101
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Lefebvre J, Hutt A, Frohlich F. Stochastic resonance mediates the state-dependent effect of periodic stimulation on cortical alpha oscillations. eLife 2017; 6:32054. [PMID: 29280733 PMCID: PMC5832422 DOI: 10.7554/elife.32054] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 12/22/2017] [Indexed: 12/14/2022] Open
Abstract
Brain stimulation can be used to engage and modulate rhythmic activity in brain networks. However, the outcomes of brain stimulation are shaped by behavioral states and endogenous fluctuations in brain activity. To better understand how this intrinsic oscillatory activity controls the susceptibility of the brain to stimulation, we analyzed a computational model of the thalamo-cortical system in two distinct states (rest and task-engaged) to identify the mechanisms by which endogenous alpha oscillations (8Hz–12Hz) are modulated by periodic stimulation. Our analysis shows that the different responses to stimulation observed experimentally in these brain states can be explained by a passage through a bifurcation combined with stochastic resonance — a mechanism by which irregular fluctuations amplify the response of a nonlinear system to weak periodic signals. Indeed, our findings suggest that modulation of brain oscillations is best achieved in states of low endogenous rhythmic activity, and that irregular state-dependent fluctuations in thalamic inputs shape the susceptibility of cortical population to periodic stimulation.
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Affiliation(s)
| | - Axel Hutt
- FE12 - Data Assimilation, Deutscher Wetterdienst, Offenbach am Main, Germany
| | - Flavio Frohlich
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, United States
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102
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Su F, Wang J, Niu S, Li H, Deng B, Liu C, Wei X. Nonlinear predictive control for adaptive adjustments of deep brain stimulation parameters in basal ganglia-thalamic network. Neural Netw 2017; 98:283-295. [PMID: 29291546 DOI: 10.1016/j.neunet.2017.12.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Revised: 09/05/2017] [Accepted: 12/01/2017] [Indexed: 11/29/2022]
Abstract
The efficacy of deep brain stimulation (DBS) for Parkinson's disease (PD) depends in part on the post-operative programming of stimulation parameters. Closed-loop stimulation is one method to realize the frequent adjustment of stimulation parameters. This paper introduced the nonlinear predictive control method into the online adjustment of DBS amplitude and frequency. This approach was tested in a computational model of basal ganglia-thalamic network. The autoregressive Volterra model was used to identify the process model based on physiological data. Simulation results illustrated the efficiency of closed-loop stimulation methods (amplitude adjustment and frequency adjustment) in improving the relay reliability of thalamic neurons compared with the PD state. Besides, compared with the 130Hz constant DBS the closed-loop stimulation methods can significantly reduce the energy consumption. Through the analysis of inter-spike-intervals (ISIs) distribution of basal ganglia neurons, the evoked network activity by the closed-loop frequency adjustment stimulation was closer to the normal state.
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Affiliation(s)
- Fei Su
- School of Electrical and Information Engineering, Tianjin University, 300072, Tianjin, China.
| | - Jiang Wang
- School of Electrical and Information Engineering, Tianjin University, 300072, Tianjin, China.
| | - Shuangxia Niu
- School of Electrical Engineering, The Hong Kong Polytechnic University, 999077, Hong Kong, China.
| | - Huiyan Li
- School of Automation and Electrical Engineering, Tianjin University of Technology and Education, 300222, Tianjin, China.
| | - Bin Deng
- School of Electrical and Information Engineering, Tianjin University, 300072, Tianjin, China.
| | - Chen Liu
- School of Electrical and Information Engineering, Tianjin University, 300072, Tianjin, China.
| | - Xile Wei
- School of Electrical and Information Engineering, Tianjin University, 300072, Tianjin, China.
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103
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Abstract
Implicit expectations induced by predictable stimuli sequences affect neuronal response to upcoming stimuli at both single cell and neural population levels. Temporally regular sensory streams also phase entrain ongoing low frequency brain oscillations but how and why this happens is unknown. Here we investigate how random recurrent neural networks without plasticity respond to stimuli streams containing oddballs. We found the neuronal correlates of sensory stream adaptation emerge if networks generate chaotic oscillations which can be phase entrained by stimulus streams. The resultant activity patterns are close to critical and support history dependent response on long timescales. Because critical network entrainment is a slow process stimulus response adapts gradually over multiple repetitions. Repeated stimuli generate suppressed responses but oddball responses are large and distinct. Oscillatory mismatch responses persist in population activity for long periods after stimulus offset while individual cell mismatch responses are strongly phasic. These effects are weakened in temporally irregular sensory streams. Thus we show that network phase entrainment provides a biologically plausible mechanism for neural oddball detection. Our results do not depend on specific network characteristics, are consistent with experimental studies and may be relevant for multiple pathologies demonstrating altered mismatch processing such as schizophrenia and depression.
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Affiliation(s)
- Adam Ponzi
- IBM T.J. Watson Research Center, Yorktown Heights, NY, USA.
- Okinawa Institute of Science and Technology Graduate University (OIST), Okinawa, Japan.
