101
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Herrera CG, Cadavieco MC, Jego S, Ponomarenko A, Korotkova T, Adamantidis A. Hypothalamic feedforward inhibition of thalamocortical network controls arousal and consciousness. Nat Neurosci 2015; 19:290-8. [PMID: 26691833 PMCID: PMC5818272 DOI: 10.1038/nn.4209] [Citation(s) in RCA: 178] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2015] [Accepted: 11/30/2015] [Indexed: 02/07/2023]
Abstract
During non-rapid eye movement (NREM) sleep, synchronous synaptic activity in the thalamocortical network generates predominantly low-frequency oscillations (<4 Hz) that are modulated by inhibitory inputs from the thalamic reticular nucleus (TRN). Whether TRN cells integrate sleep-wake signals from subcortical circuits remains unclear. We found that GABA neurons from the lateral hypothalamus (LHGABA) exert a strong inhibitory control over TRN GABA neurons (TRNGABA). We found that optogenetic activation of this circuit recapitulated state-dependent changes of TRN neuron activity in behaving mice and induced rapid arousal during NREM, but not REM, sleep. During deep anesthesia, activation of this circuit induced sustained cortical arousal. In contrast, optogenetic silencing of LHGABA-TRNGABA transmission increased the duration of NREM sleep and amplitude of delta (1-4 Hz) oscillations. Collectively, these results demonstrate that TRN cells integrate subcortical arousal inputs selectively during NREM sleep and may participate in sleep intensity.
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Affiliation(s)
- Carolina Gutierrez Herrera
- Department of Neurology, Inselspital University Hospital, University of Bern, Bern, Switzerland.,Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Canada.,Department of Preclinical Research (DKF), University of Bern, Bern, Switzerland
| | - Marta Carus Cadavieco
- Leibniz Institute for Molecular Pharmacology (FMP)/NeuroCure Cluster of Excellence, Berlin, Germany
| | - Sonia Jego
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Canada
| | - Alexey Ponomarenko
- Leibniz Institute for Molecular Pharmacology (FMP)/NeuroCure Cluster of Excellence, Berlin, Germany
| | - Tatiana Korotkova
- Leibniz Institute for Molecular Pharmacology (FMP)/NeuroCure Cluster of Excellence, Berlin, Germany
| | - Antoine Adamantidis
- Department of Neurology, Inselspital University Hospital, University of Bern, Bern, Switzerland.,Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Canada.,Department of Preclinical Research (DKF), University of Bern, Bern, Switzerland
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102
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Mazzoni A, Lindén H, Cuntz H, Lansner A, Panzeri S, Einevoll GT. Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models. PLoS Comput Biol 2015; 11:e1004584. [PMID: 26657024 PMCID: PMC4682791 DOI: 10.1371/journal.pcbi.1004584] [Citation(s) in RCA: 117] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2015] [Accepted: 10/02/2015] [Indexed: 01/27/2023] Open
Abstract
Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best "LFP proxy", we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with "ground-truth" LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo.
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Affiliation(s)
- Alberto Mazzoni
- The Biorobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Pisa, Italy
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
- * E-mail: (AM); (GTE)
| | - Henrik Lindén
- Department of Neuroscience and Pharmacology, University of Copenhagen, Copenhagen, Denmark
- Department of Computational Biology, School of Computer Science and Communication, Royal Institute of Technology–KTH, Stockholm, Sweden
| | - Hermann Cuntz
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt/Main, Germany
- Institute of Clinical Neuroanatomy, Goethe University Frankfurt, Frankfurt/Main, Germany
- Frankfurt Institute for Advanced Studies (FIAS), Frankfurt/Main, Germany
| | - Anders Lansner
- Department of Computational Biology, School of Computer Science and Communication, Royal Institute of Technology–KTH, Stockholm, Sweden
| | - Stefano Panzeri
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Gaute T. Einevoll
- Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, Ås, Norway
- Department of Physics, University of Oslo, Oslo, Norway
- * E-mail: (AM); (GTE)
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103
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Liu KKL, Bartsch RP, Lin A, Mantegna RN, Ivanov PC. Plasticity of brain wave network interactions and evolution across physiologic states. Front Neural Circuits 2015; 9:62. [PMID: 26578891 PMCID: PMC4620446 DOI: 10.3389/fncir.2015.00062] [Citation(s) in RCA: 77] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2015] [Accepted: 10/02/2015] [Indexed: 11/13/2022] Open
Abstract
Neural plasticity transcends a range of spatio-temporal scales and serves as the basis of various brain activities and physiologic functions. At the microscopic level, it enables the emergence of brain waves with complex temporal dynamics. At the macroscopic level, presence and dominance of specific brain waves is associated with important brain functions. The role of neural plasticity at different levels in generating distinct brain rhythms and how brain rhythms communicate with each other across brain areas to generate physiologic states and functions remains not understood. Here we perform an empirical exploration of neural plasticity at the level of brain wave network interactions representing dynamical communications within and between different brain areas in the frequency domain. We introduce the concept of time delay stability (TDS) to quantify coordinated bursts in the activity of brain waves, and we employ a system-wide Network Physiology integrative approach to probe the network of coordinated brain wave activations and its evolution across physiologic states. We find an association between network structure and physiologic states. We uncover a hierarchical reorganization in the brain wave networks in response to changes in physiologic state, indicating new aspects of neural plasticity at the integrated level. Globally, we find that the entire brain network undergoes a pronounced transition from low connectivity in Deep Sleep and REM to high connectivity in Light Sleep and Wake. In contrast, we find that locally, different brain areas exhibit different network dynamics of brain wave interactions to achieve differentiation in function during different sleep stages. Moreover, our analyses indicate that plasticity also emerges in frequency-specific networks, which represent interactions across brain locations mediated through a specific frequency band. Comparing frequency-specific networks within the same physiologic state we find very different degree of network connectivity and link strength, while at the same time each frequency-specific network is characterized by a different signature pattern of sleep-stage stratification, reflecting a remarkable flexibility in response to change in physiologic state. These new aspects of neural plasticity demonstrate that in addition to dominant brain waves, the network of brain wave interactions is a previously unrecognized hallmark of physiologic state and function.
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Affiliation(s)
- Kang K. L. Liu
- Laboratory for Network Physiology, Department of Physics, Boston UniversityBoston, MA, USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical SchoolBoston, MA, USA
| | | | - Aijing Lin
- Laboratory for Network Physiology, Department of Physics, Boston UniversityBoston, MA, USA
- Department of Mathematics, School of Science, Beijing Jiaotong UniversityBeijing, China
| | - Rosario N. Mantegna
- Dipartimento di Fisica e Chimica, Viale delle Scienze, University of PalermoPalermo, Italy
- Center for Network Science and Department of Economics, Central European UniversityBudapest, Hungary
| | - Plamen Ch. Ivanov
- Laboratory for Network Physiology, Department of Physics, Boston UniversityBoston, MA, USA
- Division of Sleep Medicine, Brigham and Women's Hospital, Harvard Medical SchoolBoston, MA, USA
- Institute of Solid State Physics, Bulgarian Academy of SciencesSofia, Bulgaria
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104
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Shaping Neuronal Network Activity by Presynaptic Mechanisms. PLoS Comput Biol 2015; 11:e1004438. [PMID: 26372048 PMCID: PMC4570815 DOI: 10.1371/journal.pcbi.1004438] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2015] [Accepted: 06/23/2015] [Indexed: 02/06/2023] Open
Abstract
Neuronal microcircuits generate oscillatory activity, which has been linked to basic functions such as sleep, learning and sensorimotor gating. Although synaptic release processes are well known for their ability to shape the interaction between neurons in microcircuits, most computational models do not simulate the synaptic transmission process directly and hence cannot explain how changes in synaptic parameters alter neuronal network activity. In this paper, we present a novel neuronal network model that incorporates presynaptic release mechanisms, such as vesicle pool dynamics and calcium-dependent release probability, to model the spontaneous activity of neuronal networks. The model, which is based on modified leaky integrate-and-fire neurons, generates spontaneous network activity patterns, which are similar to experimental data and robust under changes in the model's primary gain parameters such as excitatory postsynaptic potential and connectivity ratio. Furthermore, it reliably recreates experimental findings and provides mechanistic explanations for data obtained from microelectrode array recordings, such as network burst termination and the effects of pharmacological and genetic manipulations. The model demonstrates how elevated asynchronous release, but not spontaneous release, synchronizes neuronal network activity and reveals that asynchronous release enhances utilization of the recycling vesicle pool to induce the network effect. The model further predicts a positive correlation between vesicle priming at the single-neuron level and burst frequency at the network level; this prediction is supported by experimental findings. Thus, the model is utilized to reveal how synaptic release processes at the neuronal level govern activity patterns and synchronization at the network level. The activity of neuronal networks underlies basic neural functions such as sleep, learning and sensorimotor gating. Computational models of neuronal networks have been developed to capture the complexity of the network activity and predict how neuronal networks generate spontaneous activity. However, most computational models do not simulate the intricate synaptic release process that governs the interaction between neurons and has been shown to significantly impact neuronal network activity and animal behavior, learning and memory. Our paper demonstrates the importance of simulating the elaborate synaptic release process to understand how neuronal networks generate spontaneous activity and respond to manipulations of the release process. The model provides mechanistic explanations and predictions for experimental pharmacological and genetic manipulations. Thus, the model presents a novel computational platform to understand how mechanistic changes in the synaptic release process modulate network oscillatory activity that might impact basic neural functions.
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105
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Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations. PLoS Comput Biol 2015; 11:e1004490. [PMID: 26325661 PMCID: PMC4556689 DOI: 10.1371/journal.pcbi.1004490] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2014] [Accepted: 08/05/2015] [Indexed: 11/19/2022] Open
Abstract
Network models are routinely downscaled compared to nature in terms of numbers of nodes or edges because of a lack of computational resources, often without explicit mention of the limitations this entails. While reliable methods have long existed to adjust parameters such that the first-order statistics of network dynamics are conserved, here we show that limitations already arise if also second-order statistics are to be maintained. The temporal structure of pairwise averaged correlations in the activity of recurrent networks is determined by the effective population-level connectivity. We first show that in general the converse is also true and explicitly mention degenerate cases when this one-to-one relationship does not hold. The one-to-one correspondence between effective connectivity and the temporal structure of pairwise averaged correlations implies that network scalings should preserve the effective connectivity if pairwise averaged correlations are to be held constant. Changes in effective connectivity can even push a network from a linearly stable to an unstable, oscillatory regime and vice versa. On this basis, we derive conditions for the preservation of both mean population-averaged activities and pairwise averaged correlations under a change in numbers of neurons or synapses in the asynchronous regime typical of cortical networks. We find that mean activities and correlation structure can be maintained by an appropriate scaling of the synaptic weights, but only over a range of numbers of synapses that is limited by the variance of external inputs to the network. Our results therefore show that the reducibility of asynchronous networks is fundamentally limited.
