1
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Mohan UR, Zhang H, Ermentrout B, Jacobs J. The direction of theta and alpha travelling waves modulates human memory processing. Nat Hum Behav 2024:10.1038/s41562-024-01838-3. [PMID: 38459263 DOI: 10.1038/s41562-024-01838-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 01/24/2024] [Indexed: 03/10/2024]
Abstract
To support a range of behaviours, the brain must flexibly coordinate neural activity across widespread brain regions. One potential mechanism for this coordination is a travelling wave, in which a neural oscillation propagates across the brain while organizing the order and timing of activity across regions. Although travelling waves are present across the brain in various species, their potential functional relevance has remained unknown. Here, using rare direct human brain recordings, we demonstrate a distinct functional role for travelling waves of theta- and alpha-band (2-13 Hz) oscillations in the cortex. Travelling waves propagate in different directions during separate cognitive processes. In episodic memory, travelling waves tended to propagate in a posterior-to-anterior direction during successful memory encoding and in an anterior-to-posterior direction during recall. Because travelling waves of oscillations correspond to local neuronal spiking, these patterns indicate that rhythmic pulses of activity move across the brain in different directions for separate behaviours. More broadly, our results suggest a fundamental role for travelling waves and oscillations in dynamically coordinating neural connectivity, by flexibly organizing the timing and directionality of network interactions across the cortex to support cognition and behaviour.
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Affiliation(s)
- Uma R Mohan
- Surgical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD, USA
| | | | - Bard Ermentrout
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Joshua Jacobs
- Department of Biomedical Engineering, Columbia University, New York City, NY, USA.
- Department of Neurological Surgery, Columbia University, New York City, NY, USA.
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2
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Mikheev I, Steiner H, Martynova O. Detecting cognitive traits and occupational proficiency using EEG and statistical inference. Sci Rep 2024; 14:5605. [PMID: 38453969 PMCID: PMC10920811 DOI: 10.1038/s41598-024-55163-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 02/21/2024] [Indexed: 03/09/2024] Open
Abstract
Machine learning (ML) is widely used in classification tasks aimed at detecting various cognitive states or neurological diseases using noninvasive electroencephalogram (EEG) time series. However, successfully detecting specific cognitive skills in a healthy population, independent of subject, remains challenging. This study compared the subject-independent classification performance of three different pipelines: supervised and Riemann projections with logistic regression and handcrafted power spectral features with light gradient boosting machine (LightGBM). 128-channel EEGs were recorded from 26 healthy volunteers while they solved arithmetic, logical, and verbal tasks. The participants were divided into two groups based on their higher education and occupation: specialists in mathematics and humanities. The balanced accuracy of the education type was significantly above chance for all pipelines: 0.84-0.89, 0.85-0.88, and 0.86-0.88 for each type of task, respectively. All three pipelines allowed us to distinguish mathematical proficiency based on learning experience with different trade-offs between performance and explainability. Our results suggest that ML approaches could also be effective for recognizing individual cognitive traits using EEG.
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Affiliation(s)
- Ilya Mikheev
- Department of Psychology, HSE University, Moscow, 101000, Russia.
| | - Helen Steiner
- Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Moscow, 117485, Russia
| | - Olga Martynova
- Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Moscow, 117485, Russia
- Centre for Cognition and Decision Making, HSE University, Moscow, 101000, Russia
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3
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Schmidt R, Rose J, Muralidharan V. Transient oscillations as computations for cognition: Analysis, modeling and function. Curr Opin Neurobiol 2023; 83:102796. [PMID: 37804772 DOI: 10.1016/j.conb.2023.102796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 08/31/2023] [Accepted: 09/06/2023] [Indexed: 10/09/2023]
Abstract
Our view of neural oscillations is currently changing. The dominant picture of sustained oscillations is now often replaced by transient oscillations occurring in bursts. This phenomenon seems to be quite comprehensive, as it has been reported for different oscillation frequencies, including the theta, beta, and gamma bands, as well as cortical and subcortical regions in a variety of cognitive tasks and species. Here we review recent developments in their analysis, computational modeling, and functional roles. For the analysis of transient oscillations methods using lagged coherence and Hidden Markov Models have been developed and applied in recent studies to ascertain their transient nature and study their contribution to cognitive functions. Furthermore, computational models have been developed that account for their stochastic nature, which poses interesting functional constraints. Finally, as transient oscillations have been observed across many species, they are likely of functional significance and we consider challenges in characterizing their function.