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104
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Sudhakar SK, Hong S, Raikov I, Publio R, Lang C, Close T, Guo D, Negrello M, De Schutter E. Spatiotemporal network coding of physiological mossy fiber inputs by the cerebellar granular layer. PLoS Comput Biol 2017; 13:e1005754. [PMID: 28934196 PMCID: PMC5626500 DOI: 10.1371/journal.pcbi.1005754] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Revised: 10/03/2017] [Accepted: 08/31/2017] [Indexed: 11/18/2022] Open
Abstract
The granular layer, which mainly consists of granule and Golgi cells, is the first stage of the cerebellar cortex and processes spatiotemporal information transmitted by mossy fiber inputs with a wide variety of firing patterns. To study its dynamics at multiple time scales in response to inputs approximating real spatiotemporal patterns, we constructed a large-scale 3D network model of the granular layer. Patterned mossy fiber activity induces rhythmic Golgi cell activity that is synchronized by shared parallel fiber input and by gap junctions. This leads to long distance synchrony of Golgi cells along the transverse axis, powerfully regulating granule cell firing by imposing inhibition during a specific time window. The essential network mechanisms, including tunable Golgi cell oscillations, on-beam inhibition and NMDA receptors causing first winner keeps winning of granule cells, illustrate how fundamental properties of the granule layer operate in tandem to produce (1) well timed and spatially bound output, (2) a wide dynamic range of granule cell firing and (3) transient and coherent gating oscillations. These results substantially enrich our understanding of granule cell layer processing, which seems to promote spatial group selection of granule cell activity as a function of timing of mossy fiber input. The cerebellum is an organ of peculiar geometrical properties, and has been attributed the function of applying spatiotemporal transforms to sensorimotor data since Eccles. In this work we have analyzed the spatiotemporal response properties of the first part of the cerebellar circuit, the granule layer. On the basis of a biophysically plausible and large-scale model of the cerebellum, constrained by a wealth of anatomical data, we study the network dynamics and firing properties of individual cell populations in response to 'realistic' input patterns. We make specific predictions about the spatiotemporal features of granule layer processing regarding the effects of the gap junction coupled network of Golgi cells on a spatially restricted input, in an effect we denominate first-takes-all. Furthermore, we calculate that the granule cell layer has a wide dynamic range, indicating that this is a system that can transmit large variations of input intensities.
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Affiliation(s)
- Shyam Kumar Sudhakar
- Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Onna-son, Okinawa, Japan
- Laboratory of Theoretical Neurobiology and Neuro-engineering, University of Antwerp, Wilrijk, Belgium
- Department of Psychology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Sungho Hong
- Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Onna-son, Okinawa, Japan
| | - Ivan Raikov
- Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Onna-son, Okinawa, Japan
| | - Rodrigo Publio
- Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Onna-son, Okinawa, Japan
| | - Claus Lang
- Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Onna-son, Okinawa, Japan
- Bernstein Center of Computational Neuroscience Berlin, Berlin, Germany
| | - Thomas Close
- Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Onna-son, Okinawa, Japan
| | - Daqing Guo
- Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Onna-son, Okinawa, Japan
| | - Mario Negrello
- Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Onna-son, Okinawa, Japan
- Laboratory of Theoretical Neurobiology and Neuro-engineering, University of Antwerp, Wilrijk, Belgium
- Department of Neuroscience, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Erik De Schutter
- Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Onna-son, Okinawa, Japan
- Laboratory of Theoretical Neurobiology and Neuro-engineering, University of Antwerp, Wilrijk, Belgium
- * E-mail:
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105
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Inferring synaptic excitation/inhibition balance from field potentials. Neuroimage 2017; 158:70-78. [DOI: 10.1016/j.neuroimage.2017.06.078] [Citation(s) in RCA: 263] [Impact Index Per Article: 37.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Revised: 05/26/2017] [Accepted: 06/29/2017] [Indexed: 01/07/2023] Open
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106
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Refractoriness Accounts for Variable Spike Burst Responses in Somatosensory Cortex. eNeuro 2017; 4:eN-NWR-0173-17. [PMID: 28840189 PMCID: PMC5566798 DOI: 10.1523/eneuro.0173-17.2017] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Revised: 06/22/2017] [Accepted: 06/28/2017] [Indexed: 12/13/2022] Open
Abstract
Neurons in the primary somatosensory cortex (S1) respond to peripheral stimulation with synchronized bursts of spikes, which lock to the macroscopic 600-Hz EEG waves. The mechanism of burst generation and synchronization in S1 is not yet understood. Using models of single-neuron responses fitted to unit recordings from macaque monkeys, we show that these synchronized bursts are the consequence of correlated synaptic inputs combined with a refractory mechanism. In the presence of noise these models reproduce also the observed trial-to-trial response variability, where individual bursts represent one of many stereotypical temporal spike patterns. When additional slower and global excitability fluctuations are introduced the single-neuron spike patterns are correlated with the population activity, as demonstrated in experimental data. The underlying biophysical mechanism of S1 responses involves thalamic inputs arriving through depressing synapses to cortical neurons in a high-conductance state. Our findings show that a simple feedforward processing of peripheral inputs could give rise to neuronal responses with nontrivial temporal and population statistics. We conclude that neural systems could use refractoriness to encode variable cortical states into stereotypical short-term spike patterns amenable to processing at neuronal time scales (tens of milliseconds).
