characterized β as transient burst events with distinct spectro-temporal profiles influencing reaction times. Finally, we found that increased motor-to-auditory β connectivity also slowed down responses. In sum, β power, frequency, bursting properties, cortical focus, and connectivity profile all influenced behavioral outcomes. Our results imply that the study of β oscillations requires caution as β dynamics are multifaceted phenomena, and that several dynamics must be taken into account to reconcile mixed findings in the literature.
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Affiliation(s)
- Elie Rassi
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, 6525 EN Nijmegen, The Netherlands
- Department of Psychology, Centre for Cognitive Neuroscience, Paris-Lodron-University of Salzburg, 5020 Salzburg, Austria
| | - Wy Ming Lin
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, 6525 EN Nijmegen, The Netherlands
- Hector Research Institute for Education Sciences and Psychology, University of Tübingen, 72074 Tübingen, Germany
| | - Yi Zhang
- Department of Psychiatry, Columbia University, New York, NY 10032
| | - Jill Emmerzaal
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, 6525 EN Nijmegen, The Netherlands
- Human Movement Biomechanics Research Group, Department of Movement Sciences, Katholieke Universiteit Leuven, B-3001 Leuven, Belgium
- REVAL Rehabilitation Research Centre, Faculty of Rehabilitation Sciences, Hasselt University, 3500 Diepenbeek, Belgium
| | - Saskia Haegens
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, 6525 EN Nijmegen, The Netherlands
- Department of Psychiatry, Columbia University, New York, NY 10032
- Division of Systems Neuroscience, New York State Psychiatric Institute, New York, NY 10032
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40
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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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41
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Chota S, VanRullen R, Gulbinaite R. Random Tactile Noise Stimulation Reveals Beta-Rhythmic Impulse Response Function of the Somatosensory System. J Neurosci 2023; 43:3107-3119. [PMID: 36931709 PMCID: PMC10146486 DOI: 10.1523/jneurosci.1758-22.2023] [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: 09/13/2022] [Revised: 01/10/2023] [Accepted: 01/13/2023] [Indexed: 03/19/2023] Open
Abstract
Both passive tactile stimulation and motor actions result in dynamic changes in beta band (15-30 Hz Hz) oscillations over somatosensory cortex. Similar to alpha band (8-12 Hz) power decrease in the visual system, beta band power also decreases following stimulation of the somatosensory system. This relative suppression of α and β oscillations is generally interpreted as an increase in cortical excitability. Here, next to traditional single-pulse stimuli, we used a random intensity continuous right index finger tactile stimulation (white noise), which enabled us to uncover an impulse response function of the somatosensory system. Contrary to previous findings, we demonstrate a burst-like initial increase rather than decrease of beta activity following white noise stimulation (human participants, N = 18, 8 female). These β bursts, on average, lasted for 3 cycles, and their frequency was correlated with resonant frequency of somatosensory cortex, as measured by a multifrequency steady-state somatosensory evoked potential paradigm. Furthermore, beta band bursts shared spectro-temporal characteristics with evoked and resting-state β oscillations. Together, our findings not only reveal a novel oscillatory signature of somatosensory processing that mimics the previously reported visual impulse response functions, but also point to a common oscillatory generator underlying spontaneous β bursts in the absence of tactile stimulation and phase-locked β bursts following stimulation, the frequency of which is determined by the resonance properties of the somatosensory system.SIGNIFICANCE STATEMENT The investigation of the transient nature of oscillations has gained great popularity in recent years. The findings of bursting activity, rather than sustained oscillations in the beta band, have provided important insights into its role in movement planning, working memory, inhibition, and reactivation of neural ensembles. In this study, we show that also in response to tactile stimulation the somatosensory system responds with ∼3 cycle oscillatory beta band bursts, whose spectro-temporal characteristics are shared with evoked and resting-state beta band oscillatory signatures of the somatosensory system. As similar bursts have been observed in the visual domain, these oscillatory signatures might reflect an important supramodal mechanism in sensory processing.
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Affiliation(s)
- Samson Chota
- Centre de Recherche Cerveau et Cognition, Université de Toulouse, Toulouse, 31052, France
- Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, 3584 CS, The Netherlands
| | - Rufin VanRullen
- Centre de Recherche Cerveau et Cognition, Université de Toulouse, Toulouse, 31052, France
| | - Rasa Gulbinaite
- Netherlands Institute for Neuroscience, Amsterdam, 1105 BA, The Netherlands
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42
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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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43
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Tolley N, Rodrigues PLC, Gramfort A, Jones S. Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.17.537118. [PMID: 37131818 PMCID: PMC10153146 DOI: 10.1101/2023.04.17.537118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Biophysically detailed neural models are a powerful technique to study neural dynamics in health and disease with a growing number of established and openly available models. A major challenge in the use of such models is that parameter inference is an inherently difficult and unsolved problem. Identifying unique parameter distributions that can account for observed neural dynamics, and differences across experimental conditions, is essential to their meaningful use. Recently, simulation based inference (SBI) has been proposed as an approach to perform Bayesian inference to estimate parameters in detailed neural models. SBI overcomes the challenge of not having access to a likelihood function, which has severely limited inference methods in such models, by leveraging advances in deep learning to perform density estimation. While the substantial methodological advancements offered by SBI are promising, their use in large scale biophysically detailed models is challenging and methods for doing so have not been established, particularly when inferring parameters that can account for time series waveforms. We provide guidelines and considerations on how SBI can be applied to estimate time series waveforms in biophysically detailed neural models starting with a simplified example and extending to specific applications to common MEG/EEG waveforms using the the large scale neural modeling framework of the Human Neocortical Neurosolver. Specifically, we describe how to estimate and compare results from example oscillatory and event related potential simulations. We also describe how diagnostics can be used to assess the quality and uniqueness of the posterior estimates. The methods described provide a principled foundation to guide future applications of SBI in a wide variety of applications that use detailed models to study neural dynamics.
