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Ladenbauer J, Khakimova L, Malinowski R, Obst D, Tönnies E, Antonenko D, Obermayer K, Hanna J, Flöel A. Towards Optimization of Oscillatory Stimulation During Sleep. Neuromodulation 2023; 26:1592-1601. [PMID: 35981956 DOI: 10.1016/j.neurom.2022.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 05/13/2022] [Accepted: 05/16/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND Oscillatory rhythms during sleep, such as slow oscillations (SOs) and spindles and, most importantly, their coupling, are thought to underlie processes of memory consolidation. External slow oscillatory transcranial direct current stimulation (so-tDCS) with a frequency of 0.75 Hz has been shown to improve this coupling and memory consolidation; however, effects varied quite markedly between individuals, studies, and species. In this study, we aimed to determine how precisely the frequency of stimulation must match the naturally occurring SO frequency in individuals to best improve SO-spindle coupling. Moreover, we systematically tested stimulation durations necessary to induce changes. MATERIALS AND METHODS We addressed these questions by comparing so-tDCS with individualized frequency to standardized frequency of 0.75 Hz in a within-subject design with 28 older participants during napping while stimulation train durations were systematically varied between 30 seconds, 2 minutes, and 5 minutes. RESULTS Stimulation trains as short as 30 seconds were sufficient to modulate the coupling between SOs and spindle activity. Contrary to our expectations, so-tDCS with standardized frequency indicated stronger aftereffects regarding SO-spindle coupling than individualized frequency. Angle and variance of spindle maxima occurrence during the SO cycle were similarly modulated. CONCLUSIONS In sum, short stimulation trains were sufficient to induce significant changes in sleep physiology, allowing for more trains of stimulation, which provides methodological advantages and possibly even larger behavioral effects in future studies. Regarding individualized stimulation frequency, further options of optimization need to be investigated, such as closed-loop stimulation, to calibrate stimulation frequency to the SO frequency at the time of stimulation onset. CLINICAL TRIAL REGISTRATION The Clinicaltrials.gov registration number for the study is NCT04714879.
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Affiliation(s)
- Julia Ladenbauer
- Department of Neurology, Universitätsmedizin Greifswald, Greifswald, Germany
| | - Liliia Khakimova
- Department of Neurology, Universitätsmedizin Greifswald, Greifswald, Germany
| | - Robert Malinowski
- Department of Neurology, Universitätsmedizin Greifswald, Greifswald, Germany
| | - Daniela Obst
- Department of Neurology, Universitätsmedizin Greifswald, Greifswald, Germany
| | - Eric Tönnies
- Department of Neurology, Universitätsmedizin Greifswald, Greifswald, Germany
| | - Daria Antonenko
- Department of Neurology, Universitätsmedizin Greifswald, Greifswald, Germany
| | - Klaus Obermayer
- Fakultät IV and Bernstein Center for Computational Neuroscience, Technische Universität Berlin, Berlin, Germany
| | - Jeff Hanna
- Department of Neurology, Universitätsmedizin Greifswald, Greifswald, Germany
| | - Agnes Flöel
- Department of Neurology, Universitätsmedizin Greifswald, Greifswald, Germany; German Centre for Neurodegenerative Diseases (DZNE) Greifswald, Greifswald, Germany.
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2
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Safavi S, Panagiotaropoulos TI, Kapoor V, Ramirez-Villegas JF, Logothetis NK, Besserve M. Uncovering the organization of neural circuits with Generalized Phase Locking Analysis. PLoS Comput Biol 2023; 19:e1010983. [PMID: 37011110 PMCID: PMC10109521 DOI: 10.1371/journal.pcbi.1010983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 04/17/2023] [Accepted: 02/27/2023] [Indexed: 04/05/2023] Open
Abstract
Despite the considerable progress of in vivo neural recording techniques, inferring the biophysical mechanisms underlying large scale coordination of brain activity from neural data remains challenging. One obstacle is the difficulty to link high dimensional functional connectivity measures to mechanistic models of network activity. We address this issue by investigating spike-field coupling (SFC) measurements, which quantify the synchronization between, on the one hand, the action potentials produced by neurons, and on the other hand mesoscopic "field" signals, reflecting subthreshold activities at possibly multiple recording sites. As the number of recording sites gets large, the amount of pairwise SFC measurements becomes overwhelmingly challenging to interpret. We develop Generalized Phase Locking Analysis (GPLA) as an interpretable dimensionality reduction of this multivariate SFC. GPLA describes the dominant coupling between field activity and neural ensembles across space and frequencies. We show that GPLA features are biophysically interpretable when used in conjunction with appropriate network models, such that we can identify the influence of underlying circuit properties on these features. We demonstrate the statistical benefits and interpretability of this approach in various computational models and Utah array recordings. The results suggest that GPLA, used jointly with biophysical modeling, can help uncover the contribution of recurrent microcircuits to the spatio-temporal dynamics observed in multi-channel experimental recordings.