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107
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Shaping Intrinsic Neural Oscillations with Periodic Stimulation. J Neurosci 2017; 36:5328-37. [PMID: 27170129 DOI: 10.1523/jneurosci.0236-16.2016] [Citation(s) in RCA: 86] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2016] [Accepted: 04/05/2016] [Indexed: 01/07/2023] Open
Abstract
UNLABELLED Rhythmic brain activity plays an important role in neural processing and behavior. Features of these oscillations, including amplitude, phase, and spectrum, can be influenced by internal states (e.g., shifts in arousal, attention or cognitive ability) or external stimulation. Electromagnetic stimulation techniques such as transcranial magnetic stimulation, transcranial direct current stimulation, and transcranial alternating current stimulation are used increasingly in both research and clinical settings. Currently, the mechanisms whereby time-dependent external stimuli influence population-scale oscillations remain poorly understood. Here, we provide computational insights regarding the mapping between periodic pulsatile stimulation parameters such as amplitude and frequency and the response dynamics of recurrent, nonlinear spiking neural networks. Using a cortical model built of excitatory and inhibitory neurons, we explored a wide range of stimulation intensities and frequencies systematically. Our results suggest that rhythmic stimulation can form the basis of a control paradigm in which one can manipulate the intrinsic oscillatory properties of driven networks via a plurality of input-driven mechanisms. Our results show that, in addition to resonance and entrainment, nonlinear acceleration is involved in shaping the rhythmic response of our modeled network. Such nonlinear acceleration of spontaneous and synchronous oscillatory activity in a neural network occurs in regimes of intense, high-frequency rhythmic stimulation. These results open new perspectives on the manipulation of synchronous neural activity for basic and clinical research. SIGNIFICANCE STATEMENT Oscillatory activity is widely recognized as a core mechanism for information transmission within and between brain circuits. Noninvasive stimulation methods can shape this activity, something that is increasingly capitalized upon in basic research and clinical practice. Here, we provide computational insights on the mechanistic bases for such effects. Our results show that rhythmic stimulation forms the basis of a control paradigm in which one can manipulate the intrinsic oscillatory properties of driven networks via a plurality of input-driven mechanisms. In addition to resonance and entrainment, nonlinear acceleration is involved in shaping the rhythmic response of our modeled network, particularly in regimes of high-frequency rhythmic stimulation. These results open new perspectives on the manipulation of synchronous neural activity for basic and clinical research.
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108
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Kühn T, Helias M. Locking of correlated neural activity to ongoing oscillations. PLoS Comput Biol 2017; 13:e1005534. [PMID: 28604771 PMCID: PMC5484611 DOI: 10.1371/journal.pcbi.1005534] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Revised: 06/26/2017] [Accepted: 04/26/2017] [Indexed: 02/01/2023] Open
Abstract
Population-wide oscillations are ubiquitously observed in mesoscopic signals of cortical activity. In these network states a global oscillatory cycle modulates the propensity of neurons to fire. Synchronous activation of neurons has been hypothesized to be a separate channel of signal processing information in the brain. A salient question is therefore if and how oscillations interact with spike synchrony and in how far these channels can be considered separate. Experiments indeed showed that correlated spiking co-modulates with the static firing rate and is also tightly locked to the phase of beta-oscillations. While the dependence of correlations on the mean rate is well understood in feed-forward networks, it remains unclear why and by which mechanisms correlations tightly lock to an oscillatory cycle. We here demonstrate that such correlated activation of pairs of neurons is qualitatively explained by periodically-driven random networks. We identify the mechanisms by which covariances depend on a driving periodic stimulus. Mean-field theory combined with linear response theory yields closed-form expressions for the cyclostationary mean activities and pairwise zero-time-lag covariances of binary recurrent random networks. Two distinct mechanisms cause time-dependent covariances: the modulation of the susceptibility of single neurons (via the external input and network feedback) and the time-varying variances of single unit activities. For some parameters, the effectively inhibitory recurrent feedback leads to resonant covariances even if mean activities show non-resonant behavior. Our analytical results open the question of time-modulated synchronous activity to a quantitative analysis.
