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Lundqvist M, Miller EK, Nordmark J, Liljefors J, Herman P. Beta: bursts of cognition. Trends Cogn Sci 2024; 28:662-676. [PMID: 38658218 DOI: 10.1016/j.tics.2024.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 03/11/2024] [Accepted: 03/20/2024] [Indexed: 04/26/2024]
Abstract
Beta oscillations are linked to the control of goal-directed processing of sensory information and the timing of motor output. Recent evidence demonstrates they are not sustained but organized into intermittent high-power bursts mediating timely functional inhibition. This implies there is a considerable moment-to-moment variation in the neural dynamics supporting cognition. Beta bursts thus offer new opportunities for studying how sensory inputs are selectively processed, reshaped by inhibitory cognitive operations and ultimately result in motor actions. Recent method advances reveal diversity in beta bursts that provide deeper insights into their function and the underlying neural circuit activity motifs. We propose that brain-wide, spatiotemporal patterns of beta bursting reflect various cognitive operations and that their dynamics reveal nonlinear aspects of cortical processing.
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Affiliation(s)
- Mikael Lundqvist
- Division of Psychology, Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden; The Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Earl K Miller
- The Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jonatan Nordmark
- Division of Psychology, Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden
| | - Johan Liljefors
- Division of Psychology, Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden
| | - Pawel Herman
- School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden; Digital Futures, KTH Royal Institute of Technology, Stockholm, Sweden
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2
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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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3
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Diesburg DA, Wessel JR, Jones SR. Biophysical Modeling of Frontocentral ERP Generation Links Circuit-Level Mechanisms of Action-Stopping to a Behavioral Race Model. J Neurosci 2024; 44:e2016232024. [PMID: 38561227 PMCID: PMC11097283 DOI: 10.1523/jneurosci.2016-23.2024] [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: 10/25/2023] [Revised: 02/09/2024] [Accepted: 03/08/2024] [Indexed: 04/04/2024] Open
Abstract
Human frontocentral event-related potentials (FC-ERPs) are ubiquitous neural correlates of cognition and control, but their generating multiscale mechanisms remain mostly unknown. We used the Human Neocortical Neurosolver's biophysical model of a canonical neocortical circuit under exogenous thalamic and cortical drive to simulate the cell and circuit mechanisms underpinning the P2, N2, and P3 features of the FC-ERP observed after Stop-Signals in the Stop-Signal task (SST; N = 234 humans, 137 female). We demonstrate that a sequence of simulated external thalamocortical and corticocortical drives can produce the FC-ERP, similar to what has been shown for primary sensory cortices. We used this model of the FC-ERP to examine likely circuit-mechanisms underlying FC-ERP features that distinguish between successful and failed action-stopping. We also tested their adherence to the predictions of the horse-race model of the SST, with specific hypotheses motivated by theoretical links between the P3 and Stop process. These simulations revealed that a difference in P3 onset between successful and failed Stops is most likely due to a later arrival of thalamocortical drive in failed Stops, rather than, for example, a difference in the effective strength of the input. In contrast, the same model predicted that early thalamocortical drives underpinning the P2 and N2 differed in both strength and timing across stopping accuracy conditions. Overall, this model generates novel testable predictions of the thalamocortical dynamics underlying FC-ERP generation during action-stopping. Moreover, it provides a detailed cellular and circuit-level interpretation that supports links between these macroscale signatures and predictions of the behavioral race model.
