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Dura-Bernal S, Herrera B, Lupascu C, Marsh BM, Gandolfi D, Marasco A, Neymotin S, Romani A, Solinas S, Bazhenov M, Hay E, Migliore M, Reinmann M, Arkhipov A. Large-Scale Mechanistic Models of Brain Circuits with Biophysically and Morphologically Detailed Neurons. J Neurosci 2024; 44:e1236242024. [PMID: 39358017 PMCID: PMC11450527 DOI: 10.1523/jneurosci.1236-24.2024] [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: 06/28/2024] [Revised: 07/09/2024] [Accepted: 07/31/2024] [Indexed: 10/04/2024] Open
Abstract
Understanding the brain requires studying its multiscale interactions from molecules to networks. The increasing availability of large-scale datasets detailing brain circuit composition, connectivity, and activity is transforming neuroscience. However, integrating and interpreting this data remains challenging. Concurrently, advances in supercomputing and sophisticated modeling tools now enable the development of highly detailed, large-scale biophysical circuit models. These mechanistic multiscale models offer a method to systematically integrate experimental data, facilitating investigations into brain structure, function, and disease. This review, based on a Society for Neuroscience 2024 MiniSymposium, aims to disseminate recent advances in large-scale mechanistic modeling to the broader community. It highlights (1) examples of current models for various brain regions developed through experimental data integration; (2) their predictive capabilities regarding cellular and circuit mechanisms underlying experimental recordings (e.g., membrane voltage, spikes, local-field potential, electroencephalography/magnetoencephalography) and brain function; and (3) their use in simulating biomarkers for brain diseases like epilepsy, depression, schizophrenia, and Parkinson's, aiding in understanding their biophysical underpinnings and developing novel treatments. The review showcases state-of-the-art models covering hippocampus, somatosensory, visual, motor, auditory cortical, and thalamic circuits across species. These models predict neural activity at multiple scales and provide insights into the biophysical mechanisms underlying sensation, motor behavior, brain signals, neural coding, disease, pharmacological interventions, and neural stimulation. Collaboration with experimental neuroscientists and clinicians is essential for the development and validation of these models, particularly as datasets grow. Hence, this review aims to foster interest in detailed brain circuit models, leading to cross-disciplinary collaborations that accelerate brain research.
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Affiliation(s)
- Salvador Dura-Bernal
- State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, New York 11203
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York 10962
| | | | - Carmen Lupascu
- Institute of Biophysics, National Research Council/Human Brain Project, Palermo 90146, Italy
| | - Brianna M Marsh
- University of California San Diego, La Jolla, California 92093
| | - Daniela Gandolfi
- Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, Modena 41125, Italy
| | | | - Samuel Neymotin
- Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York 10962
- School of Medicine, New York University, New York 10012
| | - Armando Romani
- Swiss Federal Institute of Technology Lausanne (EPFL)/Blue Brain Project, Lausanne 1015, Switzerland
| | | | - Maxim Bazhenov
- University of California San Diego, La Jolla, California 92093
| | - Etay Hay
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario M5T 1R8, Canada
- University of Toronto, Toronto, Ontario M5S 1A1, Canada
| | - Michele Migliore
- Institute of Biophysics, National Research Council/Human Brain Project, Palermo 90146, Italy
| | - Michael Reinmann
- Swiss Federal Institute of Technology Lausanne (EPFL)/Blue Brain Project, Lausanne 1015, Switzerland
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2
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Brancaccio A, Tabarelli D, Zazio A, Bertazzoli G, Metsomaa J, Ziemann U, Bortoletto M, Belardinelli P. Towards the definition of a standard in TMS-EEG data preprocessing. Neuroimage 2024; 301:120874. [PMID: 39341472 DOI: 10.1016/j.neuroimage.2024.120874] [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: 05/13/2024] [Revised: 09/25/2024] [Accepted: 09/26/2024] [Indexed: 10/01/2024] Open
Abstract
Combining Non-Invasive Brain Stimulation (NIBS) techniques with the recording of brain electrophysiological activity is an increasingly widespread approach in neuroscience. Particularly successful has been the simultaneous combination of Transcranial Magnetic Stimulation (TMS) and Electroencephalography (EEG). Unfortunately, the strong magnetic pulse required to effectively interact with brain activity inevitably induces artifacts in the concurrent EEG acquisition. Therefore, a careful but aggressive pre-processing is required to efficiently remove artifacts. Unfortunately, as already reported in the literature, different preprocessing approaches can introduce variability in the results. Here we aim at characterizing the three main TMS-EEG preprocessing pipelines currently available, namely ARTIST (Wu et al., 2018), TESA (Rogasch et al., 2017) and SOUND/SSP-SIR (Mutanen et al., 2018, 2016), providing an insight to researchers who need to choose between different approaches. Differently from previous works, we tested the pipelines using a synthetic TMS-EEG signal with a known ground-truth (the artifacts-free to-be-reconstructed signal). In this way, it was possible to assess the reliability of each pipeline precisely and quantitatively, providing a more robust reference for future research. In summary, we found that all pipelines performed well, but with differences in terms of the spatio-temporal precision of the ground-truth reconstruction. Crucially, the three pipelines impacted differently on the inter-trial variability, with ARTIST introducing inter-trial variability not already intrinsic to the ground-truth signal.
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Affiliation(s)
- A Brancaccio
- Center for Mind/Brain Sciences-CIMeC, University of Trento, I-38123 Trento, Italy.
| | - D Tabarelli
- Center for Mind/Brain Sciences-CIMeC, University of Trento, I-38123 Trento, Italy
| | - A Zazio
- Neurophysiology lab, IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - G Bertazzoli
- Neurophysiology lab, IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - J Metsomaa
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
| | - U Ziemann
- Department of Neurology & Stroke, University of Tübingen, Germany; Hertie-Institute for Clinical Brain Research, University of Tübingen, Germany
| | - M Bortoletto
- Neurophysiology lab, IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - P Belardinelli
- Center for Mind/Brain Sciences-CIMeC, University of Trento, I-38123 Trento, Italy; Department of Neurology & Stroke, University of Tübingen, Germany; Hertie-Institute for Clinical Brain Research, University of Tübingen, Germany
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3
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Bernardo D, Xie X, Verma P, Kim J, Liu V, Numis AL, Wu Y, Glass HC, Yap PT, Nagarajan SS, Raj A. Simulation-based Inference of Developmental EEG Maturation with the Spectral Graph Model. ARXIV 2024:arXiv:2405.02524v3. [PMID: 39040639 PMCID: PMC11261974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
The spectral content of macroscopic neural activity evolves throughout development, yet how this maturation relates to underlying brain network formation and dynamics remains unknown. Here, we assess the developmental maturation of electroencephalogram spectra via Bayesian model inversion of the spectral graph model, a parsimonious whole-brain model of spatiospectral neural activity derived from linearized neural field models coupled by the structural connectome. Simulation-based inference was used to estimate age-varying spectral graph model parameter posterior distributions from electroencephalogram spectra spanning the developmental period. This model-fitting approach accurately captures observed developmental electroencephalogram spectral maturation via a neurobiologically consistent progression of key neural parameters: long-range coupling, axonal conduction speed, and excitatory:inhibitory balance. These results suggest that the spectral maturation of macroscopic neural activity observed during typical development is supported by age-dependent functional adaptations in localized neural dynamics and their long-range coupling across the macroscopic structural network.
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Affiliation(s)
- Danilo Bernardo
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Xihe Xie
- Department of Neuroscience, Weill Cornell Medicine, New York, NY, USA
| | - Parul Verma
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Jonathan Kim
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Virginia Liu
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Adam L. Numis
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Ye Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Hannah C. Glass
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Srikantan S. Nagarajan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
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4
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Johnsen KA, Cruzado NA, Menard ZC, Willats AA, Charles AS, Markowitz JE, Rozell CJ. Bridging model and experiment in systems neuroscience with Cleo: the Closed-Loop, Electrophysiology, and Optophysiology simulation testbed. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.01.27.525963. [PMID: 39026717 PMCID: PMC11257437 DOI: 10.1101/2023.01.27.525963] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Systems neuroscience has experienced an explosion of new tools for reading and writing neural activity, enabling exciting new experiments such as all-optical or closed-loop control that effect powerful causal interventions. At the same time, improved computational models are capable of reproducing behavior and neural activity with increasing fidelity. Unfortunately, these advances have drastically increased the complexity of integrating different lines of research, resulting in the missed opportunities and untapped potential of suboptimal experiments. Experiment simulation can help bridge this gap, allowing model and experiment to better inform each other by providing a low-cost testbed for experiment design, model validation, and methods engineering. Specifically, this can be achieved by incorporating the simulation of the experimental interface into our models, but no existing tool integrates optogenetics, two-photon calcium imaging, electrode recording, and flexible closed-loop processing with neural population simulations. To address this need, we have developed Cleo: the Closed-Loop, Electrophysiology, and Optophysiology experiment simulation testbed. Cleo is a Python package enabling injection of recording and stimulation devices as well as closed-loop control with realistic latency into a Brian spiking neural network model. It is the only publicly available tool currently supporting two-photon and multi-opsin/wavelength optogenetics. To facilitate adoption and extension by the community, Cleo is open-source, modular, tested, and documented, and can export results to various data formats. Here we describe the design and features of Cleo, validate output of individual components and integrated experiments, and demonstrate its utility for advancing optogenetic techniques in prospective experiments using previously published systems neuroscience models.