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Affiliation(s)
- Nicholas Tolley
- Department of Neuroscience, Brown University, Providence, RI, United States
| | | | | | - Stephanie Jones
- Department of Neuroscience, Brown University, Providence, RI, United States
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44
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Black CJ, Saab CY, Borton DA. Transient gamma events delineate somatosensory modality in S1. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.30.534945. [PMID: 37034800 PMCID: PMC10081264 DOI: 10.1101/2023.03.30.534945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Gamma band activity localized to the primary somatosensory cortex (S1) in humans and animals is implicated in the higher order neural processing of painful and tactile stimuli. However, it is unclear if gamma band activity differs between these distinct somatosensory modalities. Here, we coupled a novel behavioral approach with chronic extracellular electrophysiology to investigate differences in S1 gamma band activity elicited by noxious and innocuous hind paw stimulation in transgenic mice. Like prior studies, we found that trial-averaged gamma power in S1 increased following both noxious and innocuous stimuli. However, on individual trials, we noticed that evoked gamma band activity was not a continuous oscillatory signal but a series of transient spectral events. Upon further analysis we found that there was a significantly higher incidence of these gamma band events following noxious stimulation than innocuous stimulation. These findings suggest that somatosensory stimuli may be represented by specific features of gamma band activity at the single trial level, which may provide insight to mechanisms underlying acute pain.
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45
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Hindriks R, Tewarie PKB. Dissociation between phase and power correlation networks in the human brain is driven by co-occurrent bursts. Commun Biol 2023; 6:286. [PMID: 36934153 PMCID: PMC10024695 DOI: 10.1038/s42003-023-04648-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 03/02/2023] [Indexed: 03/20/2023] Open
Abstract
Well-known haemodynamic resting-state networks are better mirrored in power correlation networks than phase coupling networks in electrophysiological data. However, what do these power correlation networks reflect? We address this long-outstanding question in neuroscience using rigorous mathematical analysis, biophysical simulations with ground truth and application of these mathematical concepts to empirical magnetoencephalography (MEG) data. Our mathematical derivations show that for two non-Gaussian electrophysiological signals, their power correlation depends on their coherence, cokurtosis and conjugate-coherence. Only coherence and cokurtosis contribute to power correlation networks in MEG data, but cokurtosis is less affected by artefactual signal leakage and better mirrors haemodynamic resting-state networks. Simulations and MEG data show that cokurtosis may reflect co-occurrent bursting events. Our findings shed light on the origin of the complementary nature of power correlation networks to phase coupling networks and suggests that the origin of resting-state networks is partly reflected in co-occurent bursts in neuronal activity.
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Affiliation(s)
- Rikkert Hindriks
- Department of Mathematics, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Prejaas K B Tewarie
- Clinical Neurophysiology Group, University of Twente, Enschede, The Netherlands
- Sir Peter Mansfield Imaging Center, School of Physics, University of Nottingham, Nottingham, UK
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46
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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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47
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Tatz JR, Mather A, Wessel JR. β-Bursts over Frontal Cortex Track the Surprise of Unexpected Events in Auditory, Visual, and Tactile Modalities. J Cogn Neurosci 2023; 35:485-508. [PMID: 36603039 PMCID: PMC9894628 DOI: 10.1162/jocn_a_01958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
One of the fundamental ways in which the brain regulates and monitors behavior is by making predictions about the sensory environment and adjusting behavior when those expectations are violated. As such, surprise is one of the fundamental computations performed by the human brain. In recent years, it has been well established that one key aspect by which behavior is adjusted during surprise is inhibitory control of the motor system. Moreover, because surprise automatically triggers inhibitory control without much proactive influence, it can provide unique insights into largely reactive control processes. Recent years have seen tremendous interest in burst-like β frequency events in the human (and nonhuman) local field potential-especially over (p)FC-as a potential signature of inhibitory control. To date, β-bursts have only been studied in paradigms involving a substantial amount of proactive control (such as the stop-signal task). Here, we used two cross-modal oddball tasks to investigate whether surprise processing is accompanied by increases in scalp-recorded β-bursts. Indeed, we found that unexpected events in all tested sensory domains (haptic, auditory, visual) were followed by low-latency increases in β-bursting over frontal cortex. Across experiments, β-burst rates were positively correlated with estimates of surprise derived from Shannon's information theory, a type of surprise that represents the degree to which a given stimulus violates prior expectations. As such, the current work clearly implicates frontal β-bursts as a signature of surprise processing. We discuss these findings in the context of common frameworks of inhibitory and cognitive control after unexpected events.
