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Bollmann Y, Modol L, Tressard T, Vorobyev A, Dard R, Brustlein S, Sims R, Bendifallah I, Leprince E, de Sars V, Ronzitti E, Baude A, Adesnik H, Picardo MA, Platel JC, Emiliani V, Angulo-Garcia D, Cossart R. Prominent in vivo influence of single interneurons in the developing barrel cortex. Nat Neurosci 2023; 26:1555-1565. [PMID: 37653166 DOI: 10.1038/s41593-023-01405-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Accepted: 07/13/2023] [Indexed: 09/02/2023]
Abstract
Spontaneous synchronous activity is a hallmark of developing brain circuits and promotes their formation. Ex vivo, synchronous activity was shown to be orchestrated by a sparse population of highly connected GABAergic 'hub' neurons. The recent development of all-optical methods to record and manipulate neuronal activity in vivo now offers the unprecedented opportunity to probe the existence and function of hub cells in vivo. Using calcium imaging, connectivity analysis and holographic optical stimulation, we show that single GABAergic, but not glutamatergic, neurons influence population dynamics in the barrel cortex of non-anaesthetized mouse pups. Single GABAergic cells mainly exert an inhibitory influence on both spontaneous and sensory-evoked population bursts. Their network influence scales with their functional connectivity, with highly connected hub neurons displaying the strongest impact. We propose that hub neurons function in tailoring intrinsic cortical dynamics to external sensory inputs.
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Affiliation(s)
- Yannick Bollmann
- Aix Marseille Univ, Inserm, INMED, Turing Center for Living Systems, Marseille, France
| | - Laura Modol
- Aix Marseille Univ, Inserm, INMED, Turing Center for Living Systems, Marseille, France
| | - Thomas Tressard
- Aix Marseille Univ, Inserm, INMED, Turing Center for Living Systems, Marseille, France
| | - Artem Vorobyev
- Aix Marseille Univ, Inserm, INMED, Turing Center for Living Systems, Marseille, France
| | - Robin Dard
- Aix Marseille Univ, Inserm, INMED, Turing Center for Living Systems, Marseille, France
| | - Sophie Brustlein
- Aix Marseille Univ, Inserm, INMED, Turing Center for Living Systems, Marseille, France
| | - Ruth Sims
- Wavefront-Engineering Microscopy Group, Photonics Department, Vision Institute, Sorbonne University, INSERM, CNRS, Paris, France
| | - Imane Bendifallah
- Wavefront-Engineering Microscopy Group, Photonics Department, Vision Institute, Sorbonne University, INSERM, CNRS, Paris, France
| | - Erwan Leprince
- Aix Marseille Univ, Inserm, INMED, Turing Center for Living Systems, Marseille, France
| | - Vincent de Sars
- Wavefront-Engineering Microscopy Group, Photonics Department, Vision Institute, Sorbonne University, INSERM, CNRS, Paris, France
| | - Emiliano Ronzitti
- Wavefront-Engineering Microscopy Group, Photonics Department, Vision Institute, Sorbonne University, INSERM, CNRS, Paris, France
| | - Agnès Baude
- Aix Marseille Univ, Inserm, INMED, Turing Center for Living Systems, Marseille, France
| | - Hillel Adesnik
- Department of Molecular & Cell Biology, University of California, Berkeley, Berkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Michel Aimé Picardo
- Aix Marseille Univ, Inserm, INMED, Turing Center for Living Systems, Marseille, France
| | - Jean-Claude Platel
- Aix Marseille Univ, Inserm, INMED, Turing Center for Living Systems, Marseille, France
| | - Valentina Emiliani
- Wavefront-Engineering Microscopy Group, Photonics Department, Vision Institute, Sorbonne University, INSERM, CNRS, Paris, France
| | - David Angulo-Garcia
- Departamento de Matemáticas y Estadística, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Colombia, Manizales, Colombia
| | - Rosa Cossart
- Aix Marseille Univ, Inserm, INMED, Turing Center for Living Systems, Marseille, France.
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2
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Baker CM, Gong Y. Identifying properties of pattern completion neurons in a computational model of the visual cortex. PLoS Comput Biol 2023; 19:e1011167. [PMID: 37279242 DOI: 10.1371/journal.pcbi.1011167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 05/09/2023] [Indexed: 06/08/2023] Open
Abstract
Neural ensembles are found throughout the brain and are believed to underlie diverse cognitive functions including memory and perception. Methods to activate ensembles precisely, reliably, and quickly are needed to further study the ensembles' role in cognitive processes. Previous work has found that ensembles in layer 2/3 of the visual cortex (V1) exhibited pattern completion properties: ensembles containing tens of neurons were activated by stimulation of just two neurons. However, methods that identify pattern completion neurons are underdeveloped. In this study, we optimized the selection of pattern completion neurons in simulated ensembles. We developed a computational model that replicated the connectivity patterns and electrophysiological properties of layer 2/3 of mouse V1. We identified ensembles of excitatory model neurons using K-means clustering. We then stimulated pairs of neurons in identified ensembles while tracking the activity of the entire ensemble. Our analysis of ensemble activity quantified a neuron pair's power to activate an ensemble using a novel metric called pattern completion capability (PCC) based on the mean pre-stimulation voltage across the ensemble. We found that PCC was directly correlated with multiple graph theory parameters, such as degree and closeness centrality. To improve selection of pattern completion neurons in vivo, we computed a novel latency metric that was correlated with PCC and could potentially be estimated from modern physiological recordings. Lastly, we found that stimulation of five neurons could reliably activate ensembles. These findings can help researchers identify pattern completion neurons to stimulate in vivo during behavioral studies to control ensemble activation.
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Affiliation(s)
- Casey M Baker
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
| | - Yiyang Gong
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
- Department of Neurobiology, Duke University, Durham, North Carolina, United States of America
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3
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Serrano-Reyes M, García-Vilchis B, Reyes-Chapero R, Cáceres-Chávez VA, Tapia D, Galarraga E, Bargas J. Spontaneous Activity of Neuronal Ensembles in Mouse Motor Cortex: Changes after GABAergic Blockade. Neuroscience 2020; 446:304-322. [PMID: 32860933 DOI: 10.1016/j.neuroscience.2020.08.025] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 08/02/2020] [Accepted: 08/18/2020] [Indexed: 01/12/2023]
Abstract
The mouse motor cortex exhibits spontaneous activity in the form of temporal sequences of neuronal ensembles in vitro without the need of tissue stimulation. These neuronal ensembles are defined as groups of neurons with a strong correlation between its firing patterns, generating what appears to be a predetermined neural conduction mode that needs study. Each ensemble is commonly accompanied by one or more parvalbumin expressing neurons (PV+) or fast spiking interneurons. Many of these interneurons have functional connections between them, helping to form a circuit configuration similar to a small-world network. However, rich club metrics show that most connected neurons are neurons not expressing parvalbumin, mainly pyramidal neurons (PV-) suggesting feed-forward propagation through pyramidal cells. Ensembles with PV+ neurons are connected to these hubs. When ligand-gated fast GABAergic transmission is blocked, temporal sequences of ensembles collapse into a unique synchronous and recurrent ensemble, showing the need of inhibition for coding cortical spontaneous activity. This new ensemble has a duration and electrophysiological characteristics of brief recurrent interictal epileptiform discharges (IEDs) composed by the coactivity of both PV- and PV+ neurons, demonstrating that GABA transmission impedes its occurrence. Synchronous ensembles are clearly divided into two clusters one of them lasting longer and mainly composed by PV+ neurons. Because an ictal-like event was not recorded after several minutes of IEDs recording, it is inferred that an external stimulus and/or fast GABA transmission are necessary for its appearance, making this preparation ideal to study both the neuronal machinery to encode cortical spontaneous activity and its transformation into brief non-ictal epileptiform discharges.