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Affiliation(s)
- Robert Schmidt
- Institute for Neural Computation, Faculty of Computer Science, Ruhr-University Bochum, Germany.
| | - Jonas Rose
- Neural Basis of Learning, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Germany
| | - Vignesh Muralidharan
- Center for Brain Science and Application, School of AI and Data Science, Indian Institute of Technology Jodhpur, India. https://twitter.com/vigmdhrn
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4
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Hovsepyan S, Olasagasti I, Giraud AL. Rhythmic modulation of prediction errors: A top-down gating role for the beta-range in speech processing. PLoS Comput Biol 2023; 19:e1011595. [PMID: 37934766 PMCID: PMC10655987 DOI: 10.1371/journal.pcbi.1011595] [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: 06/17/2022] [Revised: 11/17/2023] [Accepted: 10/11/2023] [Indexed: 11/09/2023] Open
Abstract
Natural speech perception requires processing the ongoing acoustic input while keeping in mind the preceding one and predicting the next. This complex computational problem could be handled by a dynamic multi-timescale hierarchical inferential process that coordinates the information flow up and down the language network hierarchy. Using a predictive coding computational model (Precoss-β) that identifies online individual syllables from continuous speech, we address the advantage of a rhythmic modulation of up and down information flows, and whether beta oscillations could be optimal for this. In the model, and consistent with experimental data, theta and low-gamma neural frequency scales ensure syllable-tracking and phoneme-level speech encoding, respectively, while the beta rhythm is associated with inferential processes. We show that a rhythmic alternation of bottom-up and top-down processing regimes improves syllable recognition, and that optimal efficacy is reached when the alternation of bottom-up and top-down regimes, via oscillating prediction error precisions, is in the beta range (around 20-30 Hz). These results not only demonstrate the advantage of a rhythmic alternation of up- and down-going information, but also that the low-beta range is optimal given sensory analysis at theta and low-gamma scales. While specific to speech processing, the notion of alternating bottom-up and top-down processes with frequency multiplexing might generalize to other cognitive architectures.
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Affiliation(s)
- Sevada Hovsepyan
- Department of Basic Neurosciences, University of Geneva, Biotech Campus, Genève, Switzerland
| | - Itsaso Olasagasti
- Department of Basic Neurosciences, University of Geneva, Biotech Campus, Genève, Switzerland
| | - Anne-Lise Giraud
- Department of Basic Neurosciences, University of Geneva, Biotech Campus, Genève, Switzerland
- Institut Pasteur, Université Paris Cité, Inserm, Institut de l’Audition, France
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5
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Lu X, Wang Y, Liu Z, Gou Y, Jaeger D, St-Pierre F. Widefield imaging of rapid pan-cortical voltage dynamics with an indicator evolved for one-photon microscopy. Nat Commun 2023; 14:6423. [PMID: 37828037 PMCID: PMC10570354 DOI: 10.1038/s41467-023-41975-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 09/20/2023] [Indexed: 10/14/2023] Open
Abstract
Widefield imaging with genetically encoded voltage indicators (GEVIs) is a promising approach for understanding the role of large cortical networks in the neural coding of behavior. However, the limited performance of current GEVIs restricts their deployment for single-trial imaging of rapid neuronal voltage dynamics. Here, we developed a high-throughput platform to screen for GEVIs that combine fast kinetics with high brightness, sensitivity, and photostability under widefield one-photon illumination. Rounds of directed evolution produced JEDI-1P, a green-emitting fluorescent indicator with enhanced performance across all metrics. Next, we optimized a neonatal intracerebroventricular delivery method to achieve cost-effective and wide-spread JEDI-1P expression in mice. We also developed an approach to correct optical measurements from hemodynamic and motion artifacts effectively. Finally, we achieved stable brain-wide voltage imaging and successfully tracked gamma-frequency whisker and visual stimulations in awake mice in single trials, opening the door to investigating the role of high-frequency signals in brain computations.