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106
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Vyazovskiy VV, Olcese U, Cirelli C, Tononi G. Prolonged wakefulness alters neuronal responsiveness to local electrical stimulation of the neocortex in awake rats.. J Sleep Res 2015; 22:239-50. [PMID: 23607417 DOI: 10.1111/jsr.12009] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Prolonged wakefulness or a lack of sleep lead to cognitive deficits, but little is known about the underlying cellular mechanisms. We recently found that sleep deprivation affects spontaneous neuronal activity in the neocortex of sleeping and awake rats. While it is well known that synaptic responses are modulated by ongoing cortical activity, it remains unclear whether prolonged waking affects responsiveness of cortical neurons to incoming stimuli. By applying local electrical microstimulation to the frontal area of the neocortex, we found that after a 4 h period of waking the initial neuronal response in the contralateral frontal cortex was stronger and more synchronous, and was followed by a more profound inhibition of neuronal spiking as compared with the control condition. These changes in evoked activity suggest increased neuronal excitability and indicate that, after staying awake, cortical neurons become transiently bistable. We propose that some of the detrimental effects of sleep deprivation may be a result of altered neuronal responsiveness to incoming intrinsic and extrinsic inputs.
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Affiliation(s)
- Vladyslav V Vyazovskiy
- Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin, USA; Department of Biochemistry and Physiology, University of Surrey, Guildford, Surrey, UK
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107
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Yao S, Song J, Gao L, Yan Y, Huang C, Ding H, Huang H, He Y, Sun R, Xu G. Thalamocortical Sensorimotor Circuit Damage Associated with Disorders of Consciousness for Diffuse Axonal Injury Patients. J Neurol Sci 2015; 356:168-74. [PMID: 26165776 DOI: 10.1016/j.jns.2015.06.044] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2015] [Revised: 06/16/2015] [Accepted: 06/22/2015] [Indexed: 10/23/2022]
Abstract
The relationship of structural and functional brain damage and disorders of consciousness (DOC) for diffuse axonal injury (DAI) is still not fully explored. We employed diffusion tensor imaging (DTI) and resting-state fMRI (RS-fMRI) to examine the changes of resting activations and white matter (WM) integrity for DAI with DOC. WM damages were observed in the body and genu of the corpus callosum, right external capsule (EC) and superior corona radiate (SCR), left superior cerebellar peduncle (SCP) and posterior thalamic radiation (PTR). The RS-fMRI revealed augmented amplitude of low-frequency fluctuation (ALFF) in the anterior cingulate cortex, hippocampus, insula, amygdala and putamen, and reduced ALFF in the precuneus, thalamus, pre-central and post-central gyri. Correlation analysis identified positive associations between the Glasgow Coma Scale (GCS) and activation of the precuneus and between GCS and DTI measurements in the left PTR and SCP, but a negative correlation was found between GCS and activation of the thalamus. Cross modality association analyses indicated that activations of the amygdala and postcentral gyrus were correlated with DTI measurements of the right EC and left PTR respectively. These results implicate that the WM damages in thalamocortical sensorimotor circuit and aberrant brain activity responding to self-awareness and sensation are critical factors to DOC, which expand the current understanding of the neural mechanisms underlying DAI.
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Affiliation(s)
- Shun Yao
- Department of Neurosurgery, Wuhan General Hospital of Guangzhou Command, Wuhan 430070, China
| | - Jian Song
- Department of Neurosurgery, Wuhan General Hospital of Guangzhou Command, Wuhan 430070, China
| | - Lichen Gao
- Department of Radiology, Wuhan General Hospital of Guangzhou Command, Wuhan 430070, China
| | - Yan Yan
- Department of Neurosurgery, Wuhan General Hospital of Guangzhou Command, Wuhan 430070, China
| | - Cheng Huang
- Department of Neurosurgery, Wuhan General Hospital of Guangzhou Command, Wuhan 430070, China
| | - Huichao Ding
- Department of Neurosurgery, Wuhan General Hospital of Guangzhou Command, Wuhan 430070, China
| | - He Huang
- Department of Neurosurgery, Wuhan General Hospital of Guangzhou Command, Wuhan 430070, China
| | - Yuanzhi He
- Department of Neurosurgery, Wuhan General Hospital of Guangzhou Command, Wuhan 430070, China
| | - Ronghui Sun
- Department of Neurosurgery, Wuhan General Hospital of Guangzhou Command, Wuhan 430070, China
| | - Guozheng Xu
- Department of Neurosurgery, Wuhan General Hospital of Guangzhou Command, Wuhan 430070, China.
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108
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A distinct class of slow (~0.2-2 Hz) intrinsically bursting layer 5 pyramidal neurons determines UP/DOWN state dynamics in the neocortex. J Neurosci 2015; 35:5442-58. [PMID: 25855163 DOI: 10.1523/jneurosci.3603-14.2015] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
During sleep and anesthesia, neocortical neurons exhibit rhythmic UP/DOWN membrane potential states. Although UP states are maintained by synaptic activity, the mechanisms that underlie the initiation and robust rhythmicity of UP states are unknown. Using a physiologically validated model of UP/DOWN state generation in mouse neocortical slices whereby the cholinergic tone present in vivo is reinstated, we show that the regular initiation of UP states is driven by an electrophysiologically distinct subset of morphologically identified layer 5 neurons, which exhibit intrinsic rhythmic low-frequency burst firing at ~0.2-2 Hz. This low-frequency bursting is resistant to block of glutamatergic and GABAergic transmission but is absent when slices are maintained in a low Ca(2+) medium (an alternative, widely used model of cortical UP/DOWN states), thus explaining the lack of rhythmic UP states and abnormally prolonged DOWN states in this condition. We also characterized the activity of various other pyramidal and nonpyramidal neurons during UP/DOWN states and found that an electrophysiologically distinct subset of layer 5 regular spiking pyramidal neurons fires earlier during the onset of network oscillations compared with all other types of neurons recorded. This study, therefore, identifies an important role for cell-type-specific neuronal activity in driving neocortical UP states.
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109
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Looking for a precursor of spontaneous Sleep Slow Oscillations in human sleep: The role of the sigma activity. Int J Psychophysiol 2015; 97:99-107. [PMID: 26003553 DOI: 10.1016/j.ijpsycho.2015.05.006] [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] [Received: 01/27/2015] [Revised: 05/12/2015] [Accepted: 05/13/2015] [Indexed: 11/23/2022]
Abstract
Sleep Slow Oscillations (SSOs), paradigmatic EEG markers of cortical bistability (alternation between cellular downstates and upstates), and sleep spindles, paradigmatic EEG markers of thalamic rhythm, are two hallmarks of sleeping brain. Selective thalamic lesions are reportedly associated to reductions of spindle activity and its spectrum ~14 Hz (sigma), and to alterations of SSO features. This apparent, parallel behavior suggests that thalamo-cortical entrainment favors cortical bistability. Here we investigate temporally-causal associations between thalamic sigma activity and shape, topology, and dynamics of SSOs. We recorded sleep EEG and studied whether spatio-temporal variability of SSO amplitude, negative slope (synchronization in downstate falling) and detection rate are driven by cortical-sigma-activity expression (12-18Hz), in 3 consecutive 1s-EEG-epochs preceding each SSO event (Baselines). We analyzed: (i) spatial variability, comparing maps of baseline sigma power and of SSO features, averaged over the first sleep cycle; (ii) event-by-event shape variability, computing for each electrode correlations between baseline sigma power and amplitude/slope of related SSOs; (iii) event-by-event spreading variability, comparing baseline sigma power in electrodes showing an SSO event with the homologous ones, spared by the event. The scalp distribution of baseline sigma power mirrored those of SSO amplitude and slope; event-by-event variability in baseline sigma power was associated with that in SSO amplitude in fronto-central areas; within each SSO event, electrodes involved in cortical bistability presented higher baseline sigma activity than those free of SSO. In conclusion, spatio-temporal variability of thalamocortical entrainment, measured by background sigma activity, is a reliable estimate of the cortical proneness to bistability.
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110
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Keller CJ, Honey CJ, Mégevand P, Entz L, Ulbert I, Mehta AD. Mapping human brain networks with cortico-cortical evoked potentials. Philos Trans R Soc Lond B Biol Sci 2015; 369:rstb.2013.0528. [PMID: 25180306 DOI: 10.1098/rstb.2013.0528] [Citation(s) in RCA: 127] [Impact Index Per Article: 14.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
The cerebral cortex forms a sheet of neurons organized into a network of interconnected modules that is highly expanded in humans and presumably enables our most refined sensory and cognitive abilities. The links of this network form a fundamental aspect of its organization, and a great deal of research is focusing on understanding how information flows within and between different regions. However, an often-overlooked element of this connectivity regards a causal, hierarchical structure of regions, whereby certain nodes of the cortical network may exert greater influence over the others. While this is difficult to ascertain non-invasively, patients undergoing invasive electrode monitoring for epilepsy provide a unique window into this aspect of cortical organization. In this review, we highlight the potential for cortico-cortical evoked potential (CCEP) mapping to directly measure neuronal propagation across large-scale brain networks with spatio-temporal resolution that is superior to traditional neuroimaging methods. We first introduce effective connectivity and discuss the mechanisms underlying CCEP generation. Next, we highlight how CCEP mapping has begun to provide insight into the neural basis of non-invasive imaging signals. Finally, we present a novel approach to perturbing and measuring brain network function during cognitive processing. The direct measurement of CCEPs in response to electrical stimulation represents a potentially powerful clinical and basic science tool for probing the large-scale networks of the human cerebral cortex.
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Affiliation(s)
- Corey J Keller
- Department of Neurosurgery, Hofstra North Shore LIJ School of Medicine, and Feinstein Institute for Medical Research, Manhasset, NY, USA Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Christopher J Honey
- Department of Psychology, Princeton University, Princeton, NJ, USA Department of Psychology, University of Toronto, Toronto, Ontario M5S 3G3, Canada
| | - Pierre Mégevand
- Department of Neurosurgery, Hofstra North Shore LIJ School of Medicine, and Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Laszlo Entz
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary Department of Functional Neurosurgery, National Institute of Clinical Neuroscience, Budapest, Hungary Peter Pazmany Catholic University, Faculty of Information Technology and Bionics, Budapest, Hungary
| | - Istvan Ulbert
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary Peter Pazmany Catholic University, Faculty of Information Technology and Bionics, Budapest, Hungary
| | - Ashesh D Mehta
- Department of Neurosurgery, Hofstra North Shore LIJ School of Medicine, and Feinstein Institute for Medical Research, Manhasset, NY, USA
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111
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Feld GB, Diekelmann S. Sleep smart-optimizing sleep for declarative learning and memory. Front Psychol 2015; 6:622. [PMID: 26029150 PMCID: PMC4428077 DOI: 10.3389/fpsyg.2015.00622] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2014] [Accepted: 04/27/2015] [Indexed: 02/05/2023] Open
Abstract
The last decade has witnessed a spurt of new publications documenting sleep's essential contribution to the brains ability to form lasting memories. For the declarative memory domain, slow wave sleep (the deepest sleep stage) has the greatest beneficial effect on the consolidation of memories acquired during preceding wakefulness. The finding that newly encoded memories become reactivated during subsequent sleep fostered the idea that reactivation leads to the strengthening and transformation of the memory trace. According to the active system consolidation account, trace reactivation leads to the redistribution of the transient memory representations from the hippocampus to the long-lasting knowledge networks of the cortex. Apart from consolidating previously learned information, sleep also facilitates the encoding of new memories after sleep, which probably relies on the renormalization of synaptic weights during sleep as suggested by the synaptic homeostasis theory. During wakefulness overshooting potentiation causes an imbalance in synaptic weights that is countered by synaptic downscaling during subsequent sleep. This review briefly introduces the basic concepts and central findings of the research on sleep and memory, and discusses implications of this lab-based work for everyday applications to make the best possible use of sleep's beneficial effect on learning and memory.