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Affiliation(s)
- Miguel Serrano-Reyes
- División de Neurociencias, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, México City 04510, Mexico
| | - Brisa García-Vilchis
- División de Neurociencias, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, México City 04510, Mexico
| | - Rosa Reyes-Chapero
- División de Neurociencias, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, México City 04510, Mexico
| | | | - Dagoberto Tapia
- División de Neurociencias, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, México City 04510, Mexico
| | - Elvira Galarraga
- División de Neurociencias, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, México City 04510, Mexico
| | - José Bargas
- División de Neurociencias, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, México City 04510, Mexico.
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4
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Rozhkova EA, Lee B, Prasad JA, Liu Y, Shevchenko EV. Hypoxia-induced biosynthesis of gold nanoparticles in the living brain. NANOSCALE 2019; 11:19285-19290. [PMID: 31539009 DOI: 10.1039/c9nr05794c] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
While a large number of studies deal with biomedical applications of various types of nanoparticles synthesized using wet chemistry, we propose the concept of targeted biosynthesis of nanoparticles in the living brain. Here we demonstrate that the pathological biochemical process of accumulation of reduced pyridine nucleotides under deleterious conditions of brain hypoxia can be redirected to drive the biosynthesis of biocompatible Au nanoparticles from a precursor salt in situ in the immediate vicinity of the hypoxia site, thereby restoring the redox status of the brain.
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Affiliation(s)
- Elena A Rozhkova
- Center for Nanoscale Materials, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, Illinois 60439, USA.
| | - Byeongdu Lee
- X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, Illinois 60439, USA
| | - Judy A Prasad
- Department of Neurobiology, the University of Chicago, 947 E. 58th St., Chicago, IL 60637, USA
| | - Yuzi Liu
- Center for Nanoscale Materials, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, Illinois 60439, USA.
| | - Elena V Shevchenko
- Center for Nanoscale Materials, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, Illinois 60439, USA.
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5
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Curto C, Morrison K. Relating network connectivity to dynamics: opportunities and challenges for theoretical neuroscience. Curr Opin Neurobiol 2019; 58:11-20. [PMID: 31319287 DOI: 10.1016/j.conb.2019.06.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 06/22/2019] [Indexed: 11/29/2022]
Abstract
We review recent work relating network connectivity to the dynamics of neural activity. While concepts stemming from network science provide a valuable starting point, the interpretation of graph-theoretic structures and measures can be highly dependent on the dynamics associated to the network. Properties that are quite meaningful for linear dynamics, such as random walk and network flow models, may be of limited relevance in the neuroscience setting. Theoretical and computational neuroscience are playing a vital role in understanding the relationship between network connectivity and the nonlinear dynamics associated to neural networks.
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Affiliation(s)
- Carina Curto
- The Pennsylvania State University, PA 16802, United States.
| | - Katherine Morrison
- School of Mathematical Sciences, University of Northern Colorado, Greeley, CO 80639, USA
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6
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Sizemore AE, Bassett DS. Dynamic graph metrics: Tutorial, toolbox, and tale. Neuroimage 2018; 180:417-427. [PMID: 28698107 PMCID: PMC5758445 DOI: 10.1016/j.neuroimage.2017.06.081] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Revised: 05/24/2017] [Accepted: 06/29/2017] [Indexed: 11/23/2022] Open
Abstract
The central nervous system is composed of many individual units - from cells to areas - that are connected with one another in a complex pattern of functional interactions that supports perception, action, and cognition. One natural and parsimonious representation of such a system is a graph in which nodes (units) are connected by edges (interactions). While applicable across spatiotemporal scales, species, and cohorts, the traditional graph approach is unable to address the complexity of time-varying connectivity patterns that may be critically important for an understanding of emotional and cognitive state, task-switching, adaptation and development, or aging and disease progression. Here we survey a set of tools from applied mathematics that offer measures to characterize dynamic graphs. Along with this survey, we offer suggestions for visualization and a publicly-available MATLAB toolbox to facilitate the application of these metrics to existing or yet-to-be acquired neuroimaging data. We illustrate the toolbox by applying it to a previously published data set of time-varying functional graphs, but note that the tools can also be applied to time-varying structural graphs or to other sorts of relational data entirely. Our aim is to provide the neuroimaging community with a useful set of tools, and an intuition regarding how to use them, for addressing emerging questions that hinge on accurate and creative analyses of dynamic graphs.
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Affiliation(s)
- Ann E Sizemore
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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7
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Abstract
The central nervous system is composed of many individual units - from cells to areas - that are connected with one another in a complex pattern of functional interactions that supports perception, action, and cognition. One natural and parsimonious representation of such a system is a graph in which nodes (units) are connected by edges (interactions). While applicable across spatiotemporal scales, species, and cohorts, the traditional graph approach is unable to address the complexity of time-varying connectivity patterns that may be critically important for an understanding of emotional and cognitive state, task-switching, adaptation and development, or aging and disease progression. Here we survey a set of tools from applied mathematics that offer measures to characterize dynamic graphs. Along with this survey, we offer suggestions for visualization and a publicly-available MATLAB toolbox to facilitate the application of these metrics to existing or yet-to-be acquired neuroimaging data. We illustrate the toolbox by applying it to a previously published data set of time-varying functional graphs, but note that the tools can also be applied to time-varying structural graphs or to other sorts of relational data entirely. Our aim is to provide the neuroimaging community with a useful set of tools, and an intuition regarding how to use them, for addressing emerging questions that hinge on accurate and creative analyses of dynamic graphs.
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Affiliation(s)
- Ann E Sizemore
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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8
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Chambers B, Levy M, Dechery JB, MacLean JN. Ensemble stacking mitigates biases in inference of synaptic connectivity. Netw Neurosci 2018; 2:60-85. [PMID: 29911678 PMCID: PMC5989998 DOI: 10.1162/netn_a_00032] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Accepted: 10/11/2017] [Indexed: 01/26/2023] Open
Abstract
A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches.