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Affiliation(s)
- Xiaoyu Lu
- Systems, Synthetic, and Physical Biology Program, Rice University, Houston, TX, 77005, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Yunmiao Wang
- Neuroscience Graduate Program, Emory University, Atlanta, GA, 30322, USA
- Biology Department, Emory University, Atlanta, GA, 30322, USA
| | - Zhuohe Liu
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, 77005, USA
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Yueyang Gou
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Dieter Jaeger
- Biology Department, Emory University, Atlanta, GA, 30322, USA.
| | - François St-Pierre
- Systems, Synthetic, and Physical Biology Program, Rice University, Houston, TX, 77005, USA.
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, 77005, USA.
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, 77030, USA.
- Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, 77030, USA.
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6
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Champion KP, Gozel O, Lankow BS, Ermentrout GB, Goldman MS. An oscillatory mechanism for multi-level storage in short-term memory. Commun Biol 2023; 6:829. [PMID: 37563448 PMCID: PMC10415352 DOI: 10.1038/s42003-023-05200-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 08/01/2023] [Indexed: 08/12/2023] Open
Abstract
Oscillatory activity is commonly observed during the maintenance of information in short-term memory, but its role remains unclear. Non-oscillatory models of short-term memory storage are able to encode stimulus identity through their spatial patterns of activity, but are typically limited to either an all-or-none representation of stimulus amplitude or exhibit a biologically implausible exact-tuning condition. Here we demonstrate a simple mechanism by which oscillatory input enables a circuit to generate persistent or sequential activity that encodes information not only in the spatial pattern of activity, but also in the amplitude of activity. This is accomplished through a phase-locking phenomenon that permits many different amplitudes of persistent activity to be stored without requiring exact tuning of model parameters. Altogether, this work proposes a class of models for the storage of information in working memory, a potential role for brain oscillations, and a dynamical mechanism for maintaining multi-stable neural representations.
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Affiliation(s)
- Kathleen P Champion
- Department of Applied Mathematics, University of Washington, Seattle, WA, 98195, USA
| | - Olivia Gozel
- Departments of Neurobiology and Statistics, University of Chicago, Chicago, IL, 60637, USA
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL, 60637, USA
| | - Benjamin S Lankow
- Center for Neuroscience, University of California, Davis, Davis, CA, 95618, USA
| | - G Bard Ermentrout
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
| | - Mark S Goldman
- Center for Neuroscience, University of California, Davis, Davis, CA, 95618, USA.
- Department of Neurobiology, Physiology, and Behavior, and Department of Ophthalmology and Vision Science, University of California, Davis, Davis, CA, 95618, USA.
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7
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Johnson EL, Lin JJ, King-Stephens D, Weber PB, Laxer KD, Saez I, Girgis F, D'Esposito M, Knight RT, Badre D. A rapid theta network mechanism for flexible information encoding. Nat Commun 2023; 14:2872. [PMID: 37208373 DOI: 10.1038/s41467-023-38574-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 05/08/2023] [Indexed: 05/21/2023] Open
Abstract
Flexible behavior requires gating mechanisms that encode only task-relevant information in working memory. Extant literature supports a theoretical division of labor whereby lateral frontoparietal interactions underlie information maintenance and the striatum enacts the gate. Here, we reveal neocortical gating mechanisms in intracranial EEG patients by identifying rapid, within-trial changes in regional and inter-regional activities that predict subsequent behavioral outputs. Results first demonstrate information accumulation mechanisms that extend prior fMRI (i.e., regional high-frequency activity) and EEG evidence (inter-regional theta synchrony) of distributed neocortical networks in working memory. Second, results demonstrate that rapid changes in theta synchrony, reflected in changing patterns of default mode network connectivity, support filtering. Graph theoretical analyses further linked filtering in task-relevant information and filtering out irrelevant information to dorsal and ventral attention networks, respectively. Results establish a rapid neocortical theta network mechanism for flexible information encoding, a role previously attributed to the striatum.
