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Shin Y, Ryu J, Bai T, Qiang Y, Qi Y, Li G, Huang Y, Seo KJ, Fang H. Array-wide uniform PEDOT:PSS electroplating from potentiostatic deposition. Biosens Bioelectron 2024; 261:116418. [PMID: 38875864 PMCID: PMC11214878 DOI: 10.1016/j.bios.2024.116418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 04/04/2024] [Accepted: 05/20/2024] [Indexed: 06/16/2024]
Abstract
Electroplating of poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) is important in many neuroelectronic applications but is challenging to achieve uniformity on large-scale microelectrode arrays (MEA) using conventional galvanostatic methods. In this study, we address this challenge through a potentiostatic method and demonstrate highly uniform electroplating of PEDOT:PSS on MEA with more than one hundred electrodes, all at cellular sizes. The validation of this approach involves comparisons with galvanostatic deposition methods, showcasing unparalleled deposition yield and uniformity. Systematic electrochemical characterizations reveal similarities in structure and stability from potentiostatic deposited coatings. The advances developed here establish the potentiostatic method and detailed process to achieve a uniform coating of PEDOT:PSS on large-scale MEA, with broad utility in neuroelectronics.
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Affiliation(s)
- Yieljae Shin
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA
| | - Jaehyeon Ryu
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA
| | - Tianyu Bai
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA
| | - Yi Qiang
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA
| | - Yongli Qi
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA
| | - Gen Li
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA
| | - Yunxiang Huang
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA
| | - Kyung Jin Seo
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA.
| | - Hui Fang
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA.
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2
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Aksenov A, Renaud-D’Ambra M, Volpert V, Beuter A. Phase-shifted tACS can modulate cortical alpha waves in human subjects. Cogn Neurodyn 2024; 18:1575-1592. [PMID: 39104698 PMCID: PMC11297852 DOI: 10.1007/s11571-023-09997-1] [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: 01/11/2023] [Revised: 07/13/2023] [Accepted: 08/06/2023] [Indexed: 08/07/2024] Open
Abstract
In the present study, we investigated traveling waves induced by transcranial alternating current stimulation in the alpha frequency band of healthy subjects. Electroencephalographic data were recorded in 12 healthy subjects before, during, and after phase-shifted stimulation with a device combining both electroencephalographic and stimulation capacities. In addition, we analyzed the results of numerical simulations and compared them to the results of identical analysis on real EEG data. The results of numerical simulations indicate that imposed transcranial alternating current stimulation induces a rotating electric field. The direction of waves induced by stimulation was observed more often during at least 30 s after the end of stimulation, demonstrating the presence of aftereffects of the stimulation. Results suggest that the proposed approach could be used to modulate the interaction between distant areas of the cortex. Non-invasive transcranial alternating current stimulation can be used to facilitate the propagation of circulating waves at a particular frequency and in a controlled direction. The results presented open new opportunities for developing innovative and personalized transcranial alternating current stimulation protocols to treat various neurological disorders. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-023-09997-1.
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Affiliation(s)
| | | | - Vitaly Volpert
- Institute Camille Jordan, UMR 5208 CNRS, University Lyon 1, Villeurbanne, France
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3
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Sabater-Gárriz Á, Montoya P, Riquelme I. Enhanced EEG power density during painful stretching in individuals with cerebral palsy. RESEARCH IN DEVELOPMENTAL DISABILITIES 2024; 150:104760. [PMID: 38795555 DOI: 10.1016/j.ridd.2024.104760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 04/22/2024] [Accepted: 05/15/2024] [Indexed: 05/28/2024]
Abstract
BACKGROUND Pain perception mechanisms in cerebral palsy remain largely unclear. AIMS This study investigates brain activity in adults with cerebral palsy during painful and non-painful stretching to elucidate their pain processing characteristics. METHODS AND PROCEDURES Twenty adults with cerebral palsy and 20 controls underwent EEG in three conditions: rest, non-painful stretching, and painful stretching. Time-frequency power density of theta, alpha, and beta waves in somatosensory and frontal cortices was analyzed, alongside baseline pressure pain thresholds. OUTCOMES AND RESULTS Cerebral palsy individuals exhibited higher theta, alpha, and beta power density in both cortices during painful stretching compared to rest, and lower during non-painful stretching. Controls showed higher power density during non-painful stretching but lower during painful stretching. Cerebral palsy individuals had higher pain sensitivity, with those more sensitive experiencing greater alpha power density. CONCLUSIONS AND IMPLICATIONS These findings confirm alterations in the cerebral processing of pain in individuals with cerebral palsy. This knowledge could enhance future approaches to the diagnosis and treatment of pain in this vulnerable population.
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Affiliation(s)
- Álvaro Sabater-Gárriz
- Balearic ASPACE Foundation, Marratxí, Spain; Health Research Institute of the Balearic Islands (IUNICS-IdISBa), University of the Balearic Islands, Palma de Mallorca, Spain; Department of Nursing and Physiotherapy, University of the Balearic Islands, Palma de Mallorca, Spain
| | - Pedro Montoya
- Health Research Institute of the Balearic Islands (IUNICS-IdISBa), University of the Balearic Islands, Palma de Mallorca, Spain; Center for Mathematics, Computation and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil
| | - Inmaculada Riquelme
- Health Research Institute of the Balearic Islands (IUNICS-IdISBa), University of the Balearic Islands, Palma de Mallorca, Spain; Department of Nursing and Physiotherapy, University of the Balearic Islands, Palma de Mallorca, Spain.
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4
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Bryant AG, Aquino K, Parkes L, Fornito A, Fulcher BD. Extracting interpretable signatures of whole-brain dynamics through systematic comparison. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.10.573372. [PMID: 38915560 PMCID: PMC11195072 DOI: 10.1101/2024.01.10.573372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
The brain's complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for a given application. Here, we address this limitation by systematically comparing diverse, interpretable features of both intra-regional activity and inter-regional functional coupling from resting-state functional magnetic resonance imaging (rs-fMRI) data, demonstrating our method using case-control comparisons of four neuropsychiatric disorders. Our findings generally support the use of linear time-series analysis techniques for rs-fMRI case-control analyses, while also identifying new ways to quantify informative dynamical fMRI structures. While simple statistical representations of fMRI dynamics performed surprisingly well (e.g., properties within a single brain region), combining intra-regional properties with inter-regional coupling generally improved performance, underscoring the distributed, multifaceted changes to fMRI dynamics in neuropsychiatric disorders. The comprehensive, data-driven method introduced here enables systematic identification and interpretation of quantitative dynamical signatures of multivariate time-series data, with applicability beyond neuroimaging to diverse scientific problems involving complex time-varying systems.
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Affiliation(s)
- Annie G. Bryant
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
| | - Kevin Aquino
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
- Brain Key Incorporated, San Francisco, CA, USA
| | - Linden Parkes
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA
- Turner Institute for Brain & Mental Health, Monash University, VIC, Australia
| | - Alex Fornito
- Turner Institute for Brain & Mental Health, Monash University, VIC, Australia
| | - Ben D. Fulcher
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
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5
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Ni S, Harris B, Gong P. Distributed and dynamical communication: a mechanism for flexible cortico-cortical interactions and its functional roles in visual attention. Commun Biol 2024; 7:550. [PMID: 38719883 PMCID: PMC11078951 DOI: 10.1038/s42003-024-06228-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 04/22/2024] [Indexed: 05/12/2024] Open
Abstract
Perceptual and cognitive processing relies on flexible communication among cortical areas; however, the underlying neural mechanism remains unclear. Here we report a mechanism based on the realistic spatiotemporal dynamics of propagating wave patterns in neural population activity. Using a biophysically plausible, multiarea spiking neural circuit model, we demonstrate that these wave patterns, characterized by their rich and complex dynamics, can account for a wide variety of empirically observed neural processes. The coordinated interactions of these wave patterns give rise to distributed and dynamic communication (DDC) that enables flexible and rapid routing of neural activity across cortical areas. We elucidate how DDC unifies the previously proposed oscillation synchronization-based and subspace-based views of interareal communication, offering experimentally testable predictions that we validate through the analysis of Allen Institute Neuropixels data. Furthermore, we demonstrate that DDC can be effectively modulated during attention tasks through the interplay of neuromodulators and cortical feedback loops. This modulation process explains many neural effects of attention, underscoring the fundamental functional role of DDC in cognition.
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Affiliation(s)
- Shencong Ni
- School of Physics, University of Sydney, Sydney, NSW, Australia
| | - Brendan Harris
- School of Physics, University of Sydney, Sydney, NSW, Australia
| | - Pulin Gong
- School of Physics, University of Sydney, Sydney, NSW, Australia.
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6
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Glaeser-Khan S, Savalia NK, Cressy J, Feng J, Li Y, Kwan AC, Kaye AP. Spatiotemporal Organization of Prefrontal Norepinephrine Influences Neuronal Activity. eNeuro 2024; 11:ENEURO.0252-23.2024. [PMID: 38702188 PMCID: PMC11134306 DOI: 10.1523/eneuro.0252-23.2024] [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: 07/19/2023] [Revised: 01/08/2024] [Accepted: 01/19/2024] [Indexed: 05/06/2024] Open
Abstract
Norepinephrine (NE), a neuromodulator released by locus ceruleus (LC) neurons throughout the cortex, influences arousal and learning through extrasynaptic vesicle exocytosis. While NE within cortical regions has been viewed as a homogenous field, recent studies have demonstrated heterogeneous axonal dynamics and advances in GPCR-based fluorescent sensors permit direct observation of the local dynamics of NE at cellular scale. To investigate how the spatiotemporal dynamics of NE release in the prefrontal cortex (PFC) affect neuronal firing, we employed in vivo two-photon imaging of layer 2/3 of the PFC in order to observe fine-scale neuronal calcium and NE dynamics concurrently. In this proof of principle study, we found that local and global NE fields can decouple from one another, providing a substrate for local NE spatiotemporal activity patterns. Optic flow analysis revealed putative release and reuptake events which can occur at the same location, albeit at different times, indicating the potential to create a heterogeneous NE field. Utilizing generalized linear models, we demonstrated that cellular Ca2+ fluctuations are influenced by both the local and global NE field. However, during periods of local/global NE field decoupling, the local field drives cell firing dynamics rather than the global field. These findings underscore the significance of localized, phasic NE fluctuations for structuring cell firing, which may provide local neuromodulatory control of cortical activity.