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Affiliation(s)
- Tobias Kühn
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany
- * E-mail:
| | - Moritz Helias
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany
- Department of Physics, Faculty 1, RWTH Aachen University, Aachen, Germany
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109
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Uzilov AV, Cheesman KC, Fink MY, Newman LC, Pandya C, Lalazar Y, Hefti M, Fowkes M, Deikus G, Lau CY, Moe AS, Kinoshita Y, Kasai Y, Zweig M, Gupta A, Starcevic D, Mahajan M, Schadt EE, Post KD, Donovan MJ, Sebra R, Chen R, Geer EB. Identification of a novel RASD1 somatic mutation in a USP8-mutated corticotroph adenoma. Cold Spring Harb Mol Case Stud 2017; 3:a001602. [PMID: 28487882 PMCID: PMC5411693 DOI: 10.1101/mcs.a001602] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Accepted: 02/15/2017] [Indexed: 12/30/2022] Open
Abstract
Cushing's disease (CD) is caused by pituitary corticotroph adenomas that secrete excess adrenocorticotropic hormone (ACTH). In these tumors, somatic mutations in the gene USP8 have been identified as recurrent and pathogenic and are the sole known molecular driver for CD. Although other somatic mutations were reported in these studies, their contribution to the pathogenesis of CD remains unexplored. No molecular drivers have been established for a large proportion of CD cases and tumor heterogeneity has not yet been investigated using genomics methods. Also, even in USP8-mutant tumors, a possibility may exist of additional contributing mutations, following a paradigm from other neoplasm types where multiple somatic alterations contribute to neoplastic transformation. The current study utilizes whole-exome discovery sequencing on the Illumina platform, followed by targeted amplicon-validation sequencing on the Pacific Biosciences platform, to interrogate the somatic mutation landscape in a corticotroph adenoma resected from a CD patient. In this USP8-mutated tumor, we identified an interesting somatic mutation in the gene RASD1, which is a component of the corticotropin-releasing hormone receptor signaling system. This finding may provide insight into a novel mechanism involving loss of feedback control to the corticotropin-releasing hormone receptor and subsequent deregulation of ACTH production in corticotroph tumors.
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Affiliation(s)
- Andrew V Uzilov
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Khadeen C Cheesman
- Division of Endocrinology, Diabetes, and Bone Disease, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Marc Y Fink
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Leah C Newman
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Chetanya Pandya
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Yelena Lalazar
- Division of Endocrinology, Diabetes, and Bone Disease, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Marco Hefti
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Mary Fowkes
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Gintaras Deikus
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Chun Yee Lau
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Aye S Moe
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Yayoi Kinoshita
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Yumi Kasai
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Micol Zweig
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Arpeta Gupta
- Division of Endocrinology, Diabetes, and Bone Disease, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Daniela Starcevic
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Milind Mahajan
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Eric E Schadt
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Kalmon D Post
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Michael J Donovan
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Robert Sebra
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Rong Chen
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Eliza B Geer
- Division of Endocrinology, Diabetes, and Bone Disease, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
- Multidisciplinary Pituitary and Skull Base Tumor Center, Memorial Sloan Kettering, New York, New York 10065, USA
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110
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Nonsinusoidal Beta Oscillations Reflect Cortical Pathophysiology in Parkinson's Disease. J Neurosci 2017; 37:4830-4840. [PMID: 28416595 DOI: 10.1523/jneurosci.2208-16.2017] [Citation(s) in RCA: 134] [Impact Index Per Article: 19.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2016] [Revised: 04/03/2017] [Accepted: 04/07/2017] [Indexed: 11/21/2022] Open
Abstract
Oscillations in neural activity play a critical role in neural computation and communication. There is intriguing new evidence that the nonsinusoidal features of the oscillatory waveforms may inform underlying physiological and pathophysiological characteristics. Time-domain waveform analysis approaches stand in contrast to traditional Fourier-based methods, which alter or destroy subtle waveform features. Recently, it has been shown that the waveform features of oscillatory beta (13-30 Hz) events, a prominent motor cortical oscillation, may reflect near-synchronous excitatory synaptic inputs onto cortical pyramidal neurons. Here we analyze data from invasive human primary motor cortex (M1) recordings from patients with Parkinson's disease (PD) implanted with a deep brain stimulator (DBS) to test the hypothesis that the beta waveform becomes less sharp with DBS, suggesting that M1 input synchrony may be decreased. We find that, in PD, M1 beta oscillations have sharp, asymmetric, nonsinusoidal features, specifically asymmetries in the ratio between the sharpness of the beta peaks compared with the troughs. This waveform feature is nearly perfectly correlated with beta-high gamma phase-amplitude coupling (r = 0.94), a neural index previously shown to track PD-related motor deficit. Our results suggest that the pathophysiological beta generator is altered by DBS, smoothing out the beta waveform. This has implications not only for the interpretation of the physiological mechanism by which DBS reduces PD-related motor symptoms, but more broadly for our analytic toolkit in general. That is, the often-overlooked time-domain features of oscillatory waveforms may carry critical physiological information about neural processes and dynamics.SIGNIFICANCE STATEMENT To better understand the neural basis of cognition and disease, we need to understand how groups of neurons interact to communicate with one another. For example, there is evidence that parkinsonian bradykinesia and rigidity may arise from an oversynchronization of afferents to the motor cortex, and that these symptoms are treatable using deep brain stimulation. Here we show that the waveform shape of beta (13-30 Hz) oscillations, which may reflect input synchrony onto the cortex, is altered by deep brain stimulation. This suggests that mechanistic inferences regarding physiological and pathophysiological neural communication may be made from the temporal dynamics of oscillatory waveform shape.