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Affiliation(s)
- Shervin Safavi
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- IMPRS for Cognitive and Systems Neuroscience, University of Tübingen, Tübingen, Germany
| | - Theofanis I. Panagiotaropoulos
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Cognitive Neuroimaging Unit, INSERM, CEA, CNRS, Université Paris-Saclay, NeuroSpin center, 91191 Gif/Yvette, France
| | - Vishal Kapoor
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- International Center for Primate Brain Research (ICPBR), Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences (CAS), Shanghai 201602, China
| | - Juan F. Ramirez-Villegas
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Institute of Science and Technology Austria (IST Austria), Klosterneuburg, Austria
| | - Nikos K. Logothetis
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- International Center for Primate Brain Research (ICPBR), Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences (CAS), Shanghai 201602, China
- Centre for Imaging Sciences, Biomedical Imaging Institute, The University of Manchester, Manchester, United Kingdom
| | - Michel Besserve
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Department of Empirical Inference, Max Planck Institute for Intelligent Systems and MPI-ETH Center for Learning Systems, Tübingen, Germany
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3
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Tripathi R, Gluckman BJ. Development of Mechanistic Neural Mass (mNM) Models that Link Physiology to Mean-Field Dynamics. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 2:911090. [PMID: 36876035 PMCID: PMC9980379 DOI: 10.3389/fnetp.2022.911090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Brain rhythms emerge from the mean-field activity of networks of neurons. There have been many efforts to build mathematical and computational embodiments in the form of discrete cell-group activities-termed neural masses-to understand in particular the origins of evoked potentials, intrinsic patterns of activities such as theta, regulation of sleep, Parkinson's disease related dynamics, and mimic seizure dynamics. As originally utilized, standard neural masses convert input through a sigmoidal function to a firing rate, and firing rate through a synaptic alpha function to other masses. Here we define a process to build mechanistic neural masses (mNMs) as mean-field models of microscopic membrane-type (Hodgkin Huxley type) models of different neuron types that duplicate the stability, firing rate, and associated bifurcations as function of relevant slow variables - such as extracellular potassium - and synaptic current; and whose output is both firing rate and impact on the slow variables - such as transmembrane potassium flux. Small networks composed of just excitatory and inhibitory mNMs demonstrate expected dynamical states including firing, runaway excitation and depolarization block, and these transitions change in biologically observed ways with changes in extracellular potassium and excitatory-inhibitory balance.
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Affiliation(s)
- Richa Tripathi
- Center for Neural Engineering, The Pennsylvania State University, University Park, PA, United States.,Indian Institute of Technology Gandhinagar, Gandhinagar, India.,Center for Advanced Systems Understanding (CASUS), HZDR, Görlitz, Germany
| | - Bruce J Gluckman
- Center for Neural Engineering, The Pennsylvania State University, University Park, PA, United States.,Departments of Engineering Science and Mechanics, Biomedical Engineering, The Pennsylvania State University, University Park, PA, United States.,Department of Neurosurgery, College of Medicine, The Pennsylvania State University, Hershey, PA, United States
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4
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Jajcay N, Cakan C, Obermayer K. Cross-Frequency Slow Oscillation–Spindle Coupling in a Biophysically Realistic Thalamocortical Neural Mass Model. Front Comput Neurosci 2022; 16:769860. [PMID: 35603132 PMCID: PMC9120371 DOI: 10.3389/fncom.2022.769860] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Abstract
Sleep manifests itself by the spontaneous emergence of characteristic oscillatory rhythms, which often time-lock and are implicated in memory formation. Here, we analyze a neural mass model of the thalamocortical loop in which the cortical node can generate slow oscillations (approximately 1 Hz) while its thalamic component can generate fast sleep spindles of σ-band activity (12–15 Hz). We study the dynamics for different coupling strengths between the thalamic and cortical nodes, for different conductance values of the thalamic node's potassium leak and hyperpolarization-activated cation-nonselective currents, and for different parameter regimes of the cortical node. The latter are listed as follows: (1) a low activity (DOWN) state with noise-induced, transient excursions into a high activity (UP) state, (2) an adaptation induced slow oscillation limit cycle with alternating UP and DOWN states, and (3) a high activity (UP) state with noise-induced, transient excursions into the low activity (DOWN) state. During UP states, thalamic spindling is abolished or reduced. During DOWN states, the thalamic node generates sleep spindles, which in turn can cause DOWN to UP transitions in the cortical node. Consequently, this leads to spindle-induced UP state transitions in parameter regime (1), thalamic spindles induced in some but not all DOWN states in regime (2), and thalamic spindles following UP to DOWN transitions in regime (3). The spindle-induced σ-band activity in the cortical node, however, is typically the strongest during the UP state, which follows a DOWN state “window of opportunity” for spindling. When the cortical node is parametrized in regime (3), the model well explains the interactions between slow oscillations and sleep spindles observed experimentally during Non-Rapid Eye Movement sleep. The model is computationally efficient and can be integrated into large-scale modeling frameworks to study spatial aspects like sleep wave propagation.