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Affiliation(s)
- Kyle A. Johnsen
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | | | - Zachary C. Menard
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Adam A. Willats
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Adam S. Charles
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Jeffrey E. Markowitz
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
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5
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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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6
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Zhao Z, Shirinpour S, Tran H, Wischnewski M, Opitz A. intensity- and frequency-specific effects of transcranial alternating current stimulation are explained by network dynamics. J Neural Eng 2024; 21:026024. [PMID: 38530297 DOI: 10.1088/1741-2552/ad37d9] [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: 09/19/2023] [Accepted: 03/26/2024] [Indexed: 03/27/2024]
Abstract
Objective. Transcranial alternating current stimulation (tACS) can be used to non-invasively entrain neural activity and thereby cause changes in local neural oscillatory power. Despite its increased use in cognitive and clinical neuroscience, the fundamental mechanisms of tACS are still not fully understood.Approach. We developed a computational neuronal network model of two-compartment pyramidal neurons (PY) and inhibitory interneurons, which mimic the local cortical circuits. We modeled tACS with electric field strengths that are achievable in human applications. We then simulated intrinsic network activity and measured neural entrainment to investigate how tACS modulates ongoing endogenous oscillations.Main results. The intensity-specific effects of tACS are non-linear. At low intensities (<0.3 mV mm-1), tACS desynchronizes neural firing relative to the endogenous oscillations. At higher intensities (>0.3 mV mm-1), neurons are entrained to the exogenous electric field. We then further explore the stimulation parameter space and find that the entrainment of ongoing cortical oscillations also depends on stimulation frequency by following an Arnold tongue. Moreover, neuronal networks can amplify the tACS-induced entrainment via synaptic coupling and network effects. Our model shows that PY are directly entrained by the exogenous electric field and drive the inhibitory neurons.Significance. The results presented in this study provide a mechanistic framework for understanding the intensity- and frequency-specific effects of oscillating electric fields on neuronal networks. This is crucial for rational parameter selection for tACS in cognitive studies and clinical applications.
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Affiliation(s)
- Zhihe Zhao
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States of America
| | - Sina Shirinpour
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States of America
| | - Harry Tran
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States of America
| | - Miles Wischnewski
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States of America
| | - Alexander Opitz
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States of America
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Guet-McCreight A, Chameh HM, Mazza F, Prevot TD, Valiante TA, Sibille E, Hay E. In-silico testing of new pharmacology for restoring inhibition and human cortical function in depression. Commun Biol 2024; 7:225. [PMID: 38396202 PMCID: PMC10891083 DOI: 10.1038/s42003-024-05907-1] [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: 08/30/2023] [Accepted: 02/09/2024] [Indexed: 02/25/2024] Open
Abstract
Reduced inhibition by somatostatin-expressing interneurons is associated with depression. Administration of positive allosteric modulators of α5 subunit-containing GABAA receptor (α5-PAM) that selectively target this lost inhibition exhibit antidepressant and pro-cognitive effects in rodent models of chronic stress. However, the functional effects of α5-PAM on the human brain in vivo are unknown, and currently cannot be assessed experimentally. We modeled the effects of α5-PAM on tonic inhibition as measured in human neurons, and tested in silico α5-PAM effects on detailed models of human cortical microcircuits in health and depression. We found that α5-PAM effectively recovered impaired cortical processing as quantified by stimulus detection metrics, and also recovered the power spectral density profile of the microcircuit EEG signals. We performed an α5-PAM dose-response and identified simulated EEG biomarker candidates. Our results serve to de-risk and facilitate α5-PAM translation and provide biomarkers in non-invasive brain signals for monitoring target engagement and drug efficacy.
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Affiliation(s)
- Alexandre Guet-McCreight
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
| | | | - Frank Mazza
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Thomas D Prevot
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Taufik A Valiante
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Department of Surgery, University of Toronto, Toronto, ON, Canada
- Center for Advancing Neurotechnological Innovation to Application, Toronto, ON, Canada
- Max Planck-University of Toronto Center for Neural Science and Technology, Toronto, ON, Canada
| | - Etienne Sibille
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
| | - Etay Hay
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Department of Physiology, University of Toronto, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
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8
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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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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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10
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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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11
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Miyakoshi M, Kim H, Nakanishi M, Palmer J, Kanayama N. One out of ten independent components shows flipped polarity with poorer data quality: EEG database study. Hum Brain Mapp 2024; 45:e26540. [PMID: 38069570 PMCID: PMC10789196 DOI: 10.1002/hbm.26540] [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: 07/06/2023] [Revised: 10/30/2023] [Accepted: 11/07/2023] [Indexed: 01/16/2024] Open
Abstract
Independent component analysis (ICA) is widely used today for scalp-recorded EEG analysis. One of the limitations of ICA-based analysis is polarity indeterminacy. It is not easy to find detailed documentations that explains engineering solutions of how the polarity indeterminacy is addressed in a given implementation. We investigated how it is implemented in the case of EEGLAB and also the relation between the outcome of the polarity determination and classification of independent components (ICs) in terms of the estimated nature of the sources (brain, muscle, eye, etc.) using an open database of n = 212 EEG dataset of resting state recordings. We found that (1) about 91% of ICs showed positive-dominant IC scalp topographies; (2) positive-dominant ICs were more associated with brain-originated signals; (3) positive-dominant ICs showed more radial (peaked at 10-30 degrees deviations from the radial axis) dipolar projection pattern with less residual variance from fitting the equivalent current dipole. In conclusion, using the EEGLAB's default ICA algorithm, one out of 10 ICs results in flipping its polarity to negative, which is associated with non-radial dipole orientation with higher residual variance. Thus, we determined EEGLAB biases toward positive polarity in decomposing high-quality brain ICs.
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Affiliation(s)
- Makoto Miyakoshi
- Division of Child and Adolescent PsychiatryCincinnati Children's Hospital Medical CenterCincinnatiOhioUSA
- Department of MedicineUniversity of Cincinnati College of MedicineCincinnatiOhioUSA
- Swartz Center for Computational NeuroscienceInstitute for Neural Computation, University of California San DiegoLa JollaCaliforniaUSA
| | - Hyeonseok Kim
- Swartz Center for Computational NeuroscienceInstitute for Neural Computation, University of California San DiegoLa JollaCaliforniaUSA
| | - Masaki Nakanishi
- Swartz Center for Computational NeuroscienceInstitute for Neural Computation, University of California San DiegoLa JollaCaliforniaUSA
| | - Jason Palmer
- School of Mathematical and Data SciencesWest Virginia UniversityMorgantownWest VirginiaUSA
| | - Noriaki Kanayama
- Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST)TokyoJapan
- Center for Brain, Mind and KANSEI Sciences ResearchHiroshima UniversityTokyoJapan
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12
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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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13
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Dura-Bernal S, Griffith EY, Barczak A, O'Connell MN, McGinnis T, Moreira JVS, Schroeder CE, Lytton WW, Lakatos P, Neymotin SA. Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics. Cell Rep 2023; 42:113378. [PMID: 37925640 PMCID: PMC10727489 DOI: 10.1016/j.celrep.2023.113378] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 09/05/2023] [Accepted: 10/19/2023] [Indexed: 11/07/2023] Open
Abstract
We developed a detailed model of macaque auditory thalamocortical circuits, including primary auditory cortex (A1), medial geniculate body (MGB), and thalamic reticular nucleus, utilizing the NEURON simulator and NetPyNE tool. The A1 model simulates a cortical column with over 12,000 neurons and 25 million synapses, incorporating data on cell-type-specific neuron densities, morphology, and connectivity across six cortical layers. It is reciprocally connected to the MGB thalamus, which includes interneurons and core and matrix-layer-specific projections to A1. The model simulates multiscale measures, including physiological firing rates, local field potentials (LFPs), current source densities (CSDs), and electroencephalography (EEG) signals. Laminar CSD patterns, during spontaneous activity and in response to broadband noise stimulus trains, mirror experimental findings. Physiological oscillations emerge spontaneously across frequency bands comparable to those recorded in vivo. We elucidate population-specific contributions to observed oscillation events and relate them to firing and presynaptic input patterns. The model offers a quantitative theoretical framework to integrate and interpret experimental data and predict its underlying cellular and circuit mechanisms.
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Affiliation(s)
- Salvador Dura-Bernal
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
| | - Erica Y Griffith
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
| | - Annamaria Barczak
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Monica N O'Connell
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Tammy McGinnis
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Joao V S Moreira
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA
| | - Charles E Schroeder
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Departments of Psychiatry and Neurology, Columbia University Medical Center, New York, NY, USA
| | - William W Lytton
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Kings County Hospital Center, Brooklyn, NY, USA
| | - Peter Lakatos
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
| | - Samuel A Neymotin
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department Psychiatry, NYU Grossman School of Medicine, New York, NY, USA.
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14
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Chien VSC, Wang P, Maess B, Fishman Y, Knösche TR. Laminar neural dynamics of auditory evoked responses: Computational modeling of local field potentials in auditory cortex of non-human primates. Neuroimage 2023; 281:120364. [PMID: 37683810 DOI: 10.1016/j.neuroimage.2023.120364] [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: 01/09/2023] [Revised: 08/15/2023] [Accepted: 09/04/2023] [Indexed: 09/10/2023] Open
Abstract
Evoked neural responses to sensory stimuli have been extensively investigated in humans and animal models both to enhance our understanding of brain function and to aid in clinical diagnosis of neurological and neuropsychiatric conditions. Recording and imaging techniques such as electroencephalography (EEG), magnetoencephalography (MEG), local field potentials (LFPs), and calcium imaging provide complementary information about different aspects of brain activity at different spatial and temporal scales. Modeling and simulations provide a way to integrate these different types of information to clarify underlying neural mechanisms. In this study, we aimed to shed light on the neural dynamics underlying auditory evoked responses by fitting a rate-based model to LFPs recorded via multi-contact electrodes which simultaneously sampled neural activity across cortical laminae. Recordings included neural population responses to best-frequency (BF) and non-BF tones at four representative sites in primary auditory cortex (A1) of awake monkeys. The model considered major neural populations of excitatory, parvalbumin-expressing (PV), and somatostatin-expressing (SOM) neurons across layers 2/3, 4, and 5/6. Unknown parameters, including the connection strength between the populations, were fitted to the data. Our results revealed similar population dynamics, fitted model parameters, predicted equivalent current dipoles (ECD), tuning curves, and lateral inhibition profiles across recording sites and animals, in spite of quite different extracellular current distributions. We found that PV firing rates were higher in BF than in non-BF responses, mainly due to different strengths of tonotopic thalamic input, whereas SOM firing rates were higher in non-BF than in BF responses due to lateral inhibition. In conclusion, we demonstrate the feasibility of the model-fitting approach in identifying the contributions of cell-type specific population activity to stimulus-evoked LFPs across cortical laminae, providing a foundation for further investigations into the dynamics of neural circuits underlying cortical sensory processing.
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Affiliation(s)
- Vincent S C Chien
- Max Planck Institute for Human Cognitive and Brain Sciences, Germany; Institute of Computer Science of the Czech Academy of Sciences, Czech Republic
| | - Peng Wang
- Max Planck Institute for Human Cognitive and Brain Sciences, Germany; Institute of Psychology, University of Greifswald, Germany; Institute of Psychology, University of Regensburg, Germany
| | - Burkhard Maess
- Max Planck Institute for Human Cognitive and Brain Sciences, Germany
| | - Yonatan Fishman
- Departments of Neurology and Neuroscience, Albert Einstein College of Medicine, USA
| | - Thomas R Knösche
- Max Planck Institute for Human Cognitive and Brain Sciences, Germany.