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Affiliation(s)
- Elizabeth L Johnson
- Departments of Medical Social Sciences and Pediatrics, Northwestern University, Chicago, IL, USA.
| | - Jack J Lin
- Department of Neurology and Center for Mind and Brain, University of California, Davis, CA, USA
| | - David King-Stephens
- Department of Neurology and Neurosurgery, California Pacific Medical Center, San Francisco, CA, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Peter B Weber
- Department of Neurology and Neurosurgery, California Pacific Medical Center, San Francisco, CA, USA
| | - Kenneth D Laxer
- Department of Neurology and Neurosurgery, California Pacific Medical Center, San Francisco, CA, USA
| | - Ignacio Saez
- Department of Neurological Surgery, University of California, Davis, CA, USA
- Departments of Neuroscience, Neurosurgery, and Neurology, Ichan School of Medicine at Mt. Sinai, New York, NY, USA
| | - Fady Girgis
- Department of Neurological Surgery, University of California, Davis, CA, USA
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - Mark D'Esposito
- Helen Wills Neuroscience Institute and Department of Psychology, University of California, Berkeley, CA, USA
| | - Robert T Knight
- Helen Wills Neuroscience Institute and Department of Psychology, University of California, Berkeley, CA, USA
| | - David Badre
- Department of Cognitive, Linguistic, and Psychological Sciences, and Carney Institute for Brain Science, Brown University, Providence, RI, USA.
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8
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Safavi S, Panagiotaropoulos TI, Kapoor V, Ramirez-Villegas JF, Logothetis NK, Besserve M. Uncovering the organization of neural circuits with Generalized Phase Locking Analysis. PLoS Comput Biol 2023; 19:e1010983. [PMID: 37011110 PMCID: PMC10109521 DOI: 10.1371/journal.pcbi.1010983] [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: 06/22/2022] [Revised: 04/17/2023] [Accepted: 02/27/2023] [Indexed: 04/05/2023] Open
Abstract
Despite the considerable progress of in vivo neural recording techniques, inferring the biophysical mechanisms underlying large scale coordination of brain activity from neural data remains challenging. One obstacle is the difficulty to link high dimensional functional connectivity measures to mechanistic models of network activity. We address this issue by investigating spike-field coupling (SFC) measurements, which quantify the synchronization between, on the one hand, the action potentials produced by neurons, and on the other hand mesoscopic "field" signals, reflecting subthreshold activities at possibly multiple recording sites. As the number of recording sites gets large, the amount of pairwise SFC measurements becomes overwhelmingly challenging to interpret. We develop Generalized Phase Locking Analysis (GPLA) as an interpretable dimensionality reduction of this multivariate SFC. GPLA describes the dominant coupling between field activity and neural ensembles across space and frequencies. We show that GPLA features are biophysically interpretable when used in conjunction with appropriate network models, such that we can identify the influence of underlying circuit properties on these features. We demonstrate the statistical benefits and interpretability of this approach in various computational models and Utah array recordings. The results suggest that GPLA, used jointly with biophysical modeling, can help uncover the contribution of recurrent microcircuits to the spatio-temporal dynamics observed in multi-channel experimental recordings.
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Affiliation(s)
- Shervin Safavi
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- IMPRS for Cognitive and Systems Neuroscience, University of Tübingen, Tübingen, Germany
| | - Theofanis I. Panagiotaropoulos
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Cognitive Neuroimaging Unit, INSERM, CEA, CNRS, Université Paris-Saclay, NeuroSpin center, 91191 Gif/Yvette, France
| | - Vishal Kapoor
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- International Center for Primate Brain Research (ICPBR), Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences (CAS), Shanghai 201602, China
| | - Juan F. Ramirez-Villegas
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Institute of Science and Technology Austria (IST Austria), Klosterneuburg, Austria
| | - Nikos K. Logothetis
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- International Center for Primate Brain Research (ICPBR), Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences (CAS), Shanghai 201602, China
- Centre for Imaging Sciences, Biomedical Imaging Institute, The University of Manchester, Manchester, United Kingdom
| | - Michel Besserve
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Department of Empirical Inference, Max Planck Institute for Intelligent Systems and MPI-ETH Center for Learning Systems, Tübingen, Germany
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9
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Neurodynamical Computing at the Information Boundaries of Intelligent Systems. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10081-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
AbstractArtificial intelligence has not achieved defining features of biological intelligence despite models boasting more parameters than neurons in the human brain. In this perspective article, we synthesize historical approaches to understanding intelligent systems and argue that methodological and epistemic biases in these fields can be resolved by shifting away from cognitivist brain-as-computer theories and recognizing that brains exist within large, interdependent living systems. Integrating the dynamical systems view of cognition with the massive distributed feedback of perceptual control theory highlights a theoretical gap in our understanding of nonreductive neural mechanisms. Cell assemblies—properly conceived as reentrant dynamical flows and not merely as identified groups of neurons—may fill that gap by providing a minimal supraneuronal level of organization that establishes a neurodynamical base layer for computation. By considering information streams from physical embodiment and situational embedding, we discuss this computational base layer in terms of conserved oscillatory and structural properties of cortical-hippocampal networks. Our synthesis of embodied cognition, based in dynamical systems and perceptual control, aims to bypass the neurosymbolic stalemates that have arisen in artificial intelligence, cognitive science, and computational neuroscience.