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Affiliation(s)
- Nikola Jajcay
- Neural Information Processing Group, Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, Czechia
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
- *Correspondence: Nikola Jajcay
| | - Caglar Cakan
- Neural Information Processing Group, Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Klaus Obermayer
- Neural Information Processing Group, Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
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5
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Cakan C, Jajcay N, Obermayer K. neurolib: A Simulation Framework for Whole-Brain Neural Mass Modeling. Cognit Comput 2021. [DOI: 10.1007/s12559-021-09931-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Abstractneurolib is a computational framework for whole-brain modeling written in Python. It provides a set of neural mass models that represent the average activity of a brain region on a mesoscopic scale. In a whole-brain network model, brain regions are connected with each other based on biologically informed structural connectivity, i.e., the connectome of the brain. neurolib can load structural and functional datasets, set up a whole-brain model, manage its parameters, simulate it, and organize its outputs for later analysis. The activity of each brain region can be converted into a simulated BOLD signal in order to calibrate the model against empirical data from functional magnetic resonance imaging (fMRI). Extensive model analysis is made possible using a parameter exploration module, which allows one to characterize a model’s behavior as a function of changing parameters. An optimization module is provided for fitting models to multimodal empirical data using evolutionary algorithms. neurolib is designed to be extendable and allows for easy implementation of custom neural mass models, offering a versatile platform for computational neuroscientists for prototyping models, managing large numerical experiments, studying the structure–function relationship of brain networks, and for performing in-silico optimization of whole-brain models.
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6
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The essential role of hippocampo-cortical connections in temporal coordination of spindles and ripples. Neuroimage 2021; 243:118485. [PMID: 34425227 DOI: 10.1016/j.neuroimage.2021.118485] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 08/17/2021] [Accepted: 08/18/2021] [Indexed: 11/22/2022] Open
Abstract
The predominant activity of slow wave sleep is cortical slow oscillations (SOs), thalamic spindles and hippocampal sharp wave ripples. While the precise temporal nesting of these rhythms was shown to be essential for memory consolidation, the coordination mechanism is poorly understood. Here we develop a minimal hippocampo-cortico-thalamic network that can explain the mechanism underlying the SO-spindle-ripple coupling indicating of the succession of regional neuronal interactions. Further we verify the model predictions experimentally in naturally sleeping rodents showing our simple model provides a quantitative match to several experimental observations including the nesting of ripples in the spindle troughs and larger duration but lower amplitude of the ripples co-occurring with spindles or SOs compared to the isolated ripples. The model also predicts that the coupling of ripples to SOs and spindles monotonically enhances by increasing the strength of hippocampo-cortical connections while it is stronger at intermediate values of the cortico-hippocampal projections.
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7
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Real-Time Excitation of Slow Oscillations during Deep Sleep Using Acoustic Stimulation. SENSORS 2021; 21:s21155169. [PMID: 34372405 PMCID: PMC8347755 DOI: 10.3390/s21155169] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 07/27/2021] [Accepted: 07/28/2021] [Indexed: 11/20/2022]
Abstract
Slow-wave synchronous acoustic stimulation is a promising research and therapeutic tool. It is essential to clearly understand the principles of the synchronization methods, to know their performances and limitations, and, most importantly, to have a clear picture of the effect of stimulation on slow-wave activity (SWA). This paper covers the mentioned and currently missing parts of knowledge that are essential for the appropriate development of the method itself and future applications. Artificially streamed real sleep EEG data were used to quantitatively compare the two currently used real-time methods: the phase-locking loop (PLL) and the fixed-step stimulus in our own implementation. The fixed-step stimulation method was concluded to be more reliable and practically applicable compared to the PLL method. The sleep experiment with chronic insomnia patients in our sleep laboratory was analyzed in order to precisely characterize the effect of sound stimulation during deep sleep. We found that there is a significant phase synchronization of delta waves, which were shown to be the most sensitive metric of the effect of acoustic stimulation compared to commonly used averaged signal and power analyses. This finding may change the understanding of the effect and function of the SWA stimulation described in the literature.
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8
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Song JL, Kim JA, Struck AF, Zhang R, Westover MB. A model of metabolic supply-demand mismatch leading to secondary brain injury. J Neurophysiol 2021; 126:653-667. [PMID: 34232754 DOI: 10.1152/jn.00674.2020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Secondary brain injury (SBI) is defined as new or worsening injury to the brain after an initial neurologic insult, such as hemorrhage, trauma, ischemic stroke, or infection. It is a common and potentially preventable complication following many types of primary brain injury (PBI). However, mechanistic details about how PBI leads to additional brain injury and evolves into SBI are poorly characterized. In this work, we propose a mechanistic model for the metabolic supply demand mismatch hypothesis (MSDMH) of SBI. Our model, based on the Hodgkin-Huxley model, supplemented with additional dynamics for extracellular potassium, oxygen concentration, and excitotoxity, provides a high-level unified explanation for why patients with acute brain injury frequently develop SBI. We investigate how decreased oxygen, increased extracellular potassium, excitotoxicity, and seizures can induce SBI and suggest three underlying paths for how events following PBI may lead to SBI. The proposed model also helps explain several important empirical observations, including the common association of acute brain injury with seizures, the association of seizures with tissue hypoxia and so on. In contrast to current practices which assume that ischemia plays the predominant role in SBI, our model suggests that metabolic crisis involved in SBI can also be nonischemic. Our findings offer a more comprehensive understanding of the complex interrelationship among potassium, oxygen, excitotoxicity, seizures, and SBI.NEW & NOTEWORTHY We present a novel mechanistic model for the metabolic supply demand mismatch hypothesis (MSDMH), which attempts to explain why patients with acute brain injury frequently develop seizure activity and secondary brain injury (SBI). Specifically, we investigate how decreased oxygen, increased extracellular potassium, excitotoxicity, seizures, all common sequalae of primary brain injury (PBI), can induce SBI and suggest three underlying paths for how events following PBI may lead to SBI.