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15
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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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16
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Munn BR, Müller EJ, Medel V, Naismith SL, Lizier JT, Sanders RD, Shine JM. Neuronal connected burst cascades bridge macroscale adaptive signatures across arousal states. Nat Commun 2023; 14:6846. [PMID: 37891167 PMCID: PMC10611774 DOI: 10.1038/s41467-023-42465-2] [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: 11/29/2022] [Accepted: 10/11/2023] [Indexed: 10/29/2023] Open
Abstract
The human brain displays a rich repertoire of states that emerge from the microscopic interactions of cortical and subcortical neurons. Difficulties inherent within large-scale simultaneous neuronal recording limit our ability to link biophysical processes at the microscale to emergent macroscopic brain states. Here we introduce a microscale biophysical network model of layer-5 pyramidal neurons that display graded coarse-sampled dynamics matching those observed in macroscale electrophysiological recordings from macaques and humans. We invert our model to identify the neuronal spike and burst dynamics that differentiate unconscious, dreaming, and awake arousal states and provide insights into their functional signatures. We further show that neuromodulatory arousal can mediate different modes of neuronal dynamics around a low-dimensional energy landscape, which in turn changes the response of the model to external stimuli. Our results highlight the promise of multiscale modelling to bridge theories of consciousness across spatiotemporal scales.
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Affiliation(s)
- Brandon R Munn
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia.
- Complex Systems, School of Physics, University of Sydney, Sydney, NSW, Australia.
- Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia.
| | - Eli J Müller
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
- Complex Systems, School of Physics, University of Sydney, Sydney, NSW, Australia
- Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia
| | - Vicente Medel
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Chile
| | - Sharon L Naismith
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
- School of Psychology, Faculty of Science & Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
| | - Joseph T Lizier
- Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Robert D Sanders
- Department of Anaesthetics & Institute of Academic Surgery, Royal Prince Alfred Hospital, Camperdown, Australia
- Central Clinical School & NHMRC Clinical Trials Centre, The University of Sydney, Sydney, NSW, Australia
| | - James M Shine
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
- Complex Systems, School of Physics, University of Sydney, Sydney, NSW, Australia
- Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia
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17
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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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18
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Cho S, Choi JH. A guide towards optimal detection of transient oscillatory bursts with unknown parameters. J Neural Eng 2023; 20:046007. [PMID: 37339619 DOI: 10.1088/1741-2552/acdffd] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 06/20/2023] [Indexed: 06/22/2023]
Abstract
Objectives. Recent event-based analyses of transient neural activities have characterized the oscillatory bursts as a neural signature that bridges dynamic neural states to cognition and behaviors. Following this insight, our study aimed to (1) compare the efficacy of common burst detection algorithms under varying signal-to-noise ratios and event durations using synthetic signals and (2) establish a strategic guideline for selecting the optimal algorithm for real datasets with undefined properties.Approach.We tested the robustness of burst detection algorithms using a simulation dataset comprising bursts of multiple frequencies. To systematically assess their performance, we used a metric called 'detection confidence', quantifying classification accuracy and temporal precision in a balanced manner. Given that burst properties in empirical data are often unknown in advance, we then proposed a selection rule to identify an optimal algorithm for a given dataset and validated its application on local field potentials of basolateral amygdala recorded from male mice (n=8) exposed to a natural threat.Main Results.Our simulation-based evaluation demonstrated that burst detection is contingent upon event duration, whereas accurately pinpointing burst onsets is more susceptible to noise level. For real data, the algorithm chosen based on the selection rule exhibited superior detection and temporal accuracy, although its statistical significance differed across frequency bands. Notably, the algorithm chosen by human visual screening differed from the one recommended by the rule, implying a potential misalignment between human priors and mathematical assumptions of the algorithms.Significance.Therefore, our findings underscore that the precise detection of transient bursts is fundamentally influenced by the chosen algorithm. The proposed algorithm-selection rule suggests a potentially viable solution, while also emphasizing the inherent limitations originating from algorithmic design and volatile performances across datasets. Consequently, this study cautions against relying solely on heuristic-based approaches, advocating for a careful algorithm selection in burst detection studies.
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Affiliation(s)
- SungJun Cho
- Center for Neuroscience, Korea Institute of Science and Technology, Hwarang-ro 14-gil 5, Seongbuk-gu, Seoul 02792, Republic of Korea
- Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford OX3 7JX, United Kingdom
| | - Jee Hyun Choi
- Center for Neuroscience, Korea Institute of Science and Technology, Hwarang-ro 14-gil 5, Seongbuk-gu, Seoul 02792, Republic of Korea
- Department of Neural Sciences, University of Science and Technology, 217, Gajeong-ro, Yuseong-gu, Daejeon 34113, Republic of Korea
- Department of Physics and Center for Theoretical Physics, Seoul National University, Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
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19
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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: 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: 01/29/2023] [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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20
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Fernandez Pujol C, Blundon EG, Dykstra AR. Laminar specificity of the auditory perceptual awareness negativity: A biophysical modeling study. PLoS Comput Biol 2023; 19:e1011003. [PMID: 37384802 PMCID: PMC10337981 DOI: 10.1371/journal.pcbi.1011003] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 07/12/2023] [Accepted: 06/17/2023] [Indexed: 07/01/2023] Open
Abstract
How perception of sensory stimuli emerges from brain activity is a fundamental question of neuroscience. To date, two disparate lines of research have examined this question. On one hand, human neuroimaging studies have helped us understand the large-scale brain dynamics of perception. On the other hand, work in animal models (mice, typically) has led to fundamental insight into the micro-scale neural circuits underlying perception. However, translating such fundamental insight from animal models to humans has been challenging. Here, using biophysical modeling, we show that the auditory awareness negativity (AAN), an evoked response associated with perception of target sounds in noise, can be accounted for by synaptic input to the supragranular layers of auditory cortex (AC) that is present when target sounds are heard but absent when they are missed. This additional input likely arises from cortico-cortical feedback and/or non-lemniscal thalamic projections and targets the apical dendrites of layer-5 (L5) pyramidal neurons. In turn, this leads to increased local field potential activity, increased spiking activity in L5 pyramidal neurons, and the AAN. The results are consistent with current cellular models of conscious processing and help bridge the gap between the macro and micro levels of perception-related brain activity.
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Affiliation(s)
- Carolina Fernandez Pujol
- Department of Biomedical Engineering, University of Miami, Coral Gables, Florida, United States of America
| | - Elizabeth G. Blundon
- Department of Biomedical Engineering, University of Miami, Coral Gables, Florida, United States of America
| | - Andrew R. Dykstra
- Department of Biomedical Engineering, University of Miami, Coral Gables, Florida, United States of America
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21
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Thio BJ, Grill WM. Relative Contributions of Different Neural Sources to the EEG. Neuroimage 2023:120179. [PMID: 37225111 DOI: 10.1016/j.neuroimage.2023.120179] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 05/04/2023] [Accepted: 05/18/2023] [Indexed: 05/26/2023] Open
Abstract
Dogma dictates that the EEG signal is generated by postsynaptic currents (PSCs) because there are an enormous number of synapses in the brain, and PSCs have relatively long durations. However, PSCs are not the only potential source of electric fields in the brain. Action potentials, afterpolarizations, and presynaptic activity can also generate electric fields. Experimentally it is exceedingly difficult to delineate the contributions of different sources because they are casually linked. However, using computational modeling, we can interrogate the relative contributions of different neural elements to the EEG. We used a library of neuron models with morphologically realistic axonal arbors to quantify the relative contributions of PSCs, action potentials, and presynaptic activity to the EEG signal. Consistent with prior assertions, PSCs were the largest contributor to the EEG, but action potentials and afterpolarizations can also make appreciable contributions. For a population of neurons generating simultaneous PSCs and action potentials, we found that the action potentials accounted for up to 20% of the source strength while PSCs accounted for the other 80% and presynaptic activity negligibly contributed. Additionally, L5 PCs generated the largest PSC and action potential signals indicating that they the dominant EEG signal generator. Further, action potentials and afterpolarizations were sufficient to generate physiological oscillations, indicating that they are valid source contributors to the EEG. The EEG emerges from a combination of multiple different source, and, while PSCs are the largest contributor, other sources are non-negligible and should be included in modeling, analysis and interpretation of the EEG.
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Affiliation(s)
- Brandon J Thio
- Department of Biomedical Engineering, Duke University, Room 1427, Fitzpatrick CIEMAS, 101 Science Drive, Campus Box 90281, Durham, NC 27708
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Room 1427, Fitzpatrick CIEMAS, 101 Science Drive, Campus Box 90281, Durham, NC 27708; Duke University, Department of Electrical and Computer Engineering, Durham, NC, USA; Duke University School of Medicine, Department of Neurobiology, Durham, NC, USA; Duke University School of Medicine, Department of Neurosurgery, Durham, NC, USA.
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22
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Zhao Z, Shirinpour S, Tran H, Wischnewski M, Opitz A. Intensity- and frequency-specific effects of transcranial alternating current stimulation are explained by network dynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.19.541493. [PMID: 37293105 PMCID: PMC10245793 DOI: 10.1101/2023.05.19.541493] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Transcranial alternating current stimulation (tACS) can be used to non-invasively entrain neural activity, and thereby cause changes in local neural oscillatory power. Despite an increased use in cognitive and clinical neuroscience, the fundamental mechanisms of tACS are still not fully understood. Here, we develop a computational neuronal network model of two-compartment pyramidal neurons and inhibitory interneurons which mimic the local cortical circuits. We model tACS with electric field strengths that are achievable in human applications. We then simulate intrinsic network activity and measure neural entrainment to investigate how tACS modulates ongoing endogenous oscillations. First, we show that intensity-specific effects of tACS are non-linear. At low intensities (<0.3 mV/mm), tACS desynchronizes neural firing relative to the endogenous oscillations. At higher intensities (>0.3 mV/mm), neurons are entrained to the exogenous electric field. We then further explore the stimulation parameter space and find that entrainment of ongoing cortical oscillations also depends on frequency by following an Arnold tongue. Moreover, neuronal networks can amplify the tACS induced entrainment via excitation-inhibition balance. Our model shows that pyramidal neurons are directly entrained by the exogenous electric field and drive the inhibitory neurons. Our findings can thus provide a mechanistic framework for understanding the intensity- and frequency- specific effects of oscillating electric fields on neuronal networks. This is crucial for rational parameters selection for tACS in cognitive studies and clinical applications.