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Affiliation(s)
- Darcy A Diesburg
- Department of Neuroscience, Brown University, Providence, Rhode Island 02912
| | - Jan R Wessel
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, Iowa 52242
- Department of Neurology, Carver College of Medicine, University of Iowa Hospitals and Clinics, Iowa City, Iowa 52242
| | - Stephanie R Jones
- Department of Neuroscience, Brown University, Providence, Rhode Island 02912
- Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, Providence, Rhode Island 02908
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4
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Lankinen K, Ahveninen J, Jas M, Raij T, Ahlfors SP. Neuronal Modeling of Cross-Sensory Visual Evoked Magnetoencephalography Responses in the Auditory Cortex. J Neurosci 2024; 44:e1119232024. [PMID: 38508715 PMCID: PMC11044114 DOI: 10.1523/jneurosci.1119-23.2024] [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: 06/16/2023] [Revised: 02/13/2024] [Accepted: 02/14/2024] [Indexed: 03/22/2024] Open
Abstract
Previous studies have demonstrated that auditory cortex activity can be influenced by cross-sensory visual inputs. Intracortical laminar recordings in nonhuman primates have suggested a feedforward (FF) type profile for auditory evoked but feedback (FB) type for visual evoked activity in the auditory cortex. To test whether cross-sensory visual evoked activity in the auditory cortex is associated with FB inputs also in humans, we analyzed magnetoencephalography (MEG) responses from eight human subjects (six females) evoked by simple auditory or visual stimuli. In the estimated MEG source waveforms for auditory cortex regions of interest, auditory evoked response showed peaks at 37 and 90 ms and visual evoked response at 125 ms. The inputs to the auditory cortex were modeled through FF- and FB-type connections targeting different cortical layers using the Human Neocortical Neurosolver (HNN), which links cellular- and circuit-level mechanisms to MEG signals. HNN modeling suggested that the experimentally observed auditory response could be explained by an FF input followed by an FB input, whereas the cross-sensory visual response could be adequately explained by just an FB input. Thus, the combined MEG and HNN results support the hypothesis that cross-sensory visual input in the auditory cortex is of FB type. The results also illustrate how the dynamic patterns of the estimated MEG source activity can provide information about the characteristics of the input into a cortical area in terms of the hierarchical organization among areas.
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Affiliation(s)
- Kaisu Lankinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts 02129
- Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts 02129
- Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
| | - Mainak Jas
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts 02129
- Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
| | - Tommi Raij
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts 02129
- Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
| | - Seppo P Ahlfors
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts 02129
- Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
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5
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Lankinen K, Ahveninen J, Jas M, Raij T, Ahlfors SP. Neuronal modeling of magnetoencephalography responses in auditory cortex to auditory and visual stimuli. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.16.545371. [PMID: 37398025 PMCID: PMC10312796 DOI: 10.1101/2023.06.16.545371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Previous studies have demonstrated that auditory cortex activity can be influenced by crosssensory visual inputs. Intracortical recordings in non-human primates (NHP) have suggested a bottom-up feedforward (FF) type laminar profile for auditory evoked but top-down feedback (FB) type for cross-sensory visual evoked activity in the auditory cortex. To test whether this principle applies also to humans, we analyzed magnetoencephalography (MEG) responses from eight human subjects (six females) evoked by simple auditory or visual stimuli. In the estimated MEG source waveforms for auditory cortex region of interest, auditory evoked responses showed peaks at 37 and 90 ms and cross-sensory visual responses at 125 ms. The inputs to the auditory cortex were then modeled through FF and FB type connections targeting different cortical layers using the Human Neocortical Neurosolver (HNN), which consists of a neocortical circuit model linking the cellular- and circuit-level mechanisms to MEG. The HNN models suggested that the measured auditory response could be explained by an FF input followed by an FB input, and the crosssensory visual response by an FB input. Thus, the combined MEG and HNN results support the hypothesis that cross-sensory visual input in the auditory cortex is of FB type. The results also illustrate how the dynamic patterns of the estimated MEG/EEG source activity can provide information about the characteristics of the input into a cortical area in terms of the hierarchical organization among areas.