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Affiliation(s)
| | - Neil K Savalia
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut 06510
- Medical Scientist Training Program, Yale University School of Medicine, New Haven, Connecticut 06511
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York 14853
| | - Jianna Cressy
- Department of Psychiatry, Yale University, New Haven, Connecticut 06511
- Clinical Neuroscience Division, VA National Center for PTSD, West Haven, Connecticut 06515
| | - Jiesi Feng
- State Key Laboratory of Membrane Biology, Peking University School of Life Sciences, Beijing 100871, China
| | - Yulong Li
- State Key Laboratory of Membrane Biology, Peking University School of Life Sciences, Beijing 100871, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Alex C Kwan
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York 14853
| | - Alfred P Kaye
- Department of Psychiatry, Yale University, New Haven, Connecticut 06511
- Clinical Neuroscience Division, VA National Center for PTSD, West Haven, Connecticut 06515
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7
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Misra J, Pessoa L. Brain dynamics and spatiotemporal trajectories during threat processing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.06.588389. [PMID: 38617278 PMCID: PMC11014591 DOI: 10.1101/2024.04.06.588389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
In the past decades, functional MRI research has investigated mental states and their brain bases in largely static fashion based on evoked responses during blocked and event-related designs. Despite some progress in naturalistic designs, our understanding of threat processing remains largely limited to those obtained with standard paradigms. In the present paper, we applied Switching Linear Dynamical Systems to uncover the dynamics of threat processing during a continuous threat-of-shock paradigm. Importantly, unlike studies in systems neuroscience that frequently assume that systems are decoupled from external inputs, we characterized both endogenous and exogenous contributions to dynamics. First, we demonstrated that the SLDS model learned the regularities of the experimental paradigm, such that states and state transitions estimated from fMRI time series data from 85 ROIs reflected both the proximity of the circles and their direction (approach vs. retreat). After establishing that the model captured key properties of threat-related processing, we characterized the dynamics of the states and their transitions. The results revealed that threat processing can profitably be viewed in terms of dynamic multivariate patterns whose trajectories are a combination of intrinsic and extrinsic factors that jointly determine how the brain temporally evolves during dynamic threat. We propose that viewing threat processing through the lens of dynamical systems offers important avenues to uncover properties of the dynamics of threat that are not unveiled with standard experimental designs and analyses.
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Affiliation(s)
- Joyneel Misra
- Departmentof Electrical and Computer Engineering, University of Maryland, College Park, Maryland, United States of America
| | - Luiz Pessoa
- Departmentof Electrical and Computer Engineering, University of Maryland, College Park, Maryland, United States of America
- Department of Psychology and Maryland Neuroimaging Center, University of Maryland, College Park, Maryland, United States of America
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8
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Deng F, Wan J, Li G, Dong H, Xia X, Wang Y, Li X, Zhuang C, Zheng Y, Liu L, Yan Y, Feng J, Zhao Y, Xie H, Li Y. Improved green and red GRAB sensors for monitoring spatiotemporal serotonin release in vivo. Nat Methods 2024; 21:692-702. [PMID: 38443508 DOI: 10.1038/s41592-024-02188-8] [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: 05/11/2023] [Accepted: 01/19/2024] [Indexed: 03/07/2024]
Abstract
The serotonergic system plays important roles in both physiological and pathological processes, and is a therapeutic target for many psychiatric disorders. Although several genetically encoded GFP-based serotonin (5-HT) sensors were recently developed, their sensitivities and spectral profiles are relatively limited. To overcome these limitations, we optimized green fluorescent G-protein-coupled receptor (GPCR)-activation-based 5-HT (GRAB5-HT) sensors and developed a red fluorescent GRAB5-HT sensor. These sensors exhibit excellent cell surface trafficking and high specificity, sensitivity and spatiotemporal resolution, making them suitable for monitoring 5-HT dynamics in vivo. Besides recording subcortical 5-HT release in freely moving mice, we observed both uniform and gradient 5-HT release in the mouse dorsal cortex with mesoscopic imaging. Finally, we performed dual-color imaging and observed seizure-induced waves of 5-HT release throughout the cortex following calcium and endocannabinoid waves. In summary, these 5-HT sensors can offer valuable insights regarding the serotonergic system in both health and disease.
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Affiliation(s)
- Fei Deng
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
- Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen, China
| | - Jinxia Wan
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
| | - Guochuan Li
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
| | - Hui Dong
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
| | - Xiju Xia
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
- Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Yipan Wang
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
| | - Xuelin Li
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
| | - Chaowei Zhuang
- Department of Automation, Tsinghua University, Beijing, China
| | - Yu Zheng
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
- Peking-Tsinghua Center for Life Sciences, New Cornerstone Science Laboratory, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Laixin Liu
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
- Peking-Tsinghua Center for Life Sciences, New Cornerstone Science Laboratory, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Yuqi Yan
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
- Peking-Tsinghua Center for Life Sciences, New Cornerstone Science Laboratory, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Jiesi Feng
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
| | - Yulin Zhao
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China
| | - Hao Xie
- Department of Automation, Tsinghua University, Beijing, China
| | - Yulong Li
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing, China.
- PKU-IDG/McGovern Institute for Brain Research, Beijing, China.
- Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
- Peking-Tsinghua Center for Life Sciences, New Cornerstone Science Laboratory, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
- Chinese Institute for Brain Research, Beijing, China.
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9
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Gutzen R, De Bonis G, De Luca C, Pastorelli E, Capone C, Allegra Mascaro AL, Resta F, Manasanch A, Pavone FS, Sanchez-Vives MV, Mattia M, Grün S, Paolucci PS, Denker M. A modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets. CELL REPORTS METHODS 2024; 4:100681. [PMID: 38183979 PMCID: PMC10831958 DOI: 10.1016/j.crmeth.2023.100681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 08/11/2023] [Accepted: 12/11/2023] [Indexed: 01/08/2024]
Abstract
Neuroscience is moving toward a more integrative discipline where understanding brain function requires consolidating the accumulated evidence seen across experiments, species, and measurement techniques. A remaining challenge on that path is integrating such heterogeneous data into analysis workflows such that consistent and comparable conclusions can be distilled as an experimental basis for models and theories. Here, we propose a solution in the context of slow-wave activity (<1 Hz), which occurs during unconscious brain states like sleep and general anesthesia and is observed across diverse experimental approaches. We address the issue of integrating and comparing heterogeneous data by conceptualizing a general pipeline design that is adaptable to a variety of inputs and applications. Furthermore, we present the Collaborative Brain Wave Analysis Pipeline (Cobrawap) as a concrete, reusable software implementation to perform broad, detailed, and rigorous comparisons of slow-wave characteristics across multiple, openly available electrocorticography (ECoG) and calcium imaging datasets.
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Affiliation(s)
- Robin Gutzen
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany; Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany.
| | - Giulia De Bonis
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome, Italy
| | - Chiara De Luca
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome, Italy; Institute of Neuroinformatics, University of Zürich and ETH Zürich, Zürich, Switzerland
| | - Elena Pastorelli
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome, Italy
| | - Cristiano Capone
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma, Rome, Italy
| | - Anna Letizia Allegra Mascaro
- European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Florence, Italy; Neuroscience Institute, National Research Council, Pisa, Italy
| | - Francesco Resta
- European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Florence, Italy; Department of Physics and Astronomy, University of Florence, Florence, Italy
| | - Arnau Manasanch
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Francesco Saverio Pavone
- European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Florence, Italy; Department of Physics and Astronomy, University of Florence, Florence, Italy; National Institute of Optics, National Research Council, Sesto Fiorentino, Italy
| | - Maria V Sanchez-Vives
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Maurizio Mattia
- National Center for Radiation Protection and Computational Physics, Istituto Superiore di Sanità (ISS), Rome, Italy
| | - Sonja Grün
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany; Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany
| | | | - Michael Denker
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
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10
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Glaeser-Khan S, Savalia NK, Cressy J, Feng J, Li Y, Kwan AC, Kaye AP. Spatiotemporal organization of prefrontal norepinephrine influences neuronal activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.09.544191. [PMID: 37502881 PMCID: PMC10370029 DOI: 10.1101/2023.06.09.544191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Norepinephrine (NE), a neuromodulator released by locus coeruleus neurons throughout cortex, influences arousal and learning through extra-synaptic vesicle exocytosis. While NE within cortical regions has been viewed as a homogenous field, recent studies have demonstrated heterogeneous axonal dynamics and advances in GPCR-based fluorescent sensors permit direct observation of the local dynamics of NE at cellular scale. To investigate how the spatiotemporal dynamics of NE release in the PFC affect neuronal firing, we employed in-vivo two-photon imaging of layer 2/3 of PFC in order to observe fine-scale neuronal calcium and NE dynamics concurrently. We found that local and global NE fields can decouple from one another, providing a substrate for local NE spatiotemporal activity patterns. Optic flow analysis revealed putative release and reuptake events which can occur at the same location, albeit at different times, indicating the potential to create a heterogeneous NE field. Utilizing generalized linear models, we demonstrated that cellular Ca2+ fluctuations are influenced by both the local and global NE field. However, during periods of local/global NE field decoupling, the local field drives cell firing dynamics rather than the global field. These findings underscore the significance of localized, phasic NE fluctuations for structuring cell firing, which may provide local neuromodulatory control of cortical activity.
