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Fateh AA, Smahi A, Hassan M, Mo T, Hu Z, Mohammed AAQ, Hu Y, Massé CC, Chen L, Chen Y, Liao J, Zeng H. From brain connectivity to cognitive function: Dissecting the salience network in pediatric BECTS-ESES. Prog Neuropsychopharmacol Biol Psychiatry 2024; 135:111110. [PMID: 39069247 DOI: 10.1016/j.pnpbp.2024.111110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 07/19/2024] [Accepted: 07/24/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND Benign childhood epilepsy with centrotemporal spikes (BECTS), a common pediatric epilepsy, may lead to cognitive decline when compounded by Electrical Status Epilepticus during Sleep (ESES). Emerging evidence suggests that disruptions in the Salience Network (SN) contribute significantly to the cognitive deficits observed in BECTS-ESES. Our study rigorously investigates the dynamic functional connectivity (dFC) within the SN and its correlation with cognitive impairments in BECTS-ESES, employing advanced neuroimaging and neuropsychological assessments. METHODS In this research, 45 patients diagnosed with BECTS-ESES and 55 age-matched healthy controls (HCs) participated. We utilized resting-state functional magnetic resonance imaging (fMRI) and Independent Component Analysis (ICA) to identify three fundamental SN nodes: the right Anterior Insula (rAI), left Anterior Insula (lAI), and the Anterior Cingulate Cortex (ACC). A two-sample t-test facilitated the comparison of dFC between these pivotal regions and other brain areas. RESULTS Significantly, the BECTS-ESES group demonstrated increased dFC, particularly between the ACC and the right Middle Occipital Gyrus, and from the rAI to the right Superior Parietal Gyrus and Cerebellum, and from the lAI to the left Postcentral Gyrus. Such dFC augmentations provide neural insights potentially explaining the neuropsychological deficits in BECTS-ESES children. Employing comprehensive neuropsychological evaluations, we mapped these dFC disruptions to specific cognitive impairments encompassing memory, executive functioning, language, and attention. Through multiple regression analysis and path analysis, a preliminary but compelling association was discovered linking dFC disturbances directly to cognitive impairments. These findings underscore the critical role of SN disruptions in BECTS-ESES cognitive dysfunctions. LIMITATION Our cross-sectional design and analytic methods preclude definitive mediation models and causal inferences, leaving the precise nature of dFC's mediating role and its direct impact by BECTS-ESES partially unresolved. Future longitudinal and confirmatory studies are needed to comprehensively delineate these associations. CONCLUSION Our study heralds dFC within the SN as a vital biomarker for cognitive impairment in pediatric epilepsy, advocating for targeted cognitive-specific interventions in managing BECTS-ESES. The preliminary nature of our findings invites further studies to substantiate these associations, offering profound implications for the prognosis and therapeutic strategies in BECTS-ESES, thereby underlining the importance of this research in the field of pediatric neurology and epilepsy management.
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Affiliation(s)
- Ahmed Ameen Fateh
- Department of Radiology, Shenzhen Children's Hospital, Shenzhen 518038, China
| | - Abla Smahi
- Shenzhen Graduate School, Peking University, Shenzhen, China
| | - Muhammad Hassan
- Department of Radiology, Shenzhen Children's Hospital, Shenzhen 518038, China
| | - Tong Mo
- Department of Radiology, Shenzhen Children's Hospital, Shenzhen 518038, China
| | - Zhanqi Hu
- Department of Neurology, Shenzhen Children's Hospital, Shenzhen 518038, China
| | - Adam A Q Mohammed
- School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
| | - Yan Hu
- Department of Neurology, Shenzhen Children's Hospital, Shenzhen 518038, China
| | - Cristina Cañete Massé
- Psychology, Sciences of Education and Sport, Blanquerna, Ramon Llull University, Barcelona, Spain; Department of Social Psychology and Quantitative Psychology, Faculty of Psychology, Universitat de Barcelona, Barcelona, Spain
| | - Li Chen
- Department of Neurology, Shenzhen Children's Hospital, Shenzhen 518038, China
| | - Yan Chen
- Department of Neurology, Shenzhen Children's Hospital, Shenzhen 518038, China
| | - Jianxiang Liao
- Department of Neurology, Shenzhen Children's Hospital, Shenzhen 518038, China
| | - Hongwu Zeng
- Department of Radiology, Shenzhen Children's Hospital, Shenzhen 518038, China.
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Hong R, Zheng T, Marra V, Yang D, Liu JK. Multi-scale modelling of the epileptic brain: advantages of computational therapy exploration. J Neural Eng 2024; 21:021002. [PMID: 38621378 DOI: 10.1088/1741-2552/ad3eb4] [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: 08/29/2023] [Accepted: 04/15/2024] [Indexed: 04/17/2024]
Abstract
Objective: Epilepsy is a complex disease spanning across multiple scales, from ion channels in neurons to neuronal circuits across the entire brain. Over the past decades, computational models have been used to describe the pathophysiological activity of the epileptic brain from different aspects. Traditionally, each computational model can aid in optimizing therapeutic interventions, therefore, providing a particular view to design strategies for treating epilepsy. As a result, most studies are concerned with generating specific models of the epileptic brain that can help us understand the certain machinery of the pathological state. Those specific models vary in complexity and biological accuracy, with system-level models often lacking biological details.Approach: Here, we review various types of computational model of epilepsy and discuss their potential for different therapeutic approaches and scenarios, including drug discovery, surgical strategies, brain stimulation, and seizure prediction. We propose that we need to consider an integrated approach with a unified modelling framework across multiple scales to understand the epileptic brain. Our proposal is based on the recent increase in computational power, which has opened up the possibility of unifying those specific epileptic models into simulations with an unprecedented level of detail.Main results: A multi-scale epilepsy model can bridge the gap between biologically detailed models, used to address molecular and cellular questions, and brain-wide models based on abstract models which can account for complex neurological and behavioural observations.Significance: With these efforts, we move toward the next generation of epileptic brain models capable of connecting cellular features, such as ion channel properties, with standard clinical measures such as seizure severity.
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Affiliation(s)
- Rongqi Hong
- School of Computer Science, Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
| | - Tingting Zheng
- School of Computer Science, Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
| | | | - Dongping Yang
- Research Centre for Frontier Fundamental Studies, Zhejiang Lab, Hangzhou, People's Republic of China
| | - Jian K Liu
- School of Computer Science, Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
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3
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Polyakov D, Robinson PA, Muller EJ, Shriki O. Recruiting neural field theory for data augmentation in a motor imagery brain-computer interface. Front Robot AI 2024; 11:1362735. [PMID: 38694882 PMCID: PMC11061403 DOI: 10.3389/frobt.2024.1362735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 03/20/2024] [Indexed: 05/04/2024] Open
Abstract
We introduce a novel approach to training data augmentation in brain-computer interfaces (BCIs) using neural field theory (NFT) applied to EEG data from motor imagery tasks. BCIs often suffer from limited accuracy due to a limited amount of training data. To address this, we leveraged a corticothalamic NFT model to generate artificial EEG time series as supplemental training data. We employed the BCI competition IV '2a' dataset to evaluate this augmentation technique. For each individual, we fitted the model to common spatial patterns of each motor imagery class, jittered the fitted parameters, and generated time series for data augmentation. Our method led to significant accuracy improvements of over 2% in classifying the "total power" feature, but not in the case of the "Higuchi fractal dimension" feature. This suggests that the fit NFT model may more favorably represent one feature than the other. These findings pave the way for further exploration of NFT-based data augmentation, highlighting the benefits of biophysically accurate artificial data.