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Affiliation(s)
- Kaisu Lankinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
- Department of Radiology, Harvard Medical School, Boston, MA 02115
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
- Department of Radiology, Harvard Medical School, Boston, MA 02115
| | - Mainak Jas
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
- Department of Radiology, Harvard Medical School, Boston, MA 02115
| | - Tommi Raij
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
- Department of Radiology, Harvard Medical School, Boston, MA 02115
| | - Seppo P. Ahlfors
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
- Department of Radiology, Harvard Medical School, Boston, MA 02115
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6
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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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7
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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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8
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Jas M, Thorpe R, Tolley N, Bailey C, Brandt S, Caldwell B, Cheng H, Daniels D, Pujol CF, Khalil M, Kanekar S, Kohl C, Kolozsvári O, Lankinen K, Loi K, Neymotin S, Partani R, Pelah M, Rockhill A, Sherif M, Hamalainen M, Jones S. HNN-core: A Python software for cellular and circuit-level interpretation of human MEG/EEG. JOURNAL OF OPEN SOURCE SOFTWARE 2023; 8:5848. [PMID: 38939123 PMCID: PMC11210709 DOI: 10.21105/joss.05848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Abstract
HNN-core is a library for circuit and cellular level interpretation of non-invasive human magneto-/electro-encephalography (MEG/EEG) data. It is based on the Human Neocortical Neurosolver (HNN) software (Neymotin et al., 2020), a modeling tool designed to simulate multiscale neural mechanisms generating current dipoles in a localized patch of neocortex. HNN's foundation is a biophysically detailed neural network representing a canonical neocortical column containing populations of pyramidal and inhibitory neurons together with layer-specific exogenous synaptic drive (Figure 1 left). In addition to simulating network-level interactions, HNN produces the intracellular currents in the long apical dendrites of pyramidal cells across the cortical layers known to be responsible for macroscopic current dipole generation.
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Affiliation(s)
- Mainak Jas
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Ryan Thorpe
- Department of Neuroscience, Brown University, Providence, RI, USA
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - Nicholas Tolley
- Department of Neuroscience, Brown University, Providence, RI, USA
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | | | - Steven Brandt
- Department of Neuroscience, Brown University, Providence, RI, USA
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | | | - Huzi Cheng
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, USA
| | - Dylan Daniels
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | | | - Mostafa Khalil
- Department of Psychiatry and Behavioral Health, Penn State Milton S. Hershey Medical Center, Penn State College of Medicine, Hershey, PA, USA
| | | | - Carmen Kohl
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - Orsolya Kolozsvári
- Department of Psychology, University of Jyväskylä, Jyväskylä, Finland
- Jyväskylä Centre for Interdisciplinary Brain Research, University of Jyväskylä, Jyväskylä, Finland
| | - Kaisu Lankinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Kenneth Loi
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA
- Department of Molecular and Cell Biology; Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Sam Neymotin
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
| | - Rajat Partani
- Department of Computer Science and Engineering, National Institute of Technology Karnataka, Karnataka, India
| | - Mattan Pelah
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA
- Florida State University, Tallahassee, FL, USA
| | - Alex Rockhill
- Department of Human Physiology, University of Oregon, Eugene, OR, USA
| | - Mohamed Sherif
- Department of Psychiatry and Human Behavior, Brown University, Providence, RI, USA
- Rhode Island Hospital, Providence, RI, USA
| | - Matti Hamalainen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Stephanie Jones
- Department of Neuroscience, Brown University, Providence, RI, USA
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA
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9
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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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10
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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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11
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Diesburg DA, Wessel JR, Jones SR. Biophysical modeling of frontocentral ERP generation links circuit-level mechanisms of action-stopping to a behavioral race model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.25.564020. [PMID: 37961333 PMCID: PMC10634895 DOI: 10.1101/2023.10.25.564020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Human frontocentral event-related potentials (FC-ERPs) are ubiquitous neural correlates of cognition and control, but their generating multiscale mechanisms remain mostly unknown. We used the Human Neocortical Neurosolver(HNN)'s biophysical model of a canonical neocortical circuit under exogenous thalamic and cortical drive to simulate the cell and circuit mechanisms underpinning the P2, N2, and P3 features of the FC-ERP observed after Stop-Signals in the Stop-Signal task (SST). We demonstrate that a sequence of simulated external thalamocortical and cortico-cortical drives can produce the FC-ERP, similar to what has been shown for primary sensory cortices. We used this model of the FC-ERP to examine likely circuit-mechanisms underlying FC-ERP features that distinguish between successful and failed action-stopping. We also tested their adherence to the predictions of the horse-race model of the SST, with specific hypotheses motivated by theoretical links between the P3 and Stop process. These simulations revealed that a difference in P3 onset between successful and failed Stops is most likely due to a later arrival of thalamocortical drive in failed Stops, rather than, for example, a difference in effective strength of the input. In contrast, the same model predicted that early thalamocortical drives underpinning the P2 and N2 differed in both strength and timing across stopping accuracy conditions. Overall, this model generates novel testable predictions of the thalamocortical dynamics underlying FC-ERP generation during action-stopping. Moreover, it provides a detailed cellular and circuit-level interpretation that supports links between these macroscale signatures and predictions of the behavioral race model.