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11
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Xu Y, Long X, Feng J, Gong P. Interacting spiral wave patterns underlie complex brain dynamics and are related to cognitive processing. Nat Hum Behav 2023:10.1038/s41562-023-01626-5. [PMID: 37322235 DOI: 10.1038/s41562-023-01626-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 05/12/2023] [Indexed: 06/17/2023]
Abstract
The large-scale activity of the human brain exhibits rich and complex patterns, but the spatiotemporal dynamics of these patterns and their functional roles in cognition remain unclear. Here by characterizing moment-by-moment fluctuations of human cortical functional magnetic resonance imaging signals, we show that spiral-like, rotational wave patterns (brain spirals) are widespread during both resting and cognitive task states. These brain spirals propagate across the cortex while rotating around their phase singularity centres, giving rise to spatiotemporal activity dynamics with non-stationary features. The properties of these brain spirals, such as their rotational directions and locations, are task relevant and can be used to classify different cognitive tasks. We also demonstrate that multiple, interacting brain spirals are involved in coordinating the correlated activations and de-activations of distributed functional regions; this mechanism enables flexible reconfiguration of task-driven activity flow between bottom-up and top-down directions during cognitive processing. Our findings suggest that brain spirals organize complex spatiotemporal dynamics of the human brain and have functional correlates to cognitive processing.
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Affiliation(s)
- Yiben Xu
- School of Physics, University of Sydney, Sydney, New South Wales, Australia
- ARC Centre of Excellence for Integrative Brain Function, University of Sydney, Sydney, New South Wales, Australia
| | - Xian Long
- School of Physics, University of Sydney, Sydney, New South Wales, Australia
- ARC Centre of Excellence for Integrative Brain Function, University of Sydney, Sydney, New South Wales, Australia
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Pulin Gong
- School of Physics, University of Sydney, Sydney, New South Wales, Australia.
- ARC Centre of Excellence for Integrative Brain Function, University of Sydney, Sydney, New South Wales, Australia.
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12
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Interacting spiral waves organize brain dynamics and have functional correlates to cognition. Nat Hum Behav 2023:10.1038/s41562-023-01628-3. [PMID: 37322237 DOI: 10.1038/s41562-023-01628-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
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13
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Volpert V, Xu B, Tchechmedjiev A, Harispe S, Aksenov A, Mesnildrey Q, Beuter A. Characterization of spatiotemporal dynamics in EEG data during picture naming with optical flow patterns. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:11429-11463. [PMID: 37322989 DOI: 10.3934/mbe.2023507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
In this study, we investigate the spatiotemporal dynamics of the neural oscillations by analyzing the electric potential that arises from neural activity. We identify two types of dynamics based on the frequency and phase of oscillations: standing waves or as out-of-phase and modulated waves, which represent a combination of standing and moving waves. To characterize these dynamics, we use optical flow patterns such as sources, sinks, spirals and saddles. We compare analytical and numerical solutions with real EEG data acquired during a picture-naming task. Analytical approximation of standing waves helps us to establish some properties of pattern location and number. Specifically, sources and sinks are mainly located in the same location, while saddles are positioned between them. The number of saddles correlates with the sum of all the other patterns. These properties are confirmed in both the simulated and real EEG data. In particular, source and sink clusters in the EEG data overlap with each other with median percentages around 60%, and hence have high spatial correlation, while source/sink clusters overlap with saddle clusters in less than 1%, and have different locations. Our statistical analysis showed that saddles account for about 45% of all patterns, while the remaining patterns are present in similar proportions.
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Affiliation(s)
- V Volpert
- Institut Camille Jordan, UMR 5208 CNRS, University Lyon 1, 69622 Villeurbanne, France
| | - B Xu
- EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Ales, France
| | - A Tchechmedjiev
- EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Ales, France
| | - S Harispe
- EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Ales, France
| | | | | | - A Beuter
- CorStim SAS, Montpellier, France
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14
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Pines A, Keller AS, Larsen B, Bertolero M, Ashourvan A, Bassett DS, Cieslak M, Covitz S, Fan Y, Feczko E, Houghton A, Rueter AR, Saggar M, Shafiei G, Tapera TM, Vogel J, Weinstein SM, Shinohara RT, Williams LM, Fair DA, Satterthwaite TD. Development of top-down cortical propagations in youth. Neuron 2023; 111:1316-1330.e5. [PMID: 36803653 PMCID: PMC10121821 DOI: 10.1016/j.neuron.2023.01.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 12/08/2022] [Accepted: 01/18/2023] [Indexed: 02/19/2023]
Abstract
Hierarchical processing requires activity propagating between higher- and lower-order cortical areas. However, functional neuroimaging studies have chiefly quantified fluctuations within regions over time rather than propagations occurring over space. Here, we leverage advances in neuroimaging and computer vision to track cortical activity propagations in a large sample of youth (n = 388). We delineate cortical propagations that systematically ascend and descend a cortical hierarchy in all individuals in our developmental cohort, as well as in an independent dataset of densely sampled adults. Further, we demonstrate that top-down, descending hierarchical propagations become more prevalent with greater demands for cognitive control as well as with development in youth. These findings emphasize that hierarchical processing is reflected in the directionality of propagating cortical activity and suggest top-down propagations as a potential mechanism of neurocognitive maturation in youth.
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Affiliation(s)
- Adam Pines
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA; The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Arielle S Keller
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Bart Larsen
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Maxwell Bertolero
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Arian Ashourvan
- Department of Psychology, The University of Kansas, Lawrence, KS 66045, USA
| | - Dani S Bassett
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA; Departments of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics & Astronomy, The University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, Santa Fe, NM 87051, USA
| | - Matthew Cieslak
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney Covitz
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Fan
- Department of Radiology, The University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Eric Feczko
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Audrey Houghton
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Amanda R Rueter
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Manish Saggar
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA
| | - Golia Shafiei
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Tinashe M Tapera
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Jacob Vogel
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah M Weinstein
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Theodore D Satterthwaite
- The Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA.
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15
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Boucher-Routhier M, Thivierge JP. A deep generative adversarial network capturing complex spiral waves in disinhibited circuits of the cerebral cortex. BMC Neurosci 2023; 24:22. [PMID: 36964493 PMCID: PMC10039524 DOI: 10.1186/s12868-023-00792-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/17/2023] [Indexed: 03/26/2023] Open
Abstract
BACKGROUND In the cerebral cortex, disinhibited activity is characterized by propagating waves that spread across neural tissue. In this pathological state, a widely reported form of activity are spiral waves that travel in a circular pattern around a fixed spatial locus termed the center of mass. Spiral waves exhibit stereotypical activity and involve broad patterns of co-fluctuations, suggesting that they may be of lower complexity than healthy activity. RESULTS To evaluate this hypothesis, we performed dense multi-electrode recordings of cortical networks where disinhibition was induced by perfusing a pro-epileptiform solution containing 4-Aminopyridine as well as increased potassium and decreased magnesium. Spiral waves were identified based on a spatially delimited center of mass and a broad distribution of instantaneous phases across electrodes. Individual waves were decomposed into "snapshots" that captured instantaneous neural activation across the entire network. The complexity of these snapshots was examined using a measure termed the participation ratio. Contrary to our expectations, an eigenspectrum analysis of these snapshots revealed a broad distribution of eigenvalues and an increase in complexity compared to baseline networks. A deep generative adversarial network was trained to generate novel exemplars of snapshots that closely captured cortical spiral waves. These synthetic waves replicated key features of experimental data including a tight center of mass, a broad eigenvalue distribution, spatially-dependent correlations, and a high complexity. By adjusting the input to the model, new samples were generated that deviated in systematic ways from the experimental data, thus allowing the exploration of a broad range of states from healthy to pathologically disinhibited neural networks. CONCLUSIONS Together, results show that the complexity of population activity serves as a marker along a continuum from healthy to disinhibited brain states. The proposed generative adversarial network opens avenues for replicating the dynamics of cortical seizures and accelerating the design of optimal neurostimulation aimed at suppressing pathological brain activity.
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Affiliation(s)
- Megan Boucher-Routhier
- School of Psychology, University of Ottawa, 156 Jean-Jacques Lussier, Ottawa, ON, K1N 6N5, Canada
| | - Jean-Philippe Thivierge
- School of Psychology, University of Ottawa, 156 Jean-Jacques Lussier, Ottawa, ON, K1N 6N5, Canada.
- University of Ottawa Brain and Mind Research Institute, 451 Smyth Rd., Ottawa, ON, K1H 8M5, Canada.
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16
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Complexity of cortical wave patterns of the wake mouse cortex. Nat Commun 2023; 14:1434. [PMID: 36918572 PMCID: PMC10015011 DOI: 10.1038/s41467-023-37088-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 03/02/2023] [Indexed: 03/16/2023] Open
Abstract
Rich spatiotemporal dynamics of cortical activity, including complex and diverse wave patterns, have been identified during unconscious and conscious brain states. Yet, how these activity patterns emerge across different levels of wakefulness remain unclear. Here we study the evolution of wave patterns utilizing data from high spatiotemporal resolution optical voltage imaging of mice transitioning from barbiturate-induced anesthesia to wakefulness (N = 5) and awake mice (N = 4). We find that, as the brain transitions into wakefulness, there is a reduction in hemisphere-scale voltage waves, and an increase in local wave events and complexity. A neural mass model recapitulates the essential cellular-level features and shows how the dynamical competition between global and local spatiotemporal patterns and long-range connections can explain the experimental observations. These mechanisms possibly endow the awake cortex with enhanced integrative processing capabilities.
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17
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Chen ZS, Wilson MA. How our understanding of memory replay evolves. J Neurophysiol 2023; 129:552-580. [PMID: 36752404 PMCID: PMC9988534 DOI: 10.1152/jn.00454.2022] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 01/20/2023] [Accepted: 01/20/2023] [Indexed: 02/09/2023] Open
Abstract
Memory reactivations and replay, widely reported in the hippocampus and cortex across species, have been implicated in memory consolidation, planning, and spatial and skill learning. Technological advances in electrophysiology, calcium imaging, and human neuroimaging techniques have enabled neuroscientists to measure large-scale neural activity with increasing spatiotemporal resolution and have provided opportunities for developing robust analytic methods to identify memory replay. In this article, we first review a large body of historically important and representative memory replay studies from the animal and human literature. We then discuss our current understanding of memory replay functions in learning, planning, and memory consolidation and further discuss the progress in computational modeling that has contributed to these improvements. Next, we review past and present analytic methods for replay analyses and discuss their limitations and challenges. Finally, looking ahead, we discuss some promising analytic methods for detecting nonstereotypical, behaviorally nondecodable structures from large-scale neural recordings. We argue that seamless integration of multisite recordings, real-time replay decoding, and closed-loop manipulation experiments will be essential for delineating the role of memory replay in a wide range of cognitive and motor functions.