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Affiliation(s)
- Z. Zhao
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA
| | - S. Shirinpour
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA
| | - H. Tran
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA
| | - M. Wischnewski
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA
| | - A. Opitz
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA
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23
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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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24
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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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25
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Zich C, Quinn AJ, Bonaiuto JJ, O'Neill G, Mardell LC, Ward NS, Bestmann S. Spatiotemporal organisation of human sensorimotor beta burst activity. eLife 2023; 12:e80160. [PMID: 36961500 PMCID: PMC10110262 DOI: 10.7554/elife.80160] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 03/23/2023] [Indexed: 03/25/2023] Open
Abstract
Beta oscillations in human sensorimotor cortex are hallmark signatures of healthy and pathological movement. In single trials, beta oscillations include bursts of intermittent, transient periods of high-power activity. These burst events have been linked to a range of sensory and motor processes, but their precise spatial, spectral, and temporal structure remains unclear. Specifically, a role for beta burst activity in information coding and communication suggests spatiotemporal patterns, or travelling wave activity, along specific anatomical gradients. We here show in human magnetoencephalography recordings that burst activity in sensorimotor cortex occurs in planar spatiotemporal wave-like patterns that dominate along two axes either parallel or perpendicular to the central sulcus. Moreover, we find that the two propagation directions are characterised by distinct anatomical and physiological features. Finally, our results suggest that sensorimotor beta bursts occurring before and after a movement can be distinguished by their anatomical, spectral, and spatiotemporal characteristics, indicating distinct functional roles.
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Affiliation(s)
- Catharina Zich
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of NeurologyLondonUnited Kingdom
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of OxfordOxfordUnited Kingdom
- Medical Research Council Brain Network Dynamics Unit, 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
| | - James J Bonaiuto
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229BronFrance
- Université Claude Bernard Lyon 1, Université de LyonLyonFrance
| | - George O'Neill
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of NeurologyLondonUnited Kingdom
| | - Lydia C Mardell
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of NeurologyLondonUnited Kingdom
| | - Nick S Ward
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of NeurologyLondonUnited Kingdom
| | - Sven Bestmann
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of NeurologyLondonUnited Kingdom
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of NeurologyLondonUnited Kingdom
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26
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Developmental trajectory of transmission speed in the human brain. Nat Neurosci 2023; 26:537-541. [PMID: 36894655 PMCID: PMC10076215 DOI: 10.1038/s41593-023-01272-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 02/09/2023] [Indexed: 03/11/2023]
Abstract
The structure of the human connectome develops from childhood throughout adolescence to middle age, but how these structural changes affect the speed of neuronal signaling is not well described. In 74 subjects, we measured the latency of cortico-cortical evoked responses across association and U-fibers and calculated their corresponding transmission speeds. Decreases in conduction delays until at least 30 years show that the speed of neuronal communication develops well into adulthood.
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27
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Pujol CF, Blundon EG, Dykstra AR. Laminar Specificity of the Auditory Perceptual Awareness Negativity: A Biophysical Modeling Study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.06.531459. [PMID: 36945469 PMCID: PMC10028885 DOI: 10.1101/2023.03.06.531459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/10/2023]
Abstract
How perception of sensory stimuli emerges from brain activity is a fundamental question of neuroscience. To date, two disparate lines of research have examined this question. On one hand, human neuroimaging studies have helped us understand the large-scale brain dynamics of perception. On the other hand, work in animal models (mice, typically) has led to fundamental insight into the micro-scale neural circuits underlying perception. However, translating such fundamental insight from animal models to humans has been challenging. Here, using biophysical modeling, we show that the auditory awareness negativity (AAN), an evoked response associated with perception of target sounds in noise, can be accounted for by synaptic input to the supragranular layers of auditory cortex (AC) that is present when target sounds are heard but absent when they are missed. This additional input likely arises from cortico-cortical feedback and/or non-lemniscal thalamic projections and targets the apical dendrites of layer-V pyramidal neurons (PNs). In turn, this leads to increased local field potential activity, increased spiking activity in layer-V PNs, and the AAN. The results are consistent with current cellular models of conscious processing and help bridge the gap between the macro and micro levels of perception-related brain activity. Author Summary To date, our understanding of the brain basis of conscious perception has mostly been restricted to large-scale, network-level activity that can be measured non-invasively in human subjects. However, we lack understanding of how such network-level activity is supported by individual neurons and neural circuits. This is at least partially because conscious perception is difficult to study in experimental animals, where such detailed characterization of neural activity is possible. To address this gap, we used biophysical modeling to gain circuit-level insight into an auditory brain response known as the auditory awareness negativity (AAN). This response can be recorded non-invasively in humans and is associated with perceptual awareness of sounds of interest. Our model shows that the AAN likely arises from specific cortical layers and cell types. These data help bridge the gap between circuit- and network-level theories of consciousness, and could lead to new, targeted treatments for perceptual dysfunction and disorders of consciousness.
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Affiliation(s)
| | - Elizabeth G. Blundon
- Department of Biomedical Engineering, University of Miami, Coral Gables, FL, United States
- Present address: Department of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Andrew R. Dykstra
- Department of Biomedical Engineering, University of Miami, Coral Gables, FL, United States
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28
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Gandolfi D, Mapelli J, Solinas SMG, Triebkorn P, D'Angelo E, Jirsa V, Migliore M. Full-scale scaffold model of the human hippocampus CA1 area. NATURE COMPUTATIONAL SCIENCE 2023; 3:264-276. [PMID: 38177882 PMCID: PMC10766517 DOI: 10.1038/s43588-023-00417-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 02/09/2023] [Indexed: 01/06/2024]
Abstract
The increasing availability of quantitative data on the human brain is opening new avenues to study neural function and dysfunction, thus bringing us closer and closer to the implementation of digital twin applications for personalized medicine. Here we provide a resource to the neuroscience community: a computational method to generate full-scale scaffold model of human brain regions starting from microscopy images. We have benchmarked the method to reconstruct the CA1 region of a right human hippocampus, which accounts for about half of the entire right hippocampal formation. Together with 3D soma positioning we provide a connectivity matrix generated using a morpho-anatomical connection strategy based on axonal and dendritic probability density functions accounting for morphological properties of hippocampal neurons. The data and algorithms are supplied in a ready-to-use format, suited to implement computational models at different scales and detail.
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Affiliation(s)
- Daniela Gandolfi
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy.
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.
| | - Jonathan Mapelli
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy.
- Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Modena, Italy.
| | - Sergio M G Solinas
- Department of Biomedical Science, University of Sassari, Sassari, Italy
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Paul Triebkorn
- Institut de Neurosciences des Systèmes, Aix-Marseille University, Marseille, France
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Pavia, Italy
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes, Aix-Marseille University, Marseille, France
| | - Michele Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy.
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29
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Cobb EAW, Petroccione MA, Scimemi A. NRN-EZ: an application to streamline biophysical modeling of synaptic integration using NEURON. Sci Rep 2023; 13:464. [PMID: 36627356 PMCID: PMC9832141 DOI: 10.1038/s41598-022-27302-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 12/29/2022] [Indexed: 01/12/2023] Open
Abstract
One of the fundamental goals in neuroscience is to determine how the brain processes information and ultimately controls the execution of complex behaviors. Over the past four decades, there has been a steady growth in our knowledge of the morphological and functional diversity of neurons, the building blocks of the brain. These cells clearly differ not only for their anatomy and ion channel distribution, but also for the type, strength, location, and temporal pattern of activity of the many synaptic inputs they receive. Compartmental modeling programs like NEURON have become widely used in the neuroscience community to address a broad range of research questions, including how neurons integrate synaptic inputs and propagate information through complex neural networks. One of the main strengths of NEURON is its ability to incorporate user-defined information about the realistic morphology and biophysical properties of different cell types. Although the graphical user interface of the program can be used to run initial exploratory simulations, introducing a stochastic representation of synaptic weights, locations and activation times typically requires users to develop their own codes, a task that can be overwhelming for some beginner users. Here we describe NRN-EZ, an interactive application that allows users to specify complex patterns of synaptic input activity that can be integrated as part of NEURON simulations. Through its graphical user interface, NRN-EZ aims to ease the learning curve to run computational models in NEURON, for users that do not necessarily have a computer science background.
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Affiliation(s)
- Evan A. W. Cobb
- grid.265850.c0000 0001 2151 7947Department of Biology, SUNY Albany, 1400 Washington Avenue, Albany, NY 12222-0100 USA ,grid.265850.c0000 0001 2151 7947Department of Computer Science, SUNY Albany, 1400 Washington Avenue, Albany, NY 12222-0100 USA
| | - Maurice A. Petroccione
- grid.265850.c0000 0001 2151 7947Department of Biology, SUNY Albany, 1400 Washington Avenue, Albany, NY 12222-0100 USA
| | - Annalisa Scimemi
- Department of Biology, SUNY Albany, 1400 Washington Avenue, Albany, NY, 12222-0100, USA.
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30
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Scherer M, Wang T, Guggenberger R, Milosevic L, Gharabaghi A. FiNN: A toolbox for neurophysiological network analysis. Netw Neurosci 2022; 6:1205-1218. [PMID: 38800466 PMCID: PMC11117079 DOI: 10.1162/netn_a_00265] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 06/23/2022] [Indexed: 05/29/2024] Open
Abstract
Recently, neuroscience has seen a shift from localist approaches to network-wide investigations of brain function. Neurophysiological signals across different spatial and temporal scales provide insight into neural communication. However, additional methodological considerations arise when investigating network-wide brain dynamics rather than local effects. Specifically, larger amounts of data, investigated across a higher dimensional space, are necessary. Here, we present FiNN (Find Neurophysiological Networks), a novel toolbox for the analysis of neurophysiological data with a focus on functional and effective connectivity. FiNN provides a wide range of data processing methods and statistical and visualization tools to facilitate inspection of connectivity estimates and the resulting metrics of brain dynamics. The Python toolbox and its documentation are freely available as Supporting Information. We evaluated FiNN against a number of established frameworks on both a conceptual and an implementation level. We found FiNN to require much less processing time and memory than other toolboxes. In addition, FiNN adheres to a design philosophy of easy access and modifiability, while providing efficient data processing implementations. Since the investigation of network-level neural dynamics is experiencing increasing interest, we place FiNN at the disposal of the neuroscientific community as open-source software.