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111
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Stellino F, Mazzoni A, Storace M. Phase analysis method for burst onset prediction. Phys Rev E 2017; 95:022412. [PMID: 28297995 DOI: 10.1103/physreve.95.022412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2016] [Indexed: 06/06/2023]
Abstract
The response of bursting neurons to fluctuating inputs is usually hard to predict, due to their strong nonlinearity. For the same reason, decoding the injected stimulus from the activity of a bursting neuron is generally difficult. In this paper we propose a method describing (for neuron models) a mechanism of phase coding relating the burst onsets with the phase profile of the input current. This relation suggests that burst onset may provide a way for postsynaptic neurons to track the input phase. Moreover, we define a method of phase decoding to solve the inverse problem and estimate the likelihood of burst onset given the input state. Both methods are presented here in a unified framework, describing a complete coding-decoding procedure. This procedure is tested by using different neuron models, stimulated with different inputs (stochastic, sinusoidal, up, and down states). The results obtained show the efficacy and broad range of application of the proposed methods. Possible applications range from the study of sensory information processing, in which phase-of-firing codes are known to play a crucial role, to clinical applications such as deep brain stimulation, helping to design stimuli in order to trigger or prevent neural bursting.
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Affiliation(s)
- Flavio Stellino
- DITEN, University of Genoa, Via Opera Pia 11a, 16145 Genova, Italy
| | - Alberto Mazzoni
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, 56025 Pontedera, Italy
| | - Marco Storace
- DITEN, University of Genoa, Via Opera Pia 11a, 16145 Genova, Italy
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112
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Brain Oscillations and the Importance of Waveform Shape. Trends Cogn Sci 2017; 21:137-149. [DOI: 10.1016/j.tics.2016.12.008] [Citation(s) in RCA: 302] [Impact Index Per Article: 43.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Revised: 12/06/2016] [Accepted: 12/09/2016] [Indexed: 11/17/2022]
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113
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Teleńczuk B, Dehghani N, Le Van Quyen M, Cash SS, Halgren E, Hatsopoulos NG, Destexhe A. Local field potentials primarily reflect inhibitory neuron activity in human and monkey cortex. Sci Rep 2017; 7:40211. [PMID: 28074856 PMCID: PMC5225490 DOI: 10.1038/srep40211] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2016] [Accepted: 12/05/2016] [Indexed: 01/11/2023] Open
Abstract
The local field potential (LFP) is generated by large populations of neurons, but unitary contribution of spiking neurons to LFP is not well characterised. We investigated this contribution in multi-electrode array recordings from human and monkey neocortex by examining the spike-triggered LFP average (st-LFP). The resulting st-LFPs were dominated by broad spatio-temporal components due to ongoing activity, synaptic inputs and recurrent connectivity. To reduce the spatial reach of the st-LFP and observe the local field related to a single spike we applied a spatial filter, whose weights were adapted to the covariance of ongoing LFP. The filtered st-LFPs were limited to the perimeter of 800 μm around the neuron, and propagated at axonal speed, which is consistent with their unitary nature. In addition, we discriminated between putative inhibitory and excitatory neurons and found that the inhibitory st-LFP peaked at shorter latencies, consistently with previous findings in hippocampal slices. Thus, in human and monkey neocortex, the LFP reflects primarily inhibitory neuron activity.
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Affiliation(s)
- Bartosz Teleńczuk
- Unité de Neurosciences, Information &Complexité, Centre National de la Recherche Scientifique, 91198 Gif-sur-Yvette, France
| | - Nima Dehghani
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, USA.,New England Complex Systems Institute, Cambridge, USA
| | - Michel Le Van Quyen
- L'Institut du Cerveau et de la Moelle Épinière, UMRS 1127, CNRS UMR 7225, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, USA
| | - Eric Halgren
- Multimodal Imaging Laboratory, Departments of Neurosciences and Radiology, University of California San Diego, USA
| | - Nicholas G Hatsopoulos
- Department of Organismal Biology and Anatomy, Committee on Computational Neuroscience, University of Chicago, USA
| | - Alain Destexhe
- Unité de Neurosciences, Information &Complexité, Centre National de la Recherche Scientifique, 91198 Gif-sur-Yvette, France
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114
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Halnes G, Mäki-Marttunen T, Keller D, Pettersen KH, Andreassen OA, Einevoll GT. Effect of Ionic Diffusion on Extracellular Potentials in Neural Tissue. PLoS Comput Biol 2016; 12:e1005193. [PMID: 27820827 PMCID: PMC5098741 DOI: 10.1371/journal.pcbi.1005193] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2016] [Accepted: 10/11/2016] [Indexed: 01/06/2023] Open
Abstract
Recorded potentials in the extracellular space (ECS) of the brain is a standard measure of population activity in neural tissue. Computational models that simulate the relationship between the ECS potential and its underlying neurophysiological processes are commonly used in the interpretation of such measurements. Standard methods, such as volume-conductor theory and current-source density theory, assume that diffusion has a negligible effect on the ECS potential, at least in the range of frequencies picked up by most recording systems. This assumption remains to be verified. We here present a hybrid simulation framework that accounts for diffusive effects on the ECS potential. The framework uses (1) the NEURON simulator to compute the activity and ionic output currents from multicompartmental neuron models, and (2) the electrodiffusive Kirchhoff-Nernst-Planck framework to simulate the resulting dynamics of the potential and ion concentrations in the ECS, accounting for the effect of electrical migration as well as diffusion. Using this framework, we explore the effect that ECS diffusion has on the electrical potential surrounding a small population of 10 pyramidal neurons. The neural model was tuned so that simulations over ∼100 seconds of biological time led to shifts in ECS concentrations by a few millimolars, similar to what has been seen in experiments. By comparing simulations where ECS diffusion was absent with simulations where ECS diffusion was included, we made the following key findings: (i) ECS diffusion shifted the local potential by up to ∼0.2 mV. (ii) The power spectral density (PSD) of the diffusion-evoked potential shifts followed a 1/f2 power law. (iii) Diffusion effects dominated the PSD of the ECS potential for frequencies up to several hertz. In scenarios with large, but physiologically realistic ECS concentration gradients, diffusion was thus found to affect the ECS potential well within the frequency range picked up in experimental recordings.