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Affiliation(s)
- Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, New York, United States
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, New York, United States
- Neuroscience Institute, New York University Grossman School of Medicine, New York, New York, United States
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, New York, United States
| | - Matthew A Wilson
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
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18
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Liu M, Liang Y, Song C, Knöpfel T, Zhou C. Cortex-wide spontaneous activity non-linearly steers propagating sensory-evoked activity in awake mice. Cell Rep 2022; 41:111740. [PMID: 36476858 DOI: 10.1016/j.celrep.2022.111740] [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: 04/13/2022] [Revised: 08/27/2022] [Accepted: 11/07/2022] [Indexed: 12/12/2022] Open
Abstract
The brain responds highly variably to identical sensory inputs, but there is no consensus on the nature of this variability. We explore this question using cortex-wide optical voltage imaging and whisker stimulation in awake mice. Clustering analysis reveals that the sensory-evoked activity propagates over the cortex via distinct pathways associated with distinct behavioral states. The pathway taken by each trial is independent of the level of primary sensory-evoked activation but is partially predictable by the spatiotemporal features of the preceding cortical spontaneous activity patterns. The sensory inputs reduce trial-to-trial variability in brain activity and alter temporal autocorrelation in spatial activity pattern evolutions, suggesting non-linear interactions between evoked activities and spontaneous activities. Further, evoked activities and spontaneous activities occupy different positions in the state space, suggesting that sensory inputs can intricately interact with the internal state to generate large-scale evoked activity patterns not frequented by spontaneous brain states.
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Affiliation(s)
- Mianxin Liu
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Yuqi Liang
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Chenchen Song
- Laboratory for Neuronal Circuit Dynamics, Imperial College London, London, UK
| | - Thomas Knöpfel
- Laboratory for Neuronal Circuit Dynamics, Imperial College London, London, UK.
| | - Changsong Zhou
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong; Research Centre, HKBU Institute of Research and Continuing Education, Virtual University Park Building, South Area Hi-tech Industrial Park, Shenzhen, China.
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19
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Fractional neural sampling as a theory of spatiotemporal probabilistic computations in neural circuits. Nat Commun 2022; 13:4572. [PMID: 35931698 PMCID: PMC9356069 DOI: 10.1038/s41467-022-32279-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 07/22/2022] [Indexed: 11/08/2022] Open
Abstract
A range of perceptual and cognitive processes have been characterized from the perspective of probabilistic representations and inference. To understand the neural circuit mechanism underlying these probabilistic computations, we develop a theory based on complex spatiotemporal dynamics of neural population activity. We first implement and explore this theory in a biophysically realistic, spiking neural circuit. Population activity patterns emerging from the circuit capture realistic variability or fluctuations of neural dynamics both in time and in space. These activity patterns implement a type of probabilistic computations that we name fractional neural sampling (FNS). We further develop a mathematical model to reveal the algorithmic nature of FNS and its computational advantages for representing multimodal distributions, a major challenge faced by existing theories. We demonstrate that FNS provides a unified account of a diversity of experimental observations of neural spatiotemporal dynamics and perceptual processes such as visual perception inference, and that FNS makes experimentally testable predictions. Dynamics of neural circuits mapping brain functions such as sensory processing and decision making, can be characterized by probabilistic representations and inference. The authors elaborate the role of spatiotemporal neural dynamics for more efficient performance of probabilistic computations.
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20
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Mondal A, Mondal A, Aziz-Alaoui MA, Upadhyay RK, Sharma SK, Antonopoulos CG. The generation of diverse traveling pulses and its solution scheme in an excitable slow-fast dynamics. CHAOS (WOODBURY, N.Y.) 2022; 32:083121. [PMID: 36049912 DOI: 10.1063/5.0084606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 07/19/2022] [Indexed: 06/15/2023]
Abstract
In this article, we report on the generation and propagation of traveling pulses in a homogeneous network of diffusively coupled, excitable, slow-fast dynamical neurons. The spatially extended system is modeled using the nearest neighbor coupling theory, in which the diffusion part measures the spatial distribution of coupling topology. We derive analytically the conditions for traveling wave profiles that allow the construction of the shape of traveling nerve impulses. The analytical and numerical results are used to explore the nature of propagating pulses. The symmetric or asymmetric nature of traveling pulses is characterized, and the wave velocity is derived as a function of system parameters. Moreover, we present our results for an extended excitable medium by considering a slow-fast biophysical model with a homogeneous, diffusive coupling that can exhibit various traveling pulses. The appearance of series of pulses is an interesting phenomenon from biophysical and dynamical perspective. Varying the perturbation and coupling parameters, we observe the propagation of activities with various amplitude modulations and transition phases of different wave profiles that affect the speed of pulses in certain parameter regimes. We observe different types of traveling pulses, such as envelope solitons and multi-bump solutions, and show how system parameters and coupling play a major role in the formation of different traveling pulses. Finally, we obtain the conditions for stable and unstable plane waves.
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Affiliation(s)
- Arnab Mondal
- Department of Mathematics and Computing, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India
| | - Argha Mondal
- Department of Mathematics, Sidho-Kanho-Birsha University, Purulia 723104, West Bengal, India
| | - M A Aziz-Alaoui
- Normandie Univ, UNIHAVRE, LMAH, FR-CNRS-3335, ISCN, 76600 Le Havre, France
| | - Ranjit Kumar Upadhyay
- Department of Mathematics and Computing, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India
| | - Sanjeev Kumar Sharma
- Department of Mathematics and Computing, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India
| | - Chris G Antonopoulos
- Department of Mathematical Sciences, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, United Kingdom
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21
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Bhattacharya S, Donoghue JA, Mahnke M, Brincat SL, Brown EN, Miller EK. Propofol Anesthesia Alters Cortical Traveling Waves. J Cogn Neurosci 2022; 34:1274-1286. [PMID: 35468201 DOI: 10.1162/jocn_a_01856] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Oscillatory dynamics in cortex seem to organize into traveling waves that serve a variety of functions. Recent studies show that propofol, a widely used anesthetic, dramatically alters cortical oscillations by increasing slow-delta oscillatory power and coherence. It is not known how this affects traveling waves. We compared traveling waves across the cortex of non-human primates before, during, and after propofol-induced loss of consciousness (LOC). After LOC, traveling waves in the slow-delta (∼1 Hz) range increased, grew more organized, and traveled in different directions relative to the awake state. Higher frequency (8-30 Hz) traveling waves, by contrast, decreased, lost structure, and switched to directions where the slow-delta waves were less frequent. The results suggest that LOC may be due, in part, to increases in the strength and direction of slow-delta traveling waves that, in turn, alter and disrupt traveling waves in the higher frequencies associated with cognition.
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Affiliation(s)
| | | | | | | | - Emery N Brown
- Massachusetts Institute of Technology, Cambridge.,Massachusetts General Hospital/Harvard Medical School, Boston
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22
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An EEG-Based Neuromarketing Approach for Analyzing the Preference of an Electric Car. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9002101. [PMID: 35341175 PMCID: PMC8956417 DOI: 10.1155/2022/9002101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 02/04/2022] [Accepted: 02/22/2022] [Indexed: 11/17/2022]
Abstract
This study evaluates consumer preference from the perspective of neuroscience when a choice is made among a number of cars, one of which is an electric car. Consumer neuroscience contributes to a systematic understanding of the underlying information processing and cognitions involved in choosing or preferring a product. This study aims to evaluate whether neural measures, which were implicitly extracted from brain activities, can be reliable or consistent with self-reported measures such as preference or liking. In an EEG-based experiment, the participants viewed images of automobiles and their specifications. Emotional and attentional stimuli and the participants' responses, in the form of decisions made, were meticulously distinguished and analyzed via signal processing techniques, statistical tests, and brain mapping tools. Long-range temporal correlations (LRTCs) were also calculated to investigate whether the preference of a product could affect the dynamic of neuronal fluctuations. Statistically significant spatiotemporal dynamical differences were then evaluated between those who select an electric car (which seemingly demands specific memory and long-term attention) and participants who choose other cars. The results showed increased PSD and central-parietal and central-frontal coherences at the alpha frequency band for those who selected the electric car. In addition, the findings showed the emergence of LRTCs or the ability of this group to integrate information over extended periods. Furthermore, the result of clustering subjects into two groups, using statistically significant discriminative EEG measures, was associated with the self-report data. The obtained results highlighted the promising role of intrinsically extracted measures on consumers' buying behavior.
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23
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Li QH, Van Nieuwenhuyse E, Xia YX, Pan JT, Duytschaever M, Knecht S, Vandersickel N, Zhou C, Panfilov AV, Zhang H. Finding type and location of the source of cardiac arrhythmias from the averaged flow velocity field using the determinant-trace method. Phys Rev E 2021; 104:064401. [PMID: 35030872 DOI: 10.1103/physreve.104.064401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 11/05/2021] [Indexed: 06/14/2023]
Abstract
Life threatening cardiac arrhythmias result from abnormal propagation of nonlinear electrical excitation waves in the heart. Finding the locations of the sources of these waves remains a challenging problem. This is mainly due to the low spatial resolution of electrode recordings of these waves. Also, these recordings are subjected to noise. In this paper, we develop a different approach: the AFV-DT method based on an averaged flow velocity (AFV) technique adopted from the analysis of optical flows and the determinant-trace (DT) method used for vector field analysis of dynamical systems. This method can find the location and determine all important types of sources found in excitable media such as focal activity, spiral waves, and waves rotating around obstacles. We test this method on in silico data of various wave excitation patterns obtained using the Luo-Rudy model for cardiac tissue. We show that the method works well for data with low spatial resolutions (up to 8×8) and is stable against noise. Finally, we apply it to two clinical cases and show that it can correctly identify the arrhythmia type and location. We discuss further steps on the development and improvement of this approach.