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Affiliation(s)
- Brendan Chambers
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
| | - Maayan Levy
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
| | - Joseph B Dechery
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
| | - Jason N MacLean
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA.,Department of Neurobiology, University of Chicago, Chicago, IL, USA
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9
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Dechery JB, MacLean JN. Functional triplet motifs underlie accurate predictions of single-trial responses in populations of tuned and untuned V1 neurons. PLoS Comput Biol 2018; 14:e1006153. [PMID: 29727448 PMCID: PMC5955581 DOI: 10.1371/journal.pcbi.1006153] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 05/16/2018] [Accepted: 04/25/2018] [Indexed: 11/30/2022] Open
Abstract
Visual stimuli evoke activity in visual cortical neuronal populations. Neuronal activity can be selectively modulated by particular visual stimulus parameters, such as the direction of a moving bar of light, resulting in well-defined trial averaged tuning properties. However, given any single stimulus parameter, a large number of neurons in visual cortex remain unmodulated, and the role of this untuned population is not well understood. Here, we use two-photon calcium imaging to record, in an unbiased manner, from large populations of layer 2/3 excitatory neurons in mouse primary visual cortex to describe co-varying activity on single trials in neuronal populations consisting of both tuned and untuned neurons. Specifically, we summarize pairwise covariability with an asymmetric partial correlation coefficient, allowing us to analyze the resultant population correlation structure, or functional network, with graph theory. Using the graph neighbors of a neuron, we find that the local population, including both tuned and untuned neurons, are able to predict individual neuron activity on a moment to moment basis, while also recapitulating tuning properties of tuned neurons. Variance explained in total population activity scales with the number of neurons imaged, demonstrating larger sample sizes are required to fully capture local network interactions. We also find that a specific functional triplet motif in the graph results in the best predictions, suggesting a signature of informative correlations in these populations. In summary, we show that unbiased sampling of the local population can explain single trial response variability as well as trial-averaged tuning properties in V1, and the ability to predict responses is tied to the occurrence of a functional triplet motif.
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Affiliation(s)
- Joseph B. Dechery
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois, United States of America
| | - Jason N. MacLean
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois, United States of America
- Department of Neurobiology, University of Chicago, Chicago, Illinois, United States of America
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10
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Zhang J, Zhang S, Yu C, Zheng X, Xu K. Intrinsic optical imaging study on cortical responses to electrical stimulation in ventral posterior medial nucleus of thalamus. Brain Res 2018; 1684:40-49. [PMID: 29408501 DOI: 10.1016/j.brainres.2018.01.033] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2017] [Revised: 01/23/2018] [Accepted: 01/24/2018] [Indexed: 10/18/2022]
Abstract
Intracortical electrical micro-stimulation has been applied widely for the attempts on reconstruction of sensory functions. More recently, thalamic electrical stimulation has been proposed as a promising target for somatosensory stimulation. However, the cortical activations and mechanisms evoked by VPM stimulation remained unclear. In this report, the cortical neural responses to electrical stimulations were recorded by optical imaging of intrinsic signals. The impact of stimulation parameters was characterized to illustrate how the VPM stimulation alter cortical activities. Significant increases were found in cortical responses with increased stimulation amplitude or pulse width. However, frequency modulation exhibited significant inhibition with higher frequency stimulation. Our results suggest that optical imaging of intrinsic signals is sensitive and reliable to deep brain stimulations. These results may not only help to understand the modulation effects through thalamocortical pathway, but also show the possibility to use VPM stimulation to evoke frequency-tuned tactile sensations in rats.
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Affiliation(s)
- Jiacheng Zhang
- Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou 310027, China; Department of Biomedical Engineering, Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang University, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, China
| | - Shaomin Zhang
- Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou 310027, China; Department of Biomedical Engineering, Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang University, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, China
| | - Chaonan Yu
- Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou 310027, China; Department of Biomedical Engineering, Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang University, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, China
| | - Xiaoxiang Zheng
- Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou 310027, China; Department of Biomedical Engineering, Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang University, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, China
| | - Kedi Xu
- Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou 310027, China; Department of Biomedical Engineering, Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang University, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, China.
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11
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Anti-correlated cortical networks arise from spontaneous neuronal dynamics at slow timescales. Sci Rep 2018; 8:666. [PMID: 29330480 PMCID: PMC5766587 DOI: 10.1038/s41598-017-18097-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Accepted: 12/06/2017] [Indexed: 01/25/2023] Open
Abstract
In the highly interconnected architectures of the cerebral cortex, recurrent intracortical loops disproportionately outnumber thalamo-cortical inputs. These networks are also capable of generating neuronal activity without feedforward sensory drive. It is unknown, however, what spatiotemporal patterns may be solely attributed to intrinsic connections of the local cortical network. Using high-density microelectrode arrays, here we show that in the isolated, primary somatosensory cortex of mice, neuronal firing fluctuates on timescales from milliseconds to tens of seconds. Slower firing fluctuations reveal two spatially distinct neuronal ensembles, which correspond to superficial and deeper layers. These ensembles are anti-correlated: when one fires more, the other fires less and vice versa. This interplay is clearest at timescales of several seconds and is therefore consistent with shifts between active sensing and anticipatory behavioral states in mice.
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12
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Blackwell JM, Geffen MN. Progress and challenges for understanding the function of cortical microcircuits in auditory processing. Nat Commun 2017; 8:2165. [PMID: 29255268 PMCID: PMC5735136 DOI: 10.1038/s41467-017-01755-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2017] [Accepted: 10/12/2017] [Indexed: 12/21/2022] Open
Abstract
An important outstanding question in auditory neuroscience is to identify the mechanisms by which specific motifs within inter-connected neural circuits affect auditory processing and, ultimately, behavior. In the auditory cortex, a combination of large-scale electrophysiological recordings and concurrent optogenetic manipulations are improving our understanding of the role of inhibitory–excitatory interactions. At the same time, computational approaches have grown to incorporate diverse neuronal types and connectivity patterns. However, we are still far from understanding how cortical microcircuits encode and transmit information about complex acoustic scenes. In this review, we focus on recent results identifying the special function of different cortical neurons in the auditory cortex and discuss a computational framework for future work that incorporates ideas from network science and network dynamics toward the coding of complex auditory scenes. Advances in multi-neuron recordings and optogenetic manipulation have resulted in an interrogation of the function of specific cortical cell types in auditory cortex during sound processing. Here, the authors review this literature and discuss the merits of integrating computational approaches from dynamic network science.
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Affiliation(s)
- Jennifer M Blackwell
- Department of Otorhinolaryngology: HNS, Department of Neuroscience, Neuroscience Graduate Group, Computational Neuroscience Initiative, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Maria N Geffen
- Department of Otorhinolaryngology: HNS, Department of Neuroscience, Neuroscience Graduate Group, Computational Neuroscience Initiative, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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13
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Humphries MD. Dynamical networks: Finding, measuring, and tracking neural population activity using network science. Netw Neurosci 2017; 1:324-338. [PMID: 30090869 PMCID: PMC6063717 DOI: 10.1162/netn_a_00020] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Accepted: 06/06/2017] [Indexed: 11/04/2022] Open
Abstract
Systems neuroscience is in a headlong rush to record from as many neurons at the same time as possible. As the brain computes and codes using neuron populations, it is hoped these data will uncover the fundamentals of neural computation. But with hundreds, thousands, or more simultaneously recorded neurons come the inescapable problems of visualizing, describing, and quantifying their interactions. Here I argue that network science provides a set of scalable, analytical tools that already solve these problems. By treating neurons as nodes and their interactions as links, a single network can visualize and describe an arbitrarily large recording. I show that with this description we can quantify the effects of manipulating a neural circuit, track changes in population dynamics over time, and quantitatively define theoretical concepts of neural populations such as cell assemblies. Using network science as a core part of analyzing population recordings will thus provide both qualitative and quantitative advances to our understanding of neural computation.