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Affiliation(s)
- Geir Halnes
- Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Tuomo Mäki-Marttunen
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Daniel Keller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Klas H. Pettersen
- Letten Centre and GliaLab, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway
| | - Ole A. Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Gaute T. Einevoll
- Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, Ås, Norway
- Department of Physics, University of Oslo, Oslo, Norway
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115
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Hagen E, Dahmen D, Stavrinou ML, Lindén H, Tetzlaff T, van Albada SJ, Grün S, Diesmann M, Einevoll GT. Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks. Cereb Cortex 2016; 26:4461-4496. [PMID: 27797828 PMCID: PMC6193674 DOI: 10.1093/cercor/bhw237] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Revised: 05/31/2016] [Accepted: 07/12/2016] [Indexed: 12/21/2022] Open
Abstract
With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network model for a ∼1 mm2 patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its public implementation in hybridLFPy form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail.
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Affiliation(s)
- Espen Hagen
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, 52425 Jülich, Germany.,Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, 1430 Ås, Norway
| | - David Dahmen
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, 52425 Jülich, Germany
| | - Maria L Stavrinou
- Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, 1430 Ås, Norway.,Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Henrik Lindén
- Department of Neuroscience and Pharmacology, University of Copenhagen, 2200 Copenhagen, Denmark.,Department of Computational Biology, School of Computer Science and Communication, Royal Institute of Technology, 100 44 Stockholm, Sweden
| | - Tom Tetzlaff
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, 52425 Jülich, Germany
| | - Sacha J van Albada
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, 52425 Jülich, Germany
| | - Sonja Grün
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, 52425 Jülich, Germany.,Theoretical Systems Neurobiology, RWTH Aachen University, 52056 Aachen, Germany
| | - Markus Diesmann
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, 52425 Jülich, Germany.,Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany.,Department of Physics, Faculty 1, RWTH Aachen University, 52062 Aachen, Germany
| | - Gaute T Einevoll
- Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, 1430 Ås, Norway.,Department of Physics, University of Oslo, 0316 Oslo, Norway
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116
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Vallone F, Vannini E, Cintio A, Caleo M, Di Garbo A. Time evolution of interhemispheric coupling in a model of focal neocortical epilepsy in mice. Phys Rev E 2016; 94:032409. [PMID: 27739854 DOI: 10.1103/physreve.94.032409] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Indexed: 11/07/2022]
Abstract
Epilepsy is characterized by substantial network rearrangements leading to spontaneous seizures and little is known on how an epileptogenic focus impacts on neural activity in the contralateral hemisphere. Here, we used a model of unilateral epilepsy induced by injection of the synaptic blocker tetanus neurotoxin (TeNT) in the mouse primary visual cortex (V1). Local field potential (LFP) signals were simultaneously recorded from both hemispheres of each mouse in acute phase (peak of toxin action) and chronic condition (completion of TeNT effects). To characterize the neural electrical activities the corresponding LFP signals were analyzed with several methods of time series analysis. For the epileptic mice, the spectral analysis showed that TeNT determines a power redistribution among the different neurophysiological bands in both acute and chronic phases. Using linear and nonlinear interdependence measures in both time and frequency domains, it was found in the acute phase that TeNT injection promotes a reduction of the interhemispheric coupling for high frequencies (12-30 Hz) and small time lag (<20 ms), whereas an increase of the coupling is present for low frequencies (0.5-4 Hz) and long time lag (>40 ms). On the other hand, the chronic period is characterized by a partial or complete recovery of the interhemispheric interdependence level. Granger causality test and symbolic transfer entropy indicate a greater driving influence of the TeNT-injected side on activity in the contralateral hemisphere in the chronic phase. Lastly, based on experimental observations, we built a computational model of LFPs to investigate the role of the ipsilateral inhibition and exicitatory interhemispheric connections in the dampening of the interhemispheric coupling. The time evolution of the interhemispheric coupling in such a relevant model of epilepsy has been addressed here.