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Affiliation(s)
| | - Jan R. Wessel
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, USA
- Department of Neurology, Carver College of Medicine, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Stephanie R. Jones
- Department of Neuroscience, Brown University, Providence, RI, USA
- Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, RI, USA
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12
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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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13
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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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14
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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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15
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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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16
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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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17
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Johnston PR, McIntosh AR, Meltzer JA. Spectral slowing in chronic stroke reflects abnormalities in both periodic and aperiodic neural dynamics. Neuroimage Clin 2023; 37:103277. [PMID: 36495856 PMCID: PMC9758570 DOI: 10.1016/j.nicl.2022.103277] [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: 09/16/2022] [Revised: 11/21/2022] [Accepted: 11/30/2022] [Indexed: 12/03/2022]
Abstract
Decades of electrophysiological work have demonstrated the presence of "spectral slowing" in stroke patients - a prominent shift in the power spectrum towards lower frequencies, most evident in the vicinity of the lesion itself. Despite the reliability of this slowing as a marker of dysfunctional tissue across patient groups as well as animal models, it has yet to be explained in terms of the pathophysiological processes of stroke. To do so requires clear understanding of the neural dynamics that these differences represent, acknowledging the often overlooked fact that spectral power reflects more than just the amplitude of neural oscillations. To accomplish this, we used a combination of frequency domain and time domain measures to disambiguate and quantify periodic (oscillatory) and aperiodic (non-oscillatory) neural dynamics in resting state magnetoencephalography (MEG) recordings from chronic stroke patients. We found that abnormally elevated low frequency power in these patients was best explained by a steepening of the aperiodic component of the power spectrum, rather than an enhancement of low frequency oscillations, as is often assumed. However, genuine oscillatory activity at higher frequencies was also found to be abnormal, with patients showing alpha slowing and diminished oscillatory activity in the beta band. These aperiodic and periodic abnormalities were found to covary, and could be detected even in the un-lesioned hemisphere, however they were most prominent in perilesional tissue, where their magnitude was predictive of cognitive impairment. This work redefines spectral slowing as a pattern of changes involving both aperiodic and periodic neural dynamics and narrows the gap in understanding between non-invasive markers of dysfunctional tissue and disease processes responsible for altered neural dynamics.
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Affiliation(s)
- Phillip R Johnston
- Department of Psychology, University of Toronto, 100 St. George Street, Toronto, ON M5S 3G3, Canada; Rotman Research Institute, Baycrest Health Sciences, 3560 Bathurst Street, Toronto, ON M6A 2E1, Canada.