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Affiliation(s)
- Daniel Polyakov
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Be’er Sheva, Israel
- Agricultural, Biological, Cognitive Robotics Initiative, Ben-Gurion University of the Negev, Be’er Sheva, Israel
| | | | - Eli J. Muller
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
| | - Oren Shriki
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Be’er Sheva, Israel
- Agricultural, Biological, Cognitive Robotics Initiative, Ben-Gurion University of the Negev, Be’er Sheva, Israel
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4
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Fan D, Qi L, Hou S, Wang Q, Baier G. The seizure classification of focal epilepsy based on the network motif analysis. Brain Res Bull 2024; 207:110879. [PMID: 38237873 DOI: 10.1016/j.brainresbull.2024.110879] [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: 08/23/2023] [Revised: 12/10/2023] [Accepted: 01/13/2024] [Indexed: 01/23/2024]
Abstract
Due to the complexity of focal epilepsy and its risk for transiting to the generalized epilepsy, the development of reliable classification methods to accurately predict and classify focal and generalized seizures is critical for the clinical management of patients with epilepsy. In order to holistically understand the seizure propagation behavior of focal epilepsy, we propose a three-node motif reduced network by respectively simplifying the focal region, surrounding healthy region and their critical regions as the single node. Because three-node motif can richly characterize information evolutions, the motif analysis method could comprehensively investigate the seizure behavior of focal epilepsy. Firstly, we define a new seizure propagation marker value to capture the seizure onsets and intensity. Based on the three-node motif analysis, it is shown that the focal seizure and spreading can be categorized as inhibitory seizure, focal seizure, focal-critical seizure and generalized seizures, respectively. The four types of seizures correspond to specific modal types respectively, reflecting the strong correlation between seizure behavior and information flow evolution. In addition, it is found that the intensity difference of outflow and inflow information from the critical node (connection heterogeneity) and the excitability of the critical node significantly affected the distribution and transition of the four seizure types. In particular, the method of local linear stability analysis also verifies the effectiveness of four types of seizures classification. In sum, this paper computationally confirms the complex dynamic behavior of focal seizures, and the study of criticality is helpful to propose novel seizure control strategies.
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Affiliation(s)
- Denggui Fan
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China
| | - Lixue Qi
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China
| | - Songan Hou
- Department of Dynamics and Control, Beihang University, Beijing 100191, China
| | - Qingyun Wang
- Department of Dynamics and Control, Beihang University, Beijing 100191, China.
| | - Gerold Baier
- Cell and Developmental Biology, University College London, London WC1E 6BT, United Kingdom
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5
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Babaie-Janvier T, Gabay NC, McInnes A, Robinson PA. Neural field theory of adaptive effects on auditory evoked responses and mismatch negativity in multifrequency stimulus sequences. Front Hum Neurosci 2024; 17:1282924. [PMID: 38234595 PMCID: PMC10791997 DOI: 10.3389/fnhum.2023.1282924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 10/27/2023] [Indexed: 01/19/2024] Open
Abstract
Physiologically based neural field theory (NFT) of the corticothalamic system, including adaptation, is used to calculate the responses evoked by trains of auditory stimuli that differ in frequency. In oddball paradigms, fully distinguishable frequencies lead to different standard (common stimulus) and deviant (rare stimulus) responses; the signal obtained by subtracting the standard response from the deviant is termed the mismatch negativity (MMN). In this analysis, deviant responses are found to correspond to unadapted cortex, whereas the part of auditory cortex that processes the standard stimuli adapts over several stimulus presentations until the final standard response form is achieved. No higher-order memory processes are invoked. In multifrequency experiments, the deviant response approaches the standard one as the deviant frequency approaches that of the standard and analytic criteria for this effect to be obtained. It is shown that these criteria can also be used to understand adaptation in random tone sequences. A method of probing MMNs and adaptation in random tone sequences is suggested to makes more use of such data.
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Affiliation(s)
- Tahereh Babaie-Janvier
- School of Physics, The University of Sydney, Sydney, NSW, Australia
- Center of Excellence for Integrative Brain Function, The University of Sydney, Sydney, NSW, Australia
| | - Natasha C. Gabay
- School of Physics, The University of Sydney, Sydney, NSW, Australia
- Center of Excellence for Integrative Brain Function, The University of Sydney, Sydney, NSW, Australia
| | | | - Peter A. Robinson
- School of Physics, The University of Sydney, Sydney, NSW, Australia
- Center of Excellence for Integrative Brain Function, The University of Sydney, Sydney, NSW, Australia
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Tan X, Zhu R, Xie Y, Chai Y. Suppression of absence seizures by using different stimulations in a reduced corticothalamic-basal ganglion-pedunculopontine nucleus model. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:20468-20485. [PMID: 38124561 DOI: 10.3934/mbe.2023905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Coupled neural network models are playing an increasingly important part in the modulation of absence seizures today. However, it is currently unclear how basal ganglia, corticothalamic network and pedunculopontine nucleus can coordinate with each other to develop a whole coupling circuit, theoretically. In addition, it is still difficult to select effective parameters of electrical stimulation on the regulation of absence seizures in clinical trials. Therefore, to develop a coupled model and reduce computation cost, a new model constructed by a simplified basal ganglion, two corticothalamic circuits and a pedunculopontine nucleus was proposed. Further, to seek better inhibition therapy, three electrical stimulations, high frequency stimulation (HFS), 1:0 coordinate reset stimulation (CRS) and 3:2 CRS, were applied to the thalamic reticular nucleus (RE) in the first corticothalamic circuit in the coupled model. The simulation results revealed that increasing the frequency and pulse width of an electrical stimulation within a certain range can also suppress seizures. Under the same parameters of electrical stimulation, the inhibitory effect of HFS on seizures was better than that of 1:0 CRS and 3:2 CRS. The research established a reduced corticothalamic-basal ganglion-pedunculopontine nucleus model, which lays a theoretical foundation for future optimal parameters selection of electrical stimulation. We hope that the findings will provide new insights into the role of theoretical models in absence seizures.
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Affiliation(s)
- Xiaolong Tan
- School of Mathematics and Physics, Shanghai University of Electric Power, Shanghai 201306, China
| | - Rui Zhu
- School of Mathematics and Physics, Shanghai University of Electric Power, Shanghai 201306, China
| | - Yan Xie
- School of Mathematics and Physics, Shanghai University of Electric Power, Shanghai 201306, China
| | - Yuan Chai
- School of Mathematics and Physics, Shanghai University of Electric Power, Shanghai 201306, China
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Scarpetta S, Morrisi N, Mutti C, Azzi N, Trippi I, Ciliento R, Apicella I, Messuti G, Angiolelli M, Lombardi F, Parrino L, Vaudano AE. Criticality of neuronal avalanches in human sleep and their relationship with sleep macro- and micro-architecture. iScience 2023; 26:107840. [PMID: 37766992 PMCID: PMC10520337 DOI: 10.1016/j.isci.2023.107840] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Revised: 06/30/2023] [Accepted: 09/03/2023] [Indexed: 09/29/2023] Open
Abstract
Sleep plays a key role in preserving brain function, keeping brain networks in a state that ensures optimal computation. Empirical evidence indicates that this state is consistent with criticality, where scale-free neuronal avalanches emerge. However, the connection between sleep architecture and brain tuning to criticality remains poorly understood. Here, we characterize the critical behavior of avalanches and study their relationship with sleep macro- and micro-architectures, in particular, the cyclic alternating pattern (CAP). We show that avalanches exhibit robust scaling behaviors, with exponents obeying scaling relations consistent with the mean-field directed percolation universality class. We demonstrate that avalanche dynamics is modulated by the NREM-REM cycles and that, within NREM sleep, avalanche occurrence correlates with CAP activation phases-indicating a potential link between CAP and brain tuning to criticality. The results open new perspectives on the collective dynamics underlying CAP function, and on the relationship between sleep architecture, avalanches, and self-organization to criticality.