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Affiliation(s)
- Jiang-Ling Song
- The Medical Big Data Research Center, Northwest University, Xi'an, China.,Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jennifer A Kim
- Department of Neurology, Yale New Haven Hospital, New Haven, Connecticut
| | - Aaron F Struck
- Departments of Neurology, University of Wisconsin-Madison, Madison, Wisconsin.,William S Middleton Veterans Administration Hospital, Madison, Wisconsin
| | - Rui Zhang
- The Medical Big Data Research Center, Northwest University, Xi'an, China
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
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9
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Torres FA, Orio P, Escobar MJ. Selection of stimulus parameters for enhancing slow wave sleep events with a neural-field theory thalamocortical model. PLoS Comput Biol 2021; 17:e1008758. [PMID: 34329289 PMCID: PMC8357165 DOI: 10.1371/journal.pcbi.1008758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 08/11/2021] [Accepted: 05/28/2021] [Indexed: 11/30/2022] Open
Abstract
Slow-wave sleep cortical brain activity, conformed by slow-oscillations and sleep spindles, plays a key role in memory consolidation. The increase of the power of the slow-wave events, obtained by auditory sensory stimulation, positively correlates with memory consolidation performance. However, little is known about the experimental protocol maximizing this effect, which could be induced by the power of slow-oscillation, the number of sleep spindles, or the timing of both events' co-occurrence. Using a mean-field model of thalamocortical activity, we studied the effect of several stimulation protocols, varying the pulse shape, duration, amplitude, and frequency, as well as a target-phase using a closed-loop approach. We evaluated the effect of these parameters on slow-oscillations (SO) and sleep-spindles (SP), considering: (i) the power at the frequency bands of interest, (ii) the number of SO and SP, (iii) co-occurrences between SO and SP, and (iv) synchronization of SP with the up-peak of the SO. The first three targets are maximized using a decreasing ramp pulse with a pulse duration of 50 ms. Also, we observed a reduction in the number of SO when increasing the stimulus energy by rising its amplitude. To assess the target-phase parameter, we applied closed-loop stimulation at 0°, 45°, and 90° of the phase of the narrow-band filtered ongoing activity, at 0.85 Hz as central frequency. The 0° stimulation produces better results in the power and number of SO and SP than the rhythmic or random stimulation. On the other hand, stimulating at 45° or 90° change the timing distribution of spindles centers but with fewer co-occurrences than rhythmic and 0° phase. Finally, we propose the application of closed-loop stimulation at the rising zero-cross point using pulses with a decreasing ramp shape and 50 ms of duration for future experimental work.
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Affiliation(s)
- Felipe A. Torres
- Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso, Chile
- Advanced Center for Electrical and Electronic Engineering (AC3E), Valparaíso, Chile
| | - Patricio Orio
- Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile
- Advanced Center for Electrical and Electronic Engineering (AC3E), Valparaíso, Chile
| | - María-José Escobar
- Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso, Chile
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10
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Li Q, Westover MB, Zhang R, Chu CJ. Computational Evidence for a Competitive Thalamocortical Model of Spikes and Spindle Activity in Rolandic Epilepsy. Front Comput Neurosci 2021; 15:680549. [PMID: 34220477 PMCID: PMC8249809 DOI: 10.3389/fncom.2021.680549] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Accepted: 05/12/2021] [Indexed: 11/24/2022] Open
Abstract
Rolandic epilepsy (RE) is the most common idiopathic focal childhood epilepsy syndrome, characterized by sleep-activated epileptiform spikes and seizures and cognitive deficits in school age children. Recent evidence suggests that this disease may be caused by disruptions to the Rolandic thalamocortical circuit, resulting in both an abundance of epileptiform spikes and a paucity of sleep spindles in the Rolandic cortex during non-rapid eye movement sleep (NREM); electrographic features linked to seizures and cognitive symptoms, respectively. The neuronal mechanisms that support the competitive shared thalamocortical circuitry between pathological epileptiform spikes and physiological sleep spindles are not well-understood. In this study we introduce a computational thalamocortical model for the sleep-activated epileptiform spikes observed in RE. The cellular and neuronal circuits of this model incorporate recent experimental observations in RE, and replicate the electrophysiological features of RE. Using this model, we demonstrate that: (1) epileptiform spikes can be triggered and promoted by either a reduced NMDA current or h-type current; and (2) changes in inhibitory transmission in the thalamic reticular nucleus mediates an antagonistic dynamic between epileptiform spikes and spindles. This work provides the first computational model that both recapitulates electrophysiological features and provides a mechanistic explanation for the thalamocortical switch between the pathological and physiological electrophysiological rhythms observed during NREM sleep in this common epileptic encephalopathy.