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Affiliation(s)
- Gordon B Feld
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen Tübingen, Germany
| | - Susanne Diekelmann
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen Tübingen, Germany
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112
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Abstract
All brain normal or pathological activities occur in one of the states of vigilance: wake, slow-wave sleep, or REM sleep. Neocortical seizures preferentially occur during slow-wave sleep. We provide a description of neuronal behavior and mechanisms mediating such a behavior within neocortex taking place in natural states of vigilance as well as during seizures pointing to similarities and differences exhibited during sleep and seizures. A concept of epileptic focus is described using a model of cortical undercut, because in that model, the borders of the focus are well defined. In this model, as in other models of acquired epilepsy, the main factor altering excitability is deafferentation, which upregulates neuronal excitability that promotes generation of seizures. Periods of disfacilitation recorded during slow-wave sleep further upregulate neuronal excitability. It appears that the state of neurons and neuronal network in the epileptic focus produced by deafferentation are such that seizures cannot be generated there. Instead, seizures always start around the perimeter of the undercut cortex. Therefore, we define these areas as the seizure focus. In this zone, neuronal connectivity and excitability are moderately enhanced, lowering the threshold for seizure generation.
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113
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K-complex amplitude as a marker of sleep homeostasis in obstructive sleep apnea syndrome and healthy controls. SOMNOLOGIE 2015. [DOI: 10.1007/s11818-015-0701-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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114
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Stochastic transitions into silence cause noise correlations in cortical circuits. Proc Natl Acad Sci U S A 2015; 112:3529-34. [PMID: 25739962 DOI: 10.1073/pnas.1410509112] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The spiking activity of cortical neurons is highly variable. This variability is generally correlated among nearby neurons, an effect commonly interpreted to reflect the coactivation of neurons due to anatomically shared inputs. Recent findings, however, indicate that correlations can be dynamically modulated, suggesting that the underlying mechanisms are not well understood. Here, we investigate the hypothesis that correlations are dominated by neuronal coinactivation: the occurrence of brief silent periods during which all neurons in the local network stop firing. We recorded spiking activity from large populations of neurons in the auditory cortex of anesthetized rats across different brain states. During spontaneous activity, the reduction of correlation accompanying brain state desynchronization was largely explained by a decrease in the density of the silent periods. The presentation of a stimulus caused an initial drop of correlations followed by a rebound, a time course that was mimicked by the instantaneous silence density. We built a rate network model with fluctuation-driven transitions between a silent and an active attractor and assumed that neurons fired Poisson spike trains with a rate following the model dynamics. Variations of the network external input altered the transition rate into the silent attractor and reproduced the relation between correlation and silence density found in the data, both in spontaneous and evoked conditions. This suggests that the observed changes in correlation, occurring gradually with brain state variations or abruptly with sensory stimulation, are due to changes in the likeliness of the microcircuit to transiently cease firing.
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115
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Zhao X, Kim JW, Robinson PA. Slow-wave oscillations in a corticothalamic model of sleep and wake. J Theor Biol 2015; 370:93-102. [PMID: 25659479 DOI: 10.1016/j.jtbi.2015.01.028] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2014] [Revised: 01/21/2015] [Accepted: 01/24/2015] [Indexed: 11/27/2022]
Abstract
A physiologically-based corticothalamic neural field model is used to study slow wave oscillations including cortical UP and DOWN states in deep sleep by extending it to incorporate bursting dynamics of neurons in the thalamic reticular nucleus. The interplay of local bursting dynamics and network interactions produces the cortical UP and DOWN states of slow wave sleep while preserving previously verified model predictions in the wake state. Results show that EEG spectral features in wake and sleep are reproduced. The bursting is subthreshold but acts to intensify the amplitude of oscillations in slow wave sleep with deep UP/DOWN oscillations on the cortex emerging naturally. Furthermore, there is a continuous cycle between the two regimes, rather than a flip-flop between discrete states.
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Affiliation(s)
- X Zhao
- School of Physics, The University of Sydney, Sydney, New South Wales 2006, Australia; Center of Research Excellence, Neurosleep, 431 Glebe Point Rd, Glebe, New South Wales 2037, Australia; Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia; Cooperative Research Center for Alertness, Safety, and Productivity, University of Sydney, New South Wales 2006, Australia.
| | - J W Kim
- School of Physics, The University of Sydney, Sydney, New South Wales 2006, Australia; Center of Research Excellence, Neurosleep, 431 Glebe Point Rd, Glebe, New South Wales 2037, Australia
| | - P A Robinson
- School of Physics, The University of Sydney, Sydney, New South Wales 2006, Australia; Center of Research Excellence, Neurosleep, 431 Glebe Point Rd, Glebe, New South Wales 2037, Australia; Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia; Cooperative Research Center for Alertness, Safety, and Productivity, University of Sydney, New South Wales 2006, Australia
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Kuki T, Fujihara K, Miwa H, Tamamaki N, Yanagawa Y, Mushiake H. Contribution of parvalbumin and somatostatin-expressing GABAergic neurons to slow oscillations and the balance in beta-gamma oscillations across cortical layers. Front Neural Circuits 2015; 9:6. [PMID: 25691859 PMCID: PMC4315041 DOI: 10.3389/fncir.2015.00006] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2014] [Accepted: 01/14/2015] [Indexed: 12/23/2022] Open
Abstract
Cortical interneurons are classified into several subtypes that contribute to cortical oscillatory activity. Parvalbumin (PV)-expressing cells, a type of inhibitory interneuron, are involved in the gamma oscillations of local field potentials (LFPs). Under ketamine-xylazine anesthesia or sleep, mammalian cortical circuits exhibit slow oscillations in which the active-up state and silent-down state alternate at ~1 Hz. The up state is composed of various high-frequency oscillations, including gamma oscillations. However, it is unclear how PV cells and somatostatin (SOM) cells contribute to the slow oscillations and the high-frequency oscillations nested in the up state. To address these questions, we used mice lacking glutamate decarboxylase 67, primarily in PV cells (PV-GAD67 mice) or in SOM cells (SOM-GAD67 mice). We then compared LFPs between PV-GAD67 mice and SOM-GAD67 mice. PV cells target the proximal regions of pyramidal cells, whereas SOM cells are dendrite-preferring interneurons. We found that the up state was shortened in duration in the PV-GAD67 mice, but tended to be longer in SOM-GAD67 mice. Firing rate tended to increase in PV-GAD67 mice, but tended to decrease in SOM-GAD67 mice. We also found that delta oscillations tended to increase in SOM-GAD67 mice, but tended to decrease in PV-GAD67 mice. Current source density and wavelet analyses were performed to determine the depth profiles of various high-frequency oscillations. High gamma and ripple (60–200 Hz) power decreased in the neocortical upper layers specifically in PV-GAD67 mice, but not in SOM-GAD67. In addition, beta power (15–30 Hz) increased in the deep layers, specifically in PV-GAD67 mice. These results suggest that PV cells play important roles in persistence of the up state and in the balance between gamma and beta bands across cortical layers, whereas SOM and PV cells may make an asymmetric contribution to regulate up-state and delta oscillations.
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Affiliation(s)
- Toshinobu Kuki
- Department of Physiology, Graduate School of Medicine, Tohoku University Sendai, Japan
| | - Kazuyuki Fujihara
- Department of Genetic and Behavioral Neuroscience, Gunma University Graduate School of Medicine Maebashi, Japan ; Department of Psychiatry and Human Behavior, Gunma University Graduate School of Medicine Maebashi, Japan ; Core Research for Evolutional Science and Technology Tokyo, Japan
| | - Hideki Miwa
- Department of Genetic and Behavioral Neuroscience, Gunma University Graduate School of Medicine Maebashi, Japan ; Core Research for Evolutional Science and Technology Tokyo, Japan
| | - Nobuaki Tamamaki
- Department of Morphological Neural Science, Graduate School of Medical Sciences, Kumamoto University Kumamoto, Japan
| | - Yuchio Yanagawa
- Department of Genetic and Behavioral Neuroscience, Gunma University Graduate School of Medicine Maebashi, Japan ; Core Research for Evolutional Science and Technology Tokyo, Japan
| | - Hajime Mushiake
- Department of Physiology, Graduate School of Medicine, Tohoku University Sendai, Japan ; Core Research for Evolutional Science and Technology Tokyo, Japan
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Bellesi M, Riedner BA, Garcia-Molina GN, Cirelli C, Tononi G. Enhancement of sleep slow waves: underlying mechanisms and practical consequences. Front Syst Neurosci 2014; 8:208. [PMID: 25389394 PMCID: PMC4211398 DOI: 10.3389/fnsys.2014.00208] [Citation(s) in RCA: 142] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2014] [Accepted: 10/02/2014] [Indexed: 02/06/2023] Open
Abstract
Even modest sleep restriction, especially the loss of sleep slow wave activity (SWA), is invariably associated with slower electroencephalogram (EEG) activity during wake, the occurrence of local sleep in an otherwise awake brain, and impaired performance due to cognitive and memory deficits. Recent studies not only confirm the beneficial role of sleep in memory consolidation, but also point to a specific role for sleep slow waves. Thus, the implementation of methods to enhance sleep slow waves without unwanted arousals or lightening of sleep could have significant practical implications. Here we first review the evidence that it is possible to enhance sleep slow waves in humans using transcranial direct-current stimulation (tDCS) and transcranial magnetic stimulation. Since these methods are currently impractical and their safety is questionable, especially for chronic long-term exposure, we then discuss novel data suggesting that it is possible to enhance slow waves using sensory stimuli. We consider the physiology of the K-complex (KC), a peripheral evoked slow wave, and show that, among different sensory modalities, acoustic stimulation is the most effective in increasing the magnitude of slow waves, likely through the activation of non-lemniscal ascending pathways to the thalamo-cortical system. In addition, we discuss how intensity and frequency of the acoustic stimuli, as well as exact timing and pattern of stimulation, affect sleep enhancement. Finally, we discuss automated algorithms that read the EEG and, in real-time, adjust the stimulation parameters in a closed-loop manner to obtain an increase in sleep slow waves and avoid undesirable arousals. In conclusion, while discussing the mechanisms that underlie the generation of sleep slow waves, we review the converging evidence showing that acoustic stimulation is safe and represents an ideal tool for slow wave sleep (SWS) enhancement.