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Affiliation(s)
- Joshua R. Tatz
- University of Iowa,University of Iowa Hospital and Clinics
| | | | - Jan R. Wessel
- University of Iowa,University of Iowa Hospital and Clinics
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48
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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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49
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West TO, Duchet B, Farmer SF, Friston KJ, Cagnan H. When do bursts matter in the primary motor cortex? Investigating changes in the intermittencies of beta rhythms associated with movement states. Prog Neurobiol 2023; 221:102397. [PMID: 36565984 PMCID: PMC7614511 DOI: 10.1016/j.pneurobio.2022.102397] [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: 07/29/2022] [Revised: 11/04/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
Brain activity exhibits significant temporal structure that is not well captured in the power spectrum. Recently, attention has shifted to characterising the properties of intermittencies in rhythmic neural activity (i.e. bursts), yet the mechanisms that regulate them are unknown. Here, we present evidence from electrocorticography recordings made over the motor cortex to show that the statistics of bursts, such as duration or amplitude, in the beta frequency (14-30 Hz) band, significantly aid the classification of motor states such as rest, movement preparation, execution, and imagery. These features reflect nonlinearities not detectable in the power spectrum, with states increasing in nonlinearity from movement execution to preparation to rest. Further, we show using a computational model of the cortical microcircuit, constrained to account for burst features, that modulations of laminar specific inhibitory interneurons are responsible for the temporal organisation of activity. Finally, we show that the temporal characteristics of spontaneous activity can be used to infer the balance of cortical integration between incoming sensory information and endogenous activity. Critically, we contribute to the understanding of how transient brain rhythms may underwrite cortical processing, which in turn, could inform novel approaches for brain state classification, and modulation with novel brain-computer interfaces.
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Affiliation(s)
- Timothy O West
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK; Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK.
| | - Benoit Duchet
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK
| | - Simon F Farmer
- Department of Neurology, National Hospital for Neurology & Neurosurgery, Queen Square, London WC1N 3BG, UK; Department of Clinical and Movement Neurosciences, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Hayriye Cagnan
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK; Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
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50
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Pinzuti E, Wollstadt P, Tüscher O, Wibral M. Information theoretic evidence for layer- and frequency-specific changes in cortical information processing under anesthesia. PLoS Comput Biol 2023; 19:e1010380. [PMID: 36701388 PMCID: PMC9904504 DOI: 10.1371/journal.pcbi.1010380] [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: 07/15/2022] [Revised: 02/07/2023] [Accepted: 01/05/2023] [Indexed: 01/27/2023] Open
Abstract
Nature relies on highly distributed computation for the processing of information in nervous systems across the entire animal kingdom. Such distributed computation can be more easily understood if decomposed into the three elementary components of information processing, i.e. storage, transfer and modification, and rigorous information theoretic measures for these components exist. However, the distributed computation is often also linked to neural dynamics exhibiting distinct rhythms. Thus, it would be beneficial to associate the above components of information processing with distinct rhythmic processes where possible. Here we focus on the storage of information in neural dynamics and introduce a novel spectrally-resolved measure of active information storage (AIS). Drawing on intracortical recordings of neural activity in ferrets under anesthesia before and after loss of consciousness (LOC) we show that anesthesia- related modulation of AIS is highly specific to different frequency bands and that these frequency-specific effects differ across cortical layers and brain regions. We found that in the high/low gamma band the effects of anesthesia result in AIS modulation only in the supergranular layers, while in the alpha/beta band the strongest decrease in AIS can be seen at infragranular layers. Finally, we show that the increase of spectral power at multiple frequencies, in particular at alpha and delta bands in frontal areas, that is often observed during LOC ('anteriorization') also impacts local information processing-but in a frequency specific way: Increases in isoflurane concentration induced a decrease in AIS in the alpha frequencies, while they increased AIS in the delta frequency range < 2Hz. Thus, the analysis of spectrally-resolved AIS provides valuable additional insights into changes in cortical information processing under anaesthesia.
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Affiliation(s)
- Edoardo Pinzuti
- Leibniz Institute for Resilience Research (LIR), Mainz, Germany
- MEG Unit, Brain Imaging Center, Goethe University, Frankfurt/Main, Germany
- * E-mail:
| | - Patricia Wollstadt
- MEG Unit, Brain Imaging Center, Goethe University, Frankfurt/Main, Germany
| | - Oliver Tüscher
- Leibniz Institute for Resilience Research (LIR), Mainz, Germany
- Department of Psychiatry and Psychotherapy, Johannes Gutenberg University of Mainz, Mainz, Germany
- Institute of Molecular Biology (IMB), Mainz, Germany
| | - Michael Wibral
- Campus Institute for Dynamics of Biological Networks, Georg August University, Göttingen, Germany
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