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Affiliation(s)
- Qiang Li
- Medical Big Data Research Center, Northwest University, Xi'an, China
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - M. Brandon Westover
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Rui Zhang
- Medical Big Data Research Center, Northwest University, Xi'an, China
| | - Catherine J. Chu
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
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11
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Schöbi D, Homberg F, Frässle S, Endepols H, Moran RJ, Friston KJ, Tittgemeyer M, Heinzle J, Stephan KE. Model-based prediction of muscarinic receptor function from auditory mismatch negativity responses. Neuroimage 2021; 237:118096. [PMID: 33940149 DOI: 10.1016/j.neuroimage.2021.118096] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 04/12/2021] [Accepted: 04/19/2021] [Indexed: 01/09/2023] Open
Abstract
Drugs affecting neuromodulation, for example by dopamine or acetylcholine, take centre stage among therapeutic strategies in psychiatry. These neuromodulators can change both neuronal gain and synaptic plasticity and therefore affect electrophysiological measures. An important goal for clinical diagnostics is to exploit this effect in the reverse direction, i.e., to infer the status of specific neuromodulatory systems from electrophysiological measures. In this study, we provide proof-of-concept that the functional status of cholinergic (specifically muscarinic) receptors can be inferred from electrophysiological data using generative (dynamic causal) models. To this end, we used epidural EEG recordings over two auditory cortical regions during a mismatch negativity (MMN) paradigm in rats. All animals were treated, across sessions, with muscarinic receptor agonists and antagonists at different doses. Together with a placebo condition, this resulted in five levels of muscarinic receptor status. Using a dynamic causal model - embodying a small network of coupled cortical microcircuits - we estimated synaptic parameters and their change across pharmacological conditions. The ensuing parameter estimates associated with (the neuromodulation of) synaptic efficacy showed both graded muscarinic effects and predictive validity between agonistic and antagonistic pharmacological conditions. This finding illustrates the potential utility of generative models of electrophysiological data as computational assays of muscarinic function. In application to EEG data of patients from heterogeneous spectrum diseases, e.g. schizophrenia, such models might help identify subgroups of patients that respond differentially to cholinergic treatments. SIGNIFICANCE STATEMENT: In psychiatry, the vast majority of pharmacological treatments affect actions of neuromodulatory transmitters, e.g. dopamine or acetylcholine. As treatment is largely trial-and-error based, one of the goals for computational psychiatry is to construct mathematical models that can serve as "computational assays" and infer the status of specific neuromodulatory systems in individual patients. This translational neuromodeling strategy has great promise for electrophysiological data in particular but requires careful validation. The present study demonstrates that the functional status of cholinergic (muscarinic) receptors can be inferred from electrophysiological data using dynamic causal models of neural circuits. While accuracy needs to be enhanced and our results must be replicated in larger samples, our current results provide proof-of-concept for computational assays of muscarinic function using EEG.
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Affiliation(s)
- Dario Schöbi
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & Swiss Institute of Technology (ETH Zurich), Wilfriedstrasse 6, 8032, Zurich, Switzerland
| | - Fabienne Homberg
- Boston Scientific Medizintechnik GmbH, Daniel-Goldbach-Strasse 17-27, 40880 Ratingen, Germany
| | - Stefan Frässle
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & Swiss Institute of Technology (ETH Zurich), Wilfriedstrasse 6, 8032, Zurich, Switzerland
| | - Heike Endepols
- Preclinical Imaging Group, Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50923 Cologne, Germany
| | - Rosalyn J Moran
- Department of Neuroimaging, Institute for Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London Se5 8AF, UK
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, WC1N, 3AR, UK
| | - Marc Tittgemeyer
- Max Planck Institute for Metabolism Research, Gleueler Strasse 50, 50931 Cologne, Germany; Cluster of Excellence in Cellular Stress and Aging associated Disease (CECAD), 50931 Cologne, Germany
| | - Jakob Heinzle
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & Swiss Institute of Technology (ETH Zurich), Wilfriedstrasse 6, 8032, Zurich, Switzerland.
| | - Klaas Enno Stephan
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & Swiss Institute of Technology (ETH Zurich), Wilfriedstrasse 6, 8032, Zurich, Switzerland; Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, WC1N, 3AR, UK; Max Planck Institute for Metabolism Research, Gleueler Strasse 50, 50931 Cologne, Germany
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12
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Li Q, Song JL, Li SH, Westover MB, Zhang R. Effects of Cholinergic Neuromodulation on Thalamocortical Rhythms During NREM Sleep: A Model Study. Front Comput Neurosci 2020; 13:100. [PMID: 32038215 PMCID: PMC6990259 DOI: 10.3389/fncom.2019.00100] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 12/30/2019] [Indexed: 11/13/2022] Open
Abstract
It has been suggested that cholinergic neurons shape the oscillatory activity of the thalamocortical (TC) network in behavioral and electrophysiological experiments. However, theoretical modeling demonstrating how cholinergic neuromodulation of thalamocortical rhythms during non-rapid eye movement (NREM) sleep might occur has been lacking. In this paper, we first develop a novel computational model (TC-ACH) by incorporating a cholinergic neuron population (CH) into the classical thalamo-cortical circuitry, where connections between populations are modeled in accordance with existing knowledge. The neurotransmitter acetylcholine (ACH) released by neurons in CH, which is able to change the discharge activity of thalamocortical neurons, is the primary focus of our work. Simulation results with our TC-ACH model reveal that the cholinergic projection activity is a key factor in modulating oscillation patterns in three ways: (1) transitions between different patterns of thalamocortical oscillations are dramatically modulated through diverse projection pathways; (2) the model expresses a stable spindle oscillation state with certain parameter settings for the cholinergic projection from CH to thalamus, and more spindles appear when the strength of cholinergic input from CH to thalamocortical neurons increases; (3) the duration of oscillation patterns during NREM sleep including K-complexes, spindles, and slow oscillations is longer when cholinergic input from CH to thalamocortical neurons becomes stronger. Our modeling results provide insights into the mechanisms by which the sleep state is controlled, and provide a theoretical basis for future experimental and clinical studies.