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Affiliation(s)
- Silvia Scarpetta
- Department of Physics, University of Salerno, 84084 Fisciano, Italy
- INFN sez. Napoli Gr. Coll. Salerno, 84084 Fisciano, Italy
| | - Niccolò Morrisi
- Nephrology, Dialysis and Transplant Unit, University Hospital of Modena, 41121 Modena, Italy
| | - Carlotta Mutti
- Sleep Disorders Center, Department of Medicine and Surgery, University of Parma, 43121 Parma, Italy
| | - Nicoletta Azzi
- Sleep Disorders Center, Department of Medicine and Surgery, University of Parma, 43121 Parma, Italy
| | - Irene Trippi
- Sleep Disorders Center, Department of Medicine and Surgery, University of Parma, 43121 Parma, Italy
| | - Rosario Ciliento
- Department of Neurology, University of Wisconsin, Madison, WI 53705, USA
| | - Ilenia Apicella
- INFN sez. Napoli Gr. Coll. Salerno, 84084 Fisciano, Italy
- Department of Physics, University of Naples “Federico II”, 80126 Napoli, Italy
| | - Giovanni Messuti
- Department of Physics, University of Salerno, 84084 Fisciano, Italy
- INFN sez. Napoli Gr. Coll. Salerno, 84084 Fisciano, Italy
| | - Marianna Angiolelli
- Department of Physics, University of Salerno, 84084 Fisciano, Italy
- INFN sez. Napoli Gr. Coll. Salerno, 84084 Fisciano, Italy
- Engineering Department, University Campus Bio-Medico of Rome, 00128 Roma, Italy
| | - Fabrizio Lombardi
- Institute of Science and Technology Austria, Am Campus 1, 3400 Klosterneuburg, Austria
- Department of Biomedical Sciences, University of Padova, Via Ugo Bassi 58B, 35131 Padova, Italy
| | - Liborio Parrino
- Sleep Disorders Center, Department of Medicine and Surgery, University of Parma, 43121 Parma, Italy
| | - Anna Elisabetta Vaudano
- Neurology Unit, Azienda Ospedaliero-Universitaria of Modena, OCB Hospital, 41125 Modena, Italy
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, 41125 Modena, Italy
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8
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Assadzadeh S, Annen J, Sanz L, Barra A, Bonin E, Thibaut A, Boly M, Laureys S, Gosseries O, Robinson PA. Method for quantifying arousal and consciousness in healthy states and severe brain injury via EEG-based measures of corticothalamic physiology. J Neurosci Methods 2023; 398:109958. [PMID: 37661056 DOI: 10.1016/j.jneumeth.2023.109958] [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: 05/24/2023] [Revised: 08/09/2023] [Accepted: 08/27/2023] [Indexed: 09/05/2023]
Abstract
BACKGROUND Characterization of normal arousal states has been achieved by fitting predictions of corticothalamic neural field theory (NFT) to electroencephalographic (EEG) spectra to yield relevant physiological parameters. NEW METHOD A prior fitting method is extended to distinguish conscious and unconscious states in healthy and brain injured subjects by identifying additional parameters and clusters in parameter space. RESULTS Fits of NFT predictions to EEG spectra are used to estimate neurophysiological parameters in healthy and brain injured subjects. Spectra are used from healthy subjects in wake and sleep and from patients with unresponsive wakefulness syndrome, in a minimally conscious state (MCS), and emerged from MCS. Subjects cluster into three groups in parameter space: conscious healthy (wake and REM), sleep, and brain injured. These are distinguished by the difference X-Y between corticocortical (X) and corticothalamic (Y) feedbacks, and by mean neural response rates α and β to incoming spikes. X-Y tracks consciousness in healthy individuals, with smaller values in wake/REM than sleep, but cannot distinguish between brain injuries. Parameters α and β differentiate deep sleep from wake/REM and brain injury. COMPARISON WITH EXISTING METHODS Other methods typically rely on laborious clinical assessment, manual EEG scoring, or evaluation of measures like Φ from integrated information theory, for which no efficient method exists. In contrast, the present method can be automated on a personal computer. CONCLUSION The method provides a means to quantify consciousness and arousal in healthy and brain injured subjects, but does not distinguish subtypes of brain injury.
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Affiliation(s)
- S Assadzadeh
- School of Physics, The University of Sydney, NSW 2006, Australia; Center for Integrative Brain Function, The University of Sydney, NSW 2006, Australia
| | - J Annen
- Coma Science Group, GIGA-Consciousness, University of Liège, Belgium; Centre du Cerveau, University Hospital of Liège, Belgium
| | - L Sanz
- Coma Science Group, GIGA-Consciousness, University of Liège, Belgium; Centre du Cerveau, University Hospital of Liège, Belgium
| | - A Barra
- Coma Science Group, GIGA-Consciousness, University of Liège, Belgium; Centre du Cerveau, University Hospital of Liège, Belgium
| | - E Bonin
- Coma Science Group, GIGA-Consciousness, University of Liège, Belgium; Centre du Cerveau, University Hospital of Liège, Belgium
| | - A Thibaut
- Coma Science Group, GIGA-Consciousness, University of Liège, Belgium; Centre du Cerveau, University Hospital of Liège, Belgium
| | - M Boly
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA; Department of Neurology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - S Laureys
- Coma Science Group, GIGA-Consciousness, University of Liège, Belgium; Centre du Cerveau, University Hospital of Liège, Belgium; Joint International Research Unit on Consciousness, CERVO Brain Research Centre, U Laval, Canada; International Consciousness Science Institute, Hangzhou Normal University, Hangzhou, China
| | - O Gosseries
- Coma Science Group, GIGA-Consciousness, University of Liège, Belgium; Centre du Cerveau, University Hospital of Liège, Belgium
| | - P A Robinson
- School of Physics, The University of Sydney, NSW 2006, Australia; Center for Integrative Brain Function, The University of Sydney, NSW 2006, Australia.
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9
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Müller EJ, Munn BR, Redinbaugh MJ, Lizier J, Breakspear M, Saalmann YB, Shine JM. The non-specific matrix thalamus facilitates the cortical information processing modes relevant for conscious awareness. Cell Rep 2023; 42:112844. [PMID: 37498741 DOI: 10.1016/j.celrep.2023.112844] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 04/25/2023] [Accepted: 07/06/2023] [Indexed: 07/29/2023] Open
Abstract
The neurobiological mechanisms of arousal and anesthesia remain poorly understood. Recent evidence highlights the key role of interactions between the cerebral cortex and the diffusely projecting matrix thalamic nuclei. Here, we interrogate these processes in a whole-brain corticothalamic neural mass model endowed with targeted and diffusely projecting thalamocortical nuclei inferred from empirical data. This model captures key features seen in propofol anesthesia, including diminished network integration, lowered state diversity, impaired susceptibility to perturbation, and decreased corticocortical coherence. Collectively, these signatures reflect a suppression of information transfer across the cerebral cortex. We recover these signatures of conscious arousal by selectively stimulating the matrix thalamus, recapitulating empirical results in macaque, as well as wake-like information processing states that reflect the thalamic modulation of large-scale cortical attractor dynamics. Our results highlight the role of matrix thalamocortical projections in shaping many features of complex cortical dynamics to facilitate the unique communication states supporting conscious awareness.