| | - Anthony R McIntosh
- Biomedical Physiology and Kinesiology, Simon Fraser University, 8888 University Drive E K9625, Burnaby, BC V5A 1S6, Canada; Institute for Neuroscience and Neurotechnology, Simon Fraser University, 8888 University Drive E K9625, Burnaby, BC V5A 1S6, Canada
| | - Jed A Meltzer
- Department of Psychology, University of Toronto, 100 St. George Street, Toronto, ON M5S 3G3, Canada; Rotman Research Institute, Baycrest Health Sciences, 3560 Bathurst Street, Toronto, ON M6A 2E1, Canada; Department of Speech-Language Pathology, University of Toronto, 500 University Avenue, Toronto, ON M5G 1V7, Canada
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18
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Herrera B, Westerberg JA, Schall MS, Maier A, Woodman GF, Schall JD, Riera JJ. Resolving the mesoscopic missing link: Biophysical modeling of EEG from cortical columns in primates. Neuroimage 2022; 263:119593. [PMID: 36031184 PMCID: PMC9968827 DOI: 10.1016/j.neuroimage.2022.119593] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 08/16/2022] [Accepted: 08/24/2022] [Indexed: 10/31/2022] Open
Abstract
Event-related potentials (ERP) are among the most widely measured indices for studying human cognition. While their timing and magnitude provide valuable insights, their usefulness is limited by our understanding of their neural generators at the circuit level. Inverse source localization offers insights into such generators, but their solutions are not unique. To address this problem, scientists have assumed the source space generating such signals comprises a set of discrete equivalent current dipoles, representing the activity of small cortical regions. Based on this notion, theoretical studies have employed forward modeling of scalp potentials to understand how changes in circuit-level dynamics translate into macroscopic ERPs. However, experimental validation is lacking because it requires in vivo measurements of intracranial brain sources. Laminar local field potentials (LFP) offer a mechanism for estimating intracranial current sources. Yet, a theoretical link between LFPs and intracranial brain sources is missing. Here, we present a forward modeling approach for estimating mesoscopic intracranial brain sources from LFPs and predict their contribution to macroscopic ERPs. We evaluate the accuracy of this LFP-based representation of brain sources utilizing synthetic laminar neurophysiological measurements and then demonstrate the power of the approach in vivo to clarify the source of a representative cognitive ERP component. To that end, LFP was measured across the cortical layers of visual area V4 in macaque monkeys performing an attention demanding task. We show that area V4 generates dipoles through layer-specific transsynaptic currents that biophysically recapitulate the ERP component through the detailed forward modeling. The constraints imposed on EEG production by this method also revealed an important dissociation between computational and biophysical contributors. As such, this approach represents an important bridge between laminar microcircuitry, through the mesoscopic activity of cortical columns to the patterns of EEG we measure at the scalp.
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Affiliation(s)
- Beatriz Herrera
- Department of Biomedical Engineering, Florida International University, Miami, FL 33174, United States
| | - Jacob A. Westerberg
- Department of Psychology, Vanderbilt Brain Institute, Vanderbilt Vision Research Center, Vanderbilt University, 111 21st Avenue South, 301 Wilson Hall, Nashville, TN 37240, United States,Corresponding author. (J.A. Westerberg)
| | - Michelle S. Schall
- Department of Psychology, Vanderbilt Brain Institute, Vanderbilt Vision Research Center, Vanderbilt University, 111 21st Avenue South, 301 Wilson Hall, Nashville, TN 37240, United States
| | - Alexander Maier
- Department of Psychology, Vanderbilt Brain Institute, Vanderbilt Vision Research Center, Vanderbilt University, 111 21st Avenue South, 301 Wilson Hall, Nashville, TN 37240, United States
| | - Geoffrey F. Woodman
- Department of Psychology, Vanderbilt Brain Institute, Vanderbilt Vision Research Center, Vanderbilt University, 111 21st Avenue South, 301 Wilson Hall, Nashville, TN 37240, United States
| | - Jeffrey D. Schall
- Centre for Vision Research, Departments of Biology and Psychology, Vision: Science to Applications Program, York University, Toronto, ON M3J 1P3, Canada
| | - Jorge J. Riera
- Department of Biomedical Engineering, Florida International University, Miami, FL 33174, United States
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