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Affiliation(s)
- Qi-Hao Li
- Department of Physics, Zhejiang University, Hangzhou 310027, China
| | | | - Yuan-Xun Xia
- Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Jun-Ting Pan
- Ocean College, Zhejiang University, Zhoushan 316021, China
| | | | | | - Nele Vandersickel
- Department of Physics and Astronomy, Ghent University, Ghent 9000, Belgium
| | - Changsong Zhou
- Department of Physics, Zhejiang University, Hangzhou 310027, China
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong
- Research Centre, HKBU Institute of Research and Continuing Education, Shenzhen 518057, China
- Beijing Computational Science Research Center, Beijing 100084, China
| | - Alexander V Panfilov
- Department of Physics and Astronomy, Ghent University, Ghent 9000, Belgium
- Laboratory of Computational Biology and Medicine, Ural Federal University, Ekaterinburg 620002, Russia
- World-Class Research Center "Digital biodesign and personalized healthcare," Sechenov University, Moscow 119146, Russia
| | - Hong Zhang
- Department of Physics, Zhejiang University, Hangzhou 310027, China
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24
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Cao L, Varga V, Chen ZS. Uncovering spatial representations from spatiotemporal patterns of rodent hippocampal field potentials. CELL REPORTS METHODS 2021; 1:100101. [PMID: 34888543 PMCID: PMC8654278 DOI: 10.1016/j.crmeth.2021.100101] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 07/27/2021] [Accepted: 09/28/2021] [Indexed: 12/23/2022]
Abstract
Spatiotemporal patterns of large-scale spiking and field potentials of the rodent hippocampus encode spatial representations during maze runs, immobility, and sleep. Here, we show that multisite hippocampal field potential amplitude at ultra-high-frequency band (FPAuhf), a generalized form of multiunit activity, provides not only a fast and reliable reconstruction of the rodent's position when awake, but also a readout of replay content during sharp-wave ripples. This FPAuhf feature may serve as a robust real-time decoding strategy from large-scale recordings in closed-loop experiments. Furthermore, we develop unsupervised learning approaches to extract low-dimensional spatiotemporal FPAuhf features during run and ripple periods and to infer latent dynamical structures from lower-rank FPAuhf features. We also develop an optical flow-based method to identify propagating spatiotemporal LFP patterns from multisite array recordings, which can be used as a decoding application. Finally, we develop a prospective decoding strategy to predict an animal's future decision in goal-directed navigation.
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Affiliation(s)
- Liang Cao
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Physics, East China Normal University, Shanghai 200241, China
| | - Viktor Varga
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
- Institute of Experimental Medicine, 43 Szigony Street, 1083 Budapest, Hungary
| | - Zhe S. Chen
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Psychiatry, Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA
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25
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Baker V, Cruz L. Traveling Waves in Quasi-One-Dimensional Neuronal Minicolumns. Neural Comput 2021; 34:78-103. [PMID: 34758481 DOI: 10.1162/neco_a_01451] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 07/03/2021] [Indexed: 11/04/2022]
Abstract
Traveling waves of neuronal activity in the cortex have been observed in vivo. These traveling waves have been correlated to various features of observed cortical dynamics, including spike timing variability and correlated fluctuations in neuron membrane potential. Although traveling waves are typically studied as either strictly one-dimensional or two-dimensional excitations, here we investigate the conditions for the existence of quasi-one-dimensional traveling waves that could be sustainable in parts of the brain containing cortical minicolumns. For that, we explore a quasi-one-dimensional network of heterogeneous neurons with a biologically influenced computational model of neuron dynamics and connectivity. We find that background stimulus reliably evokes traveling waves in networks with local connectivity between neurons. We also observe traveling waves in fully connected networks when a model for action potential propagation speed is incorporated. The biological properties of the neurons influence the generation and propagation of the traveling waves. Our quasi-one-dimensional model is not only useful for studying the basic properties of traveling waves in neuronal networks; it also provides a simplified representation of possible wave propagation in columnar or minicolumnar networks found in the cortex.
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Affiliation(s)
- Vincent Baker
- Department of Physics, Drexel University, Philadelphia, PA 19104, U.S.A.
| | - Luis Cruz
- Department of Physics, Drexel University, Philadelphia, PA 19104, U.S.A.
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Mondal A, Mondal A, Kumar Sharma S, Kumar Upadhyay R, Antonopoulos CG. Spatiotemporal characteristics in systems of diffusively coupled excitable slow-fast FitzHugh-Rinzel dynamical neurons. CHAOS (WOODBURY, N.Y.) 2021; 31:103122. [PMID: 34717324 DOI: 10.1063/5.0055389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 09/27/2021] [Indexed: 06/13/2023]
Abstract
In this paper, we study an excitable, biophysical system that supports wave propagation of nerve impulses. We consider a slow-fast, FitzHugh-Rinzel neuron model where only the membrane voltage interacts diffusively, giving rise to the formation of spatiotemporal patterns. We focus on local, nonlinear excitations and diverse neural responses in an excitable one- and two-dimensional configuration of diffusively coupled FitzHugh-Rinzel neurons. The study of the emerging spatiotemporal patterns is essential in understanding the working mechanism in different brain areas. We derive analytically the coefficients of the amplitude equations in the vicinity of Hopf bifurcations and characterize various patterns, including spirals exhibiting complex geometric substructures. Furthermore, we derive analytically the condition for the development of antispirals in the neighborhood of the bifurcation point. The emergence of broken target waves can be observed to form spiral-like profiles. The spatial dynamics of the excitable system exhibits two- and multi-arm spirals for small diffusive couplings. Our results reveal a multitude of neural excitabilities and possible conditions for the emergence of spiral-wave formation. Finally, we show that the coupled excitable systems with different firing characteristics participate in a collective behavior that may contribute significantly to irregular neural dynamics.
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Affiliation(s)
- Arnab Mondal
- Department of Mathematics and Computing, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India
| | - Argha Mondal
- School of Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam 690525, India
| | - Sanjeev Kumar Sharma
- Department of Mathematics and Computing, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India
| | - Ranjit Kumar Upadhyay
- Department of Mathematics and Computing, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India
| | - Chris G Antonopoulos
- Department of Mathematical Sciences, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, United Kingdom
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Mishra A, Marzban N, Cohen MX, Englitz B. Dynamics of Neural Microstates in the VTA-Striatal-Prefrontal Loop during Novelty Exploration in the Rat. J Neurosci 2021; 41:6864-6877. [PMID: 34193560 PMCID: PMC8360694 DOI: 10.1523/jneurosci.2256-20.2021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 05/17/2021] [Accepted: 05/21/2021] [Indexed: 11/21/2022] Open
Abstract
Neural activity at the large-scale population level has been suggested to be consistent with a sequence of brief, quasistable spatial patterns. These "microstates" and their temporal dynamics have been linked to myriad cognitive functions and brain diseases. Most of this research has been performed using EEG, leaving many questions, such as the existence, dynamics, and behavioral relevance of microstates at the level of local field potentials (LFPs), unaddressed. Here, we adapted the standard EEG microstate analysis to triple-area LFP recordings from 192 electrodes in rats to investigate the mesoscopic dynamics of neural microstates within and across brain regions during novelty exploration. We performed simultaneous recordings from the prefrontal cortex, striatum, and ventral tegmental area in male rats during awake behavior (object novelty and exploration). We found that the LFP data can be accounted for by multiple, recurring microstates that were stable for ∼60-100 ms. The simultaneous microstate activity across brain regions revealed rhythmic patterns of coactivations, which we interpret as a novel indicator of inter-regional, mesoscale synchronization. Furthermore, these rhythmic coactivation patterns across microstates were modulated by behavioral states such as movement and exploration of a novel object. These results support the existence of a functional mesoscopic organization across multiple brain areas and present a possible link of the origin of macroscopic EEG microstates to zero-lag neuronal synchronization within and between brain areas, which is of particular interest to the human research community.SIGNIFICANCE STATEMENT The coordination of neural activity across the entire brain has remained elusive. Here we combine large-scale neural recordings at fine spatial resolution with the analysis of microstates (i.e., short-lived, recurring spatial patterns of neural activity). We demonstrate that the local activity in different brain areas can be accounted for by only a few microstates per region. These microstates exhibited temporal dynamics that were correlated across regions in rhythmic patterns. We demonstrate that these microstates are linked to behavior and exhibit different properties in the frequency domain during different behavioral states. In summary, LFP microstates provide an insightful approach to studying both mesoscopic and large-scale brain activation within and across regions.
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Affiliation(s)
- Ashutosh Mishra
- Synchronisation in Neural Systems Laboratory, Department of Neuroinformatics, Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, 6500 HB, Nijmegen, The Netherlands
- Computational Neuroscience Laboratory, Department of Neurophysiology, Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, 6525 AJ, Nijmegen, The Netherlands
| | - Nader Marzban
- Synchronisation in Neural Systems Laboratory, Department of Neuroinformatics, Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, 6500 HB, Nijmegen, The Netherlands
| | - Michael X Cohen
- Synchronisation in Neural Systems Laboratory, Department of Neuroinformatics, Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, 6500 HB, Nijmegen, The Netherlands
| | - Bernhard Englitz
- Computational Neuroscience Laboratory, Department of Neurophysiology, Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, 6525 AJ, Nijmegen, The Netherlands
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Linden NJ, Tabuena DR, Steinmetz NA, Moody WJ, Brunton SL, Brunton BW. Go with the FLOW: visualizing spatiotemporal dynamics in optical widefield calcium imaging. J R Soc Interface 2021; 18:20210523. [PMID: 34428947 PMCID: PMC8385384 DOI: 10.1098/rsif.2021.0523] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 08/02/2021] [Indexed: 12/11/2022] Open
Abstract
Widefield calcium imaging has recently emerged as a powerful experimental technique to record coordinated large-scale brain activity. These measurements present a unique opportunity to characterize spatiotemporally coherent structures that underlie neural activity across many regions of the brain. In this work, we leverage analytic techniques from fluid dynamics to develop a visualization framework that highlights features of flow across the cortex, mapping wavefronts that may be correlated with behavioural events. First, we transform the time series of widefield calcium images into time-varying vector fields using optic flow. Next, we extract concise diagrams summarizing the dynamics, which we refer to as FLOW (flow lines in optical widefield imaging) portraits. These FLOW portraits provide an intuitive map of dynamic calcium activity, including regions of initiation and termination, as well as the direction and extent of activity spread. To extract these structures, we use the finite-time Lyapunov exponent technique developed to analyse time-varying manifolds in unsteady fluids. Importantly, our approach captures coherent structures that are poorly represented by traditional modal decomposition techniques. We demonstrate the application of FLOW portraits on three simple synthetic datasets and two widefield calcium imaging datasets, including cortical waves in the developing mouse and spontaneous cortical activity in an adult mouse.