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Affiliation(s)
- Eli J Müller
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia; Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia; School of Computer Science, The University of Sydney, Sydney, NSW, Australia.
| | - Brandon R Munn
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia; Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia
| | | | - Joseph Lizier
- Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia; School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | | | - Yuri B Saalmann
- Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA; Wisconsin National Primate Research Centre, Madison, WI, USA
| | - James M Shine
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia; Centre for Complex Systems, The University of Sydney, Sydney, NSW, Australia
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10
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Zhao J, Yu Y, Han F, Wang Q. Regulating epileptiform discharges by heterogeneous interneurons in thalamocortical model. CHAOS (WOODBURY, N.Y.) 2023; 33:083128. [PMID: 37561121 DOI: 10.1063/5.0163243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 07/25/2023] [Indexed: 08/11/2023]
Abstract
Inhibitory interneurons in the cortex are abundant and have diverse roles, classified as parvalbumin (PV), somatostatin (SOM), and vasoactive intestinal polypeptide (VIP) according to chemically defined categories. Currently, their involvement with seizures has been partially uncovered in physiological terms. Here, we propose a corticothalamic model containing heterogeneous interneurons to study the effects of various interneurons on absence seizure dynamics by means of optogenetic stimulation. First, the important role of feedforward inhibition caused by SRN→PV→PN projections on seizures is verified. Then, we demonstrate that light activation targeting either PV or SOM INs can control seizures. Finally, with different inhibition contributions from PV INs and SOM INs, the possible disinhibitory effect of blue light acting on VIP INs is mainly discussed. The results suggest that depending on the inhibition degree of both types, the disinhibition brought about by the VIP INs will trigger seizures, will control seizures, and will not work or cause the PNs to tend toward a high saturation state with high excitability. The circuit mechanism and the related bifurcation characteristics in various cases are emphatically revealed. In the model presented, in addition to Hopf and saddle-node bifurcations, the system may also undergo period-doubling and torus bifurcations under stimulus action, with more complex dynamics. Our work may provide a theoretical basis for understanding and further exploring the role of heterogeneous interneurons, in particular, the VIP INs, a novel target, in absence seizures.
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Affiliation(s)
- Jinyi Zhao
- Department of Dynamics and Control, Beihang University, Beijing 100191, China
| | - Ying Yu
- School of Engineering Medicine, Beihang University, Beijing 100191, China
| | - Fang Han
- College of Information Science and Technology, Donghua University, Shanghai 201620, China
| | - Qingyun Wang
- Department of Dynamics and Control, Beihang University, Beijing 100191, China
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11
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Galinsky VL, Frank LR. Neuronal avalanches: Sandpiles of self-organized criticality or critical dynamics of brain waves? FRONTIERS OF PHYSICS 2023; 18:45301. [PMID: 37008280 PMCID: PMC10062440 DOI: 10.1007/s11467-023-1273-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 02/23/2023] [Indexed: 06/19/2023]
Abstract
Analytical expressions for scaling of brain wave spectra derived from the general nonlinear wave Hamiltonian form show excellent agreement with experimental "neuronal avalanche" data. The theory of the weakly evanescent nonlinear brain wave dynamics [Phys. Rev. Research 2, 023061 (2020); J. Cognitive Neurosci. 32, 2178 (2020)] reveals the underlying collective processes hidden behind the phenomenological statistical description of the neuronal avalanches and connects together the whole range of brain activity states, from oscillatory wave-like modes, to neuronal avalanches, to incoherent spiking, showing that the neuronal avalanches are just the manifestation of the different nonlinear side of wave processes abundant in cortical tissue. In a more broad way these results show that a system of wave modes interacting through all possible combinations of the third order nonlinear terms described by a general wave Hamiltonian necessarily produces anharmonic wave modes with temporal and spatial scaling properties that follow scale free power laws. To the best of our knowledge this has never been reported in the physical literature and may be applicable to many physical systems that involve wave processes and not just to neuronal avalanches.
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Affiliation(s)
- Vitaly L. Galinsky
- Center for Scientific Computation in Imaging, University of California at San Diego, La Jolla, CA 92037-0854, USA
| | - Lawrence R. Frank
- Center for Scientific Computation in Imaging, University of California at San Diego, La Jolla, CA 92037-0854, USA
- Center for Functional MRI, University of California at San Diego, La Jolla, CA 92037-0677, USA
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12
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Kloucek MB, Machon T, Kajimura S, Royall CP, Masuda N, Turci F. Biases in inverse Ising estimates of near-critical behavior. Phys Rev E 2023; 108:014109. [PMID: 37583208 DOI: 10.1103/physreve.108.014109] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 04/27/2023] [Indexed: 08/17/2023]
Abstract
Inverse Ising inference allows pairwise interactions of complex binary systems to be reconstructed from empirical correlations. Typical estimators used for this inference, such as pseudo-likelihood maximization (PLM), are biased. Using the Sherrington-Kirkpatrick model as a benchmark, we show that these biases are large in critical regimes close to phase boundaries, and they may alter the qualitative interpretation of the inferred model. In particular, we show that the small-sample bias causes models inferred through PLM to appear closer to criticality than one would expect from the data. Data-driven methods to correct this bias are explored and applied to a functional magnetic resonance imaging data set from neuroscience. Our results indicate that additional care should be taken when attributing criticality to real-world data sets.
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Affiliation(s)
- Maximilian B Kloucek
- School of Physics, HH Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, United Kingdom
- Bristol Centre for Functional Nanomaterials, HH Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, United Kingdom
| | - Thomas Machon
- School of Physics, HH Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, United Kingdom
| | - Shogo Kajimura
- Faculty of Information and Human Sciences, Kyoto Institute of Technology, Kyoto 606-8585, Japan
| | - C Patrick Royall
- Gulliver UMR CNRS 7083, ESPCI Paris, Université PSL, 75005 Paris, France
| | - Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York 14260-2900, USA
- Computational and Data-Enabled Science and Engineering Program, State University of New York at Buffalo, Buffalo, New York 14260-5030, USA
| | - Francesco Turci
- School of Physics, HH Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, United Kingdom
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13
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Tewarie PKB, Tjepkema-Cloostermans MC, Abeysuriya RG, Hofmeijer J, van Putten MJAM. Preservation of thalamocortical circuitry is essential for good recovery after cardiac arrest. PNAS NEXUS 2023; 2:pgad119. [PMID: 37143862 PMCID: PMC10153639 DOI: 10.1093/pnasnexus/pgad119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 03/10/2023] [Accepted: 04/04/2023] [Indexed: 05/06/2023]
Abstract
Continuous electroencephalographam (EEG) monitoring contributes to prediction of neurological outcome in comatose cardiac arrest survivors. While the phenomenology of EEG abnormalities in postanoxic encephalopathy is well known, the pathophysiology, especially the presumed role of selective synaptic failure, is less understood. To further this understanding, we estimate biophysical model parameters from the EEG power spectra from individual patients with a good or poor recovery from a postanoxic encephalopathy. This biophysical model includes intracortical, intrathalamic, and corticothalamic synaptic strengths, as well as synaptic time constants and axonal conduction delays. We used continuous EEG measurements from hundred comatose patients recorded during the first 48 h postcardiac arrest, 50 with a poor neurological outcome [cerebral performance category ( CPC = 5 ) ] and 50 with a good neurological outcome ( CPC = 1 ). We only included patients that developed (dis-)continuous EEG activity within 48 h postcardiac arrest. For patients with a good outcome, we observed an initial relative excitation in the corticothalamic loop and corticothalamic propagation that subsequently evolved towards values observed in healthy controls. For patients with a poor outcome, we observed an initial increase in the cortical excitation-inhibition ratio, increased relative inhibition in the corticothalamic loop, delayed corticothalamic propagation of neuronal activity, and severely prolonged synaptic time constants that did not return to physiological values. We conclude that the abnormal EEG evolution in patients with a poor neurological recovery after cardiac arrest may result from persistent and selective synaptic failure that includes corticothalamic circuitry and also delayed corticothalamic propagation.