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Affiliation(s)
- Michele Bellesi
- Department of Psychiatry, University of Wisconsin-MadisonMadison, WI, USA
| | - Brady A. Riedner
- Department of Psychiatry, University of Wisconsin-MadisonMadison, WI, USA
| | - Gary N. Garcia-Molina
- Department of Psychiatry, University of Wisconsin-MadisonMadison, WI, USA
- Clinical Sites Research Program, Philips Group InnovationBriarcliff, NY, USA
| | - Chiara Cirelli
- Department of Psychiatry, University of Wisconsin-MadisonMadison, WI, USA
| | - Giulio Tononi
- Department of Psychiatry, University of Wisconsin-MadisonMadison, WI, USA
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118
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Kaufman M, Reinartz S, Ziv NE. Adaptation to prolonged neuromodulation in cortical cultures: an invariable return to network synchrony. BMC Biol 2014; 12:83. [PMID: 25339462 PMCID: PMC4237737 DOI: 10.1186/s12915-014-0083-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2014] [Accepted: 09/29/2014] [Indexed: 11/17/2022] Open
Abstract
Background Prolonged neuromodulatory regimes, such as those critically involved in promoting arousal and suppressing sleep-associated synchronous activity patterns, might be expected to trigger adaptation processes and, consequently, a decline in neuromodulator-driven effects. This possibility, however, has rarely been addressed. Results Using networks of cultured cortical neurons, acetylcholine microinjections and a novel closed-loop ‘synchrony-clamp’ system, we found that acetylcholine pulses strongly suppressed network synchrony. Over the course of many hours, however, synchrony invariably reemerged, even when feedback was used to compensate for declining cholinergic efficacy. Network synchrony also reemerged following its initial suppression by noradrenaline, but this did not occlude the suppression of synchrony or its gradual reemergence following subsequent cholinergic input. Importantly, cholinergic efficacy could be restored and preserved over extended time scales by periodically withdrawing cholinergic input. Conclusions These findings indicate that the capacity of neuromodulators to suppress network synchrony is constrained by slow-acting, reactive processes. A multiplicity of neuromodulators and ultimately neuromodulator withdrawal periods might thus be necessary to cope with an inevitable reemergence of network synchrony. Electronic supplementary material The online version of this article (doi:10.1186/s12915-014-0083-3) contains supplementary material, which is available to authorized users.
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119
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Fu Y, Tucciarone JM, Espinosa JS, Sheng N, Darcy DP, Nicoll RA, Huang ZJ, Stryker MP. A cortical circuit for gain control by behavioral state. Cell 2014; 156:1139-1152. [PMID: 24630718 DOI: 10.1016/j.cell.2014.01.050] [Citation(s) in RCA: 588] [Impact Index Per Article: 58.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2013] [Revised: 11/25/2013] [Accepted: 01/10/2014] [Indexed: 11/30/2022]
Abstract
The brain's response to sensory input is strikingly modulated by behavioral state. Notably, the visual response of mouse primary visual cortex (V1) is enhanced by locomotion, a tractable and accessible example of a time-locked change in cortical state. The neural circuits that transmit behavioral state to sensory cortex to produce this modulation are unknown. In vivo calcium imaging of behaving animals revealed that locomotion activates vasoactive intestinal peptide (VIP)-positive neurons in mouse V1 independent of visual stimulation and largely through nicotinic inputs from basal forebrain. Optogenetic activation of VIP neurons increased V1 visual responses in stationary awake mice, artificially mimicking the effect of locomotion, and photolytic damage of VIP neurons abolished the enhancement of V1 responses by locomotion. These findings establish a cortical circuit for the enhancement of visual response by locomotion and provide a potential common circuit for the modulation of sensory processing by behavioral state.
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Affiliation(s)
- Yu Fu
- Center for Integrative Neuroscience, Department of Physiology, University of California, 675 Nelson Rising Road, San Francisco, CA 94158, USA.
| | - Jason M Tucciarone
- Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA; MSTP/Neuroscience graduate Program, Stony Brook University, Stony Brook, NY 11790, USA
| | - J Sebastian Espinosa
- Center for Integrative Neuroscience, Department of Physiology, University of California, 675 Nelson Rising Road, San Francisco, CA 94158, USA
| | - Nengyin Sheng
- Departments of Cellular and Molecular Pharmacology and Physiology, University of California, San Francisco, CA 94158, USA
| | - Daniel P Darcy
- Center for Integrative Neuroscience, Department of Physiology, University of California, 675 Nelson Rising Road, San Francisco, CA 94158, USA
| | - Roger A Nicoll
- Departments of Cellular and Molecular Pharmacology and Physiology, University of California, San Francisco, CA 94158, USA
| | - Z Josh Huang
- Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA
| | - Michael P Stryker
- Center for Integrative Neuroscience, Department of Physiology, University of California, 675 Nelson Rising Road, San Francisco, CA 94158, USA.
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Bhattacharya BS, Patterson C, Galluppi F, Durrant SJ, Furber S. Engineering a thalamo-cortico-thalamic circuit on SpiNNaker: a preliminary study toward modeling sleep and wakefulness. Front Neural Circuits 2014; 8:46. [PMID: 24904294 PMCID: PMC4033042 DOI: 10.3389/fncir.2014.00046] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2013] [Accepted: 04/21/2014] [Indexed: 11/23/2022] Open
Abstract
We present a preliminary study of a thalamo-cortico-thalamic (TCT) implementation on SpiNNaker (Spiking Neural Network architecture), a brain inspired hardware platform designed to incorporate the inherent biological properties of parallelism, fault tolerance and energy efficiency. These attributes make SpiNNaker an ideal platform for simulating biologically plausible computational models. Our focus in this work is to design a TCT framework that can be simulated on SpiNNaker to mimic dynamical behavior similar to Electroencephalogram (EEG) time and power-spectra signatures in sleep-wake transition. The scale of the model is minimized for simplicity in this proof-of-concept study; thus the total number of spiking neurons is ≈1000 and represents a "mini-column" of the thalamocortical tissue. All data on model structure, synaptic layout and parameters is inspired from previous studies and abstracted at a level that is appropriate to the aims of the current study as well as computationally suitable for model simulation on a small 4-chip SpiNNaker system. The initial results from selective deletion of synaptic connectivity parameters in the model show similarity with EEG power spectra characteristics of sleep and wakefulness. These observations provide a positive perspective and a basis for future implementation of a very large scale biologically plausible model of thalamo-cortico-thalamic interactivity-the essential brain circuit that regulates the biological sleep-wake cycle and associated EEG rhythms.
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Affiliation(s)
| | - Cameron Patterson
- School of Computer Science, APT Group, University of ManchesterManchester, Lancashire, UK
| | - Francesco Galluppi
- School of Computer Science, APT Group, University of ManchesterManchester, Lancashire, UK
| | - Simon J. Durrant
- School of Psychology, Lincoln Sleep and Cognition Laboratory, University of LincolnLincoln, Lincolnshire, UK
| | - Steve Furber
- School of Computer Science, APT Group, University of ManchesterManchester, Lancashire, UK
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Gardner RJ, Kersanté F, Jones MW, Bartsch U. Neural oscillations during non-rapid eye movement sleep as biomarkers of circuit dysfunction in schizophrenia. Eur J Neurosci 2014; 39:1091-106. [DOI: 10.1111/ejn.12533] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2013] [Revised: 01/06/2014] [Accepted: 01/29/2014] [Indexed: 12/25/2022]
Affiliation(s)
- Richard J. Gardner
- School of Physiology and Pharmacology; University of Bristol; Medical Sciences Building University Walk Bristol BS8 1TD UK
| | - Flavie Kersanté
- School of Physiology and Pharmacology; University of Bristol; Medical Sciences Building University Walk Bristol BS8 1TD UK
| | - Matthew W. Jones
- School of Physiology and Pharmacology; University of Bristol; Medical Sciences Building University Walk Bristol BS8 1TD UK
| | - Ullrich Bartsch
- School of Physiology and Pharmacology; University of Bristol; Medical Sciences Building University Walk Bristol BS8 1TD UK
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Zaytsev YV, Morrison A. CyNEST: a maintainable Cython-based interface for the NEST simulator. Front Neuroinform 2014; 8:23. [PMID: 24672470 PMCID: PMC3953856 DOI: 10.3389/fninf.2014.00023] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2013] [Accepted: 02/22/2014] [Indexed: 11/13/2022] Open
Abstract
NEST is a simulator for large-scale networks of spiking point neuron models (Gewaltig and Diesmann, 2007). Originally, simulations were controlled via the Simulation Language Interpreter (SLI), a built-in scripting facility implementing a language derived from PostScript (Adobe Systems, Inc., 1999). The introduction of PyNEST (Eppler et al., 2008), the Python interface for NEST, enabled users to control simulations using Python. As the majority of NEST users found PyNEST easier to use and to combine with other applications, it immediately displaced SLI as the default NEST interface. However, developing and maintaining PyNEST has become increasingly difficult over time. This is partly because adding new features requires writing low-level C++ code intermixed with calls to the Python/C API, which is unrewarding. Moreover, the Python/C API evolves with each new version of Python, which results in a proliferation of version-dependent code branches. In this contribution we present the re-implementation of PyNEST in the Cython language, a superset of Python that additionally supports the declaration of C/C++ types for variables and class attributes, and provides a convenient foreign function interface (FFI) for invoking C/C++ routines (Behnel et al., 2011). Code generation via Cython allows the production of smaller and more maintainable bindings, including increased compatibility with all supported Python releases without additional burden for NEST developers. Furthermore, this novel approach opens up the possibility to support alternative implementations of the Python language at no cost given a functional Cython back-end for the corresponding implementation, and also enables cross-compilation of Python bindings for embedded systems and supercomputers alike.
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Affiliation(s)
- Yury V Zaytsev
- Simulation Laboratory Neuroscience - Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich Research Center Jülich, Germany ; Faculty of Biology, Albert-Ludwig University of Freiburg Freiburg im Breisgau, Germany
| | - Abigail Morrison
- Simulation Laboratory Neuroscience - Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich Research Center Jülich, Germany ; Institute for Advanced Simulation (IAS-6), Theoretical Neuroscience and Institute of Neuroscience and Medicine (INM-6), Computational and Systems Neuroscience, Jülich Research Center and JARA Jülich, Germany ; Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr-University Bochum Bochum, Germany
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123
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Ching S, Brown EN. Modeling the dynamical effects of anesthesia on brain circuits. Curr Opin Neurobiol 2014; 25:116-22. [PMID: 24457211 DOI: 10.1016/j.conb.2013.12.011] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2013] [Revised: 12/14/2013] [Accepted: 12/18/2013] [Indexed: 11/28/2022]
Abstract
General anesthesia is a neurophysiological state that consists of unconsciousness, amnesia, analgesia, and immobility along with maintenance of physiological stability. General anesthesia has been used in the United States for more than 167 years. Now, using systems neuroscience paradigms how anesthetics act in the brain and central nervous system to create the states of general anesthesia is being understood. Propofol is one of the most widely used and the most widely studied anesthetics. When administered for general anesthesia or sedation, the electroencephalogram (EEG) under propofol shows highly structured, rhythmic activity that is strongly associated with changes in the patient's level of arousal. These highly structured oscillations lend themselves readily to mathematical descriptions using dynamical systems models. We review recent model descriptions of the commonly observed EEG patterns associated with propofol: paradoxical excitation, strong frontal alpha oscillations, anteriorization and burst suppression. Our analysis suggests that propofol's actions at GABAergic networks in the cortex, thalamus and brainstem induce profound brain dynamics that are one of the likely mechanisms through which this anesthetic induces altered arousal states from sedation to unconsciousness. Because these dynamical effects are readily observed in the EEG, the mathematical descriptions of how propofol's EEG signatures relate to its mechanisms of action in neural circuits provide anesthesiologists with a neurophysiologically based approach to monitoring the brain states of patients receiving anesthesia care.