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Affiliation(s)
- Qiang Li
- Medical Big Data Research Center, Northwest University, Xi'an, China
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Jiang-Ling Song
- Medical Big Data Research Center, Northwest University, Xi'an, China
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Si-Hui Li
- Medical Big Data Research Center, Northwest University, Xi'an, China
| | - M. Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Rui Zhang
- Medical Big Data Research Center, Northwest University, Xi'an, China
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13
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Rosen BQ, Krishnan GP, Sanda P, Komarov M, Sejnowski T, Rulkov N, Ulbert I, Eross L, Madsen J, Devinsky O, Doyle W, Fabo D, Cash S, Bazhenov M, Halgren E. Simulating human sleep spindle MEG and EEG from ion channel and circuit level dynamics. J Neurosci Methods 2019; 316:46-57. [PMID: 30300700 PMCID: PMC6380919 DOI: 10.1016/j.jneumeth.2018.10.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Revised: 10/03/2018] [Accepted: 10/04/2018] [Indexed: 11/16/2022]
Abstract
BACKGROUND Although they form a unitary phenomenon, the relationship between extracranial M/EEG and transmembrane ion flows is understood only as a general principle rather than as a well-articulated and quantified causal chain. METHOD We present an integrated multiscale model, consisting of a neural simulation of thalamus and cortex during stage N2 sleep and a biophysical model projecting cortical current densities to M/EEG fields. Sleep spindles were generated through the interactions of local and distant network connections and intrinsic currents within thalamocortical circuits. 32,652 cortical neurons were mapped onto the cortical surface reconstructed from subjects' MRI, interconnected based on geodesic distances, and scaled-up to current dipole densities based on laminar recordings in humans. MRIs were used to generate a quasi-static electromagnetic model enabling simulated cortical activity to be projected to the M/EEG sensors. RESULTS The simulated M/EEG spindles were similar in amplitude and topography to empirical examples in the same subjects. Simulated spindles with more core-dominant activity were more MEG weighted. COMPARISON WITH EXISTING METHODS Previous models lacked either spindle-generating thalamic neural dynamics or whole head biophysical modeling; the framework presented here is the first to simultaneously capture these disparate scales. CONCLUSIONS This multiscale model provides a platform for the principled quantitative integration of existing information relevant to the generation of sleep spindles, and allows the implications of future findings to be explored. It provides a proof of principle for a methodological framework allowing large-scale integrative brain oscillations to be understood in terms of their underlying channels and synapses.
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Affiliation(s)
- B Q Rosen
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, United States.
| | - G P Krishnan
- Department of Medicine, University of California, San Diego, La Jolla, CA, United States.
| | - P Sanda
- Department of Medicine, University of California, San Diego, La Jolla, CA, United States; Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic.
| | - M Komarov
- Department of Medicine, University of California, San Diego, La Jolla, CA, United States.
| | - T Sejnowski
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, United States; The Salk Institute, La Jolla, CA, United States.
| | - N Rulkov
- BioCiruits Institute, University of California, San Diego, La Jolla, CA, United States.
| | - I Ulbert
- Institute of Cognitive Neuroscience and Psychology, Hungarian Academy of Science, Budapest, Hungary; Faculty of Information Technology and Bionics, Peter Pazmany Catholic University, Budapest, Hungary.
| | - L Eross
- Faculty of Information Technology and Bionics, Peter Pazmany Catholic University, Budapest, Hungary; Department of Functional Neurosurgery, National Institute of Clinical Neurosciences, Budapest, Hungary.
| | - J Madsen
- Departments of Neurosurgery, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States.
| | - O Devinsky
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY, United States.
| | - W Doyle
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY, United States.
| | - D Fabo
- Epilepsy Centrum, National Institute of Clinical Neurosciences, Budapest, Hungary.
| | - S Cash
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, United States; Department of Medicine, University of California, San Diego, La Jolla, CA, United States; Departments of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
| | - M Bazhenov
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, United States; Department of Medicine, University of California, San Diego, La Jolla, CA, United States.
| | - E Halgren
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, United States; Department of Radiology, University of California, San Diego, La Jolla, CA, United States; Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States.
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14
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Abstract
Sleep and circadian rhythms are regulated across multiple functional, spatial and temporal levels: from genes to networks of coupled neurons and glial cells, to large scale brain dynamics and behaviour. The dynamics at each of these levels are complex and the interaction between the levels is even more so, so research have mostly focused on interactions within the levels to understand the underlying mechanisms—the so-called reductionist approach. Mathematical models were developed to test theories of sleep regulation and guide new experiments at each of these levels and have become an integral part of the field. The advantage of modelling, however, is that it allows us to simulate and test the dynamics of complex biological systems and thus provides a tool to investigate the connections between the different levels and study the system as a whole. In this paper I review key models of sleep developed at different physiological levels and discuss the potential for an integrated systems biology approach for sleep regulation across these levels. I also highlight the necessity of building mechanistic connections between models of sleep and circadian rhythms across these levels.