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Affiliation(s)
| | - Marleen C Tjepkema-Cloostermans
- Clinical Neurophysiology Group, University of Twente, 7522 NH Enschede, Netherlands
- Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, 7512 KZ Enschede, Netherlands
| | - Romesh G Abeysuriya
- Computational Epidemic Modelling, Burnet Institute, 3004 Melbourne, Australia
| | - Jeannette Hofmeijer
- Clinical Neurophysiology Group, University of Twente, 7522 NH Enschede, Netherlands
- Department of Neurology, Rijnstate Hospital, 6815 AD Arnhem, Netherlands
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14
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Hindriks R, Tewarie PKB. Dissociation between phase and power correlation networks in the human brain is driven by co-occurrent bursts. Commun Biol 2023; 6:286. [PMID: 36934153 PMCID: PMC10024695 DOI: 10.1038/s42003-023-04648-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 03/02/2023] [Indexed: 03/20/2023] Open
Abstract
Well-known haemodynamic resting-state networks are better mirrored in power correlation networks than phase coupling networks in electrophysiological data. However, what do these power correlation networks reflect? We address this long-outstanding question in neuroscience using rigorous mathematical analysis, biophysical simulations with ground truth and application of these mathematical concepts to empirical magnetoencephalography (MEG) data. Our mathematical derivations show that for two non-Gaussian electrophysiological signals, their power correlation depends on their coherence, cokurtosis and conjugate-coherence. Only coherence and cokurtosis contribute to power correlation networks in MEG data, but cokurtosis is less affected by artefactual signal leakage and better mirrors haemodynamic resting-state networks. Simulations and MEG data show that cokurtosis may reflect co-occurrent bursting events. Our findings shed light on the origin of the complementary nature of power correlation networks to phase coupling networks and suggests that the origin of resting-state networks is partly reflected in co-occurent bursts in neuronal activity.
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Affiliation(s)
- Rikkert Hindriks
- Department of Mathematics, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Prejaas K B Tewarie
- Clinical Neurophysiology Group, University of Twente, Enschede, The Netherlands
- Sir Peter Mansfield Imaging Center, School of Physics, University of Nottingham, Nottingham, UK
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15
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Sun Z, Liu Y, Yang X, Xu W. Control of epileptic activities in a cortex network of multiple coupled neural populations under electromagnetic induction. APPLIED MATHEMATICS AND MECHANICS 2023; 44:499-514. [PMID: 36880095 PMCID: PMC9976671 DOI: 10.1007/s10483-023-2969-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 12/30/2022] [Indexed: 06/18/2023]
Abstract
Epilepsy is believed to be associated with the abnormal synchronous neuronal activity in the brain, which results from large groups or circuits of neurons. In this paper, we choose to focus on the temporal lobe epilepsy, and establish a cortex network of multiple coupled neural populations to explore the epileptic activities under electromagnetic induction. We demonstrate that the epileptic activities can be controlled and modulated by electromagnetic induction and coupling among regions. In certain regions, these two types of control are observed to show exactly reverse effects. The results show that the strong electromagnetic induction is conducive to eliminating the epileptic seizures. The coupling among regions has a conduction effect that the previous normal background activity of the region gives way to the epileptic discharge, owing to coupling with spike wave discharge regions. Overall, these results highlight the role of electromagnetic induction and coupling among the regions in controlling and modulating epileptic activities, and might provide novel insights into the treatments of epilepsy.
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Affiliation(s)
- Zhongkui Sun
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an, 710129 China
| | - Yuanyuan Liu
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an, 710129 China
| | - Xiaoli Yang
- School of Mathematics and Statistics, Shaanxi Normal University, Xi’an, 710062 China
| | - Wei Xu
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an, 710129 China
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16
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Xie Y, Zhang H, Pan Y, Chai Y. Combined effect of stimulation and electromagnetic induction on absence seizure inhibition in coupled thalamocortical circuits. Eur J Neurosci 2023; 57:867-879. [PMID: 36696966 DOI: 10.1111/ejn.15923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 01/13/2023] [Indexed: 01/27/2023]
Abstract
Deep brain stimulation (DBS) and electromagnetic induction are new techniques that are increasingly used in modern epilepsy treatments; however, the mechanism of action remains unclear. In this study, we constructed a bidirectional-coupled cortico-thalamic model, based on which we proposed three regulation schemes: isolated regulation of DBS, isolated regulation of electromagnetic induction and combined regulation of the previous two. In particular, we introduced DBS with a lower amplitude and considered the influence of electromagnetic induction caused by the transmembrane current on the membrane potential. The most striking finding of this study is that the three therapeutic schemes could effectively control abnormal discharge, and combined regulation could reduce the occurrence of epileptic seizures more effectively. The present study bridges the gap between the bidirectional coupling model and combined control. In this way, the damage induced by electrical stimulation of the patient's brain tissue could be reduced, and the abnormal physiological discharge pattern of the cerebral cortex was simultaneously regulated by different techniques. This work opens new avenues for improving brain dysfunction in patients with epilepsy, expands ideas for promoting the development of neuroscience and is meaningful for improving the health of modern society and developing the field of science.
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Affiliation(s)
- Yan Xie
- School of Mathematics and Physics, Shanghai University of Electric Power, Shanghai, China
| | - Hudong Zhang
- School of Mathematics and Physics, Shanghai University of Electric Power, Shanghai, China
| | - Yufeng Pan
- School of Mathematics and Physics, Shanghai University of Electric Power, Shanghai, China
| | - Yuan Chai
- School of Mathematics and Physics, Shanghai University of Electric Power, Shanghai, China
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17
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Abstract
Analytical expressions for scaling of brain wave spectra derived from the general non-linear wave Hamiltonian form show excellent agreement with experimental "neuronal avalanche" data. The theory of the weakly evanescent non-linear brain wave dynamics reveals the underlying collective processes hidden behind the phenomenological statistical description of the neuronal avalanches and connects together the whole range of brain activity states, from oscillatory wave-like modes, to neuronal avalanches, to incoherent spiking, showing that the neuronal avalanches are just the manifestation of the different non-linear side of wave processes abundant in cortical tissue. In a more broad way these results show that a system of wave modes interacting through all possible combinations of the third order non-linear terms described by a general wave Hamiltonian necessarily produces anharmonic wave modes with temporal and spatial scaling properties that follow scale free power laws. To the best of our knowledge this has never been reported in the physical literature and may be applicable to many physical systems that involve wave processes and not just to neuronal avalanches.
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Affiliation(s)
- Vitaly L. Galinsky
- Center for Scientific Computation in Imaging, University of California, San Diego, San Diego, CA, United States
| | - Lawrence R. Frank
- Center for Scientific Computation in Imaging, University of California, San Diego, San Diego, CA, United States
- Center for Functional MRI, University of California, San Diego, San Diego, CA, United States
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18
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Suppression of seizure in childhood absence epilepsy using robust control of deep brain stimulation: a simulation study. Sci Rep 2023; 13:461. [PMID: 36627375 PMCID: PMC9832016 DOI: 10.1038/s41598-023-27527-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 01/03/2023] [Indexed: 01/11/2023] Open
Abstract
Deep brain stimulation (DBS) is a promising technique to relieve the symptoms in patients with intractable seizures. Although the DBS therapy for seizure suppression dates back more than 40 years, determining stimulation parameters is a significant challenge to the success of this technique. One solution to this challenge with application in a real DBS system is to design a closed-loop control system to regulate the stimulation intensity using computational models of epilepsy automatically. The main goal of the current study is to develop a robust control technique based on adaptive fuzzy terminal sliding mode control (AFTSMC) for eliminating the oscillatory spiking behavior in childhood absence epilepsy (CAE) dynamical model consisting of cortical, thalamic relay, and reticular nuclei neurons. To this end, the membrane voltage dynamics of the three coupled neurons are considered as a three-input three-output nonlinear state delay system. A fuzzy logic system is developed to estimate the unknown nonlinear dynamics of the current and delayed states of the model embedded in the control input. Chattering-free control input (continuous DBS pulses) without any singularity problem is the superiority of the proposed control method. To guarantee the bounded stability of the closed-loop system in a finite time, the upper bounds of the external disturbance and minimum estimation errors are updated online with adaptive laws without any offline tuning phase. Simulation results are provided to show the robustness of AFTSMC in the presence of uncertainty and external disturbances.