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Affiliation(s)
- Mark D. Humphries
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
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14
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Dechery JB, MacLean JN. Emergent cortical circuit dynamics contain dense, interwoven ensembles of spike sequences. J Neurophysiol 2017; 118:1914-1925. [PMID: 28724786 DOI: 10.1152/jn.00394.2017] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 07/05/2017] [Accepted: 07/14/2017] [Indexed: 01/30/2023] Open
Abstract
Temporal codes are theoretically powerful encoding schemes, but their precise form in the neocortex remains unknown in part because of the large number of possible codes and the difficulty in disambiguating informative spikes from statistical noise. A biologically plausible and computationally powerful temporal coding scheme is the Hebbian assembly phase sequence (APS), which predicts reliable propagation of spikes between functionally related assemblies of neurons. Here, we sought to measure the inherent capacity of neocortical networks to produce reliable sequences of spikes, as would be predicted by an APS code. To record microcircuit activity, the scale at which computation is implemented, we used two-photon calcium imaging to densely sample spontaneous activity in murine neocortical networks ex vivo. We show that the population spike histogram is sufficient to produce a spatiotemporal progression of activity across the population. To more comprehensively evaluate the capacity for sequential spiking that cannot be explained by the overall population spiking, we identify statistically significant spike sequences. We found a large repertoire of sequence spikes that collectively comprise the majority of spiking in the circuit. Sequences manifest probabilistically and share neuron membership, resulting in unique ensembles of interwoven sequences characterizing individual spatiotemporal progressions of activity. Distillation of population dynamics into its constituent sequences provides a way to capture trial-to-trial variability and may prove to be a powerful decoding substrate in vivo. Informed by these data, we suggest that the Hebbian APS be reformulated as interwoven sequences with flexible assembly membership due to shared overlapping neurons.NEW & NOTEWORTHY Neocortical computation occurs largely within microcircuits comprised of individual neurons and their connections within small volumes (<500 μm3). We found evidence for a long-postulated temporal code, the Hebbian assembly phase sequence, by identifying repeated and co-occurring sequences of spikes. Variance in population activity across trials was explained in part by the ensemble of active sequences. The presence of interwoven sequences suggests that neuronal assembly structure can be variable and is determined by previous activity.
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Affiliation(s)
- Joseph B Dechery
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois; and
| | - Jason N MacLean
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois; and .,Department of Neurobiology, University of Chicago, Illinois
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15
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On the Structure of Cortical Microcircuits Inferred from Small Sample Sizes. J Neurosci 2017; 37:8498-8510. [PMID: 28760860 DOI: 10.1523/jneurosci.0984-17.2017] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Revised: 06/23/2017] [Accepted: 07/18/2017] [Indexed: 02/05/2023] Open
Abstract
The structure in cortical microcircuits deviates from what would be expected in a purely random network, which has been seen as evidence of clustering. To address this issue, we sought to reproduce the nonrandom features of cortical circuits by considering several distinct classes of network topology, including clustered networks, networks with distance-dependent connectivity, and those with broad degree distributions. To our surprise, we found that all of these qualitatively distinct topologies could account equally well for all reported nonrandom features despite being easily distinguishable from one another at the network level. This apparent paradox was a consequence of estimating network properties given only small sample sizes. In other words, networks that differ markedly in their global structure can look quite similar locally. This makes inferring network structure from small sample sizes, a necessity given the technical difficulty inherent in simultaneous intracellular recordings, problematic. We found that a network statistic called the sample degree correlation (SDC) overcomes this difficulty. The SDC depends only on parameters that can be estimated reliably given small sample sizes and is an accurate fingerprint of every topological family. We applied the SDC criterion to data from rat visual and somatosensory cortex and discovered that the connectivity was not consistent with any of these main topological classes. However, we were able to fit the experimental data with a more general network class, of which all previous topologies were special cases. The resulting network topology could be interpreted as a combination of physical spatial dependence and nonspatial, hierarchical clustering.SIGNIFICANCE STATEMENT The connectivity of cortical microcircuits exhibits features that are inconsistent with a simple random network. Here, we show that several classes of network models can account for this nonrandom structure despite qualitative differences in their global properties. This apparent paradox is a consequence of the small numbers of simultaneously recorded neurons in experiment: when inferred via small sample sizes, many networks may be indistinguishable despite being globally distinct. We develop a connectivity measure that successfully classifies networks even when estimated locally with a few neurons at a time. We show that data from rat cortex is consistent with a network in which the likelihood of a connection between neurons depends on spatial distance and on nonspatial, asymmetric clustering.
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16
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Hillary FG, Grafman JH. Injured Brains and Adaptive Networks: The Benefits and Costs of Hyperconnectivity. Trends Cogn Sci 2017; 21:385-401. [PMID: 28372878 PMCID: PMC6664441 DOI: 10.1016/j.tics.2017.03.003] [Citation(s) in RCA: 184] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Revised: 03/01/2017] [Accepted: 03/03/2017] [Indexed: 01/15/2023]
Abstract
A common finding in human functional brain-imaging studies is that damage to neural systems paradoxically results in enhanced functional connectivity between network regions, a phenomenon commonly referred to as 'hyperconnectivity'. Here, we describe the various ways that hyperconnectivity operates to benefit a neural network following injury while simultaneously negotiating the trade-off between metabolic cost and communication efficiency. Hyperconnectivity may be optimally expressed by increasing connections through the most central and metabolically efficient regions (i.e., hubs). While adaptive in the short term, we propose that chronic hyperconnectivity may leave network hubs vulnerable to secondary pathological processes over the life span due to chronically elevated metabolic stress. We conclude by offering novel, testable hypotheses for advancing our understanding of the role of hyperconnectivity in systems-level brain plasticity in neurological disorders.