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Affiliation(s)
- Svetlana Postnova
- School of Physics, University of Sydney, Sydney 2006, NSW, Australia;
- Center of Excellence for Integrative Brain Function, University of Sydney, Sydney 2006, NSW, Australia
- Charles Perkins Center, University of Sydney, Sydney 2006, NSW, Australia
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15
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Antony JW, Piloto L, Wang M, Pacheco P, Norman KA, Paller KA. Sleep Spindle Refractoriness Segregates Periods of Memory Reactivation. Curr Biol 2018; 28:1736-1743.e4. [PMID: 29804809 PMCID: PMC5992601 DOI: 10.1016/j.cub.2018.04.020] [Citation(s) in RCA: 114] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Revised: 03/14/2018] [Accepted: 04/05/2018] [Indexed: 11/18/2022]
Abstract
The stability of long-term memories is enhanced by reactivation during sleep. Correlative evidence has linked memory reactivation with thalamocortical sleep spindles, although their functional role is not fully understood. Our initial study replicated this correlation and also demonstrated a novel rhythmicity to spindles, such that a spindle is more likely to occur approximately 3-6 s following a prior spindle. We leveraged this rhythmicity to test the role of spindles in memory by using real-time spindle tracking to present cues within versus just after the presumptive refractory period; as predicted, cues presented just after the refractory period led to better memory. Our findings demonstrate a precise temporal link between sleep spindles and memory reactivation. Moreover, they reveal a previously undescribed neural mechanism whereby spindles may segment sleep into two distinct substates: prime opportunities for reactivation and gaps that segregate reactivation events.
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Affiliation(s)
- James W Antony
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA.
| | - Luis Piloto
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Margaret Wang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Paula Pacheco
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Kenneth A Norman
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Ken A Paller
- Department of Psychology, Northwestern University, Evanston, IL 60208, USA
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16
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Ferrat LA, Goodfellow M, Terry JR. Classifying dynamic transitions in high dimensional neural mass models: A random forest approach. PLoS Comput Biol 2018; 14:e1006009. [PMID: 29499044 PMCID: PMC5851637 DOI: 10.1371/journal.pcbi.1006009] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 03/14/2018] [Accepted: 01/30/2018] [Indexed: 02/04/2023] Open
Abstract
Neural mass models (NMMs) are increasingly used to uncover the large-scale mechanisms of brain rhythms in health and disease. The dynamics of these models is dependent upon the choice of parameters, and therefore it is crucial to be able to understand how dynamics change when parameters are varied. Despite being considered low dimensional in comparison to micro-scale, neuronal network models, with regards to understanding the relationship between parameters and dynamics, NMMs are still prohibitively high dimensional for classical approaches such as numerical continuation. Therefore, we need alternative methods to characterise dynamics of NMMs in high dimensional parameter spaces. Here, we introduce a statistical framework that enables the efficient exploration of the relationship between model parameters and selected features of the simulated, emergent model dynamics of NMMs. We combine the classical machine learning approaches of trees and random forests to enable studying the effect that varying multiple parameters has on the dynamics of a model. The method proceeds by using simulations to transform the mathematical model into a database. This database is then used to partition parameter space with respect to dynamic features of interest, using random forests. This allows us to rapidly explore dynamics in high dimensional parameter space, capture the approximate location of qualitative transitions in dynamics and assess the relative importance of all parameters in the model in all dimensions simultaneously. We apply this method to a commonly used NMM in the context of transitions to seizure dynamics. We find that the inhibitory sub-system is most crucial for the generation of seizure dynamics, confirm and expand previous findings regarding the ratio of excitation and inhibition, and demonstrate that previously overlooked parameters can have a significant impact on model dynamics. We advocate the use of this method in future to constrain high dimensional parameter spaces enabling more efficient, person-specific, model calibration.
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Affiliation(s)
- Lauric A. Ferrat
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
- Living Systems Institute, University of Exeter, Exeter, UK
- Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, UK
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK
| | - Marc Goodfellow
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
- Living Systems Institute, University of Exeter, Exeter, UK
- Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, UK
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK
| | - John R. Terry
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
- Living Systems Institute, University of Exeter, Exeter, UK
- Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, UK
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK
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17
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Li G, Henriquez CS, Fröhlich F. Unified thalamic model generates multiple distinct oscillations with state-dependent entrainment by stimulation. PLoS Comput Biol 2017; 13:e1005797. [PMID: 29073146 PMCID: PMC5675460 DOI: 10.1371/journal.pcbi.1005797] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2017] [Revised: 11/07/2017] [Accepted: 09/26/2017] [Indexed: 11/21/2022] Open
Abstract
The thalamus plays a critical role in the genesis of thalamocortical oscillations, yet the underlying mechanisms remain elusive. To understand whether the isolated thalamus can generate multiple distinct oscillations, we developed a biophysical thalamic model to test the hypothesis that generation of and transition between distinct thalamic oscillations can be explained as a function of neuromodulation by acetylcholine (ACh) and norepinephrine (NE) and afferent synaptic excitation. Indeed, the model exhibited four distinct thalamic rhythms (delta, sleep spindle, alpha and gamma oscillations) that span the physiological states corresponding to different arousal levels from deep sleep to focused attention. Our simulation results indicate that generation of these distinct thalamic oscillations is a result of both intrinsic oscillatory cellular properties and specific network connectivity patterns. We then systematically varied the ACh/NE and input levels to generate a complete map of the different oscillatory states and their transitions. Lastly, we applied periodic stimulation to the thalamic network and found that entrainment of thalamic oscillations is highly state-dependent. Our results support the hypothesis that ACh/NE modulation and afferent excitation define thalamic oscillatory states and their response to brain stimulation. Our model proposes a broader and more central role of the thalamus in the genesis of multiple distinct thalamo-cortical rhythms than previously assumed. Computational modeling has served as an important tool to understand the cellular and circuit mechanisms of thalamocortical oscillations. However, most of the existing thalamic models focus on only one particular oscillatory pattern such as alpha or spindle oscillations. Thus, it remains unclear whether the same thalamic circuitry on its own could generate all major oscillatory patterns and if so what mechanisms underlie the transition among these distinct states. Here we present a unified model of the thalamus that is capable of independently generating multiple distinct oscillations corresponding to different physiological conditions. We then mapped out the different thalamic oscillations by varying the ACh/NE modulatory level and input level systematically. Our simulation results offer a mechanistic understanding of thalamic oscillations and support the long standing notion of a thalamic “pacemaker”. It also suggests that pathological oscillations associated with neurological and psychiatric disorders may stem from malfunction of the thalamic circuitry.