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10
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Medalla M, Chang W, Ibañez S, Guillamon-Vivancos T, Nittmann M, Kapitonava A, Busch SE, Moore TL, Rosene DL, Luebke JI. Layer-specific pyramidal neuron properties underlie diverse anterior cingulate cortical motor and limbic networks. Cereb Cortex 2022; 32:2170-2196. [PMID: 34613380 PMCID: PMC9113240 DOI: 10.1093/cercor/bhab347] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 08/26/2021] [Accepted: 08/27/2021] [Indexed: 11/13/2022] Open
Abstract
The laminar cellular and circuit mechanisms by which the anterior cingulate cortex (ACC) exerts flexible control of motor and affective information for goal-directed behavior have not been elucidated. Using multimodal tract-tracing, in vitro patch-clamp recording and computational approaches in rhesus monkeys (M. mulatta), we provide evidence that specialized motor and affective network dynamics can be conferred by layer-specific biophysical and structural properties of ACC pyramidal neurons targeting two key downstream structures -the dorsal premotor cortex (PMd) and the amygdala (AMY). AMY-targeting neurons exhibited significant laminar differences, with L5 more excitable (higher input resistance and action potential firing rates) than L3 neurons. Between-pathway differences were found within L5, with AMY-targeting neurons exhibiting greater excitability, apical dendritic complexity, spine densities, and diversity of inhibitory inputs than PMd-targeting neurons. Simulations using a pyramidal-interneuron network model predict that these layer- and pathway-specific single-cell differences contribute to distinct network oscillatory dynamics. L5 AMY-targeting networks are more tuned to slow oscillations well-suited for affective and contextual processing timescales, while PMd-targeting networks showed strong beta/gamma synchrony implicated in rapid sensorimotor processing. These findings are fundamental to our broad understanding of how layer-specific cellular and circuit properties can drive diverse laminar activity found in flexible behavior.
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Affiliation(s)
- Maria Medalla
- Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA, 02118, USA
- Center for Systems Neuroscience, Boston University, Boston, MA, 02215, USA
| | - Wayne Chang
- Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA, 02118, USA
- Center for Systems Neuroscience, Boston University, Boston, MA, 02215, USA
| | - Sara Ibañez
- Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA, 02118, USA
- Center for Systems Neuroscience, Boston University, Boston, MA, 02215, USA
| | - Teresa Guillamon-Vivancos
- Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA, 02118, USA
- Instituto de Neurociencias de Alicante, Alicante, Spain
| | - Mathias Nittmann
- Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA, 02118, USA
- University of South Florida, Morsani College of Medicine, Tampa, FL, 33612, USA
| | - Anastasia Kapitonava
- Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA, 02118, USA
| | - Silas E Busch
- Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA, 02118, USA
- Department of Neurobiology, University of Chicago, Chicago, IL, 60637, USA
| | - Tara L Moore
- Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA, 02118, USA
- Center for Systems Neuroscience, Boston University, Boston, MA, 02215, USA
| | - Douglas L Rosene
- Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA, 02118, USA
- Center for Systems Neuroscience, Boston University, Boston, MA, 02215, USA
| | - Jennifer I Luebke
- Department of Anatomy & Neurobiology, Boston University School of Medicine, Boston, MA, 02118, USA
- Center for Systems Neuroscience, Boston University, Boston, MA, 02215, USA
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11
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Constraints on Persistent Activity in a Biologically Detailed Network Model of the Prefrontal Cortex with Heterogeneities. Prog Neurobiol 2022; 215:102287. [DOI: 10.1016/j.pneurobio.2022.102287] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 02/25/2022] [Accepted: 05/04/2022] [Indexed: 11/18/2022]
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12
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Susin E, Destexhe A. Integration, coincidence detection and resonance in networks of spiking neurons expressing Gamma oscillations and asynchronous states. PLoS Comput Biol 2021; 17:e1009416. [PMID: 34529655 PMCID: PMC8478196 DOI: 10.1371/journal.pcbi.1009416] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 09/28/2021] [Accepted: 09/02/2021] [Indexed: 12/29/2022] Open
Abstract