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19
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Hu B, Wang Z, Xu M, Zhang D, Wang D. The adjustment mechanism of the spike and wave discharges in thalamic neurons: a simulation analysis. Cogn Neurodyn 2022; 16:1449-1460. [PMID: 36408065 PMCID: PMC9666587 DOI: 10.1007/s11571-022-09788-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 01/18/2022] [Accepted: 01/25/2022] [Indexed: 11/28/2022] Open
Abstract
Different from many previous theoretical studies, this paper explores the regulatory mechanism of the spike and wave discharges (SWDs) in the reticular thalamic nucleus (TRN) by a dynamic computational model. We observe that the SWDs appears in the TRN by changing the coupling weights and delays in the thalamocortical circuit. The abundant poly-spikes wave discharges is also induced when the delay increases to large enough. These discharges can be inhibited by tuning the inhibitory output from the basal ganglia to the thalamus. The mechanisms of these waves can be explained in this model together with simulation results, which are different from the mechanisms in the cortex. The TRN is an important target in treating epilepsy, and the results may be a theoretical evidence for experimental study in the future.
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Affiliation(s)
- Bing Hu
- Department of Applied Mathematics, Zhejiang University of Technology, Hangzhou, 310023 China
| | - Zhizhi Wang
- Department of Applied Mathematics, Zhejiang University of Technology, Hangzhou, 310023 China
| | - Minbo Xu
- Department of Applied Mathematics, Zhejiang University of Technology, Hangzhou, 310023 China
| | - Dongmei Zhang
- Department of Applied Mathematics, Zhejiang University of Technology, Hangzhou, 310023 China
| | - Dingjiang Wang
- Department of Applied Mathematics, Zhejiang University of Technology, Hangzhou, 310023 China
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20
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Huiskamp M, Kiljan S, Kulik S, Witte ME, Jonkman LE, Gjm Bol J, Schenk GJ, Hulst HE, Tewarie P, Schoonheim MM, Geurts JJ. Inhibitory synaptic loss drives network changes in multiple sclerosis: An ex vivo to in silico translational study. Mult Scler 2022; 28:2010-2019. [PMID: 36189828 PMCID: PMC9574900 DOI: 10.1177/13524585221125381] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Background: Synaptic and neuronal loss contribute to network dysfunction and disability
in multiple sclerosis (MS). However, it is unknown whether excitatory or
inhibitory synapses and neurons are more vulnerable and how their losses
impact network functioning. Objective: To quantify excitatory and inhibitory synapses and neurons and to investigate
how synaptic loss affects network functioning through computational
modeling. Methods: Using immunofluorescent staining and confocal microscopy, densities of
glutamatergic and GABAergic synapses and neurons were compared between
post-mortem MS and non-neurological control cases. Then, a corticothalamic
biophysical model was employed to study how MS-induced excitatory and
inhibitory synaptic loss affect network functioning. Results: In layer VI of normal-appearing MS cortex, excitatory and inhibitory synaptic
densities were significantly lower than controls (reductions up to 14.9%),
but demyelinated cortex showed larger losses of inhibitory synapses (29%).
In our computational model, reducing inhibitory synapses impacted the
network most, leading to a disinhibitory increase in neuronal activity and
connectivity. Conclusion: In MS, excitatory and inhibitory synaptic losses were observed, predominantly
for inhibitory synapses in demyelinated cortex. Inhibitory synaptic loss
affected network functioning most, leading to increased neuronal activity
and connectivity. As network disinhibition relates to cognitive impairment,
inhibitory synaptic loss seems particularly relevant in MS.
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Affiliation(s)
- Marijn Huiskamp
- Anatomy and Neurosciences, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Svenja Kiljan
- Anatomy and Neurosciences, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Shanna Kulik
- Anatomy and Neurosciences, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Maarteen E Witte
- Molecular Cell Biology and Inflammation, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Laura E Jonkman
- Anatomy and Neurosciences, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - John Gjm Bol
- Anatomy and Neurosciences, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Geert J Schenk
- Anatomy and Neurosciences, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Hanneke E Hulst
- Anatomy and Neurosciences, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands/Health, Medical and Neuropsychology Unit, Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Prejaas Tewarie
- Neurology, MS center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands/Clinical Neurophysiology and MEG Center, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Menno M Schoonheim
- Anatomy and Neurosciences, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Jeroen Jg Geurts
- Anatomy and Neurosciences, MS Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
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21
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Hou S, Fan D, Wang Q. Regulating absence seizures by tri-phase delay stimulation applied to globus pallidus internal. APPLIED MATHEMATICS AND MECHANICS 2022; 43:1399-1414. [PMID: 36092985 PMCID: PMC9438882 DOI: 10.1007/s10483-022-2896-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 06/15/2022] [Indexed: 06/15/2023]
Abstract
In this paper, a reduced globus pallidus internal (GPI)-corticothalamic (GCT) model is developed, and a tri-phase delay stimulation (TPDS) with sequentially applying three pulses on the GPI representing the inputs from the striatal D 1 neurons, subthalamic nucleus (STN), and globus pallidus external (GPE), respectively, is proposed. The GPI is evidenced to control absence seizures characterized by 2 Hz-4 Hz spike and wave discharge (SWD). Hence, based on the basal ganglia-thalamocortical (BGCT) model, we firstly explore the triple effects of D l-GPI, GPE-GPI, and STN-GPI pathways on seizure patterns. Then, using the GCT model, we apply the TPDS on the GPI to potentially investigate the alternative and improved approach if these pathways to the GPI are blocked. The results show that the striatum D 1, GPE, and STN can indeed jointly and significantly affect seizure patterns. In particular, the TPDS can effectively reproduce the seizure pattern if the D 1-GPI, GPE-GPI, and STN-GPI pathways are cut off. In addition, the seizure abatement can be obtained by well tuning the TPDS stimulation parameters. This implies that the TPDS can play the surrogate role similar to the modulation of basal ganglia, which hopefully can be helpful for the development of the brain-computer interface in the clinical application of epilepsy.
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Affiliation(s)
- Songan Hou
- Department of Dynamics and Control, Beihang University, Beijing, 100191 China
| | - Denggui Fan
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, 100083 China
| | - Qingyun Wang
- Department of Dynamics and Control, Beihang University, Beijing, 100191 China
- Beijing Institute of Brain Disorders, Capital Medical University, Beijing, 100069 China
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22
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Classification of EEG Signals for Prediction of Epileptic Seizures. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Epilepsy is a common brain disorder that causes patients to face multiple seizures in a single day. Around 65 million people are affected by epilepsy worldwide. Patients with focal epilepsy can be treated with surgery, whereas generalized epileptic seizures can be managed with medications. It has been noted that in more than 30% of cases, these medications fail to control epileptic seizures, resulting in accidents and limiting the patient’s life. Predicting epileptic seizures in such patients prior to the commencement of an oncoming seizure is critical so that the seizure can be treated with preventive medicines before it occurs. Electroencephalogram (EEG) signals of patients recorded to observe brain electrical activity during a seizure can be quite helpful in predicting seizures. Researchers have proposed methods that use machine and/or deep learning techniques to predict epileptic seizures using scalp EEG signals; however, prediction of seizures with increased accuracy is still a challenge. Therefore, we propose a three-step approach. It includes preprocessing of scalp EEG signals with PREP pipeline, which is a more sophisticated alternative to basic notch filtering. This method uses a regression-based technique to further enhance the SNR, with a combination of handcrafted, i.e., statistical features such as temporal mean, variance, and skewness, and automated features using CNN, followed by classification of interictal state and preictal state segments using LSTM to predict seizures. We train and validate our proposed technique on the CHB-MIT scalp EEG dataset and achieve accuracy of 94%, sensitivity of 93.8%, and 91.2% specificity. The proposed technique achieves better sensitivity and specificity than existing methods.