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Affiliation(s)
- Frank G Hillary
- Pennsylvania State University, University Park, PA, USA; Social Life and Engineering Sciences Imaging Center, University Park, PA, USA; Department of Neurology, Hershey Medical Center, Hershey, PA, USA.
| | - Jordan H Grafman
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
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17
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Memory replay in balanced recurrent networks. PLoS Comput Biol 2017; 13:e1005359. [PMID: 28135266 PMCID: PMC5305273 DOI: 10.1371/journal.pcbi.1005359] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2016] [Revised: 02/13/2017] [Accepted: 01/09/2017] [Indexed: 11/19/2022] Open
Abstract
Complex patterns of neural activity appear during up-states in the neocortex and sharp waves in the hippocampus, including sequences that resemble those during prior behavioral experience. The mechanisms underlying this replay are not well understood. How can small synaptic footprints engraved by experience control large-scale network activity during memory retrieval and consolidation? We hypothesize that sparse and weak synaptic connectivity between Hebbian assemblies are boosted by pre-existing recurrent connectivity within them. To investigate this idea, we connect sequences of assemblies in randomly connected spiking neuronal networks with a balance of excitation and inhibition. Simulations and analytical calculations show that recurrent connections within assemblies allow for a fast amplification of signals that indeed reduces the required number of inter-assembly connections. Replay can be evoked by small sensory-like cues or emerge spontaneously by activity fluctuations. Global-potentially neuromodulatory-alterations of neuronal excitability can switch between network states that favor retrieval and consolidation.
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18
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Chambers B, MacLean JN. Higher-Order Synaptic Interactions Coordinate Dynamics in Recurrent Networks. PLoS Comput Biol 2016; 12:e1005078. [PMID: 27542093 PMCID: PMC4991791 DOI: 10.1371/journal.pcbi.1005078] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2016] [Accepted: 07/21/2016] [Indexed: 01/13/2023] Open
Abstract
Linking synaptic connectivity to dynamics is key to understanding information processing in neocortex. Circuit dynamics emerge from complex interactions of interconnected neurons, necessitating that links between connectivity and dynamics be evaluated at the network level. Here we map propagating activity in large neuronal ensembles from mouse neocortex and compare it to a recurrent network model, where connectivity can be precisely measured and manipulated. We find that a dynamical feature dominates statistical descriptions of propagating activity for both neocortex and the model: convergent clusters comprised of fan-in triangle motifs, where two input neurons are themselves connected. Fan-in triangles coordinate the timing of presynaptic inputs during ongoing activity to effectively generate postsynaptic spiking. As a result, paradoxically, fan-in triangles dominate the statistics of spike propagation even in randomly connected recurrent networks. Interplay between higher-order synaptic connectivity and the integrative properties of neurons constrains the structure of network dynamics and shapes the routing of information in neocortex.
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Affiliation(s)
- Brendan Chambers
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois, United States of America
| | - Jason N. MacLean
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois, United States of America
- Department of Neurobiology, University of Chicago, Chicago, Illinois, United States of America
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19
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Frye CG, MacLean JN. Spontaneous activations follow a common developmental course across primary sensory areas in mouse neocortex. J Neurophysiol 2016; 116:431-7. [PMID: 27146981 DOI: 10.1152/jn.00172.2016] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2016] [Accepted: 05/01/2016] [Indexed: 11/22/2022] Open
Abstract
Spontaneous propagation of spiking within the local neocortical circuits of mature primary sensory areas is highly nonrandom, engaging specific sets of interconnected and functionally related neurons. These spontaneous activations promise insight into neocortical structure and function, but their properties in the first 2 wk of perinatal development are incompletely characterized. Previously, we have found that there is a minimal numerical sample, on the order of 400 cells, necessary to fully capture mature neocortical circuit dynamics. Therefore we maximized our numerical sample by using two-photon calcium imaging to observe spontaneous activity in populations of up to 1,062 neurons spanning multiple columns and layers in 52 acute coronal slices of mouse neocortex at each day from postnatal day (PND) 3 to PND 15. Slices contained either primary auditory cortex (A1) or somatosensory barrel field (S1BF), which allowed us to compare sensory modalities with markedly different developmental timelines. Between PND 3 and PND 8, populations in both areas exhibited activations of anatomically compact subgroups on the order of dozens of cells. Between PND 9 and PND 13, the spatiotemporal structure of the activity diversified to include spatially distributed activations encompassing hundreds of cells. Sparse activations covering the entire field of view dominated in slices taken on or after PND 14. These and other findings demonstrate that the developmental progression of spontaneous activations from active local modules in the first postnatal week to sparse, intermingled groups of neurons at the beginning of the third postnatal week generalizes across primary sensory areas, consistent with an intrinsic developmental trajectory independent of sensory input.
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Affiliation(s)
- Charles G Frye
- Helen Wills Neuroscience Institute, University of California, Berkeley, California; and Department of Neurobiology, University of Chicago, Chicago, Illinois
| | - Jason N MacLean
- Department of Neurobiology, University of Chicago, Chicago, Illinois
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20
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Pyle R, Rosenbaum R. Highly connected neurons spike less frequently in balanced networks. Phys Rev E 2016; 93:040302. [PMID: 27176240 DOI: 10.1103/physreve.93.040302] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2016] [Indexed: 11/07/2022]
Abstract
Biological neuronal networks exhibit highly variable spiking activity. Balanced networks offer a parsimonious model of this variability in which strong excitatory synaptic inputs are canceled by strong inhibitory inputs on average, and irregular spiking activity is driven by fluctuating synaptic currents. Most previous studies of balanced networks assume a homogeneous or distance-dependent connectivity structure, but connectivity in biological cortical networks is more intricate. We use a heterogeneous mean-field theory of balanced networks to show that heterogeneous in-degrees can break balance. Moreover, heterogeneous architectures that achieve balance promote lower firing rates in neurons with larger in-degrees, consistent with some recent experimental observations.
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Affiliation(s)
- Ryan Pyle
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana 46556, USA
| | - Robert Rosenbaum
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana 46556, USA.,Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, Indiana 46556, USA
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21
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Luongo F, Horn M, Sohal VS. Putative Microcircuit-Level Substrates for Attention Are Disrupted in Mouse Models of Autism. Biol Psychiatry 2016; 79:667-75. [PMID: 26022075 PMCID: PMC4624609 DOI: 10.1016/j.biopsych.2015.04.014] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2015] [Revised: 04/10/2015] [Accepted: 04/14/2015] [Indexed: 12/27/2022]
Abstract
BACKGROUND Deep layer excitatory circuits in the prefrontal cortex represent the strongest locus for genetic convergence in autism, but specific abnormalities within these circuits that mediate key features of autism, such as cognitive or attentional deficits, remain unknown. Attention normally increases the sensitivity of neural populations to incoming signals by decorrelating ongoing cortical circuit activity. Here, we investigated whether mechanisms underlying this phenomenon might be disrupted within deep layer prefrontal circuits in mouse models of autism. METHODS We isolated deep layer prefrontal circuits in brain slices then used single-photon GCaMP imaging to record activity from many (50 to 100) neurons simultaneously to study patterns of spontaneous activity generated by these circuits under normal conditions and in two etiologically distinct models of autism: mice exposed to valproic acid in utero and Fmr1 knockout mice. RESULTS We found that modest doses of the cholinergic agonist carbachol normally decorrelate spontaneous activity generated by deep layer prefrontal networks. This effect was disrupted in both valproic acid-exposed and Fmr1 knockout mice but intact following other manipulations that did not model autism. CONCLUSIONS Our results suggest that cholinergic modulation may contribute to attention by acting on local cortical microcircuits to decorrelate spontaneous activity. Furthermore, defects in this mechanism represent a microcircuit-level endophenotype that could link diverse genetic and developmental disruptions to attentional deficits in autism. Future studies could elucidate pathways leading from various etiologies to this circuit-level abnormality or use this abnormality itself as a target and identify novel therapeutic strategies that restore normal circuit function.