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Affiliation(s)
- Maximilian Scherer
- Institute for Neuromodulation and Neurotechnology, University Hospital and University of Tübingen, Tübingen, Germany
- Krembil Brain Institute, University Health Network, and Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Tianlu Wang
- Institute for Neuromodulation and Neurotechnology, University Hospital and University of Tübingen, Tübingen, Germany
| | - Robert Guggenberger
- Institute for Neuromodulation and Neurotechnology, University Hospital and University of Tübingen, Tübingen, Germany
| | - Luka Milosevic
- Institute for Neuromodulation and Neurotechnology, University Hospital and University of Tübingen, Tübingen, Germany
- Krembil Brain Institute, University Health Network, and Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Alireza Gharabaghi
- Institute for Neuromodulation and Neurotechnology, University Hospital and University of Tübingen, Tübingen, Germany
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31
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Studenova AA, Villringer A, Nikulin VV. Non-zero mean alpha oscillations revealed with computational model and empirical data. PLoS Comput Biol 2022; 18:e1010272. [PMID: 35802619 PMCID: PMC9269450 DOI: 10.1371/journal.pcbi.1010272] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 06/01/2022] [Indexed: 11/18/2022] Open
Abstract
Ongoing oscillations and evoked responses are two main types of neuronal activity obtained with diverse electrophysiological recordings (EEG/MEG/iEEG/LFP). Although typically studied separately, they might in fact be closely related. One possibility to unite them is to demonstrate that neuronal oscillations have non-zero mean which predicts that stimulus- or task-triggered amplitude modulation of oscillations can contribute to the generation of evoked responses. We validated this mechanism using computational modelling and analysis of a large EEG data set. With a biophysical model, we indeed demonstrated that intracellular currents in the neuron are asymmetric and, consequently, the mean of alpha oscillations is non-zero. To understand the effect that neuronal currents exert on oscillatory mean, we varied several biophysical and morphological properties of neurons in the network, such as voltage-gated channel densities, length of dendrites, and intensity of incoming stimuli. For a very large range of model parameters, we observed evidence for non-zero mean of oscillations. Complimentary, we analysed empirical rest EEG recordings of 90 participants (50 young, 40 elderly) and, with spatio-spectral decomposition, detected at least one spatially-filtred oscillatory component of non-zero mean alpha oscillations in 93% of participants. In order to explain a complex relationship between the dynamics of amplitude-envelope and corresponding baseline shifts, we performed additional simulations with simple oscillators coupled with different time delays. We demonstrated that the extent of spatial synchronisation may obscure macroscopic estimation of alpha rhythm modulation while leaving baseline shifts unchanged. Overall, our results predict that amplitude modulation of neural oscillations should at least partially explain the generation of evoked responses. Therefore, inference about changes in evoked responses with respect to cognitive conditions, age or neuropathologies should be constructed while taking into account oscillatory neuronal dynamics.
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Affiliation(s)
- Alina A. Studenova
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
- * E-mail:
| | - Arno Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Department of Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany
| | - Vadim V. Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
- Neurophysics Group, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
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32
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Neymotin SA, Tal I, Barczak A, O'Connell MN, McGinnis T, Markowitz N, Espinal E, Griffith E, Anwar H, Dura-Bernal S, Schroeder CE, Lytton WW, Jones SR, Bickel S, Lakatos P. Detecting Spontaneous Neural Oscillation Events in Primate Auditory Cortex. eNeuro 2022; 9:ENEURO.0281-21.2022. [PMID: 35906065 PMCID: PMC9395248 DOI: 10.1523/eneuro.0281-21.2022] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 05/20/2022] [Accepted: 06/20/2022] [Indexed: 11/21/2022] Open
Abstract
Electrophysiological oscillations in the brain have been shown to occur as multicycle events, with onset and offset dependent on behavioral and cognitive state. To provide a baseline for state-related and task-related events, we quantified oscillation features in resting-state recordings. We developed an open-source wavelet-based tool to detect and characterize such oscillation events (OEvents) and exemplify the use of this tool in both simulations and two invasively-recorded electrophysiology datasets: one from human, and one from nonhuman primate (NHP) auditory system. After removing incidentally occurring event-related potentials (ERPs), we used OEvents to quantify oscillation features. We identified ∼2 million oscillation events, classified within traditional frequency bands: δ, θ, α, β, low γ, γ, and high γ. Oscillation events of 1-44 cycles could be identified in at least one frequency band 90% of the time in human and NHP recordings. Individual oscillation events were characterized by nonconstant frequency and amplitude. This result necessarily contrasts with prior studies which assumed frequency constancy, but is consistent with evidence from event-associated oscillations. We measured oscillation event duration, frequency span, and waveform shape. Oscillations tended to exhibit multiple cycles per event, verifiable by comparing filtered to unfiltered waveforms. In addition to the clear intraevent rhythmicity, there was also evidence of interevent rhythmicity within bands, demonstrated by finding that coefficient of variation of interval distributions and Fano factor (FF) measures differed significantly from a Poisson distribution assumption. Overall, our study provides an easy-to-use tool to study oscillation events at the single-trial level or in ongoing recordings, and demonstrates that rhythmic, multicycle oscillation events dominate auditory cortical dynamics.
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Affiliation(s)
- Samuel A Neymotin
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962
- Department Psychiatry, New York University Grossman School of Medicine, New York, NY 10016
| | - Idan Tal
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962
- Departments of Neurosurgery and Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY 10032
| | - Annamaria Barczak
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962
| | - Monica N O'Connell
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962
| | - Tammy McGinnis
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962
| | - Noah Markowitz
- Department Neurology and Neurosurgery, The Feinstein Institutes for Medical Research at Northwell Health, Manhasset, NY 11030
| | - Elizabeth Espinal
- Department Neurology and Neurosurgery, The Feinstein Institutes for Medical Research at Northwell Health, Manhasset, NY 11030
| | - Erica Griffith
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962
- Department Physiology and Pharmacology, State University of New York Downstate Medical Center, Brooklyn, NY 11203
| | - Haroon Anwar
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962
| | - Salvador Dura-Bernal
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962
- Department Physiology and Pharmacology, State University of New York Downstate Medical Center, Brooklyn, NY 11203
| | - Charles E Schroeder
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962
- Departments of Neurosurgery and Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY 10032
| | - William W Lytton
- Department Physiology and Pharmacology, State University of New York Downstate Medical Center, Brooklyn, NY 11203
- Department Neurology, Kings County Hospital Center, Brooklyn, NY 11203
| | - Stephanie R Jones
- Department Neuroscience and Carney Institute for Brain Science, Brown University, Providence, RI 02906
| | - Stephan Bickel
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962
- Department Neurology and Neurosurgery, The Feinstein Institutes for Medical Research at Northwell Health, Manhasset, NY 11030
| | - Peter Lakatos
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962
- Department Psychiatry, New York University Grossman School of Medicine, New York, NY 10016
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33
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Awile O, Kumbhar P, Cornu N, Dura-Bernal S, King JG, Lupton O, Magkanaris I, McDougal RA, Newton AJH, Pereira F, Săvulescu A, Carnevale NT, Lytton WW, Hines ML, Schürmann F. Modernizing the NEURON Simulator for Sustainability, Portability, and Performance. Front Neuroinform 2022; 16:884046. [PMID: 35832575 PMCID: PMC9272742 DOI: 10.3389/fninf.2022.884046] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 05/26/2022] [Indexed: 12/25/2022] Open
Abstract
The need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURON's ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURON's reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.
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Affiliation(s)
- Omar Awile
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Pramod Kumbhar
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Nicolas Cornu
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Salvador Dura-Bernal
- Department Physiology and Pharmacology, SUNY Downstate, Brooklyn, NY, United States
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | - James Gonzalo King
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Olli Lupton
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Ioannis Magkanaris
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Robert A. McDougal
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States
- Yale Center for Medical Informatics, Yale University, New Haven, CT, United States
| | - Adam J. H. Newton
- Department Physiology and Pharmacology, SUNY Downstate, Brooklyn, NY, United States
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States
| | - Fernando Pereira
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Alexandru Săvulescu
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | | | - William W. Lytton
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | - Michael L. Hines
- Department of Neuroscience, Yale University, New Haven, CT, United States
| | - Felix Schürmann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
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Donoghue T, Schaworonkow N, Voytek B. Methodological considerations for studying neural oscillations. Eur J Neurosci 2022; 55:3502-3527. [PMID: 34268825 PMCID: PMC8761223 DOI: 10.1111/ejn.15361] [Citation(s) in RCA: 92] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 05/25/2021] [Accepted: 06/16/2021] [Indexed: 12/29/2022]
Abstract
Neural oscillations are ubiquitous across recording methodologies and species, broadly associated with cognitive tasks, and amenable to computational modelling that investigates neural circuit generating mechanisms and neural population dynamics. Because of this, neural oscillations offer an exciting potential opportunity for linking theory, physiology and mechanisms of cognition. However, despite their prevalence, there are many concerns-new and old-about how our analysis assumptions are violated by known properties of field potential data. For investigations of neural oscillations to be properly interpreted, and ultimately developed into mechanistic theories, it is necessary to carefully consider the underlying assumptions of the methods we employ. Here, we discuss seven methodological considerations for analysing neural oscillations. The considerations are to (1) verify the presence of oscillations, as they may be absent; (2) validate oscillation band definitions, to address variable peak frequencies; (3) account for concurrent non-oscillatory aperiodic activity, which might otherwise confound measures; measure and account for (4) temporal variability and (5) waveform shape of neural oscillations, which are often bursty and/or nonsinusoidal, potentially leading to spurious results; (6) separate spatially overlapping rhythms, which may interfere with each other; and (7) consider the required signal-to-noise ratio for obtaining reliable estimates. For each topic, we provide relevant examples, demonstrate potential errors of interpretation, and offer suggestions to address these issues. We primarily focus on univariate measures, such as power and phase estimates, though we discuss how these issues can propagate to multivariate measures. These considerations and recommendations offer a helpful guide for measuring and interpreting neural oscillations.