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23
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Pan Y, Zhang H, Xie Y, Chai Y. Role of coupling distances in a coupled thalamocortical network for regulation of epilepsy. J Theor Biol 2022; 550:111206. [PMID: 35850254 DOI: 10.1016/j.jtbi.2022.111206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 04/24/2022] [Accepted: 06/20/2022] [Indexed: 11/17/2022]
Abstract
The recent theoretical modeling of coupled cortical thalamic network is an important advance toward the spatiotemporal dynamics of the brain. However, the diversity of coupling distances is ignored, and the better choice of deep brain stimulation (DBS) parameters to control epilepsy is still a challenge so far. A modeling object of this paper is to establish a coupled cortical thalamic model with uncertain coupling distances including nine combinations. Based on the pathways formed by pyramidal neuronal population (PY), thalamic reticular nucleus (RE) and thalamic relay nucleus (TC), we simulate the spike-wave discharges (SWD) at 2-4Hz which are the main manifestations of absence episodes. It is demonstrated that combination (1/3, 1/9) between the left and right ventricles is the optimal coupling distance of the proposed model by analyzing the percentage of SWD. A stimulating object of this paper is to find an optimum parameter range of DBS. One of the important results is that the number of SWD is inversely proportional to the amplitude, another one is that the number of SWD shows a U-shaped trend with the change of frequency. The present study has laidtheoryfoundationforthebrainplasticity, which will provide an important theoretical basis and direction for the treatment of absence epilepsy in the future. In brief, hopefully our simulation results will provide some help to patients.
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Affiliation(s)
- Yufeng Pan
- School of Mathematics and Physics, Shanghai University of Electric Power, Shanghai 201306, China
| | - Hudong Zhang
- School of Mathematics and Physics, Shanghai University of Electric Power, Shanghai 201306, China
| | - Yan Xie
- School of Mathematics and Physics, Shanghai University of Electric Power, Shanghai 201306, China
| | - Yuan Chai
- School of Mathematics and Physics, Shanghai University of Electric Power, Shanghai 201306, China.
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24
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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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25
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Luyao Yan A, Honghui Zhang B, Zhongkui Sun C, Zilu Cao D, Zhuan Shen E, Yuzhi Zhao F. Mechanism analysis for excitatory interneurons dominating poly-spike wave and optimization of electrical stimulation. CHAOS (WOODBURY, N.Y.) 2022; 32:033110. [PMID: 35364840 DOI: 10.1063/5.0076439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 02/21/2022] [Indexed: 06/14/2023]
Abstract
In addition to inhibitory interneurons, there exist excitatory interneurons (EINs) in the cortex, which mainly have excitatory projections to pyramidal neurons. In this study, we improve a thalamocortical model by introducing EIN, investigate the dominant role of EIN in generating spike and slow wave discharges (SWDs), and consider a non-rectangular pulse to control absence seizures. First, we display here that the improved model can reproduce typical SWDs of absence seizures. Moreover, we focus on the function of EIN by means of bifurcation analysis and find that EIN can induce transition behaviors under Hopf-type and fold limit cycle bifurcations. Specifically, the system has three stable solutions composing a tri-stable region. In this region, there are three attraction basins, which hints that external stimulation can drive the system trajectory from one basin to another, thereby eliminating abnormal oscillations. Furthermore, we compare the increasing ramp with rectangular pulse and optimize stimulation waveforms from the perspective of electrical charges input. The controlling role of the single increasing ramp to absence seizures is remarkable and the optimal stimulus parameters have been found theoretically. This work provides a computational model containing EIN and a theoretical basis for future physiological experiments and clinical research studies.
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Affiliation(s)
- A Luyao Yan
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an, Shaanxi 710129, China
| | - B Honghui Zhang
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an, Shaanxi 710129, China
| | - C Zhongkui Sun
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an, Shaanxi 710129, China
| | - D Zilu Cao
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an, Shaanxi 710129, China
| | - E Zhuan Shen
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an, Shaanxi 710129, China
| | - F Yuzhi Zhao
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an, Shaanxi 710129, China
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26
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Cao M, Galvis D, Vogrin SJ, Woods WP, Vogrin S, Wang F, Woldman W, Terry JR, Peterson A, Plummer C, Cook MJ. Virtual intracranial EEG signals reconstructed from MEG with potential for epilepsy surgery. Nat Commun 2022; 13:994. [PMID: 35194035 PMCID: PMC8863890 DOI: 10.1038/s41467-022-28640-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 01/28/2022] [Indexed: 12/17/2022] Open
Abstract
Modelling the interactions that arise from neural dynamics in seizure genesis is challenging but important in the effort to improve the success of epilepsy surgery. Dynamical network models developed from physiological evidence offer insights into rapidly evolving brain networks in the epileptic seizure. A limitation of previous studies in this field is the dependence on invasive cortical recordings with constrained spatial sampling of brain regions that might be involved in seizure dynamics. Here, we propose virtual intracranial electroencephalography (ViEEG), which combines non-invasive ictal magnetoencephalographic imaging (MEG), dynamical network models and a virtual resection technique. In this proof-of-concept study, we show that ViEEG signals reconstructed from MEG alone preserve critical temporospatial characteristics for dynamical approaches to identify brain areas involved in seizure generation. We show the non-invasive ViEEG approach may have some advantage over intracranial electroencephalography (iEEG). Future work may be designed to test the potential of the virtual iEEG approach for use in surgical management of epilepsy.
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Affiliation(s)
- Miao Cao
- Department of Medicine St Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, Australia
| | - Daniel Galvis
- Translational Research Exchange at Exeter, University of Exeter, Exeter, UK.,Living Systems Institute, University of Exeter, Exeter, UK.,Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, UK.,Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - Simon J Vogrin
- Department of Medicine St Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, Australia.,Faculty of Health, Art and Design, Swinburne University of Technology, Melbourne, Australia
| | - William P Woods
- Faculty of Health, Art and Design, Swinburne University of Technology, Melbourne, Australia
| | - Sara Vogrin
- Department of Medicine St Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Department of Medicine Western Health, The University of Melbourne, Melbourne, Australia
| | - Fan Wang
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.,CAS Centre for Excellence in Brain Science and Intelligence Technology, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Wessel Woldman
- Translational Research Exchange at Exeter, University of Exeter, Exeter, UK.,Living Systems Institute, University of Exeter, Exeter, UK.,Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, UK.,Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - John R Terry
- Translational Research Exchange at Exeter, University of Exeter, Exeter, UK.,Living Systems Institute, University of Exeter, Exeter, UK.,Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, UK.,Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - Andre Peterson
- Department of Medicine St Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, Australia.,Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia
| | - Chris Plummer
- Department of Medicine St Vincent's Hospital, The University of Melbourne, Melbourne, Australia. .,Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, Australia. .,Faculty of Health, Art and Design, Swinburne University of Technology, Melbourne, Australia.
| | - Mark J Cook
- Department of Medicine St Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, Australia.,Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia
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27
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Aghili Yajadda MM, Robinson PA, Henderson JA. Generalized neural field theory of cortical plasticity illustrated by an application to the linear phase of ocular dominance column formation in primary visual cortex. BIOLOGICAL CYBERNETICS 2022; 116:33-52. [PMID: 34773503 DOI: 10.1007/s00422-021-00901-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 09/30/2021] [Indexed: 06/13/2023]
Abstract
Physiologically based neural field theory (NFT) is extended to encompass cortical plasticity dynamics. An illustrative application is provided which treats the evolution of the connectivity of left- and right-eye visual stimuli to neuronal populations in the primary visual cortex (V1), and the initial, linear phase of formation of approximately one-dimensional (1D) ocular dominance columns (ODCs) that sets their transverse spatial scale. This links V1 activity, structure, and physiology within a single theory that already accounts for a range of other brain activity and connectivity phenomena, thereby enabling ODC formation and many other phenomena to be interrelated and cortical parameters to be constrained across multiple domains. The results accord with experimental ODC widths for realistic cortical parameters and are based directly on a unified description of the neuronal populations involved, their connection strengths, and the neuronal activity they support. Other key results include simple analytic approximations for ODC widths and the parameters of maximum growth rate, constraints on cortical excitatory and inhibitory gains, elucidation of the roles of specific poles of the V1 response function, and the fact that ODCs are not formed when input stimuli are fully correlated between eyes. This work provides a basis for further generalization of NFT to model other plasticity phenomena, thereby linking them to the range multiscale phenomena accounted for by NFT.