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Affiliation(s)
- Shinung Ching
- Department of Electrical & Systems Engineering, Division of Biology & Biomedical Sciences, Washington University in St. Louis, St. Louis, MO 63130, United States
| | - Emery N Brown
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital/Harvard Medical School, Boston, MA 02114, United States; Institute for Medical Engineering and Science, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
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Sarasso S, Rosanova M, Casali AG, Casarotto S, Fecchio M, Boly M, Gosseries O, Tononi G, Laureys S, Massimini M. Quantifying cortical EEG responses to TMS in (un)consciousness. Clin EEG Neurosci 2014; 45:40-9. [PMID: 24403317 DOI: 10.1177/1550059413513723] [Citation(s) in RCA: 76] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
We normally assess another individual's level of consciousness based on her or his ability to interact with the surrounding environment and communicate. Usually, if we observe purposeful behavior, appropriate responses to sensory inputs, and, above all, appropriate answers to questions, we can be reasonably sure that the person is conscious. However, we know that consciousness can be entirely within the brain, even in the absence of any interaction with the external world; this happens almost every night, while we dream. Yet, to this day, we lack an objective, dependable measure of the level of consciousness that is independent of processing sensory inputs and producing appropriate motor outputs. Theoretically, consciousness is thought to require the joint presence of functional integration and functional differentiation, otherwise defined as brain complexity. Here we review a series of recent studies in which Transcranial Magnetic Stimulation combined with electroencephalography (TMS/EEG) has been employed to quantify brain complexity in wakefulness and during physiological (sleep), pharmacological (anesthesia) and pathological (brain injury) loss of consciousness. These studies invariably show that the complexity of the cortical response to TMS collapses when consciousness is lost during deep sleep, anesthesia and vegetative state following severe brain injury, while it recovers when consciousness resurges in wakefulness, during dreaming, in the minimally conscious state or locked-in syndrome. The present paper will also focus on how this approach may contribute to unveiling the pathophysiology of disorders of consciousness affecting brain-injured patients. Finally, we will underline some crucial methodological aspects concerning TMS/EEG measurements of brain complexity.
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Affiliation(s)
- Simone Sarasso
- Department of Biomedical and Clinical Sciences "Luigi Sacco," University of Milan, Milan, Italy
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125
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Abstract
In the last decades a substantial knowledge about sleep mechanisms has been accumulated. However, the function of sleep still remains elusive. The difficulty with unraveling sleep's function may arise from the lack of understanding of how the multitude of processes associated with waking and sleep-from gene expression and single neuron activity to the whole brain dynamics and behavior-functionally and mechanistically relate to each other. Therefore, novel conceptual frameworks, which integrate and take into account the variety of phenomena occurring during waking and sleep at different levels, will likely lead to advances in our understanding of the function of sleep, above and beyond what merely descriptive or correlative approaches can provide. One such framework, the synaptic homeostasis hypothesis, focuses on wake- and sleep-dependent changes in synaptic strength. The core claim of this hypothesis is that learning and experience during wakefulness are associated with a net increase in synaptic strength. In turn, the proposed function of sleep is to provide synaptic renormalization, which has important implications with respect to energy needs, intracranial space, metabolic supplies, and, importantly, enables further plastic changes. In this article we review the empirical evidence for this hypothesis, which was obtained at several levels-from gene expression and cellular excitability to structural synaptic modifications and behavioral outcomes. We conclude that although the mechanisms behind the proposed role of sleep in synaptic homeostasis are undoubtedly complex, this conceptual framework offers a unique opportunity to provide mechanistic and functional explanation for many previously disparate observations, and define future research strategies.
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126
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Abstract
Following stroke, patients are commonly left with debilitating motor and speech impairments. This article reviews the state of the art in neurological repair for stroke and proposes a new model for the future. We suggest that stroke treatment--from the time of the ictus itself to living with the consequences--must be fundamentally neurological, from limiting the extent of injury at the outset, to repairing the consequent damage. Our model links brain and behaviour by targeting brain circuits, and we illustrate the model though action observation treatment, which aims to enhance brain network connectivity. The model is based on the assumptions that the mechanisms of neural repair inherently involve cellular and circuit plasticity, that brain plasticity is a synaptic phenomenon that is largely stimulus-dependent, and that brain repair required both physical and behavioural interventions that are tailored to reorganize specific brain circuits. We review current approaches to brain repair after stroke and present our new model, and discuss the biological foundations, rationales, and data to support our novel approach to upper-extremity and language rehabilitation. We believe that by enhancing plasticity at the level of brain network interactions, this neurological model for brain repair could ultimately lead to a cure for stroke.
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Affiliation(s)
- Steven L Small
- Department of Neurology, University of California, Irvine, 200 Manchester Avenue, Suite 206, Orange, CA 92697, USA
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127
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Nakagawa TT, Woolrich M, Luckhoo H, Joensson M, Mohseni H, Kringelbach ML, Jirsa V, Deco G. How delays matter in an oscillatory whole-brain spiking-neuron network model for MEG alpha-rhythms at rest. Neuroimage 2013; 87:383-94. [PMID: 24246492 DOI: 10.1016/j.neuroimage.2013.11.009] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2013] [Revised: 09/23/2013] [Accepted: 11/05/2013] [Indexed: 10/26/2022] Open
Abstract
In recent years the study of the intrinsic brain dynamics in a relaxed awake state in the absence of any specific task has gained increasing attention, as spontaneous neural activity has been found to be highly structured at a large scale. This so called resting-state activity has been found to be comprised by nonrandom spatiotemporal patterns and fluctuations, and several Resting-State Networks (RSN) have been found in BOLD-fMRI as well as in MEG signal power envelope correlations. The underlying anatomical connectivity structure between areas of the brain has been identified as being a key to the observed functional network connectivity, but the mechanisms behind this are still underdetermined. Theoretical large-scale brain models for fMRI data have corroborated the importance of the connectome in shaping network dynamics, while the importance of delays and noise differ between studies and depend on the models' specific dynamics. In the current study, we present a spiking neuron network model that is able to produce noisy, distributed alpha-oscillations, matching the power peak in the spectrum of group resting-state MEG recordings. We studied how well the model captured the inter-node correlation structure of the alpha-band power envelopes for different delays between brain areas, and found that the model performs best for propagation delays inside the physiological range (5-10 m/s). Delays also shift the transition from noisy to bursting oscillations to higher global coupling values in the model. Thus, in contrast to the asynchronous fMRI state, delays are important to consider in the presence of oscillation.
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Affiliation(s)
- Tristan T Nakagawa
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona 08018, Spain.
| | - Mark Woolrich
- Oxford Ctr. For Human Brain Activity, Univ. of Oxford, Oxford, United Kingdom
| | - Henry Luckhoo
- Oxford Ctr. For Human Brain Activity, Univ. of Oxford, Oxford, United Kingdom
| | - Morten Joensson
- Department of Psychiatry, University of Oxford, Oxford, UK; Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, Denmark
| | - Hamid Mohseni
- Oxford Ctr. For Human Brain Activity, Univ. of Oxford, Oxford, United Kingdom
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK; Center of Functionally Integrative Neuroscience (CFIN), Aarhus University, Denmark
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes UMR INSERM 1106, Aix-Marseille Universitè, 13005 Marseille, France
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats, Universitat Pompeu Fabra, Barcelona 08010, Spain
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128
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Sarasso S, Määttä S, Ferrarelli F, Poryazova R, Tononi G, Small SL. Plastic Changes Following Imitation-Based Speech and Language Therapy for Aphasia. Neurorehabil Neural Repair 2013; 28:129-38. [DOI: 10.1177/1545968313498651] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background. Objective measurement of plastic brain changes induced by a novel rehabilitative approach is a key requirement for validating its biological rationale linking the potential therapeutic gains to the changes in brain physiology. Objective. Based on an emerging notion linking cortical plastic changes to EEG sleep slow-wave activity (SWA) regulation, we aimed to assess the acute plastic changes induced by an imitation-based speech therapy in individuals with aphasia by comparing sleep SWA changes before and after therapy. Methods. A total of 13 left-hemispheric stroke patients underwent language assessment with the Western Aphasia Battery (WAB) before and after 2 consecutive high-density (hd) EEG sleep recordings interleaved by a daytime session of imitation-based speech therapy (Intensive Mouth Imitation and Talking for Aphasia Therapeutic Effects [IMITATE]). This protocol is thought to stimulate bilateral connections between the inferior parietal lobule and the ventral premotor areas. Results. A single exposure to IMITATE resulted in increases in local EEG SWA during subsequent sleep over the same regions predicted by the therapeutic rationale, particularly over the right hemisphere (unaffected by the lesion). Furthermore, changes in SWA over the left-precentral areas predicted changes in WAB repetition scores in our group, supporting the role of perilesional areas in predicting positive functional responses. Conclusions. Our results suggest that SWA changes occurring in brain areas activated during imitation-based aphasia therapy may reflect the acute plastic changes induced by this intervention. Further testing will be needed to evaluate SWA as a non-invasive assessment of changes induced by the therapy and as a predictor of positive long-term clinical outcome.
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Affiliation(s)
- Simone Sarasso
- University of Wisconsin–Madison, WI, USA
- University of Milan, Milan, Italy
| | - Sara Määttä
- University of Eastern Finland, Kuopio, Finland
| | | | | | | | - Steven L. Small
- University of Chicago, IL, USA
- University of California–Irvine, CA, USA
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129
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Chizhov AV. Conductance-based refractory density model of primary visual cortex. J Comput Neurosci 2013; 36:297-319. [PMID: 23888313 DOI: 10.1007/s10827-013-0473-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2013] [Revised: 06/17/2013] [Accepted: 06/27/2013] [Indexed: 10/26/2022]
Abstract
A layered continual population model of primary visual cortex has been constructed, which reproduces a set of experimental data, including postsynaptic responses of single neurons on extracellular electric stimulation and spatially distributed activity patterns in response to visual stimulation. In the model, synaptically interacting excitatory and inhibitory neuronal populations are described by a conductance-based refractory density approach. Populations of two-compartment excitatory and inhibitory neurons in cortical layers 2/3 and 4 are distributed in the 2-d cortical space and connected by AMPA, NMDA and GABA type synapses. The external connections are pinwheel-like, according to the orientation of a stimulus. Intracortical connections are isotropic local and patchy between neurons with similar orientations. The model proposes better temporal resolution and more detailed elaboration than conventional mean-field models. In comparison to large network simulations, it excludes a posteriori statistical data manipulation and provides better computational efficiency and minimal parametrization.