Gamma oscillations are widely seen in the awake and sleeping cerebral cortex, but the exact role of these oscillations is still debated. Here, we used biophysical models to examine how Gamma oscillations may participate to the processing of afferent stimuli. We constructed conductance-based network models of Gamma oscillations, based on different cell types found in cerebral cortex. The models were adjusted to extracellular unit recordings in humans, where Gamma oscillations always coexist with the asynchronous firing mode. We considered three different mechanisms to generate Gamma, first a mechanism based on the interaction between pyramidal neurons and interneurons (PING), second a mechanism in which Gamma is generated by interneuron networks (ING) and third, a mechanism which relies on Gamma oscillations generated by pacemaker chattering neurons (CHING). We find that all three mechanisms generate features consistent with human recordings, but that the ING mechanism is most consistent with the firing rate change inside Gamma bursts seen in the human data. We next evaluated the responsiveness and resonant properties of these networks, contrasting Gamma oscillations with the asynchronous mode. We find that for both slowly-varying stimuli and precisely-timed stimuli, the responsiveness is generally lower during Gamma compared to asynchronous states, while resonant properties are similar around the Gamma band. We could not find conditions where Gamma oscillations were more responsive. We therefore predict that asynchronous states provide the highest responsiveness to external stimuli, while Gamma oscillations tend to overall diminish responsiveness. In the awake and attentive brain, the activity of neurons is typically asynchronous and irregular. It also occasionally displays oscillations in the Gamma frequency range (30–90 Hz), which are believed to be involved in information processing. Here, we use computational models to investigate how brain circuits generate oscillations in a manner consistent with microelectrode recordings in humans. We then study how these networks respond to external input, comparing asynchronous and oscillatory states. This is tested according to several paradigms, an integrative mode, where slowly varying inputs are progressively integrated, a coincidence detection mode, where brief inputs are processed according to the phase of the oscillations, and a resonance mode where the network is probed with oscillatory inputs. Surprisingly, we find that in all cases, the presence of Gamma oscillations tends to diminish the responsiveness to external inputs. We discuss possible implications of this responsiveness decrease on information processing and propose new directions for further exploration.
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Affiliation(s)
- Eduarda Susin
- Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Centre National de la Recherche Scientifique (CNRS), Gif-sur-Yvette, France
- * E-mail:
| | - Alain Destexhe
- Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Centre National de la Recherche Scientifique (CNRS), Gif-sur-Yvette, France
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13
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Banaie Boroujeni K, Tiesinga P, Womelsdorf T. Interneuron-specific gamma synchronization indexes cue uncertainty and prediction errors in lateral prefrontal and anterior cingulate cortex. eLife 2021; 10:69111. [PMID: 34142661 PMCID: PMC8248985 DOI: 10.7554/elife.69111] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 06/17/2021] [Indexed: 12/27/2022] Open
Abstract
Inhibitory interneurons are believed to realize critical gating functions in cortical circuits, but it has been difficult to ascertain the content of gated information for well-characterized interneurons in primate cortex. Here, we address this question by characterizing putative interneurons in primate prefrontal and anterior cingulate cortex while monkeys engaged in attention demanding reversal learning. We find that subclasses of narrow spiking neurons have a relative suppressive effect on the local circuit indicating they are inhibitory interneurons. One of these interneuron subclasses showed prominent firing rate modulations and (35–45 Hz) gamma synchronous spiking during periods of uncertainty in both, lateral prefrontal cortex (LPFC) and anterior cingulate cortex (ACC). In LPFC, this interneuron subclass activated when the uncertainty of attention cues was resolved during flexible learning, whereas in ACC it fired and gamma-synchronized when outcomes were uncertain and prediction errors were high during learning. Computational modeling of this interneuron-specific gamma band activity in simple circuit motifs suggests it could reflect a soft winner-take-all gating of information having high degree of uncertainty. Together, these findings elucidate an electrophysiologically characterized interneuron subclass in the primate, that forms gamma synchronous networks in two different areas when resolving uncertainty during adaptive goal-directed behavior.