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Affiliation(s)
- F Vallone
- Institute of Biophysics, CNR-National Research Council, 56124 Pisa, Italy.,The Biorobotics Institute, Scuola Superiore Sant'Anna, 56026 Pisa, Italy
| | - E Vannini
- Neuroscience Institute, CNR-National Research Council, 56124 Pisa, Italy
| | - A Cintio
- Institute of Biophysics, CNR-National Research Council, 56124 Pisa, Italy
| | - M Caleo
- Neuroscience Institute, CNR-National Research Council, 56124 Pisa, Italy
| | - A Di Garbo
- Institute of Biophysics, CNR-National Research Council, 56124 Pisa, Italy.,INFN-Section of Pisa, 56127 Pisa, Italy
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117
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Cui J, Ng LJ, Volman V. Callosal dysfunction explains injury sequelae in a computational network model of axonal injury. J Neurophysiol 2016; 116:2892-2908. [PMID: 27683891 DOI: 10.1152/jn.00603.2016] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Accepted: 09/22/2016] [Indexed: 12/28/2022] Open
Abstract
Mild traumatic brain injury (mTBI) often results in neurobehavioral aberrations such as impaired attention and increased reaction time. Diffusion imaging and postmortem analysis studies suggest that mTBI primarily affects myelinated axons in white matter tracts. In particular, corpus callosum, mediating interhemispheric information exchange, has been shown to be affected in mTBI. Yet little is known about the mechanisms linking the injury of myelinated callosal axons to the neurobehavioral sequelae of mTBI. To address this issue, we devised and studied a large, biologically plausible neuronal network model of cortical tissue. Importantly, the model architecture incorporated intra- and interhemispheric organization, including myelinated callosal axons and distance-dependent axonal conduction delays. In the resting state, the intact model network exhibited several salient features, including alpha-band (8-12 Hz) collective activity with low-frequency irregular spiking of individual neurons. The network model of callosal injury captured several clinical observations, including 1) "slowing down" of the network rhythms, manifested as an increased resting-state theta-to-alpha power ratio, 2) reduced response to attention-like network stimulation, manifested as a reduced spectral power of collective activity, and 3) increased population response time in response to stimulation. Importantly, these changes were positively correlated with injury severity, supporting proposals to use neurobehavioral indices as biomarkers for determining the severity of injury. Our modeling effort helps to understand the role played by the injury of callosal myelinated axons in defining the neurobehavioral sequelae of mTBI.
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Affiliation(s)
- Jianxia Cui
- L-3 Applied Technologies, Inc., San Diego, California
| | - Laurel J Ng
- L-3 Applied Technologies, Inc., San Diego, California
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118
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Barardi A, Garcia-Ojalvo J, Mazzoni A. Transition between Functional Regimes in an Integrate-And-Fire Network Model of the Thalamus. PLoS One 2016; 11:e0161934. [PMID: 27598260 PMCID: PMC5012668 DOI: 10.1371/journal.pone.0161934] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Accepted: 08/15/2016] [Indexed: 01/07/2023] Open
Abstract
The thalamus is a key brain element in the processing of sensory information. During the sleep and awake states, this brain area is characterized by the presence of two distinct dynamical regimes: in the sleep state activity is dominated by spindle oscillations (7 − 15 Hz) weakly affected by external stimuli, while in the awake state the activity is primarily driven by external stimuli. Here we develop a simple and computationally efficient model of the thalamus that exhibits two dynamical regimes with different information-processing capabilities, and study the transition between them. The network model includes glutamatergic thalamocortical (TC) relay neurons and GABAergic reticular (RE) neurons described by adaptive integrate-and-fire models in which spikes are induced by either depolarization or hyperpolarization rebound. We found a range of connectivity conditions under which the thalamic network composed by these neurons displays the two aforementioned dynamical regimes. Our results show that TC-RE loops generate spindle-like oscillations and that a minimum level of clustering (i.e. local connectivity density) in the RE-RE connections is necessary for the coexistence of the two regimes. We also observe that the transition between the two regimes occurs when the external excitatory input on TC neurons (mimicking sensory stimulation) is large enough to cause a significant fraction of them to switch from hyperpolarization-rebound-driven firing to depolarization-driven firing. Overall, our model gives a novel and clear description of the role that the two types of neurons and their connectivity play in the dynamical regimes observed in the thalamus, and in the transition between them. These results pave the way for the development of efficient models of the transmission of sensory information from periphery to cortex.