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Affiliation(s)
- Anton V Chizhov
- A.F. Ioffe Physical-Technical Institute of RAS, Politekhnicheskaya str., 26, 194021, St.-Petersburg, Russia,
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130
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Deco G, Hagmann P, Hudetz AG, Tononi G. Modeling resting-state functional networks when the cortex falls asleep: local and global changes. Cereb Cortex 2013; 24:3180-94. [PMID: 23845770 DOI: 10.1093/cercor/bht176] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The transition from wakefulness to sleep represents the most conspicuous change in behavior and the level of consciousness occurring in the healthy brain. It is accompanied by similarly conspicuous changes in neural dynamics, traditionally exemplified by the change from "desynchronized" electroencephalogram activity in wake to globally synchronized slow wave activity of early sleep. However, unit and local field recordings indicate that the transition is more gradual than it might appear: On one hand, local slow waves already appear during wake; on the other hand, slow sleep waves are only rarely global. Studies with functional magnetic resonance imaging also reveal changes in resting-state functional connectivity (FC) between wake and slow wave sleep. However, it remains unclear how resting-state networks may change during this transition period. Here, we employ large-scale modeling of the human cortico-cortical anatomical connectivity to evaluate changes in resting-state FC when the model "falls asleep" due to the progressive decrease in arousal-promoting neuromodulation. When cholinergic neuromodulation is parametrically decreased, local slow waves appear, while the overall organization of resting-state networks does not change. Furthermore, we show that these local slow waves are structured macroscopically in networks that resemble the resting-state networks. In contrast, when the neuromodulator decrease further to very low levels, slow waves become global and resting-state networks merge into a single undifferentiated, broadly synchronized network.
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Affiliation(s)
- Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona 08018, Spain Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Barcelona 08010, Spain
| | - Patric Hagmann
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne 1011, Switzerland Signal Processing Lab 5, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Anthony G Hudetz
- Department of Anesthesiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA and
| | - Giulio Tononi
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA
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131
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Is sleep essential for neural plasticity in humans, and how does it affect motor and cognitive recovery? Neural Plast 2013; 2013:103949. [PMID: 23840970 PMCID: PMC3693176 DOI: 10.1155/2013/103949] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2013] [Revised: 05/28/2013] [Accepted: 05/29/2013] [Indexed: 02/05/2023] Open
Abstract
There is a general consensus that sleep is strictly linked to memory, learning, and, in general, to the mechanisms of neural plasticity, and that this link may directly affect recovery processes. In fact, a coherent pattern of empirical findings points to beneficial effect of sleep on learning and plastic processes, and changes in synaptic plasticity during wakefulness induce coherent modifications in EEG slow wave cortical topography during subsequent sleep. However, the specific nature of the relation between sleep and synaptic plasticity is not clear yet. We reported findings in line with two models conflicting with respect to the underlying mechanisms, that is, the “synaptic homeostasis hypothesis” and the “consolidation” hypothesis, and some recent results that may reconcile them. Independently from the specific mechanisms involved, sleep loss is associated with detrimental effects on plastic processes at a molecular and electrophysiological level. Finally, we reviewed growing evidence supporting the notion that plasticity-dependent recovery could be improved managing sleep quality, while monitoring EEG during sleep may help to explain how specific rehabilitative paradigms work. We conclude that a better understanding of the sleep-plasticity link could be crucial from a rehabilitative point of view.
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132
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Tan AYY, Andoni S, Priebe NJ. A spontaneous state of weakly correlated synaptic excitation and inhibition in visual cortex. Neuroscience 2013; 247:364-75. [PMID: 23727451 DOI: 10.1016/j.neuroscience.2013.05.037] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2013] [Revised: 05/13/2013] [Accepted: 05/14/2013] [Indexed: 11/18/2022]
Abstract
Cortical spontaneous activity reflects an animal's behavioral state and affects neural responses to sensory stimuli. The correlation between excitatory and inhibitory synaptic input to single neurons is a key parameter in models of cortical circuitry. Recent measurements demonstrated highly correlated synaptic excitation and inhibition during spontaneous "up-and-down" states, during which excitation accounted for approximately 80% of inhibitory variance (Shu et al., 2003; Haider et al., 2006). Here we report in vivo whole-cell estimates of the correlation between excitation and inhibition in the rat visual cortex under pentobarbital anesthesia, during which up-and-down states are absent. Excitation and inhibition are weakly correlated, relative to the up-and-down state: excitation accounts for less than 40% of inhibitory variance. Although these correlations are lower than when the circuit cycles between up-and-down states, both behaviors may arise from the same circuitry. Our observations provide evidence that different correlational patterns of excitation and inhibition underlie different cortical states.
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Affiliation(s)
- A Y Y Tan
- Center for Perceptual Systems, Section of Neurobiology, School of Biological Sciences, College of Natural Sciences, The University of Texas at Austin, 2400 Speedway, Austin, TX 78705, USA.
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133
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Abstract
BACKGROUND Ketamine is a commonly used anesthetic, but the mechanistic basis for its clinically relevant actions remains to be determined. The authors previously showed that HCN1 channels are inhibited by ketamine and demonstrated that global HCN1 knockout mice are twofold less sensitive to hypnotic actions of ketamine. Although that work identified HCN1 channels as a viable molecular target for ketamine, it did not determine the relevant neural substrate. METHODS To localize the brain region responsible for HCN1-mediated hypnotic actions of ketamine, the authors used a conditional knockout strategy to delete HCN1 channels selectively in excitatory cells of the mouse forebrain. A combination of molecular, immunohistochemical, and cellular electrophysiologic approaches was used to verify conditional HCN1 deletion; a loss-of-righting reflex assay served to ascertain effects of forebrain HCN1 channel ablation on hypnotic actions of ketamine. RESULTS In conditional knockout mice, HCN1 channels were selectively deleted in cortex and hippocampus, with expression retained in cerebellum. In cortical pyramidal neurons from forebrain-selective HCN1 knockout mice, effects of ketamine on HCN1-dependent membrane properties were absent; notably, ketamine was unable to evoke membrane hyperpolarization or enhance synaptic inputs. Finally, the EC50 for ketamine-induced loss-of-righting reflex was shifted to significantly higher concentrations (by approximately 31%). CONCLUSIONS These data indicate that forebrain principal cells represent a relevant neural substrate for HCN1-mediated hypnotic actions of ketamine. The authors suggest that ketamine inhibition of HCN1 shifts cortical neuron electroresponsive properties to contribute to ketamine-induced hypnosis.
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134
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Piantoni G, Cheung BLP, Van Veen BD, Romeijn N, Riedner BA, Tononi G, Van Der Werf YD, Van Someren EJW. Disrupted directed connectivity along the cingulate cortex determines vigilance after sleep deprivation. Neuroimage 2013; 79:213-22. [PMID: 23643925 DOI: 10.1016/j.neuroimage.2013.04.103] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2012] [Revised: 04/25/2013] [Accepted: 04/26/2013] [Indexed: 11/19/2022] Open
Abstract
The cingulate cortex is regarded as the backbone of structural and functional connectivity of the brain. While its functional connectivity has been intensively studied, little is known about its effective connectivity, its modulation by behavioral states, and its involvement in cognitive performance. Given the previously reported effects on cingulate functional connectivity, we investigated how eye-closure and sleep deprivation changed cingulate effective connectivity, estimated from resting-state high-density electroencephalography (EEG) using a novel method to calculate Granger Causality directly in source space. Effective connectivity along the cingulate cortex was dominant in the forward direction. Eyes-open connectivity in the forward direction was greater compared to eyes-closed, in well-rested participants. The difference between eyes-open and eyes-closed connectivity was attenuated and no longer significant after sleep deprivation. Individual variability in the forward connectivity after sleep deprivation predicted subsequent task performance, such that those subjects who showed a greater increase in forward connectivity between the eyes-open and the eyes-closed periods also performed better on a sustained attention task. Effective connectivity in the opposite, backward, direction was not affected by whether the eyes were open or closed or by sleep deprivation. These findings indicate that the effective connectivity from posterior to anterior cingulate regions is enhanced when a well-rested subject has his eyes open compared to when they are closed. Sleep deprivation impairs this directed information flow, proportional to its deleterious effect on vigilance. Therefore, sleep may play a role in the maintenance of waking effective connectivity.
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Affiliation(s)
- Giovanni Piantoni
- Dept of Sleep and Cognition, Netherlands Institute for Neuroscience, Meibergdreef 47, 1105BA Amsterdam, The Netherlands.
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135
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Kerr CC, Van Albada SJ, Neymotin SA, Chadderdon GL, Robinson PA, Lytton WW. Cortical information flow in Parkinson's disease: a composite network/field model. Front Comput Neurosci 2013; 7:39. [PMID: 23630492 PMCID: PMC3635017 DOI: 10.3389/fncom.2013.00039] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 04/02/2013] [Indexed: 11/30/2022] Open
Abstract
The basal ganglia play a crucial role in the execution of movements, as demonstrated by the severe motor deficits that accompany Parkinson's disease (PD). Since motor commands originate in the cortex, an important question is how the basal ganglia influence cortical information flow, and how this influence becomes pathological in PD. To explore this, we developed a composite neuronal network/neural field model. The network model consisted of 4950 spiking neurons, divided into 15 excitatory and inhibitory cell populations in the thalamus and cortex. The field model consisted of the cortex, thalamus, striatum, subthalamic nucleus, and globus pallidus. Both models have been separately validated in previous work. Three field models were used: one with basal ganglia parameters based on data from healthy individuals, one based on data from individuals with PD, and one purely thalamocortical model. Spikes generated by these field models were then used to drive the network model. Compared to the network driven by the healthy model, the PD-driven network had lower firing rates, a shift in spectral power toward lower frequencies, and higher probability of bursting; each of these findings is consistent with empirical data on PD. In the healthy model, we found strong Granger causality between cortical layers in the beta and low gamma frequency bands, but this causality was largely absent in the PD model. In particular, the reduction in Granger causality from the main “input” layer of the cortex (layer 4) to the main “output” layer (layer 5) was pronounced. This may account for symptoms of PD that seem to reflect deficits in information flow, such as bradykinesia. In general, these results demonstrate that the brain's large-scale oscillatory environment, represented here by the field model, strongly influences the information processing that occurs within its subnetworks. Hence, it may be preferable to drive spiking network models with physiologically realistic inputs rather than pure white noise.
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Affiliation(s)
- Cliff C Kerr
- Department of Physiology and Pharmacology, State University of New York Downstate Medical Center Brooklyn, NY, USA ; School of Physics, University of Sydney NSW, Australia ; Brain Dynamics Centre, Westmead Millennium Institute Westmead, NSW, Australia
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136
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STURA ILARIA, PRIANO LORENZO, MAURO ALESSANDRO, GUIOT CATERINA, VENTURINO EZIO. DOUBLE-LAYERED MODELS CAN EXPLAIN MACRO AND MICRO STRUCTURE OF HUMAN SLEEP. Int J Neural Syst 2013; 23:1350008. [DOI: 10.1142/s0129065713500081] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The model simulates the activity of three neural populations using a Lotka–Volterra predator–prey system and, based on neuro-anatomical and neuro-physiological recent findings, assumes that a functional thalamo-cortical gate should be crossed by 'queuing' thalamic signals and that a sleep promoting substance acts as a modulator. The resultant activity accounts for the sleep stage transitions. In accordance with sleep cycles timing, the model proves to be able to reproduce the clustering and randomness of those peculiar transient synchronized EEG patterns (TSEP) described in normal human sleep and supposed to be related to the dynamic building up of NREM sleep until its stabilization against perturbations.