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Affiliation(s)
- Francisco Luongo
- Department of Psychiatry, 675 Nelson Rising Lane University of California, San Francisco San Francisco, CA 94143-0444,Center for Integrative Neuroscience, 675 Nelson Rising Lane University of California, San Francisco San Francisco, CA 94143-0444,Sloan-Swartz Center for Theoretical Neurobiology, 675 Nelson Rising Lane University of California, San Francisco San Francisco, CA 94143-0444,Neuroscience Graduate Program, 675 Nelson Rising Lane University of California, San Francisco San Francisco, CA 94143-0444
| | - Meryl Horn
- Neuroscience Graduate Program, 675 Nelson Rising Lane University of California, San Francisco San Francisco, CA 94143-0444
| | - Vikaas S. Sohal
- Department of Psychiatry, 675 Nelson Rising Lane University of California, San Francisco San Francisco, CA 94143-0444,Center for Integrative Neuroscience, 675 Nelson Rising Lane University of California, San Francisco San Francisco, CA 94143-0444,Sloan-Swartz Center for Theoretical Neurobiology, 675 Nelson Rising Lane University of California, San Francisco San Francisco, CA 94143-0444, To whom correspondence should be addressed at
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22
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Pérez-Ortega J, Duhne M, Lara-González E, Plata V, Gasca D, Galarraga E, Hernández-Cruz A, Bargas J. Pathophysiological signatures of functional connectomics in parkinsonian and dyskinetic striatal microcircuits. Neurobiol Dis 2016; 91:347-61. [PMID: 26951948 DOI: 10.1016/j.nbd.2016.02.023] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2016] [Revised: 02/19/2016] [Accepted: 02/24/2016] [Indexed: 12/12/2022] Open
Abstract
A challenge in neuroscience is to integrate the cellular and system levels. For instance, we still do not know how a few dozen neurons organize their activity and relations in a microcircuit or module of histological scale. By using network theory and Ca(2+) imaging with single-neuron resolution we studied the way in which striatal microcircuits of dozens of cells orchestrate their activity. In addition, control and diseased striatal tissues were compared in rats. In the control tissue, functional connectomics revealed small-world, scale-free and hierarchical network properties. These properties were lost during pathological conditions in ways that could be quantitatively analyzed. Decorticated striatal circuits disclosed that corticostriatal interactions depend on privileged connections with a set of highly connected neurons or "hubs". In the 6-OHDA model of Parkinson's disease there was a decrease in hubs number; but the ones that remained were linked to dominant network states. l-DOPA induced dyskinesia provoked a loss in the hierarchical structure of the circuit. All these conditions conferred distinct temporal sequences to circuit activity. Temporal sequences appeared as particular signatures of disease process thus bringing the possibility of a future quantitative pathophysiology at a histological scale.
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Affiliation(s)
- Jesús Pérez-Ortega
- División de Neurociencias, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, México City, DF, Mexico
| | - Mariana Duhne
- División de Neurociencias, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, México City, DF, Mexico
| | - Esther Lara-González
- División de Neurociencias, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, México City, DF, Mexico
| | - Victor Plata
- División de Neurociencias, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, México City, DF, Mexico
| | - Deisy Gasca
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, Mexico
| | - Elvira Galarraga
- División de Neurociencias, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, México City, DF, Mexico
| | - Arturo Hernández-Cruz
- División de Neurociencias, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, México City, DF, Mexico
| | - José Bargas
- División de Neurociencias, Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, México City, DF, Mexico.
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23
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Tsytsarev V, Pumbo E, Tang Q, Chen CW, Kalchenko V, Chen Y. Study of the cortical representation of whisker frequency selectivity using voltage-sensitive dye optical imaging. INTRAVITAL 2016; 5:e1142637. [PMID: 28243518 DOI: 10.1080/21659087.2016.1142637] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2015] [Revised: 01/06/2016] [Accepted: 01/08/2016] [Indexed: 12/17/2022]
Abstract
The facial whiskers of rodents act as a high-resolution tactile apparatus that allow the animal to detect the finest details of its environment. Previously it was shown that whisker-sensitive neurons in the somatosensory cortex show frequency selectivity to small amplitude stimuli, An intravital voltage-sensitive dye optical imaging (VSDi) method in combination with the different frequency whisker stimulation was used in order to visualize neural activity in the mice somatosensory cortex in response to the stimulation of a single whisker by different frequencies. Using the intravital voltage-sensitive dye optical imaging (VSDi) method in combination with the different frequency whisker stimulation we visualized neural activity in the mice somatosensory cortex in response to the stimulation of a single whisker by different frequencies. We found that whisker stimuli with different frequencies led to different optical signals in the barrel field. Our results provide evidence that different neurons of the barrel cortex have different frequency preferences. This supports prior research that whisker deflections cause responses in cortical neurons within the barrel field according to the frequency of the stimulation. Many studies of the whisker frequency selectivity were performed using unit recording but to map spatial organization, imaging methods are essential. In the work described in the present paper, we take a serious step toward detailed functional mapping of the somatosensory cortex using VSDi. To our knowledge, this is the first demonstration of whisker frequency sensitivity and selectivity of barrel cortex neurons with optical imaging methods.
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Affiliation(s)
- Vassiliy Tsytsarev
- Department of Anatomy and Neurobiology, University of Maryland School of Medicine , Baltimore, MD, USA
| | - Elena Pumbo
- Center for Genetic Medicine, Children's National Medical Center , Washington, DC, USA
| | - Qinggong Tang
- Department of Bioengineering, University of Maryland , College Park, MD, USA
| | - Chao-Wei Chen
- Department of Bioengineering, University of Maryland , College Park, MD, USA
| | - Vyacheslav Kalchenko
- Department of Veterinary Resources, Weizmann Institute of Science , Rehovot, Israel
| | - Yu Chen
- Department of Bioengineering, University of Maryland , College Park, MD, USA
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24
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Luongo FJ, Zimmerman CA, Horn ME, Sohal VS. Correlations between prefrontal neurons form a small-world network that optimizes the generation of multineuron sequences of activity. J Neurophysiol 2016; 115:2359-75. [PMID: 26888108 DOI: 10.1152/jn.01043.2015] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Accepted: 02/15/2016] [Indexed: 12/11/2022] Open
Abstract
Sequential patterns of prefrontal activity are believed to mediate important behaviors, e.g., working memory, but it remains unclear exactly how they are generated. In accordance with previous studies of cortical circuits, we found that prefrontal microcircuits in young adult mice spontaneously generate many more stereotyped sequences of activity than expected by chance. However, the key question of whether these sequences depend on a specific functional organization within the cortical microcircuit, or emerge simply as a by-product of random interactions between neurons, remains unanswered. We observed that correlations between prefrontal neurons do follow a specific functional organization-they have a small-world topology. However, until now it has not been possible to directly link small-world topologies to specific circuit functions, e.g., sequence generation. Therefore, we developed a novel analysis to address this issue. Specifically, we constructed surrogate data sets that have identical levels of network activity at every point in time but nevertheless represent various network topologies. We call this method shuffling activity to rearrange correlations (SHARC). We found that only surrogate data sets based on the actual small-world functional organization of prefrontal microcircuits were able to reproduce the levels of sequences observed in actual data. As expected, small-world data sets contained many more sequences than surrogate data sets with randomly arranged correlations. Surprisingly, small-world data sets also outperformed data sets in which correlations were maximally clustered. Thus the small-world functional organization of cortical microcircuits, which effectively balances the random and maximally clustered regimes, is optimal for producing stereotyped sequential patterns of activity.