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Affiliation(s)
- Thomas Donoghue
- Department of Cognitive Science, University of California, San Diego
| | | | - Bradley Voytek
- Department of Cognitive Science, University of California, San Diego
- Neurosciences Graduate Program, University of California, San Diego
- Halıcıoğlu Data Science Institute, University of California, San Diego
- Kavli Institute for Brain and Mind, University of California, San Diego
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35
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Anwar H, Caby S, Dura-Bernal S, D’Onofrio D, Hasegan D, Deible M, Grunblatt S, Chadderdon GL, Kerr CC, Lakatos P, Lytton WW, Hazan H, Neymotin SA. Training a spiking neuronal network model of visual-motor cortex to play a virtual racket-ball game using reinforcement learning. PLoS One 2022; 17:e0265808. [PMID: 35544518 PMCID: PMC9094569 DOI: 10.1371/journal.pone.0265808] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 03/08/2022] [Indexed: 11/18/2022] Open
Abstract
Recent models of spiking neuronal networks have been trained to perform behaviors in static environments using a variety of learning rules, with varying degrees of biological realism. Most of these models have not been tested in dynamic visual environments where models must make predictions on future states and adjust their behavior accordingly. The models using these learning rules are often treated as black boxes, with little analysis on circuit architectures and learning mechanisms supporting optimal performance. Here we developed visual/motor spiking neuronal network models and trained them to play a virtual racket-ball game using several reinforcement learning algorithms inspired by the dopaminergic reward system. We systematically investigated how different architectures and circuit-motifs (feed-forward, recurrent, feedback) contributed to learning and performance. We also developed a new biologically-inspired learning rule that significantly enhanced performance, while reducing training time. Our models included visual areas encoding game inputs and relaying the information to motor areas, which used this information to learn to move the racket to hit the ball. Neurons in the early visual area relayed information encoding object location and motion direction across the network. Neuronal association areas encoded spatial relationships between objects in the visual scene. Motor populations received inputs from visual and association areas representing the dorsal pathway. Two populations of motor neurons generated commands to move the racket up or down. Model-generated actions updated the environment and triggered reward or punishment signals that adjusted synaptic weights so that the models could learn which actions led to reward. Here we demonstrate that our biologically-plausible learning rules were effective in training spiking neuronal network models to solve problems in dynamic environments. We used our models to dissect the circuit architectures and learning rules most effective for learning. Our model shows that learning mechanisms involving different neural circuits produce similar performance in sensory-motor tasks. In biological networks, all learning mechanisms may complement one another, accelerating the learning capabilities of animals. Furthermore, this also highlights the resilience and redundancy in biological systems.
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Affiliation(s)
- Haroon Anwar
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
| | - Simon Caby
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
| | - Salvador Dura-Bernal
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
- Dept. Physiology & Pharmacology, State University of New York Downstate, Brooklyn, New York, United States of America
| | - David D’Onofrio
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
| | - Daniel Hasegan
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
| | - Matt Deible
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Sara Grunblatt
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
| | - George L. Chadderdon
- Dept. Physiology & Pharmacology, State University of New York Downstate, Brooklyn, New York, United States of America
| | - Cliff C. Kerr
- Dept Physics, University of Sydney, Sydney, Australia
- Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America
| | - Peter Lakatos
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
- Dept. Psychiatry, NYU Grossman School of Medicine, New York, New York, United States of America
| | - William W. Lytton
- Dept. Physiology & Pharmacology, State University of New York Downstate, Brooklyn, New York, United States of America
- Dept Neurology, Kings County Hospital Center, Brooklyn, New York, United States of America
| | - Hananel Hazan
- Dept of Biology, Tufts University, Medford, Massachusetts, United States of America
| | - Samuel A. Neymotin
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
- Dept. Psychiatry, NYU Grossman School of Medicine, New York, New York, United States of America
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36
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Brady B, Bardouille T. Periodic/Aperiodic Parameterization of Transient Oscillations (PAPTO): Implications for Healthy Ageing. Neuroimage 2022; 251:118974. [DOI: 10.1016/j.neuroimage.2022.118974] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 01/28/2022] [Accepted: 02/03/2022] [Indexed: 12/11/2022] Open
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37
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Kupers ER, Benson NC, Winawer J. A visual encoding model links magnetoencephalography signals to neural synchrony in human cortex. Neuroimage 2021; 245:118655. [PMID: 34687857 PMCID: PMC8788390 DOI: 10.1016/j.neuroimage.2021.118655] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 10/11/2021] [Indexed: 01/23/2023] Open
Abstract
Synchronization of neuronal responses over large distances is hypothesized to be important for many cortical functions. However, no straightforward methods exist to estimate synchrony non-invasively in the living human brain. MEG and EEG measure the whole brain, but the sensors pool over large, overlapping cortical regions, obscuring the underlying neural synchrony. Here, we developed a model from stimulus to cortex to MEG sensors to disentangle neural synchrony from spatial pooling of the instrument. We find that synchrony across cortex has a surprisingly large and systematic effect on predicted MEG spatial topography. We then conducted visual MEG experiments and separated responses into stimulus-locked and broadband components. The stimulus-locked topography was similar to model predictions assuming synchronous neural sources, whereas the broadband topography was similar to model predictions assuming asynchronous sources. We infer that visual stimulation elicits two distinct types of neural responses, one highly synchronous and one largely asynchronous across cortex.
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Affiliation(s)
- Eline R Kupers
- Department of Psychology, New York University, New York, NY 10003, United States; Center for Neural Science, New York University, New York, NY 10003, United States; Department of Psychology, Stanford University, Stanford, CA 94305, United States.
| | - Noah C Benson
- Department of Psychology, New York University, New York, NY 10003, United States; Center for Neural Science, New York University, New York, NY 10003, United States; eSciences Institute, University of Washington, Seattle, WA 98195, United States
| | - Jonathan Winawer
- Department of Psychology, New York University, New York, NY 10003, United States; Center for Neural Science, New York University, New York, NY 10003, United States
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38
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Schöbi D, Do CT, Frässle S, Tittgemeyer M, Heinzle J, Stephan KE. Technical note: A fast and robust integrator of delay differential equations in DCM for electrophysiological data. Neuroimage 2021; 244:118567. [PMID: 34530135 DOI: 10.1016/j.neuroimage.2021.118567] [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: 01/21/2021] [Revised: 07/05/2021] [Accepted: 09/07/2021] [Indexed: 10/20/2022] Open
Abstract
Dynamic causal models (DCMs) of electrophysiological data allow, in principle, for inference on hidden, bulk synaptic function in neural circuits. The directed influences between the neuronal elements of modeled circuits are subject to delays due to the finite transmission speed of axonal connections. Ordinary differential equations are therefore not adequate to capture the ensuing circuit dynamics, and delay differential equations (DDEs) are required instead. Previous work has illustrated that the integration of DDEs in DCMs benefits from sophisticated integration schemes in order to ensure rigorous parameter estimation and correct model identification. However, integration schemes that have been proposed for DCMs either emphasize speed (at the possible expense of accuracy) or robustness (but with computational costs that are problematic in practice). In this technical note, we propose an alternative integration scheme that overcomes these shortcomings and offers high computational efficiency while correctly preserving the nature of delayed effects. This integration scheme is available as open-source code in the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) toolbox and can be easily integrated into existing software (SPM) for the analysis of DCMs for electrophysiological data. While this paper focuses on its application to the convolution-based formalism of DCMs, the new integration scheme can be equally applied to more advanced formulations of DCMs (e.g. conductance based models). Our method provides a new option for electrophysiological DCMs that offers the speed required for scientific projects, but also the accuracy required for rigorous translational applications, e.g. in computational psychiatry.
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Affiliation(s)
- Dario Schöbi
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & Swiss Institute of Technology (ETH Zurich), Wilfriedstrasse 6, Zurich 8032, Switzerland
| | - Cao-Tri Do
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & Swiss Institute of Technology (ETH Zurich), Wilfriedstrasse 6, Zurich 8032, Switzerland
| | - Stefan Frässle
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & Swiss Institute of Technology (ETH Zurich), Wilfriedstrasse 6, Zurich 8032, Switzerland
| | - Marc Tittgemeyer
- Max Planck Institute for Metabolism Research, Gleuler Strasse 50, Cologne 50931, Germany; Cluster of Excellence in Cellular Stress and Aging associated Disease (CECAD), Cologne 50931, Germany
| | - Jakob Heinzle
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & Swiss Institute of Technology (ETH Zurich), Wilfriedstrasse 6, Zurich 8032, Switzerland.
| | - Klaas Enno Stephan
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & Swiss Institute of Technology (ETH Zurich), Wilfriedstrasse 6, Zurich 8032, Switzerland; Max Planck Institute for Metabolism Research, Gleuler Strasse 50, Cologne 50931, Germany
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Bonaiuto JJ, Little S, Neymotin SA, Jones SR, Barnes GR, Bestmann S. Laminar dynamics of high amplitude beta bursts in human motor cortex. Neuroimage 2021; 242:118479. [PMID: 34407440 PMCID: PMC8463839 DOI: 10.1016/j.neuroimage.2021.118479] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 08/12/2021] [Accepted: 08/14/2021] [Indexed: 12/28/2022] Open
Abstract
Motor cortical activity in the beta frequency range is one of the strongest and most studied movement-related neural signals. At the single trial level, beta band activity is often characterized by transient, high amplitude, bursting events rather than slowly modulating oscillations. The timing of these bursting events is tightly linked to behavior, suggesting a more dynamic functional role for beta activity than previously believed. However, the neural mechanisms underlying beta bursts in sensorimotor circuits are poorly understood. To address this, we here leverage and extend recent developments in high precision MEG for temporally resolved laminar analysis of burst activity, combined with a neocortical circuit model that simulates the biophysical generators of the electrical currents which drive beta bursts. This approach pinpoints the generation of beta bursts in human motor cortex to distinct excitatory synaptic inputs to deep and superficial cortical layers, which drive current flow in opposite directions. These laminar dynamics of beta bursts in motor cortex align with prior invasive animal recordings within the somatosensory cortex, and suggest a conserved mechanism for somatosensory and motor cortical beta bursts. More generally, we demonstrate the ability for uncovering the laminar dynamics of event-related neural signals in human non-invasive recordings. This provides important constraints to theories about the functional role of burst activity for movement control in health and disease, and crucial links between macro-scale phenomena measured in humans and micro-circuit activity recorded from animal models.