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Affiliation(s)
- Nathaniel J. Linden
- Department of Bioengineering, University of Washington, Seattle, WA 98195-0005, USA
- Department of Biology, University of Washington, Seattle, WA 98195-0005, USA
| | - Dennis R. Tabuena
- Department of Biology, University of Washington, Seattle, WA 98195-0005, USA
- Graduate Program in Neuroscience, University of Washington, Seattle, WA 98195-0005, USA
| | - Nicholas A. Steinmetz
- Department of Biological Structure, University of Washington, Seattle, WA 98195-0005, USA
| | - William J. Moody
- Department of Biology, University of Washington, Seattle, WA 98195-0005, USA
| | - Steven L. Brunton
- Department of Mechanical Engineering, University of Washington, Seattle, WA 98195-0005, USA
| | - Bingni W. Brunton
- Department of Biology, University of Washington, Seattle, WA 98195-0005, USA
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Raut RV, Snyder AZ, Mitra A, Yellin D, Fujii N, Malach R, Raichle ME. Global waves synchronize the brain's functional systems with fluctuating arousal. SCIENCE ADVANCES 2021; 7:7/30/eabf2709. [PMID: 34290088 PMCID: PMC8294763 DOI: 10.1126/sciadv.abf2709] [Citation(s) in RCA: 87] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 06/04/2021] [Indexed: 05/04/2023]
Abstract
We propose and empirically support a parsimonious account of intrinsic, brain-wide spatiotemporal organization arising from traveling waves linked to arousal. We hypothesize that these waves are the predominant physiological process reflected in spontaneous functional magnetic resonance imaging (fMRI) signal fluctuations. The correlation structure ("functional connectivity") of these fluctuations recapitulates the large-scale functional organization of the brain. However, a unifying physiological account of this structure has so far been lacking. Here, using fMRI in humans, we show that ongoing arousal fluctuations are associated with global waves of activity that slowly propagate in parallel throughout the neocortex, thalamus, striatum, and cerebellum. We show that these waves can parsimoniously account for many features of spontaneous fMRI signal fluctuations, including topographically organized functional connectivity. Last, we demonstrate similar, cortex-wide propagation of neural activity measured with electrocorticography in macaques. These findings suggest that traveling waves spatiotemporally pattern brain-wide excitability in relation to arousal.
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Affiliation(s)
- Ryan V Raut
- Department of Radiology, Washington University, St. Louis, MO 63110, USA.
| | - Abraham Z Snyder
- Department of Radiology, Washington University, St. Louis, MO 63110, USA
- Department of Neurology, Washington University, St. Louis, MO 63110, USA
| | - Anish Mitra
- Department of Psychiatry, Stanford University, Stanford, CA 94305, USA
| | - Dov Yellin
- Department of Neurobiology, Weizmann Institute of Science, 76100 Rehovot, Israel
| | - Naotaka Fujii
- Laboratory for Adaptive Intelligence, RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan
| | - Rafael Malach
- Department of Neurobiology, Weizmann Institute of Science, 76100 Rehovot, Israel
| | - Marcus E Raichle
- Department of Radiology, Washington University, St. Louis, MO 63110, USA
- Department of Neurology, Washington University, St. Louis, MO 63110, USA
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Liu Y, Long X, Martin PR, Solomon SG, Gong P. Lévy walk dynamics explain gamma burst patterns in primate cerebral cortex. Commun Biol 2021; 4:739. [PMID: 34131276 PMCID: PMC8206356 DOI: 10.1038/s42003-021-02256-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 05/21/2021] [Indexed: 11/21/2022] Open
Abstract
Lévy walks describe patterns of intermittent motion with variable step sizes. In complex biological systems, Lévy walks (non-Brownian, superdiffusive random walks) are associated with behaviors such as search patterns of animals foraging for food. Here we show that Lévy walks also describe patterns of oscillatory activity in primate cerebral cortex. We used a combination of empirical observation and modeling to investigate high-frequency (gamma band) local field potential activity in visual motion-processing cortical area MT of marmoset monkeys. We found that gamma activity is organized as localized burst patterns that propagate across the cortical surface with Lévy walk dynamics. Lévy walks are fundamentally different from either global synchronization, or regular propagating waves, because they include large steps that enable activity patterns to move rapidly over cortical modules. The presence of Lévy walk dynamics therefore represents a previously undiscovered mode of brain activity, and implies a novel way for the cortex to compute. We apply a biophysically realistic circuit model to explain that the Lévy walk dynamics arise from critical-state transitions between asynchronous and localized propagating wave states, and that these dynamics yield optimal spatial sampling of the cortical sheet. We hypothesise that Lévy walk dynamics could help the cortex to efficiently process variable inputs, and to find links in patterns of activity among sparsely spiking populations of neurons.
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Affiliation(s)
- Yuxi Liu
- School of Physics, University of Sydney, Sydney, NSW, Australia
- ARC Centre of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Xian Long
- School of Physics, University of Sydney, Sydney, NSW, Australia
- ARC Centre of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Paul R Martin
- ARC Centre of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
- Discipline of Physiology, University of Sydney, Sydney, NSW, Australia
- Save Sight Institute, University of Sydney, Sydney, NSW, Australia
| | - Samuel G Solomon
- Department of Experimental Psychology, University College London, London, UK
| | - Pulin Gong
- School of Physics, University of Sydney, Sydney, NSW, Australia.
- ARC Centre of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia.
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Cecchini G, Scaglione A, Allegra Mascaro AL, Checcucci C, Conti E, Adam I, Fanelli D, Livi R, Pavone FS, Kreuz T. Cortical propagation tracks functional recovery after stroke. PLoS Comput Biol 2021; 17:e1008963. [PMID: 33999967 PMCID: PMC8159272 DOI: 10.1371/journal.pcbi.1008963] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 05/27/2021] [Accepted: 04/13/2021] [Indexed: 12/04/2022] Open
Abstract
Stroke is a debilitating condition affecting millions of people worldwide. The development of improved rehabilitation therapies rests on finding biomarkers suitable for tracking functional damage and recovery. To achieve this goal, we perform a spatiotemporal analysis of cortical activity obtained by wide-field calcium images in mice before and after stroke. We compare spontaneous recovery with three different post-stroke rehabilitation paradigms, motor training alone, pharmacological contralesional inactivation and both combined. We identify three novel indicators that are able to track how movement-evoked global activation patterns are impaired by stroke and evolve during rehabilitation: the duration, the smoothness, and the angle of individual propagation events. Results show that, compared to pre-stroke conditions, propagation of cortical activity in the subacute phase right after stroke is slowed down and more irregular. When comparing rehabilitation paradigms, we find that mice treated with both motor training and pharmacological intervention, the only group associated with generalized recovery, manifest new propagation patterns, that are even faster and smoother than before the stroke. In conclusion, our new spatiotemporal propagation indicators could represent promising biomarkers that are able to uncover neural correlates not only of motor deficits caused by stroke but also of functional recovery during rehabilitation. In turn, these insights could pave the way towards more targeted post-stroke therapies. Millions of people worldwide suffer from long-lasting motor deficits caused by stroke. Very recently, the two basic therapeutic approaches, motor training and pharmacological intervention, have been combined in order to achieve a more efficient functional recovery. In this study, we analyze the neurophysiological activity in the brain of mice observed with in vivo calcium imaging before and after the induction of a stroke. We use a newly developed universal approach based on the temporal sequence of local activation in different brain regions to quantify three properties of global propagation patterns: duration, smoothness and angle. These innovative spatiotemporal propagation indicators allow us to track damage and functional recovery following stroke and to quantify the relative success of motor training, pharmacological inactivation, and a combination of both, compared to spontaneous recovery. We show that all three treatments reverse the alterations observed during the subacute phase right after stroke. We also find that combining motor training and pharmacological intervention does not restore pre-stroke features but rather leads to the emergence of new propagation patterns that, surprisingly, are even faster and smoother than the pre-stroke patterns.
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Affiliation(s)
- Gloria Cecchini
- Department of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy
- CSDC, University of Florence, Sesto Fiorentino, Italy
- * E-mail:
| | - Alessandro Scaglione
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy
- European Laboratory for Non-linear Spectroscopy, University of Florence, Sesto Fiorentino, Italy
| | - Anna Letizia Allegra Mascaro
- European Laboratory for Non-linear Spectroscopy, University of Florence, Sesto Fiorentino, Italy
- Neuroscience Institute, National Research Council, Pisa, Italy
| | - Curzio Checcucci
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy
- European Laboratory for Non-linear Spectroscopy, University of Florence, Sesto Fiorentino, Italy
| | - Emilia Conti
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy
- European Laboratory for Non-linear Spectroscopy, University of Florence, Sesto Fiorentino, Italy
- Neuroscience Institute, National Research Council, Pisa, Italy
| | - Ihusan Adam
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy
- CSDC, University of Florence, Sesto Fiorentino, Italy
- Department of Information Engineering, University of Florence, Sesto Fiorentino, Italy
| | - Duccio Fanelli
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy
- CSDC, University of Florence, Sesto Fiorentino, Italy
- INFN, Florence Section, Sesto Fiorentino, Italy
| | - Roberto Livi
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy
- CSDC, University of Florence, Sesto Fiorentino, Italy
- INFN, Florence Section, Sesto Fiorentino, Italy
| | - Francesco Saverio Pavone
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy
- European Laboratory for Non-linear Spectroscopy, University of Florence, Sesto Fiorentino, Italy
- National Institute of Optics (INO), National Research Council (CNR), Sesto Fiorentino, Italy
| | - Thomas Kreuz
- Institute for Complex Systems (ISC), National Research Council (CNR), Sesto Fiorentino, Italy
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Hamid AA, Frank MJ, Moore CI. Wave-like dopamine dynamics as a mechanism for spatiotemporal credit assignment. Cell 2021; 184:2733-2749.e16. [PMID: 33861952 PMCID: PMC8122079 DOI: 10.1016/j.cell.2021.03.046] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 12/31/2020] [Accepted: 03/23/2021] [Indexed: 12/17/2022]
Abstract
Significant evidence supports the view that dopamine shapes learning by encoding reward prediction errors. However, it is unknown whether striatal targets receive tailored dopamine dynamics based on regional functional specialization. Here, we report wave-like spatiotemporal activity patterns in dopamine axons and release across the dorsal striatum. These waves switch between activational motifs and organize dopamine transients into localized clusters within functionally related striatal subregions. Notably, wave trajectories were tailored to task demands, propagating from dorsomedial to dorsolateral striatum when rewards are contingent on animal behavior and in the opponent direction when rewards are independent of behavioral responses. We propose a computational architecture in which striatal dopamine waves are sculpted by inference about agency and provide a mechanism to direct credit assignment to specialized striatal subregions. Supporting model predictions, dorsomedial dopamine activity during reward-pursuit signaled the extent of instrumental control and interacted with reward waves to predict future behavioral adjustments.