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Affiliation(s)
- Francisco J Luongo
- Department of Psychiatry, University of California, San Francisco, California; Center for Integrative Neuroscience, University of California, San Francisco, California; Sloan-Swartz Center for Theoretical Neurobiology, University of California, San Francisco, California; and Neuroscience Graduate Program, University of California, San Francisco, California
| | - Chris A Zimmerman
- Neuroscience Graduate Program, University of California, San Francisco, California
| | - Meryl E Horn
- Neuroscience Graduate Program, University of California, San Francisco, California
| | - Vikaas S Sohal
- Department of Psychiatry, University of California, San Francisco, California; Center for Integrative Neuroscience, University of California, San Francisco, California; Sloan-Swartz Center for Theoretical Neurobiology, University of California, San Francisco, California; and
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25
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Kim S, Callier T, Tabot GA, Gaunt RA, Tenore FV, Bensmaia SJ. Behavioral assessment of sensitivity to intracortical microstimulation of primate somatosensory cortex. Proc Natl Acad Sci U S A 2015; 112:15202-7. [PMID: 26504211 PMCID: PMC4679002 DOI: 10.1073/pnas.1509265112] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Intracortical microstimulation (ICMS) is a powerful tool to investigate the functional role of neural circuits and may provide a means to restore sensation for patients for whom peripheral stimulation is not an option. In a series of psychophysical experiments with nonhuman primates, we investigate how stimulation parameters affect behavioral sensitivity to ICMS. Specifically, we deliver ICMS to primary somatosensory cortex through chronically implanted electrode arrays across a wide range of stimulation regimes. First, we investigate how the detectability of ICMS depends on stimulation parameters, including pulse width, frequency, amplitude, and pulse train duration. Then, we characterize the degree to which ICMS pulse trains that differ in amplitude lead to discriminable percepts across the range of perceptible and safe amplitudes. We also investigate how discriminability of pulse amplitude is modulated by other stimulation parameters-namely, frequency and duration. Perceptual judgments obtained across these various conditions will inform the design of stimulation regimes for neuroscience and neuroengineering applications.
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Affiliation(s)
- Sungshin Kim
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL 60637
| | - Thierri Callier
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL 60637
| | - Gregg A Tabot
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL 60637
| | - Robert A Gaunt
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213
| | - Francesco V Tenore
- Research and Exploratory Development Department, Applied Physics Laboratory, Johns Hopkins University, Laurel, MD 20723
| | - Sliman J Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL 60637; Committee on Computational Neuroscience, University of Chicago, Chicago, IL 60637;
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26
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Chambers B, MacLean JN. Multineuronal activity patterns identify selective synaptic connections under realistic experimental constraints. J Neurophysiol 2015. [PMID: 26203109 DOI: 10.1152/jn.00429.2015] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Structured multineuronal activity patterns within local neocortical circuitry are strongly linked to sensory input, motor output, and behavioral choice. These reliable patterns of pairwise lagged firing are the consequence of connectivity since they are not present in rate-matched but unconnected Poisson nulls. It is important to relate multineuronal patterns to their synaptic underpinnings, but it is unclear how effectively statistical dependencies in spiking between neurons identify causal synaptic connections. To assess the feasibility of mapping function onto structure we used a network model that showed a diversity of multineuronal activity patterns and replicated experimental constraints on data acquisition. Using an iterative Bayesian inference algorithm, we detected a select subset of monosynaptic connections substantially more precisely than correlation-based inference, a common alternative approach. We found that precise inference of synaptic connections improved with increasing numbers of diverse multineuronal activity patterns in contrast to increased observations of a single pattern. Surprisingly, neuronal spiking was most effective and precise at revealing causal synaptic connectivity when the lags considered by the iterative Bayesian algorithm encompassed the timescale of synaptic conductance and integration (∼10 ms), rather than synaptic transmission time (∼2 ms), highlighting the importance of synaptic integration in driving postsynaptic spiking. Last, strong synaptic connections were detected preferentially, underscoring their special importance in cortical computation. Even after simulating experimental constraints, top down approaches to cortical connectivity, from function to structure, identify synaptic connections underlying multineuronal activity. These select connections are closely tied to cortical processing.
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Affiliation(s)
- Brendan Chambers
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois; and
| | - Jason N MacLean
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois; and Department of Neurobiology, University of Chicago, Chicago, Illinois
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27
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Sederberg AJ, Palmer SE, MacLean JN. Decoding thalamic afferent input using microcircuit spiking activity. J Neurophysiol 2015; 113:2921-33. [PMID: 25695647 DOI: 10.1152/jn.00885.2014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2014] [Accepted: 02/18/2015] [Indexed: 11/22/2022] Open
Abstract
A behavioral response appropriate to a sensory stimulus depends on the collective activity of thousands of interconnected neurons. The majority of cortical connections arise from neighboring neurons, and thus understanding the cortical code requires characterizing information representation at the scale of the cortical microcircuit. Using two-photon calcium imaging, we densely sampled the thalamically evoked response of hundreds of neurons spanning multiple layers and columns in thalamocortical slices of mouse somatosensory cortex. We then used a biologically plausible decoder to characterize the representation of two distinct thalamic inputs, at the level of the microcircuit, to reveal those aspects of the activity pattern that are likely relevant to downstream neurons. Our data suggest a sparse code, distributed across lamina, in which a small population of cells carries stimulus-relevant information. Furthermore, we find that, within this subset of neurons, decoder performance improves when noise correlations are taken into account.