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Affiliation(s)
- James J Bonaiuto
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Bron, France; Université Claude Bernard Lyon 1, Université de Lyon, France; Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK; Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK.
| | - Simon Little
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK; Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Samuel A Neymotin
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department of Neuroscience, Brown University, Providence, RI, USA; Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
| | - Stephanie R Jones
- Department of Neuroscience, Brown University, Providence, RI, USA; Center for Neurorestoration and Neurotechnology, Providence VAMC, Providence, RI, USA
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK
| | - Sven Bestmann
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK; Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK
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40
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Kobayashi T, Kuriyama R, Yamazaki T. Testing an Explicit Method for Multi-compartment Neuron Model Simulation on a GPU. Cognit Comput 2021. [DOI: 10.1007/s12559-021-09942-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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41
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Sinha M, Narayanan R. Active Dendrites and Local Field Potentials: Biophysical Mechanisms and Computational Explorations. Neuroscience 2021; 489:111-142. [PMID: 34506834 PMCID: PMC7612676 DOI: 10.1016/j.neuroscience.2021.08.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 08/30/2021] [Accepted: 08/31/2021] [Indexed: 10/27/2022]
Abstract
Neurons and glial cells are endowed with membranes that express a rich repertoire of ion channels, transporters, and receptors. The constant flux of ions across the neuronal and glial membranes results in voltage fluctuations that can be recorded from the extracellular matrix. The high frequency components of this voltage signal contain information about the spiking activity, reflecting the output from the neurons surrounding the recording location. The low frequency components of the signal, referred to as the local field potential (LFP), have been traditionally thought to provide information about the synaptic inputs that impinge on the large dendritic trees of various neurons. In this review, we discuss recent computational and experimental studies pointing to a critical role of several active dendritic mechanisms that can influence the genesis and the location-dependent spectro-temporal dynamics of LFPs, spanning different brain regions. We strongly emphasize the need to account for the several fast and slow dendritic events and associated active mechanisms - including gradients in their expression profiles, inter- and intra-cellular spatio-temporal interactions spanning neurons and glia, heterogeneities and degeneracy across scales, neuromodulatory influences, and activitydependent plasticity - towards gaining important insights about the origins of LFP under different behavioral states in health and disease. We provide simple but essential guidelines on how to model LFPs taking into account these dendritic mechanisms, with detailed methodology on how to account for various heterogeneities and electrophysiological properties of neurons and synapses while studying LFPs.
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Affiliation(s)
- Manisha Sinha
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka 560012, India
| | - Rishikesh Narayanan
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka 560012, India.
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42
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Riddle J, Frohlich F. Targeting neural oscillations with transcranial alternating current stimulation. Brain Res 2021; 1765:147491. [PMID: 33887251 PMCID: PMC8206031 DOI: 10.1016/j.brainres.2021.147491] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 03/26/2021] [Accepted: 04/13/2021] [Indexed: 11/16/2022]
Abstract
Neural oscillations at the network level synchronize activity between regions and temporal scales. Transcranial alternating current stimulation (tACS), the delivery of low-amplitude electric current to the scalp, provides a tool for investigating the causal role of neural oscillations in cognition. The parameter space for tACS is vast and optimization is required in terms of temporal and spatial targeting. We review emerging techniques and suggest novel approaches that capitalize on the non-sinusoidal and transient nature of neural oscillations and leverage the flexibility provided by a customizable electrode montage and electrical waveform. The customizability and safety profile of tACS make it a promising tool for precision intervention in psychiatric illnesses.
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Affiliation(s)
- Justin Riddle
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA; Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Flavio Frohlich
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA; Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA; Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA; Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA; Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA; Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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43
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Bertazzoli G, Esposito R, Mutanen TP, Ferrari C, Ilmoniemi RJ, Miniussi C, Bortoletto M. The impact of artifact removal approaches on TMS-EEG signal. Neuroimage 2021; 239:118272. [PMID: 34144161 DOI: 10.1016/j.neuroimage.2021.118272] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 04/07/2021] [Accepted: 05/19/2021] [Indexed: 12/30/2022] Open
Abstract
Transcranial magnetic stimulation (TMS)-evoked potentials (TEPs) allow one to assess cortical excitability and effective connectivity in clinical and basic research. However, obtaining clean TEPs is challenging due to the various TMS-related artifacts that contaminate the electroencephalographic (EEG) signal when the TMS pulse is delivered. Different preprocessing approaches have been employed to remove the artifacts, but the degree of artifact reduction or signal distortion introduced in this phase of analysis is still unknown. Knowing and controlling this potential source of uncertainty will increase the inter-rater reliability of TEPs and improve the comparability between TMS-EEG studies. The goal of this study was to assess the variability in TEP waveforms due to of the use of different preprocessing pipelines. To accomplish this aim, we preprocessed the same TMS-EEG data with four different pipelines and compared the results. The dataset was obtained from 16 subjects in two identical recording sessions, each session consisting of both left dorsolateral prefrontal cortex and left inferior parietal lobule stimulation at 100% of the resting motor threshold. Considerable differences in TEP amplitudes and global mean field power (GMFP) were found between the preprocessing pipelines. Topographies of TEPs from the different pipelines were all highly correlated (ρ>0.8) at latencies over 100 ms. By contrast, waveforms at latencies under 100 ms showed a variable level of correlation, with ρ ranging between 0.2 and 0.9. Moreover, the test-retest reliability of TEPs depended on the preprocessing pipeline. Taken together, these results take us to suggest that the choice of the preprocessing approach has a marked impact on the final TEP, and that further studies are needed to understand advantages and disadvantages of the different approaches.
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Affiliation(s)
- Giacomo Bertazzoli
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, TN, Italy; Neurophysiology lab, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Via Pilastroni 4, 25125 Brescia, Italy
| | - Romina Esposito
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, TN, Italy
| | - Tuomas P Mutanen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Clarissa Ferrari
- Service of Statistics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Carlo Miniussi
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, TN, Italy
| | - Marta Bortoletto
- Neurophysiology lab, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Via Pilastroni 4, 25125 Brescia, Italy.
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44
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Jas M, Jones SR, Hämäläinen MS. Whole-head OPM-MEG enables noninvasive assessment of functional connectivity. Trends Neurosci 2021; 44:510-512. [PMID: 33965213 DOI: 10.1016/j.tins.2021.04.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 04/22/2021] [Indexed: 11/29/2022]
Abstract
A recent paper by Boto et al. established reliability of noninvasive functional connectivity measurements with a new whole-head optically pumped magnetometer magnetoencephalography (OPM-MEG) system. This rapidly developing technology enables a conformal sensor array to accommodate different head sizes and opens up new avenues for experiments in more naturalistic settings.
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Affiliation(s)
- Mainak Jas
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Stephanie R Jones
- Department of Neuroscience, Brown University, Providence, RI, USA; Center for Neurorestoration and Neurotechnology, Providence Veterans' Administration Medical Center (VAMC), Providence, RI, USA
| | - Matti S Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland
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45
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Kohl C, Parviainen T, Jones SR. Neural Mechanisms Underlying Human Auditory Evoked Responses Revealed By Human Neocortical Neurosolver. Brain Topogr 2021; 35:19-35. [PMID: 33876329 PMCID: PMC8813713 DOI: 10.1007/s10548-021-00838-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 03/30/2021] [Indexed: 12/19/2022]
Abstract
Auditory evoked fields (AEFs) are commonly studied, yet their underlying neural mechanisms remain poorly understood. Here, we used the biophysical modelling software Human Neocortical Neurosolver (HNN) whose foundation is a canonical neocortical circuit model to interpret the cell and network mechanisms contributing to macroscale AEFs elicited by a simple tone, measured with magnetoencephalography. We found that AEFs can be reproduced by activating the neocortical circuit through a layer specific sequence of feedforward and feedback excitatory synaptic drives, similar to prior simulation of somatosensory evoked responses, supporting the notion that basic structures and activation patterns are preserved across sensory regions. We also applied the modeling framework to develop and test predictions on neural mechanisms underlying AEF differences in the left and right hemispheres, as well as in hemispheres contralateral and ipsilateral to the presentation of the auditory stimulus. We found that increasing the strength of the excitatory synaptic cortical feedback inputs to supragranular layers simulates the commonly observed right hemisphere dominance, while decreasing the input latencies and simultaneously increasing the number of cells contributing to the signal accounted for the contralateral dominance. These results provide a direct link between human data and prior animal studies and lay the foundation for future translational research examining the mechanisms underlying alteration in this fundamental biomarker of auditory processing in healthy cognition and neuropathology.
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Affiliation(s)
- Carmen Kohl
- Department of Neuroscience, Carney Institute for Brain Sciences, Brown University, Providence, USA.
| | - Tiina Parviainen
- Centre for Interdisciplinary Brain Research, Department of Psychology, University of Jyväskylä, P.O. Box 35, 40014, Jyväskylä, Finland
- Meg Core Aalto Neuroimaging, Aalto University, AALTO, P.O. Box 15100, 00076, Espoo, Finland
| | - Stephanie R Jones
- Department of Neuroscience, Carney Institute for Brain Sciences, Brown University, Providence, USA
- Center for Neurorestoration and Neurotechnology, Providence VAMC, Providence, USA
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46
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Martínez-Cañada P, Ness TV, Einevoll GT, Fellin T, Panzeri S. Computation of the electroencephalogram (EEG) from network models of point neurons. PLoS Comput Biol 2021; 17:e1008893. [PMID: 33798190 PMCID: PMC8046357 DOI: 10.1371/journal.pcbi.1008893] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 04/14/2021] [Accepted: 03/18/2021] [Indexed: 12/28/2022] Open
Abstract
The electroencephalogram (EEG) is a major tool for non-invasively studying brain function and dysfunction. Comparing experimentally recorded EEGs with neural network models is important to better interpret EEGs in terms of neural mechanisms. Most current neural network models use networks of simple point neurons. They capture important properties of cortical dynamics, and are numerically or analytically tractable. However, point neurons cannot generate an EEG, as EEG generation requires spatially separated transmembrane currents. Here, we explored how to compute an accurate approximation of a rodent's EEG with quantities defined in point-neuron network models. We constructed different approximations (or proxies) of the EEG signal that can be computed from networks of leaky integrate-and-fire (LIF) point neurons, such as firing rates, membrane potentials, and combinations of synaptic currents. We then evaluated how well each proxy reconstructed a ground-truth EEG obtained when the synaptic currents of the LIF model network were fed into a three-dimensional network model of multicompartmental neurons with realistic morphologies. Proxies based on linear combinations of AMPA and GABA currents performed better than proxies based on firing rates or membrane potentials. A new class of proxies, based on an optimized linear combination of time-shifted AMPA and GABA currents, provided the most accurate estimate of the EEG over a wide range of network states. The new linear proxies explained 85-95% of the variance of the ground-truth EEG for a wide range of network configurations including different cell morphologies, distributions of presynaptic inputs, positions of the recording electrode, and spatial extensions of the network. Non-linear EEG proxies using a convolutional neural network (CNN) on synaptic currents increased proxy performance by a further 2-8%. Our proxies can be used to easily calculate a biologically realistic EEG signal directly from point-neuron simulations thus facilitating a quantitative comparison between computational models and experimental EEG recordings.