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Affiliation(s)
- Arif A Hamid
- Department of Neuroscience, Brown University, Providence, RI 02912, USA; Carney Institute for Brain Science, Brown University, Providence, RI 02912, USA.
| | - Michael J Frank
- Department of Cognitive Linguistics & Psychological Sciences, Brown University, Providence, RI 02912, USA; Carney Institute for Brain Science, Brown University, Providence, RI 02912, USA.
| | - Christopher I Moore
- Department of Neuroscience, Brown University, Providence, RI 02912, USA; Carney Institute for Brain Science, Brown University, Providence, RI 02912, USA.
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Liang Y, Song C, Liu M, Gong P, Zhou C, Knöpfel T. Cortex-Wide Dynamics of Intrinsic Electrical Activities: Propagating Waves and Their Interactions. J Neurosci 2021; 41:3665-3678. [PMID: 33727333 PMCID: PMC8055070 DOI: 10.1523/jneurosci.0623-20.2021] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 02/18/2021] [Accepted: 02/22/2021] [Indexed: 11/21/2022] Open
Abstract
Cortical circuits generate patterned activities that reflect intrinsic brain dynamics that lay the foundation for any, including stimuli-evoked, cognition and behavior. However, the spatiotemporal organization properties and principles of this intrinsic activity have only been partially elucidated because of previous poor resolution of experimental data and limited analysis methods. Here we investigated continuous wave patterns in the 0.5-4 Hz (delta band) frequency range on data from high-spatiotemporal resolution optical voltage imaging of the upper cortical layers in anesthetized mice. Waves of population activities propagate in heterogeneous directions to coordinate neuronal activities between different brain regions. The complex wave patterns show characteristics of both stereotypy and variety. The location and type of wave patterns determine the dynamical evolution when different waves interact with each other. Local wave patterns of source, sink, or saddle emerge at preferred spatial locations. Specifically, "source" patterns are predominantly found in cortical regions with low multimodal hierarchy such as the primary somatosensory cortex. Our findings reveal principles that govern the spatiotemporal dynamics of spontaneous cortical activities and associate them with the structural architecture across the cortex.SIGNIFICANCE STATEMENT Intrinsic brain activities, as opposed to external stimulus-evoked responses, have increasingly gained attention, but it remains unclear how these intrinsic activities are spatiotemporally organized at the cortex-wide scale. By taking advantage of the high spatiotemporal resolution of optical voltage imaging, we identified five wave pattern types, and revealed the organization properties of different wave patterns and the dynamical mechanisms when they interact with each other. Moreover, we found a relationship between the emergence probability of local wave patterns and the multimodal structure hierarchy across cortical areas. Our findings reveal the principles of spatiotemporal wave dynamics of spontaneous activities and associate them with the underlying hierarchical architecture across the cortex.
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Affiliation(s)
- Yuqi Liang
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong, People's Republic of China
- The HKBU Institute of Research and Continuing Education, Shenzhen 518000, People's Republic of China
| | - Chenchen Song
- Laboratory for Neuronal Circuit Dynamics, Imperial College London, London SW7 2AZ, United Kingdom
| | - Mianxin Liu
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong, People's Republic of China
- School of Biomedical Engineering, Shanghai Tech University, Shanghai 201210, People's Republic of China
| | - Pulin Gong
- School of Physics, University of Sydney, Sydney 2006, New South Wales, Australia
- Australian Research Council Centre of Excellence for Integrative Brain Function, University of Sydney, Sydney 2001, New South Wales, Australia
| | - Changsong Zhou
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong, People's Republic of China
- The HKBU Institute of Research and Continuing Education, Shenzhen 518000, People's Republic of China
- Department of Physics, Zhejiang University, Hangzhou 310027, People's Republic of China
- Beijing Computational Science Research Center, Beijing 100193, People's Republic of China
| | - Thomas Knöpfel
- Laboratory for Neuronal Circuit Dynamics, Imperial College London, London SW7 2AZ, United Kingdom
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Travelling spindles create necessary conditions for spike-timing-dependent plasticity in humans. Nat Commun 2021; 12:1027. [PMID: 33589639 PMCID: PMC7884835 DOI: 10.1038/s41467-021-21298-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 01/12/2021] [Indexed: 12/22/2022] Open
Abstract
Sleep spindles facilitate memory consolidation in the cortex during mammalian non-rapid eye movement sleep. In rodents, phase-locked firing during spindles may facilitate spike-timing-dependent plasticity by grouping pre-then-post-synaptic cell firing within ~25 ms. Currently, microphysiological evidence in humans for conditions conducive for spike-timing-dependent plasticity during spindles is absent. Here, we analyze field potentials and unit firing from middle/upper layers during spindles from 10 × 10 microelectrode arrays at 400 μm pitch in humans. We report strong tonic and phase-locked increases in firing and co-firing within 25 ms during spindles, especially those co-occurring with down-to-upstate transitions. Co-firing, spindle co-occurrence, and spindle coherence are greatest within ~2 mm, and high co-firing of units on different contacts depends on high spindle coherence between those contacts. Spindles propagate at ~0.28 m/s in distinct patterns, with correlated cell co-firing sequences. Spindles hence organize spatiotemporal patterns of neuronal co-firing in ways that may provide pre-conditions for plasticity during non-rapid eye movement sleep. Sleep spindles during non-rapid eye movement are important for memory consolidation and require specific neuronal firing conditions in non-human mammals. Here, the authors show these conditions are present in humans, potentially facilitating spike-timing-dependent plasticity.
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Oprea L, Pack CC, Khadra A. Machine classification of spatiotemporal patterns: automated parameter search in a rebounding spiking network. Cogn Neurodyn 2020; 14:267-280. [PMID: 32399070 PMCID: PMC7203379 DOI: 10.1007/s11571-020-09568-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2019] [Revised: 11/20/2019] [Accepted: 01/03/2020] [Indexed: 12/20/2022] Open
Abstract
Various patterns of electrical activities, including travelling waves, have been observed in cortical experimental data from animal models as well as humans. By applying machine learning techniques, we investigate the spatiotemporal patterns, found in a spiking neuronal network with inhibition-induced firing (rebounding). Our cortical sheet model produces a wide variety of network activities including synchrony, target waves, and travelling wavelets. Pattern formation is controlled by modifying a Gaussian derivative coupling kernel through varying the level of inhibition, coupling strength, and kernel geometry. We have designed a computationally efficient machine classifier, based on statistical, textural, and temporal features, to identify the parameter regimes associated with different spatiotemporal patterns. Our results reveal that switching between synchrony and travelling waves can occur transiently and spontaneously without a stimulus, in a noise-dependent fashion, or in the presence of stimulus when the coupling strength and level of inhibition are at moderate values. They also demonstrate that when a target wave is formed, its wave speed is most sensitive to perturbations in the coupling strength between model neurons. This study provides an automated method to characterize activities produced by a novel spiking network that phenomenologically models large scale dynamics in the cortex.
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Affiliation(s)
- Lawrence Oprea
- Department of Physiology, McGill University, Montréal, QC Canada
| | - Christopher C. Pack
- Department of Neurology and Neurosurgery, McGill University, Montréal, QC Canada
| | - Anmar Khadra
- Department of Physiology, McGill University, Montréal, QC Canada
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Celotto M, De Luca C, Muratore P, Resta F, Allegra Mascaro AL, Pavone FS, De Bonis G, Paolucci PS. Analysis and Model of Cortical Slow Waves Acquired with Optical Techniques. Methods Protoc 2020; 3:E14. [PMID: 32023996 PMCID: PMC7189682 DOI: 10.3390/mps3010014] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 01/10/2020] [Accepted: 01/22/2020] [Indexed: 12/25/2022] Open
Abstract
Slow waves (SWs) are spatio-temporal patterns of cortical activity that occur both during natural sleep and anesthesia and are preserved across species. Even though electrophysiological recordings have been largely used to characterize brain states, they are limited in the spatial resolution and cannot target specific neuronal population. Recently, large-scale optical imaging techniques coupled with functional indicators overcame these restrictions, and new pipelines of analysis and novel approaches of SWs modelling are needed to extract relevant features of the spatio-temporal dynamics of SWs from these highly spatially resolved data-sets. Here we combined wide-field fluorescence microscopy and a transgenic mouse model expressing a calcium indicator (GCaMP6f) in excitatory neurons to study SW propagation over the meso-scale under ketamine anesthesia. We developed a versatile analysis pipeline to identify and quantify the spatio-temporal propagation of the SWs. Moreover, we designed a computational simulator based on a simple theoretical model, which takes into account the statistics of neuronal activity, the response of fluorescence proteins and the slow waves dynamics. The simulator was capable of synthesizing artificial signals that could reliably reproduce several features of the SWs observed in vivo, thus enabling a calibration tool for the analysis pipeline. Comparison of experimental and simulated data shows the robustness of the analysis tools and its potential to uncover mechanistic insights of the Slow Wave Activity (SWA).