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Affiliation(s)
- ILARIA STURA
- Dip di Matematica "Giuseppe Peano"Università di Torino, via Carlo Alberto 10, 10123 Torino, Italy
| | - LORENZO PRIANO
- Dip. Neuroscienze, Università di Torino, V. Cherasco 15 10126 Torino, Italy
- Department Neurology and Neurorehabilitation, IRCCS Ist Auxologico Italiano, Piancavallo (VB), Italy
| | - ALESSANDRO MAURO
- Dip. Neuroscienze, Università di Torino, V. Cherasco 15 10126 Torino, Italy
- Department Neurology and Neurorehabilitation, IRCCS Ist Auxologico Italiano, Piancavallo (VB), Italy
| | - CATERINA GUIOT
- Dip. Neuroscienze, Università di Torino, C. Raffaello 30 10125 Torino, Italy
| | - EZIO VENTURINO
- Dip di Matematica "Giuseppe Peano"Università di Torino, via Carlo Alberto 10, 10123 Torino, Italy
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137
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Lavi A, Sheinin A, Shapira R, Zelmanoff D, Ashery U. DOC2B and Munc13-1 differentially regulate neuronal network activity. ACTA ACUST UNITED AC 2013; 24:2309-23. [PMID: 23537531 DOI: 10.1093/cercor/bht081] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Alterations in the levels of synaptic proteins affect synaptic transmission and synaptic plasticity. However, the precise effects on neuronal network activity are still enigmatic. Here, we utilized microelectrode array (MEA) to elucidate how manipulation of the presynaptic release process affects the activity of neuronal networks. By combining pharmacological tools and genetic manipulation of synaptic proteins, we show that overexpression of DOC2B and Munc13-1, proteins known to promote vesicular maturation and release, elicits opposite effects on the activity of the neuronal network. Although both cause an increase in the overall number of spikes, the distribution of spikes is different. While DOC2B enhances, Munc13-1 reduces the firing rate within bursts of spikes throughout the network; however, Munc13-1 increases the rate of network bursts. DOC2B's effects were mimicked by Strontium that elevates asynchronous release but not by a DOC2B mutant that enhances spontaneous release rate. This suggests for the first time that increased asynchronous release on the single-neuron level promotes bursting activity in the network level. This innovative study demonstrates the complementary role of the network level in explaining the physiological relevance of the cellular activity of presynaptic proteins and the transformation of synaptic release manipulation from the neuron to the network level.
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Affiliation(s)
- Ayal Lavi
- Department of Neurobiology, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
| | - Anton Sheinin
- Department of Neurobiology, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
| | - Ronit Shapira
- Department of Neurobiology, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Daniel Zelmanoff
- Department of Neurobiology, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Uri Ashery
- Department of Neurobiology, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
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138
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van Albada SJ, Robinson PA. Relationships between Electroencephalographic Spectral Peaks Across Frequency Bands. Front Hum Neurosci 2013; 7:56. [PMID: 23483663 PMCID: PMC3586764 DOI: 10.3389/fnhum.2013.00056] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2012] [Accepted: 02/11/2013] [Indexed: 11/18/2022] Open
Abstract
The degree to which electroencephalographic spectral peaks are independent, and the relationships between their frequencies have been debated. A novel fitting method was used to determine peak parameters in the range 2-35 Hz from a large sample of eyes-closed spectra, and their interrelationships were investigated. Findings were compared with a mean-field model of thalamocortical activity, which predicts near-harmonic relationships between peaks. The subject set consisted of 1424 healthy subjects from the Brain Resource International Database. Peaks in the theta range occurred on average near half the alpha peak frequency, while peaks in the beta range tended to occur near twice and three times the alpha peak frequency on an individual-subject basis. Moreover, for the majority of subjects, alpha peak frequencies were significantly positively correlated with frequencies of peaks in the theta and low and high beta ranges. Such a harmonic progression agrees semiquantitatively with theoretical predictions from the mean-field model. These findings indicate a common or analogous source for different rhythms, and help to define appropriate individual frequency bands for peak identification.
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Affiliation(s)
- S. J. van Albada
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6), Jülich Research Centre and Jülich-Aachen Research AllianceJülich, Germany
- School of Physics, The University of SydneySydney, NSW, Australia
- Brain Dynamics Center, Sydney Medical School – Western, University of SydneySydney, NSW, Australia
| | - P. A. Robinson
- School of Physics, The University of SydneySydney, NSW, Australia
- Brain Dynamics Center, Sydney Medical School – Western, University of SydneySydney, NSW, Australia
- Center for Integrated Research and Understanding of SleepGlebe, NSW, Australia
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139
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Abstract
Cortical circuits encode sensory stimuli through the firing of neuronal ensembles, and also produce spontaneous population patterns in the absence of sensory drive. This population activity is often characterized experimentally by the distribution of multineuron "words" (binary firing vectors), and a match between spontaneous and evoked word distributions has been suggested to reflect learning of a probabilistic model of the sensory world. We analyzed multineuron word distributions in sensory cortex of anesthetized rats and cats, and found that they are dominated by fluctuations in population firing rate rather than precise interactions between individual units. Furthermore, cortical word distributions change when brain state shifts, and similar behavior is seen in simulated networks with fixed, random connectivity. Our results suggest that similarity or dissimilarity in multineuron word distributions could primarily reflect similarity or dissimilarity in population firing rate dynamics, and not necessarily the precise interactions between neurons that would indicate learning of sensory features.
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140
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Taxidis J, Mizuseki K, Mason R, Owen MR. Influence of slow oscillation on hippocampal activity and ripples through cortico-hippocampal synaptic interactions, analyzed by a cortical-CA3-CA1 network model. Front Comput Neurosci 2013; 7:3. [PMID: 23386827 PMCID: PMC3564232 DOI: 10.3389/fncom.2013.00003] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2012] [Accepted: 01/21/2013] [Indexed: 11/13/2022] Open
Abstract
Hippocampal sharp wave-ripple complexes (SWRs) involve the synchronous discharge of thousands of cells throughout the CA3-CA1-subiculum-entorhinal cortex axis. Their strong transient output affects cortical targets, rendering SWRs a possible means for memory transfer from the hippocampus to the neocortex for long-term storage. Neurophysiological observations of hippocampal activity modulation by the cortical slow oscillation (SO) during deep sleep and anesthesia, and correlations between ripples and UP states, support the role of SWRs in memory consolidation through a cortico-hippocampal feedback loop. We couple a cortical network exhibiting SO with a hippocampal CA3-CA1 computational network model exhibiting SWRs, in order to model such cortico-hippocampal correlations and uncover important parameters and coupling mechanisms controlling them. The cortical oscillatory output entrains the CA3 network via connections representing the mossy fiber input, and the CA1 network via the temporoammonic pathway (TA). The spiking activity in CA3 and CA1 is shown to depend on the excitation-to-inhibition ratio, induced by combining the two hippocampal inputs, with mossy fiber input controlling the UP-state correlation of CA3 population bursts and corresponding SWRs, whereas the temporoammonic input affects the overall CA1 spiking activity. Ripple characteristics and pyramidal spiking participation to SWRs are shaped by the strength of the Schaffer collateral drive. A set of in vivo recordings from the rat hippocampus confirms a model-predicted segregation of pyramidal cells into subgroups according to the SO state where they preferentially fire and their response to SWRs. These groups can potentially play distinct functional roles in the replay of spike sequences.
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Affiliation(s)
- Jiannis Taxidis
- Centre for Mathematical Biology and Medicine, School of Mathematical Sciences, University of Nottingham Nottingham, UK ; Division of Biology, Computation and Neural Systems, California Institute of Technology Pasadena, CA, USA
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141
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Hung CS, Sarasso S, Ferrarelli F, Riedner B, Ghilardi MF, Cirelli C, Tononi G. Local experience-dependent changes in the wake EEG after prolonged wakefulness. Sleep 2013; 36:59-72. [PMID: 23288972 DOI: 10.5665/sleep.2302] [Citation(s) in RCA: 141] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
STUDY OBJECTIVES Prolonged wakefulness leads to a progressive increase in sleep pressure, reflected in a global increase in slow wave activity (SWA, 0.5-4.5 Hz) in the sleep electroencephalogram (EEG). A global increase in wake theta activity (5-9 Hz) also occurs. Recently, it was shown that prolonged wakefulness in rodents leads to signs of "local sleep" in an otherwise awake brain, accompanied by a slow/theta wave (2-6 Hz) in the local EEG that occurs at different times in different cortical areas. Compelling evidence in animals and humans also indicates that sleep is locally regulated by the amount of experience-dependent plasticity. Here, we asked whether the extended practice of tasks that involve specific brain circuits results in increased occurrence of local intermittent theta waves in the human EEG, above and beyond the global EEG changes previously described. DESIGN Participants recorded with high-density EEG completed 2 experiments during which they stayed awake ≥ 24 h practicing a language task (audiobook listening [AB]) or a visuomotor task (driving simulator [DS]). SETTING Sleep laboratory. PATIENTS OR PARTICIPANTS 16 healthy participants (7 females). INTERVENTIONS Two extended wake periods. MEASUREMENTS AND RESULTS Both conditions resulted in global increases in resting wake EEG theta power at the end of 24 h of wake, accompanied by increased sleepiness. Moreover, wake theta power as well as the occurrence and amplitude of theta waves showed regional, task-dependent changes, increasing more over left frontal derivations in AB, and over posterior parietal regions in DS. These local changes in wake theta power correlated with similar local changes in sleep low frequencies including SWA. CONCLUSIONS Extended experience-dependent plasticity of specific circuits results in a local increase of the wake theta EEG power in those regions, followed by more intense sleep, as reflected by SWA, over the same areas.
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Affiliation(s)
- Ching-Sui Hung
- Department of Psychiatry, University of Wisconsin, Madison, Madison, WI 53719, USA
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142
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Murillo-Rodríguez E, Palomero-Rivero M, Millán-Aldaco D, Di Marzo V. The administration of endocannabinoid uptake inhibitors OMDM-2 or VDM-11 promotes sleep and decreases extracellular levels of dopamine in rats. Physiol Behav 2013; 109:88-95. [DOI: 10.1016/j.physbeh.2012.11.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2012] [Revised: 11/22/2012] [Accepted: 11/27/2012] [Indexed: 01/14/2023]
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143
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Potjans TC, Diesmann M. The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model. ACTA ACUST UNITED AC 2012. [PMID: 23203991 PMCID: PMC3920768 DOI: 10.1093/cercor/bhs358] [Citation(s) in RCA: 202] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
In the past decade, the cell-type specific connectivity and activity of local cortical networks have been characterized experimentally to some detail. In parallel, modeling has been established as a tool to relate network structure to activity dynamics. While available comprehensive connectivity maps (
Thomson, West, et al. 2002; Binzegger et al. 2004) have been used in various computational studies, prominent features of the simulated activity such as the spontaneous firing rates do not match the experimental findings. Here, we analyze the properties of these maps to compile an integrated connectivity map, which additionally incorporates insights on the specific selection of target types. Based on this integrated map, we build a full-scale spiking network model of the local cortical microcircuit. The simulated spontaneous activity is asynchronous irregular and cell-type specific firing rates are in agreement with in vivo recordings in awake animals, including the low rate of layer 2/3 excitatory cells. The interplay of excitation and inhibition captures the flow of activity through cortical layers after transient thalamic stimulation. In conclusion, the integration of a large body of the available connectivity data enables us to expose the dynamical consequences of the cortical microcircuitry.