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Affiliation(s)
- Pablo Martínez-Cañada
- Neural Coding Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
- Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
| | - Torbjørn V. Ness
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Gaute T. Einevoll
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
- Department of Physics, University of Oslo, Oslo, Norway
| | - Tommaso Fellin
- Neural Coding Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
- Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
| | - Stefano Panzeri
- Neural Coding Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
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47
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Petrosyan A, Sinkin M, Lebedev MA, Ossadtchi A. Decoding and interpreting cortical signals with a compact convolutional neural network. J Neural Eng 2021; 18. [PMID: 33524962 DOI: 10.1088/1741-2552/abe20e] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 02/01/2021] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Brain-computer interfaces (BCIs) decode information from neural activity and send it to external devices. The use of Deep Learning approaches for decoding allows for automatic feature engineering within the specific decoding task. Physiologically plausible interpretation of the network parameters ensures the robustness of the learned decision rules and opens the exciting opportunity for automatic knowledge discovery. APPROACH We describe a compact convolutional network-based architecture for adaptive decoding of electrocorticographic (ECoG) data into finger kinematics. We also propose a novel theoretically justified approach to interpreting the spatial and temporal weights in the architectures that combine adaptation in both space and time. The obtained spatial and frequency patterns characterizing the neuronal populations pivotal to the specific decoding task can then be interpreted by fitting appropriate spatial and dynamical models. MAIN RESULTS We first tested our solution using realistic Monte-Carlo simulations. Then, when applied to the ECoG data from Berlin BCI competition IV dataset, our architecture performed comparably to the competition winners without requiring explicit feature engineering. Using the proposed approach to the network weights interpretation we could unravel the spatial and the spectral patterns of the neuronal processes underlying the successful decoding of finger kinematics from an ECoG dataset. Finally we have also applied the entire pipeline to the analysis of a 32-channel EEG motor-imagery dataset and observed physiologically plausible patterns specific to the task. SIGNIFICANCE We described a compact and interpretable CNN architecture derived from the basic principles and encompassing the knowledge in the field of neural electrophysiology. For the first time in the context of such multibranch architectures with factorized spatial and temporal processing we presented theoretically justified weights interpretation rules. We verified our recipes using simulations and real data and demonstrated that the proposed solution offers a good decoder and a tool for investigating motor control neural mechanisms.
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Affiliation(s)
- Artur Petrosyan
- Center for Bioelectric Interfaces, Higher School of Economics, Krivokolennyi per., 3, Moscow, Russia, 10100, RUSSIAN FEDERATION
| | - Mikhail Sinkin
- A I Yevdokimov Moscow State University of Medicine and Dentistry of the Ministry of Healthcare of the Russian Federation Faculty of Dentistry, Delegatskaya St., 20, p. 1, Moskva, Moskva, 127473, RUSSIAN FEDERATION
| | - M A Lebedev
- Neurobiology, Duke University, Hudson Hall 136, Durham, NC 27708-0281, USA, Durham, 27517, UNITED STATES
| | - Alexei Ossadtchi
- Center for Bioelectric Interfaces, Higher School of Economics, Krivokolennyi per., 3, Moscow, Russia, 101000, RUSSIAN FEDERATION
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48
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Dai K, Gratiy SL, Billeh YN, Xu R, Cai B, Cain N, Rimehaug AE, Stasik AJ, Einevoll GT, Mihalas S, Koch C, Arkhipov A. Brain Modeling ToolKit: An open source software suite for multiscale modeling of brain circuits. PLoS Comput Biol 2020; 16:e1008386. [PMID: 33253147 PMCID: PMC7728187 DOI: 10.1371/journal.pcbi.1008386] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 12/10/2020] [Accepted: 09/16/2020] [Indexed: 11/26/2022] Open
Abstract
Experimental studies in neuroscience are producing data at a rapidly increasing rate, providing exciting opportunities and formidable challenges to existing theoretical and modeling approaches. To turn massive datasets into predictive quantitative frameworks, the field needs software solutions for systematic integration of data into realistic, multiscale models. Here we describe the Brain Modeling ToolKit (BMTK), a software suite for building models and performing simulations at multiple levels of resolution, from biophysically detailed multi-compartmental, to point-neuron, to population-statistical approaches. Leveraging the SONATA file format and existing software such as NEURON, NEST, and others, BMTK offers a consistent user experience across multiple levels of resolution. It permits highly sophisticated simulations to be set up with little coding required, thus lowering entry barriers to new users. We illustrate successful applications of BMTK to large-scale simulations of a cortical area. BMTK is an open-source package provided as a resource supporting modeling-based discovery in the community.
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Affiliation(s)
- Kael Dai
- Allen Institute, Seattle, Washington, United States of America
| | | | - Yazan N. Billeh
- Allen Institute, Seattle, Washington, United States of America
| | - Richard Xu
- Allen Institute, Seattle, Washington, United States of America
| | - Binghuang Cai
- Allen Institute, Seattle, Washington, United States of America
| | - Nicholas Cain
- Allen Institute, Seattle, Washington, United States of America
| | - Atle E. Rimehaug
- Norwegian University of Life Sciences & University of Oslo, Oslo, Norway
| | | | - Gaute T. Einevoll
- Norwegian University of Life Sciences & University of Oslo, Oslo, Norway
| | - Stefan Mihalas
- Allen Institute, Seattle, Washington, United States of America
| | - Christof Koch
- Allen Institute, Seattle, Washington, United States of America
| | - Anton Arkhipov
- Allen Institute, Seattle, Washington, United States of America
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49
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Srivastava P, Nozari E, Kim JZ, Ju H, Zhou D, Becker C, Pasqualetti F, Pappas GJ, Bassett DS. Models of communication and control for brain networks: distinctions, convergence, and future outlook. Netw Neurosci 2020; 4:1122-1159. [PMID: 33195951 PMCID: PMC7655113 DOI: 10.1162/netn_a_00158] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 07/21/2020] [Indexed: 12/13/2022] Open
Abstract
Recent advances in computational models of signal propagation and routing in the human brain have underscored the critical role of white-matter structure. A complementary approach has utilized the framework of network control theory to better understand how white matter constrains the manner in which a region or set of regions can direct or control the activity of other regions. Despite the potential for both of these approaches to enhance our understanding of the role of network structure in brain function, little work has sought to understand the relations between them. Here, we seek to explicitly bridge computational models of communication and principles of network control in a conceptual review of the current literature. By drawing comparisons between communication and control models in terms of the level of abstraction, the dynamical complexity, the dependence on network attributes, and the interplay of multiple spatiotemporal scales, we highlight the convergence of and distinctions between the two frameworks. Based on the understanding of the intertwined nature of communication and control in human brain networks, this work provides an integrative perspective for the field and outlines exciting directions for future work.
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Affiliation(s)
- Pragya Srivastava
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA
| | - Erfan Nozari
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA USA
| | - Jason Z. Kim
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA
| | - Harang Ju
- Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Dale Zhou
- Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Cassiano Becker
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, CA USA
| | - George J. Pappas
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA USA
| | - Danielle S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA USA
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA
- Santa Fe Institute, Santa Fe, NM USA
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50
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Næss S, Halnes G, Hagen E, Hagler DJ, Dale AM, Einevoll GT, Ness TV. Biophysically detailed forward modeling of the neural origin of EEG and MEG signals. Neuroimage 2020; 225:117467. [PMID: 33075556 DOI: 10.1016/j.neuroimage.2020.117467] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 09/28/2020] [Accepted: 10/12/2020] [Indexed: 12/22/2022] Open
Abstract
Electroencephalography (EEG) and magnetoencephalography (MEG) are among the most important techniques for non-invasively studying cognition and disease in the human brain. These signals are known to originate from cortical neural activity, typically described in terms of current dipoles. While the link between cortical current dipoles and EEG/MEG signals is relatively well understood, surprisingly little is known about the link between different kinds of neural activity and the current dipoles themselves. Detailed biophysical modeling has played an important role in exploring the neural origin of intracranial electric signals, like extracellular spikes and local field potentials. However, this approach has not yet been taken full advantage of in the context of exploring the neural origin of the cortical current dipoles that are causing EEG/MEG signals. Here, we present a method for reducing arbitrary simulated neural activity to single current dipoles. We find that the method is applicable for calculating extracranial signals, but less suited for calculating intracranial electrocorticography (ECoG) signals. We demonstrate that this approach can serve as a powerful tool for investigating the neural origin of EEG/MEG signals. This is done through example studies of the single-neuron EEG contribution, the putative EEG contribution from calcium spikes, and from calculating EEG signals from large-scale neural network simulations. We also demonstrate how the simulated current dipoles can be used directly in combination with detailed head models, allowing for simulated EEG signals with an unprecedented level of biophysical details. In conclusion, this paper presents a framework for biophysically detailed modeling of EEG and MEG signals, which can be used to better our understanding of non-inasively measured neural activity in humans.
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Affiliation(s)
- Solveig Næss
- Department of Informatics, University of Oslo, Oslo 0316, Norway
| | - Geir Halnes
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway
| | - Espen Hagen
- Department of Physics, University of Oslo, Oslo 0316, Norway
| | - Donald J Hagler
- Department of Radiology, University of California, La Jolla, CA 92093, USA
| | - Anders M Dale
- Department of Radiology, University of California, La Jolla, CA 92093, USA; Department of Neurosciences, University of California, La Jolla, CA 92093, USA
| | - Gaute T Einevoll
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway; Department of Physics, University of Oslo, Oslo 0316, Norway.
| | - Torbjørn V Ness
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway.
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