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Affiliation(s)
- Marco Celotto
- Department of Physics, “Sapienza” University of Rome, 00185 Rome, Italy; (M.C.); (C.D.L.); (P.M.)
- IIT—Neural Computation Lab, CNCS@UniTn, 38068 Rovereto, Italy
| | - Chiara De Luca
- Department of Physics, “Sapienza” University of Rome, 00185 Rome, Italy; (M.C.); (C.D.L.); (P.M.)
- INFN, 00185 Rome, Italy;
- PhD Program in Behavioural Neuroscience,“Sapienza” University of Rome, 00185 Rome, Italy
| | - Paolo Muratore
- Department of Physics, “Sapienza” University of Rome, 00185 Rome, Italy; (M.C.); (C.D.L.); (P.M.)
- PhD Program in Cognitive Neuroscience, SISSA, 34136 Trieste, Italy
| | - Francesco Resta
- LENS, University of Florence, 50019 Florence, Italy; (F.R.); (A.L.A.M.); (F.S.P.)
| | - Anna Letizia Allegra Mascaro
- LENS, University of Florence, 50019 Florence, Italy; (F.R.); (A.L.A.M.); (F.S.P.)
- Istituto di Neuroscienze, CNR, 56124 Pisa, Italy
| | - Francesco Saverio Pavone
- LENS, University of Florence, 50019 Florence, Italy; (F.R.); (A.L.A.M.); (F.S.P.)
- Department of Physics, University of Florence, 50019 Florence, Italy
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Iraji A, Miller R, Adali T, Calhoun VD. Space: A Missing Piece of the Dynamic Puzzle. Trends Cogn Sci 2020; 24:135-149. [PMID: 31983607 DOI: 10.1016/j.tics.2019.12.004] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 11/15/2019] [Accepted: 12/03/2019] [Indexed: 01/24/2023]
Abstract
There has been growing interest in studying the temporal reconfiguration of brain functional connectivity to understand the role of dynamic interaction (e.g., integration and segregation) among neuronal populations in cognitive functions. However, it is crucial to differentiate between various dynamic properties because nearly all existing dynamic connectivity studies are presented as spatiotemporally dynamic, even though they fall into different categories. As a result, variation in the spatial patterns of functional structures are not well characterized. Here, we present the concepts of spatially, temporally, and spatiotemporally dynamics and use this terminology to categorize existing approaches. We review current spatially dynamic connectivity work, emphasizing that explicit incorporation of space into dynamic analyses can expand our understanding of brain function and disorder.
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Affiliation(s)
- Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Robyn Miller
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Tulay Adali
- Department of CSEE, University of Maryland Baltimore County, Baltimore, MD 21250, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA.
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38
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Chen G, Gong P. Computing by modulating spontaneous cortical activity patterns as a mechanism of active visual processing. Nat Commun 2019; 10:4915. [PMID: 31664052 PMCID: PMC6820766 DOI: 10.1038/s41467-019-12918-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Accepted: 10/07/2019] [Indexed: 01/23/2023] Open
Abstract
Cortical populations produce complex spatiotemporal activity spontaneously without sensory inputs. However, the fundamental computational roles of such spontaneous activity remain unclear. Here, we propose a new neural computation mechanism for understanding how spontaneous activity is actively involved in cortical processing: Computing by Modulating Spontaneous Activity (CMSA). Using biophysically plausible circuit models, we demonstrate that spontaneous activity patterns with dynamical properties, as found in empirical observations, are modulated or redistributed by external stimuli to give rise to neural responses. We find that this CMSA mechanism of generating neural responses provides profound computational advantages, such as actively speeding up cortical processing. We further reveal that the CMSA mechanism provides a unifying explanation for many experimental findings at both the single-neuron and circuit levels, and that CMSA in response to natural stimuli such as face images is the underlying neurophysiological mechanism of perceptual "bubbles" as found in psychophysical studies.
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Affiliation(s)
- Guozhang Chen
- School of Physics, University of Sydney, Sydney, New South Wales 2006, Australia.,ARC Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, New South Wales 2006, Australia
| | - Pulin Gong
- School of Physics, University of Sydney, Sydney, New South Wales 2006, Australia. .,ARC Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, New South Wales 2006, Australia.
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39
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Engel TA, Steinmetz NA. New perspectives on dimensionality and variability from large-scale cortical dynamics. Curr Opin Neurobiol 2019; 58:181-190. [PMID: 31585331 PMCID: PMC6859189 DOI: 10.1016/j.conb.2019.09.003] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Revised: 07/27/2019] [Accepted: 09/05/2019] [Indexed: 12/21/2022]
Abstract
The neocortex is a multi-scale network, with intricate local circuitry interwoven into a global mesh of long-range connections. Neural activity propagates within this network on a wide range of temporal and spatial scales. At the micro scale, neurophysiological recordings reveal coordinated dynamics in local neural populations, which support behaviorally relevant computations. At the macro scale, neuroimaging modalities measure global activity fluctuations organized into spatiotemporal patterns across the entire brain. Here we review recent advances linking the local and global scales of cortical dynamics and their relationship to behavior. We argue that diverse experimental observations on the dimensionality and variability of neural activity can be reconciled by considering how activity propagates in space and time on multiple spatial scales.
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Affiliation(s)
- Tatiana A Engel
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, United States.
| | - Nicholas A Steinmetz
- Department of Biological Structure, University of Washington, Seattle, WA 98195, United States.
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40
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Naoumenko D, Gong P. Complex Dynamics of Propagating Waves in a Two-Dimensional Neural Field. Front Comput Neurosci 2019; 13:50. [PMID: 31417385 PMCID: PMC6682636 DOI: 10.3389/fncom.2019.00050] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 07/02/2019] [Indexed: 11/13/2022] Open
Abstract
Propagating waves with complex dynamics have been widely observed in neural population activity. To understand their formation mechanisms, we investigate a type of two-dimensional neural field model by systematically varying its recurrent excitatory and inhibitory inputs. We show that the neural field model exhibits a rich repertoire of dynamical activity states when the relevant strength of excitation and inhibition is increased, ranging from localized rotating and traveling waves to global waves. Particularly, near the transition between stable states of rotating and traveling waves, the model exhibits a bistable state; that is, both the rotating and the traveling waves can exist, and the inclusion of noise can induce spontaneous transitions between them. Furthermore, we demonstrate that when there are multiple propagating waves, they exhibit rich collective propagation dynamics with variable propagating speeds and trajectories. We use techniques from time series analysis such detrended fluctuation analysis to characterize the effect of the strength of excitation and inhibition on these collective dynamics, which range from purely random motion to motion with long-range spatiotemporal correlations. These results provide insights into the possible contribution of excitation and inhibition toward a range of previously observed spatiotemporal wave phenomena.
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Affiliation(s)
| | - Pulin Gong
- School of Physics, University of Sydney, Sydney, NSW, Australia.,ARC Centre of Excellence for Integrative Brain Function, The University of Sydney, Sydney, NSW, Australia
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41
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Gu Y, Qi Y, Gong P. Rich-club connectivity, diverse population coupling, and dynamical activity patterns emerging from local cortical circuits. PLoS Comput Biol 2019; 15:e1006902. [PMID: 30939135 PMCID: PMC6461296 DOI: 10.1371/journal.pcbi.1006902] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Revised: 04/12/2019] [Accepted: 02/25/2019] [Indexed: 11/19/2022] Open
Abstract
Experimental studies have begun revealing essential properties of the structural connectivity and the spatiotemporal activity dynamics of cortical circuits. To integrate these properties from anatomy and physiology, and to elucidate the links between them, we develop a novel cortical circuit model that captures a range of realistic features of synaptic connectivity. We show that the model accounts for the emergence of higher-order connectivity structures, including highly connected hub neurons that form an interconnected rich-club. The circuit model exhibits a rich repertoire of dynamical activity states, ranging from asynchronous to localized and global propagating wave states. We find that around the transition between asynchronous and localized propagating wave states, our model quantitatively reproduces a variety of major empirical findings regarding neural spatiotemporal dynamics, which otherwise remain disjointed in existing studies. These dynamics include diverse coupling (correlation) between spiking activity of individual neurons and the population, dynamical wave patterns with variable speeds and precise temporal structures of neural spikes. We further illustrate how these neural dynamics are related to the connectivity properties by analysing structural contributions to variable spiking dynamics and by showing that the rich-club structure is related to the diverse population coupling. These findings establish an integrated account of structural connectivity and activity dynamics of local cortical circuits, and provide new insights into understanding their working mechanisms. To integrate essential anatomical and physiological properties of local cortical circuits and to elucidate mechanistic links between them, we develop a novel circuit model capturing key synaptic connectivity features. We show that the model explains the emergence of a range of connectivity patterns such as rich-club connectivity, and gives rise to a rich repertoire of cortical states. We identify both the anatomical and physiological mechanisms underlying the transition of these cortical states, and show that our model reconciles an otherwise disparate set of key physiological findings on neural activity dynamics. We further illustrate how these neural dynamics are related to the connectivity properties by analysing structural contributions to variable spiking dynamics and by showing that the rich-club structure is related to diverse neural population correlations as observed recently. Our model thus provides a framework for integrating and explaining a variety of neural connectivity properties and spatiotemporal activity dynamics observed in experimental studies, and provides novel experimentally testable predictions.
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Affiliation(s)
- Yifan Gu
- School of Physics, University of Sydney, New South Wales, Australia
- ARC Centre of Excellence for Integrative Brain Function, University of Sydney, New South Wales, Australia
| | - Yang Qi
- School of Physics, University of Sydney, New South Wales, Australia
- ARC Centre of Excellence for Integrative Brain Function, University of Sydney, New South Wales, Australia
| | - Pulin Gong
- School of Physics, University of Sydney, New South Wales, Australia
- ARC Centre of Excellence for Integrative Brain Function, University of Sydney, New South Wales, Australia
- * E-mail:
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