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Affiliation(s)
- Tobias C Potjans
- Institute of Neuroscience and Medicine (INM-6), Computational and Systems Neuroscience, Research Center Juelich, Juelich, Germany
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144
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Crook SM, Bednar JA, Berger S, Cannon R, Davison AP, Djurfeldt M, Eppler J, Kriener B, Furber S, Graham B, Plesser HE, Schwabe L, Smith L, Steuber V, van Albada S. Creating, documenting and sharing network models. NETWORK (BRISTOL, ENGLAND) 2012; 23:131-149. [PMID: 22994683 DOI: 10.3109/0954898x.2012.722743] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
As computational neuroscience matures, many simulation environments are available that are useful for neuronal network modeling. However, methods for successfully documenting models for publication and for exchanging models and model components among these projects are still under development. Here we briefly review existing software and applications for network model creation, documentation and exchange. Then we discuss a few of the larger issues facing the field of computational neuroscience regarding network modeling and suggest solutions to some of these problems, concentrating in particular on standardized network model terminology, notation, and descriptions and explicit documentation of model scaling. We hope this will enable and encourage computational neuroscientists to share their models more systematically in the future.
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Affiliation(s)
- Sharon M Crook
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, USA.
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145
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Benita JM, Guillamon A, Deco G, Sanchez-Vives MV. Synaptic depression and slow oscillatory activity in a biophysical network model of the cerebral cortex. Front Comput Neurosci 2012; 6:64. [PMID: 22973221 PMCID: PMC3428579 DOI: 10.3389/fncom.2012.00064] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2012] [Accepted: 08/09/2012] [Indexed: 11/30/2022] Open
Abstract
Short-term synaptic depression (STD) is a form of synaptic plasticity that has a large impact on network computations. Experimental results suggest that STD is modulated by cortical activity, decreasing with activity in the network and increasing during silent states. Here, we explored different activity-modulation protocols in a biophysical network model for which the model displayed less STD when the network was active than when it was silent, in agreement with experimental results. Furthermore, we studied how trains of synaptic potentials had lesser decay during periods of activity (UP states) than during silent periods (DOWN states), providing new experimental predictions. We next tackled the inverse question of what is the impact of modifying STD parameters on the emergent activity of the network, a question difficult to answer experimentally. We found that synaptic depression of cortical connections had a critical role to determine the regime of rhythmic cortical activity. While low STD resulted in an emergent rhythmic activity with short UP states and long DOWN states, increasing STD resulted in longer and more frequent UP states interleaved with short silent periods. A still higher synaptic depression set the network into a non-oscillatory firing regime where DOWN states no longer occurred. The speed of propagation of UP states along the network was not found to be modulated by STD during the oscillatory regime; it remained relatively stable over a range of values of STD. Overall, we found that the mutual interactions between synaptic depression and ongoing network activity are critical to determine the mechanisms that modulate cortical emergent patterns.
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Affiliation(s)
- Jose M Benita
- Department of Applied Mathematics I - EPSEB, Universitat Politècnica de Catalunya Barcelona, Spain
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146
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Fellin T, Ellenbogen JM, De Pittà M, Ben-Jacob E, Halassa MM. Astrocyte regulation of sleep circuits: experimental and modeling perspectives. Front Comput Neurosci 2012; 6:65. [PMID: 22973222 PMCID: PMC3428699 DOI: 10.3389/fncom.2012.00065] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2012] [Accepted: 08/10/2012] [Indexed: 12/20/2022] Open
Abstract
Integrated within neural circuits, astrocytes have recently been shown to modulate brain rhythms thought to mediate sleep function. Experimental evidence suggests that local impact of astrocytes on single synapses translates into global modulation of neuronal networks and behavior. We discuss these findings in the context of current conceptual models of sleep generation and function, each of which have historically focused on neural mechanisms. We highlight the implications and the challenges introduced by these results from a conceptual and computational perspective. We further provide modeling directions on how these data might extend our knowledge of astrocytic properties and sleep function. Given our evolving understanding of how local cellular activities during sleep lead to functional outcomes for the brain, further mechanistic and theoretical understanding of astrocytic contribution to these dynamics will undoubtedly be of great basic and translational benefit.
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Affiliation(s)
- Tommaso Fellin
- Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia Genova, Italy
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147
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Patriarca M, Postnova S, Braun HA, Hernández-García E, Toral R. Diversity and noise effects in a model of homeostatic regulation of the sleep-wake cycle. PLoS Comput Biol 2012; 8:e1002650. [PMID: 22927806 PMCID: PMC3426568 DOI: 10.1371/journal.pcbi.1002650] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2012] [Accepted: 06/20/2012] [Indexed: 11/18/2022] Open
Abstract
Recent advances in sleep neurobiology have allowed development of physiologically based mathematical models of sleep regulation that account for the neuronal dynamics responsible for the regulation of sleep-wake cycles and allow detailed examination of the underlying mechanisms. Neuronal systems in general, and those involved in sleep regulation in particular, are noisy and heterogeneous by their nature. It has been shown in various systems that certain levels of noise and diversity can significantly improve signal encoding. However, these phenomena, especially the effects of diversity, are rarely considered in the models of sleep regulation. The present paper is focused on a neuron-based physiologically motivated model of sleep-wake cycles that proposes a novel mechanism of the homeostatic regulation of sleep based on the dynamics of a wake-promoting neuropeptide orexin. Here this model is generalized by the introduction of intrinsic diversity and noise in the orexin-producing neurons, in order to study the effect of their presence on the sleep-wake cycle. A simple quantitative measure of the quality of a sleep-wake cycle is introduced and used to systematically study the generalized model for different levels of noise and diversity. The model is shown to exhibit a clear diversity-induced resonance: that is, the best wake-sleep cycle turns out to correspond to an intermediate level of diversity at the synapses of the orexin-producing neurons. On the other hand, only a mild evidence of stochastic resonance is found, when the level of noise is varied. These results show that disorder, especially in the form of quenched diversity, can be a key-element for an efficient or optimal functioning of the homeostatic regulation of the sleep-wake cycle. Furthermore, this study provides an example of a constructive role of diversity in a neuronal system that can be extended beyond the system studied here.
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Affiliation(s)
- Marco Patriarca
- IFISC, Instituto de Física Interdisciplinar y Sistemas Complejos-CSIC-UIB, Campus Universitat de les Illes Balears, Palma de Mallorca, Spain.
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148
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Information coding in a laminar computational model of cat primary visual cortex. J Comput Neurosci 2012; 34:273-83. [PMID: 22907135 DOI: 10.1007/s10827-012-0420-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2012] [Revised: 07/21/2012] [Accepted: 07/29/2012] [Indexed: 10/28/2022]
Abstract
Neural populations across cortical layers perform different computational tasks. However, it is not known whether information in different layers is encoded using a common neural code or whether it depends on the specific layer. Here we studied the laminar distribution of information in a large-scale computational model of cat primary visual cortex. We analyzed the amount of information about the input stimulus conveyed by the different representations of the cortical responses. In particular, we compared the information encoded in four possible neural codes: (1) the information carried by the firing rate of individual neurons; (2) the information carried by spike patterns within a time window; (3) the rate-and-phase information carried by the firing rate labelled by the phase of the Local Field Potentials (LFP); (4) the pattern-and-phase information carried by the spike patterns tagged with the LFP phase. We found that there is substantially more information in the rate-and-phase code compared with the firing rate alone for low LFP frequency bands (less than 30 Hz). When comparing how information is encoded across layers, we found that the extra information contained in a rate-and-phase code may reach 90 % in Layer 4, while in other layers it reaches only 60 %, compared to the information carried by the firing rate alone. These results suggest that information processing in primary sensory cortices could rely on different coding strategies across different layers.
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149
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Long-term relationships between cholinergic tone, synchronous bursting and synaptic remodeling. PLoS One 2012; 7:e40980. [PMID: 22911726 PMCID: PMC3402441 DOI: 10.1371/journal.pone.0040980] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2012] [Accepted: 06/15/2012] [Indexed: 01/20/2023] Open
Abstract
Cholinergic neuromodulation plays key roles in the regulation of neuronal excitability, network activity, arousal, and behavior. On longer time scales, cholinergic systems play essential roles in cortical development, maturation, and plasticity. Presumably, these processes are associated with substantial synaptic remodeling, yet to date, long-term relationships between cholinergic tone and synaptic remodeling remain largely unknown. Here we used automated microscopy combined with multielectrode array recordings to study long-term relationships between cholinergic tone, excitatory synapse remodeling, and network activity characteristics in networks of cortical neurons grown on multielectrode array substrates. Experimental elevations of cholinergic tone led to the abrupt suppression of episodic synchronous bursting activity (but not of general activity), followed by a gradual growth of excitatory synapses over hours. Subsequent blockage of cholinergic receptors led to an immediate restoration of synchronous bursting and the gradual reversal of synaptic growth. Neither synaptic growth nor downsizing was governed by multiplicative scaling rules. Instead, these occurred in a subset of synapses, irrespective of initial synaptic size. Synaptic growth seemed to depend on intrinsic network activity, but not on the degree to which bursting was suppressed. Intriguingly, sustained elevations of cholinergic tone were associated with a gradual recovery of synchronous bursting but not with a reversal of synaptic growth. These findings show that cholinergic tone can strongly affect synaptic remodeling and synchronous bursting activity, but do not support a strict coupling between the two. Finally, the reemergence of synchronous bursting in the presence of elevated cholinergic tone indicates that the capacity of cholinergic neuromodulation to indefinitely suppress synchronous bursting might be inherently limited.
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150
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Abstract
This review summarizes the brain mechanisms controlling sleep and wakefulness. Wakefulness promoting systems cause low-voltage, fast activity in the electroencephalogram (EEG). Multiple interacting neurotransmitter systems in the brain stem, hypothalamus, and basal forebrain converge onto common effector systems in the thalamus and cortex. Sleep results from the inhibition of wake-promoting systems by homeostatic sleep factors such as adenosine and nitric oxide and GABAergic neurons in the preoptic area of the hypothalamus, resulting in large-amplitude, slow EEG oscillations. Local, activity-dependent factors modulate the amplitude and frequency of cortical slow oscillations. Non-rapid-eye-movement (NREM) sleep results in conservation of brain energy and facilitates memory consolidation through the modulation of synaptic weights. Rapid-eye-movement (REM) sleep results from the interaction of brain stem cholinergic, aminergic, and GABAergic neurons which control the activity of glutamatergic reticular formation neurons leading to REM sleep phenomena such as muscle atonia, REMs, dreaming, and cortical activation. Strong activation of limbic regions during REM sleep suggests a role in regulation of emotion. Genetic studies suggest that brain mechanisms controlling waking and NREM sleep are strongly conserved throughout evolution, underscoring their enormous importance for brain function. Sleep disruption interferes with the normal restorative functions of NREM and REM sleep, resulting in disruptions of breathing and cardiovascular function, changes in emotional reactivity, and cognitive impairments in attention, memory, and decision making.
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Affiliation(s)
- Ritchie E Brown
- Laboratory of Neuroscience, VA Boston Healthcare System and Harvard Medical School, Brockton, Massachusetts 02301, USA
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