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Affiliation(s)
- Audrey J Sederberg
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois; Department of Neurobiology, University of Chicago, Chicago, Illinois; and
| | - Stephanie E Palmer
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois; Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois
| | - Jason N MacLean
- Department of Neurobiology, University of Chicago, Chicago, Illinois; and Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois
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28
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Tomov P, Pena RFO, Zaks MA, Roque AC. Sustained oscillations, irregular firing, and chaotic dynamics in hierarchical modular networks with mixtures of electrophysiological cell types. Front Comput Neurosci 2014; 8:103. [PMID: 25228879 PMCID: PMC4151042 DOI: 10.3389/fncom.2014.00103] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2014] [Accepted: 08/13/2014] [Indexed: 11/13/2022] Open
Abstract
The cerebral cortex exhibits neural activity even in the absence of external stimuli. This self-sustained activity is characterized by irregular firing of individual neurons and population oscillations with a broad frequency range. Questions that arise in this context, are: What are the mechanisms responsible for the existence of neuronal spiking activity in the cortex without external input? Do these mechanisms depend on the structural organization of the cortical connections? Do they depend on intrinsic characteristics of the cortical neurons? To approach the answers to these questions, we have used computer simulations of cortical network models. Our networks have hierarchical modular architecture and are composed of combinations of neuron models that reproduce the firing behavior of the five main cortical electrophysiological cell classes: regular spiking (RS), chattering (CH), intrinsically bursting (IB), low threshold spiking (LTS), and fast spiking (FS). The population of excitatory neurons is built of RS cells (always present) and either CH or IB cells. Inhibitory neurons belong to the same class, either LTS or FS. Long-lived self-sustained activity states in our network simulations display irregular single neuron firing and oscillatory activity similar to experimentally measured ones. The duration of self-sustained activity strongly depends on the initial conditions, suggesting a transient chaotic regime. Extensive analysis of the self-sustained activity states showed that their lifetime expectancy increases with the number of network modules and is favored when the network is composed of excitatory neurons of the RS and CH classes combined with inhibitory neurons of the LTS class. These results indicate that the existence and properties of the self-sustained cortical activity states depend on both the topology of the network and the neuronal mixture that comprises the network.
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Affiliation(s)
- Petar Tomov
- Institute of Mathematics, Humboldt University of Berlin Berlin, Germany
| | - Rodrigo F O Pena
- Laboratory of Neural Systems, Department of Physics, School of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo Ribeirão Preto, Brazil
| | - Michael A Zaks
- Institute of Mathematics, Humboldt University of Berlin Berlin, Germany
| | - Antonio C Roque
- Laboratory of Neural Systems, Department of Physics, School of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo Ribeirão Preto, Brazil
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Gururangan SS, Sadovsky AJ, MacLean JN. Analysis of graph invariants in functional neocortical circuitry reveals generalized features common to three areas of sensory cortex. PLoS Comput Biol 2014; 10:e1003710. [PMID: 25010654 PMCID: PMC4091703 DOI: 10.1371/journal.pcbi.1003710] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2014] [Accepted: 05/24/2014] [Indexed: 11/25/2022] Open
Abstract
Correlations in local neocortical spiking activity can provide insight into the underlying organization of cortical microcircuitry. However, identifying structure in patterned multi-neuronal spiking remains a daunting task due to the high dimensionality of the activity. Using two-photon imaging, we monitored spontaneous circuit dynamics in large, densely sampled neuronal populations within slices of mouse primary auditory, somatosensory, and visual cortex. Using the lagged correlation of spiking activity between neurons, we generated functional wiring diagrams to gain insight into the underlying neocortical circuitry. By establishing the presence of graph invariants, which are label-independent characteristics common to all circuit topologies, our study revealed organizational features that generalized across functionally distinct cortical regions. Regardless of sensory area, random and -nearest neighbors null graphs failed to capture the structure of experimentally derived functional circuitry. These null models indicated that despite a bias in the data towards spatially proximal functional connections, functional circuit structure is best described by non-random and occasionally distal connections. Eigenvector centrality, which quantifies the importance of a neuron in the temporal flow of circuit activity, was highly related to feedforwardness in all functional circuits. The number of nodes participating in a functional circuit did not scale with the number of neurons imaged regardless of sensory area, indicating that circuit size is not tied to the sampling of neocortex. Local circuit flow comprehensively covered angular space regardless of the spatial scale that we tested, demonstrating that circuitry itself does not bias activity flow toward pia. Finally, analysis revealed that a minimal numerical sample size of neurons was necessary to capture at least 90 percent of functional circuit topology. These data and analyses indicated that functional circuitry exhibited rules of organization which generalized across three areas of sensory neocortex. Information in the brain is represented and processed by populations of interconnected neurons. However, there is a lack of a clear understanding of the structure and organization of circuit wiring, particularly at the mesoscale which spans multiple columns and layers. In this study, we sought to evaluate whether functional circuit architecture generalizes across the neocortex, testing the existence of a functional analogue to the neocortical microcircuit hypothesis. We analyzed the correlational structure of spontaneous circuit activations in primary auditory, somatosensory, and visual neocortex to generate functional topologies. In these graphs, neurons were represented as nodes, and time-lagged firing between neurons were directed edges. Edge weights reflected how many times the lagged firing occurred and was synonymous to the strength of the functional connection between two neurons. The presence of label-independent features, identified by investigating functional circuit topologies under a graph invariant framework, suggest that functionally distinct areas of the neocortex carry features of a generalized functional cortical circuit. Furthermore, our analyses show that the simultaneous recording of large sections of cortical circuitry is necessary to recognize these features and avoid undersampling errors.
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Affiliation(s)
- Suchin S. Gururangan
- Department of Neurobiology, University of Chicago, Chicago, Illinois, United States of America
| | - Alexander J. Sadovsky
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois, United States of America
| | - Jason N. MacLean
- Department of Neurobiology, University of Chicago, Chicago, Illinois, United States of America
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois, United States of America
- * E-mail:
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Runfeldt MJ, Sadovsky AJ, MacLean JN. Acetylcholine functionally reorganizes neocortical microcircuits. J Neurophysiol 2014; 112:1205-16. [PMID: 24872527 DOI: 10.1152/jn.00071.2014] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Sensory information is processed and transmitted through the synaptic structure of local cortical circuits, but it is unclear how modulation of this architecture influences the cortical representation of sensory stimuli. Acetylcholine (ACh) promotes attention and arousal and is thought to increase the signal-to-noise ratio of sensory input in primary sensory cortices. Using high-speed two-photon calcium imaging in a thalamocortical somatosensory slice preparation, we recorded action potential activity of up to 900 neurons simultaneously and compared local cortical circuit activations with and without bath presence of ACh. We found that ACh reduced weak pairwise relationships and excluded neurons that were already unreliable during circuit activity. Using action potential activity from the imaged population, we generated functional wiring diagrams based on the statistical dependencies of activity between neurons. ACh pruned weak functional connections from spontaneous circuit activations and yielded a more modular and hierarchical circuit structure, which biased activity to flow in a more feedforward fashion. Neurons that were active in response to thalamic input had reduced pairwise dependencies overall, but strong correlations were conserved. This coincided with a prolonged period during which neurons showed temporally precise responses to thalamic input. Our results demonstrate that ACh reorganizes functional circuit structure in a manner that may enhance the integration and discriminability of thalamic afferent input within local neocortical circuitry.
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Affiliation(s)
- Melissa J Runfeldt
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois; and
| | - Alexander J Sadovsky
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois; and
| | - Jason N MacLean
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois; and Department of Neurobiology, University of Chicago, Chicago, Illinois
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