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Affiliation(s)
| | - Paul Tiesinga
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Thilo Womelsdorf
- Department of Psychology, Vanderbilt University, Nashville, United States.,Department of Biology, Centre for Vision Research, York University, Toronto, Canada
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14
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Fischer P. Mechanisms of Network Interactions for Flexible Cortico-Basal Ganglia-Mediated Action Control. eNeuro 2021; 8:ENEURO.0009-21.2021. [PMID: 33883192 PMCID: PMC8205496 DOI: 10.1523/eneuro.0009-21.2021] [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: 01/10/2021] [Revised: 03/23/2021] [Accepted: 03/24/2021] [Indexed: 01/28/2023] Open
Abstract
In humans, finely tuned γ synchronization (60-90 Hz) rapidly appears at movement onset in a motor control network involving primary motor cortex, the basal ganglia and motor thalamus. Yet the functional consequences of brief movement-related synchronization are still unclear. Distinct synchronization phenomena have also been linked to different forms of motor inhibition, including relaxing antagonist muscles, rapid movement interruption and stabilizing network dynamics for sustained contractions. Here, I will introduce detailed hypotheses about how intrasite and intersite synchronization could interact with firing rate changes in different parts of the network to enable flexible action control. The here proposed cause-and-effect relationships shine a spotlight on potential key mechanisms of cortico-basal ganglia-thalamo-cortical (CBGTC) communication. Confirming or revising these hypotheses will be critical in understanding the neuronal basis of flexible movement initiation, invigoration and inhibition. Ultimately, the study of more complex cognitive phenomena will also become more tractable once we understand the neuronal mechanisms underlying behavioral readouts.
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Affiliation(s)
- Petra Fischer
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, OX3 9DU Oxford, United Kingdom
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15
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Novikov N, Zakharov D, Moiseeva V, Gutkin B. Activity Stabilization in a Population Model of Working Memory by Sinusoidal and Noisy Inputs. Front Neural Circuits 2021; 15:647944. [PMID: 33967703 PMCID: PMC8096914 DOI: 10.3389/fncir.2021.647944] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 03/19/2021] [Indexed: 01/22/2023] Open
Abstract
According to mechanistic theories of working memory (WM), information is retained as stimulus-dependent persistent spiking activity of cortical neural networks. Yet, how this activity is related to changes in the oscillatory profile observed during WM tasks remains a largely open issue. We explore joint effects of input gamma-band oscillations and noise on the dynamics of several firing rate models of WM. The considered models have a metastable active regime, i.e., they demonstrate long-lasting transient post-stimulus firing rate elevation. We start from a single excitatory-inhibitory circuit and demonstrate that either gamma-band or noise input could stabilize the active regime, thus supporting WM retention. We then consider a system of two circuits with excitatory intercoupling. We find that fast coupling allows for better stabilization by common noise compared to independent noise and stronger amplification of this effect by in-phase gamma inputs compared to anti-phase inputs. Finally, we consider a multi-circuit system comprised of two clusters, each containing a group of circuits receiving a common noise input and a group of circuits receiving independent noise. Each cluster is associated with its own local gamma generator, so all its circuits receive gamma-band input in the same phase. We find that gamma-band input differentially stabilizes the activity of the "common-noise" groups compared to the "independent-noise" groups. If the inter-cluster connections are fast, this effect is more pronounced when the gamma-band input is delivered to the clusters in the same phase rather than in the anti-phase. Assuming that the common noise comes from a large-scale distributed WM representation, our results demonstrate that local gamma oscillations can stabilize the activity of the corresponding parts of this representation, with stronger effect for fast long-range connections and synchronized gamma oscillations.
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Affiliation(s)
- Nikita Novikov
- Centre for Cognition and Decision Making, HSE University, Moscow, Russia
| | - Denis Zakharov
- Centre for Cognition and Decision Making, HSE University, Moscow, Russia
| | - Victoria Moiseeva
- Centre for Cognition and Decision Making, HSE University, Moscow, Russia
| | - Boris Gutkin
- Centre for Cognition and Decision Making, HSE University, Moscow, Russia.,Group for Neural Theory, LNC2 INSERM U960, Départment d'Études Cognitives, École Normale Supérieure, PSL Research Université, Paris, France
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