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Affiliation(s)
- Alessandro Barardi
- Departament of Experimental and Health Sciences, Universitat Pompeu Fabra, Dr. Aiguader 88, 08003 Barcelona, Spain
- Departament de Física i Enginyeria Nuclear, Universitat Politècnica de Catalunya, Rambla Sant Nebridi 22, 08222 Terrassa, Spain
| | - Jordi Garcia-Ojalvo
- Departament of Experimental and Health Sciences, Universitat Pompeu Fabra, Dr. Aiguader 88, 08003 Barcelona, Spain
- * E-mail: (JGO); (AM)
| | - Alberto Mazzoni
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, 56026, Italy
- * E-mail: (JGO); (AM)
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119
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Gips B, van der Eerden JPJM, Jensen O. A biologically plausible mechanism for neuronal coding organized by the phase of alpha oscillations. Eur J Neurosci 2016; 44:2147-61. [PMID: 27320148 PMCID: PMC5129495 DOI: 10.1111/ejn.13318] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2015] [Revised: 06/15/2016] [Accepted: 06/17/2016] [Indexed: 01/18/2023]
Abstract
The visual system receives a wealth of sensory information of which only little is relevant for behaviour. We present a mechanism in which alpha oscillations serve to prioritize different components of visual information. By way of simulated neuronal networks, we show that inhibitory modulation in the alpha range (~ 10 Hz) can serve to temporally segment the visual information to prevent information overload. Coupled excitatory and inhibitory neurons generate a gamma rhythm in which information is segmented and sorted according to excitability in each alpha cycle. Further details are coded by distributed neuronal firing patterns within each gamma cycle. The network model produces coupling between alpha phase and gamma (40–100 Hz) amplitude in the simulated local field potential similar to that observed experimentally in human and animal recordings.
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Affiliation(s)
- Bart Gips
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525 EN, Nijmegen, The Netherlands
| | - Jan P J M van der Eerden
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525 EN, Nijmegen, The Netherlands
| | - Ole Jensen
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525 EN, Nijmegen, The Netherlands
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120
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Osinski BL, Kay LM. Granule cell excitability regulates gamma and beta oscillations in a model of the olfactory bulb dendrodendritic microcircuit. J Neurophysiol 2016; 116:522-39. [PMID: 27121582 DOI: 10.1152/jn.00988.2015] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Accepted: 04/25/2016] [Indexed: 01/03/2023] Open
Abstract
Odors evoke gamma (40-100 Hz) and beta (20-30 Hz) oscillations in the local field potential (LFP) of the mammalian olfactory bulb (OB). Gamma (and possibly beta) oscillations arise from interactions in the dendrodendritic microcircuit between excitatory mitral cells (MCs) and inhibitory granule cells (GCs). When cortical descending inputs to the OB are blocked, beta oscillations are extinguished whereas gamma oscillations become larger. Much of this centrifugal input targets inhibitory interneurons in the GC layer and regulates the excitability of GCs, which suggests a causal link between the emergence of beta oscillations and GC excitability. We investigate the effect that GC excitability has on network oscillations in a computational model of the MC-GC dendrodendritic network with Ca(2+)-dependent graded inhibition. Results from our model suggest that when GC excitability is low, the graded inhibitory current mediated by NMDA channels and voltage-dependent Ca(2+) channels (VDCCs) is also low, allowing MC populations to fire in the gamma frequency range. When GC excitability is increased, the activation of NMDA receptors and other VDCCs is also increased, allowing the slow decay time constants of these channels to sustain beta-frequency oscillations. Our model argues that Ca(2+) flow through VDCCs alone could sustain beta oscillations and that the switch between gamma and beta oscillations can be triggered by an increase in the excitability state of a subpopulation of GCs.
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Affiliation(s)
- Bolesław L Osinski
- Graduate Program in Biophysical Sciences, The University of Chicago, Chicago, Illinois; Institute for Mind and Biology, The University of Chicago, Chicago, Illinois; and
| | - Leslie M Kay
- Institute for Mind and Biology, The University of Chicago, Chicago, Illinois; and Department of Psychology, The University of Chicago, Chicago, Illinois
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121
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Prestimulus influences on auditory perception from sensory representations and decision processes. Proc Natl Acad Sci U S A 2016; 113:4842-7. [PMID: 27071110 DOI: 10.1073/pnas.1524087113] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
The qualities of perception depend not only on the sensory inputs but also on the brain state before stimulus presentation. Although the collective evidence from neuroimaging studies for a relation between prestimulus state and perception is strong, the interpretation in the context of sensory computations or decision processes has remained difficult. In the auditory system, for example, previous studies have reported a wide range of effects in terms of the perceptually relevant frequency bands and state parameters (phase/power). To dissociate influences of state on earlier sensory representations and higher-level decision processes, we collected behavioral and EEG data in human participants performing two auditory discrimination tasks relying on distinct acoustic features. Using single-trial decoding, we quantified the relation between prestimulus activity, relevant sensory evidence, and choice in different task-relevant EEG components. Within auditory networks, we found that phase had no direct influence on choice, whereas power in task-specific frequency bands affected the encoding of sensory evidence. Within later-activated frontoparietal regions, theta and alpha phase had a direct influence on choice, without involving sensory evidence. These results delineate two consistent mechanisms by which prestimulus activity shapes perception. However, the timescales of the relevant neural activity depend on the specific brain regions engaged by the respective task.
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