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Affiliation(s)
- Guoshi Li
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Craig S. Henriquez
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
| | - Flavio Fröhlich
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
- Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
- Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
- * E-mail:
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18
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Sohanian Haghighi H, Markazi AHD. A new description of epileptic seizures based on dynamic analysis of a thalamocortical model. Sci Rep 2017; 7:13615. [PMID: 29051507 PMCID: PMC5648785 DOI: 10.1038/s41598-017-13126-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Accepted: 09/13/2017] [Indexed: 12/11/2022] Open
Abstract
Increasing evidence suggests that the brain dynamics can be interpreted from the viewpoint of nonlinear dynamical systems. The aim of this paper is to investigate the behavior of a thalamocortical model from this perspective. The model includes both cortical and sensory inputs that can affect the dynamic nature of the model. Driving response of the model subjected to various harmonic stimulations is considered to identify the effects of stimulus parameters on the cortical output. Detailed numerical studies including phase portraits, Poincare maps and bifurcation diagrams reveal a wide range of complex dynamics including period doubling and chaos in the output. Transition between different states can occur as the stimulation parameters are changed. In addition, the amplitude jump phenomena and hysteresis are shown to be possible as a result of the bending in the frequency response curve. These results suggest that the jump phenomenon due to the brain nonlinear resonance can be responsible for the transitions between ictal and interictal states.
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Affiliation(s)
- H Sohanian Haghighi
- School of Mechanical Engineering, Iran University of Science and Technology, Tehran, 16844, Iran.
| | - A H D Markazi
- School of Mechanical Engineering, Iran University of Science and Technology, Tehran, 16844, Iran
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19
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Krishnan GP, Chauvette S, Shamie I, Soltani S, Timofeev I, Cash SS, Halgren E, Bazhenov M. Cellular and neurochemical basis of sleep stages in the thalamocortical network. eLife 2016; 5:e18607. [PMID: 27849520 PMCID: PMC5111887 DOI: 10.7554/elife.18607] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2016] [Accepted: 10/11/2016] [Indexed: 01/05/2023] Open
Abstract
The link between the combined action of neuromodulators in the brain and global brain states remains a mystery. In this study, using biophysically realistic models of the thalamocortical network, we identified the critical intrinsic and synaptic mechanisms, associated with the putative action of acetylcholine (ACh), GABA and monoamines, which lead to transitions between primary brain vigilance states (waking, non-rapid eye movement sleep [NREM] and REM sleep) within an ultradian cycle. Using ECoG recordings from humans and LFP recordings from cats and mice, we found that during NREM sleep the power of spindle and delta oscillations is negatively correlated in humans and positively correlated in animal recordings. We explained this discrepancy by the differences in the relative level of ACh. Overall, our study revealed the critical intrinsic and synaptic mechanisms through which different neuromodulators acting in combination result in characteristic brain EEG rhythms and transitions between sleep stages.
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Affiliation(s)
- Giri P Krishnan
- Department of Medicine, University of California, San Diego, La Jolla, CA, United States
| | - Sylvain Chauvette
- Department of Psychiatry and Neuroscience, Université Laval, Québec, Canada
- Centre de Recherche de l’Institut Universitaire en Santé Mentale de Québec, Université Laval, Québec, Canada
| | - Isaac Shamie
- Departments of Radiology and Neurosciences, University of California, San Diego, La Jolla, CA, United States
| | - Sara Soltani
- Department of Psychiatry and Neuroscience, Université Laval, Québec, Canada
- Centre de Recherche de l’Institut Universitaire en Santé Mentale de Québec, Université Laval, Québec, Canada
| | - Igor Timofeev
- Department of Psychiatry and Neuroscience, Université Laval, Québec, Canada
- Centre de Recherche de l’Institut Universitaire en Santé Mentale de Québec, Université Laval, Québec, Canada
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, United States
| | - Eric Halgren
- Departments of Radiology and Neurosciences, University of California, San Diego, La Jolla, CA, United States
| | - Maxim Bazhenov
- Department of Medicine, University of California, San Diego, La Jolla, CA, United States
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