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Affiliation(s)
- M M Aghili Yajadda
- School of Physics, University of Sydney, Sydney, NSW, 2006, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, NSW, 2006, Australia
| | - P A Robinson
- School of Physics, University of Sydney, Sydney, NSW, 2006, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, NSW, 2006, Australia
| | - J A Henderson
- School of Physics, University of Sydney, Sydney, NSW, 2006, Australia.
- Center for Integrative Brain Function, University of Sydney, Sydney, NSW, 2006, Australia.
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28
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Faheem M, Ameer S, Khan AW, Haseeb M, Raza Q, Ali Shah F, Khusro A, Aarti C, Umar Khayam Sahibzada M, El-Saber Batiha G, Koirala N, Adnan M, Alghamdi S, Assaggaf H, Alsiwiehri NO. A comprehensive review on antiepileptic properties of medicinal plants. ARAB J CHEM 2022. [DOI: 10.1016/j.arabjc.2021.103478] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
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29
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Bondar A, Shubina L. The Relationship Between the Rhythmic Components of the Brain Electrical Activity During the Development of Status Epilepticus: An Operational Model of Brain Rhythms Generation. Brain Connect 2021; 12:571-583. [PMID: 34486376 DOI: 10.1089/brain.2021.0108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background/Introduction: Despite the fact that brain rhythms are widely studied and officially classified, there is no consensus on their relationship, which can shed light on the genesis of rhythmic activity, its synchronization, functional role, and the formation of pathological reactions. Using the experimental status epilepticus (SE) as a model of brain in a hypersynchronized state with well-defined rhythms, we aimed to study the relationship between the rhythmic components of the brain electrical activity. Materials and Methods: Local field potentials (LFPs) were recorded simultaneously from the hippocampus, entorhinal cortex, medial septum, and amygdala during normal conditions and after kainic acid (KA) administration in waking guinea pigs. The dynamical spectral LFP properties were analyzed with the aid of Fast Fourier transform. Results: KA induces prominent SE with periodic combination of epileptiform discharge complexes and relatively quiet interdischarge intervals in the electrical activity of the brain. We have shown that new components appeared in the LFP spectra during the development of SE, representing a sequential doubling of the frequency, which had initially been dominating in the background records. Discussion: The phenomenon of frequency doubling can be interpreted as the octave principle of the LFP spectrum rhythmic carcass structure. The spectra of discharge complexes represent an alternation of harmonic spectra, where fundamental frequency coincides with one of the doubled frequencies dominating in the interdischarge activity. Using a nonlinear recurrent operation of rhythm multiplication and the obtained data we propose an operational model of the generation of rhythms and pathological discharges in the brain based on the octave principle. Impact statement In this study, we examined the relationship between the rhythmic components of the electrical activity of the limbic structures during the experimental status epilepticus and propose an operational model of brain rhythms generation based on the octave principle. Our study demonstrates that using fundamental principles (nonlinearity and the presence of recurrence), it is possible to explain the genesis and phenomenology of the electrical activity of brain structures in normal and pathological conditions.
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Affiliation(s)
- Alexandr Bondar
- Department of Reception Mechanisms, Institute of Cell Biophysics of Russian Academy of Sciences, Pushchino, Russian Federation
| | - Liubov Shubina
- Laboratory of Systemic organization of Neurons, Institute of Theoretical and Experimental Biophysics of Russian Academy of Sciences, Pushchino, Russian Federation
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30
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Robinson PA. Discrete spectral eigenmode-resonance network of brain dynamics and connectivity. Phys Rev E 2021; 104:034411. [PMID: 34654199 DOI: 10.1103/physreve.104.034411] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 09/02/2021] [Indexed: 12/27/2022]
Abstract
The problem of finding a compact natural representation of brain dynamics and connectivity is addressed using an expansion in terms of physical spatial eigenmodes and their frequency resonances. It is demonstrated that this discrete expansion via the system transfer function enables linear and nonlinear dynamics to be analyzed in compact form in terms of natural dynamic "atoms," each of which is a frequency resonance of an eigenmode. Because these modal resonances are determined by the system dynamics, not the investigator, they are privileged over widely used phenomenological patterns, and obviate the need for artificial discretizations and thresholding in coordinate space. It is shown that modal resonances participate as nodes of a discrete spectral network, are noninteracting in the linear regime, but are linked nonlinearly by wave-wave coalescence and decay processes. The modal resonance formulation is shown to be capable of speeding numerical calculations of strongly nonlinear interactions. Recent work in brain dynamics, especially based on neural field theory (NFT) approaches, allows eigenmodes and their resonances to be estimated from data without assuming a specific brain model. This means that dynamic equations can be inferred using system identification methods from control theory, rather than being assumed, and resonances can be interpreted as control-systems data filters. The results link brain activity and connectivity with control-systems functions such as prediction and attention via gain control and can also be linked to specific NFT predictions if desired, thereby providing a convenient bridge between physiologically based theories and experiment. Amplitudes of modes and resonances can also be tracked to provide a more direct and temporally localized representation of the dynamics than correlations and covariances, which are widely used in the field. By synthesizing many different lines of research, this work provides a way to link quantitative electrophysiological and imaging measurements, connectivity, brain dynamics, and function. This underlines the need to move between coordinate and spectral representations as required. Moreover, standard theoretical-physics approaches and mathematical methods can be used in place of ad hoc statistical measures such as those based on graph theory of artificially discretized and decimated networks, which are highly prone to selection effects and artifacts.
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Affiliation(s)
- P A Robinson
- School of Physics, University of Sydney, New South Wales 2006, Australia and Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
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31
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Robinson PA, Gabay NC, Babaie-Janvier T. Neural Field Theory of Evoked Response Sequences and Mismatch Negativity With Adaptation. Front Hum Neurosci 2021; 15:655505. [PMID: 34483860 PMCID: PMC8415526 DOI: 10.3389/fnhum.2021.655505] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 07/20/2021] [Indexed: 12/02/2022] Open
Abstract
Physiologically based neural field theory of the corticothalamic system is used to calculate the responses evoked by trains of auditory stimuli that correspond to different cortical locations via the tonotopic map. The results are shown to account for standard and deviant evoked responses to frequent and rare stimuli, respectively, in the auditory oddball paradigms widely used in human cognitive studies, and the so-called mismatch negativity between them. It also reproduces a wide range of other effects and variants, including the mechanism by which a change in standard responses relative to deviants can develop through adaptation, different responses when two deviants are presented in a row or a standard is presented after two deviants, relaxation of standard responses back to deviant form after a stimulus-free period, and more complex sequences. Some cases are identified in which adaptation does not account for the whole difference between standard and deviant responses. The results thus provide a systematic means to determine how much of the response is due to adaptation in the system comprising the primary auditory cortex and medial geniculate nucleus, and how much requires involvement of higher-level processing.
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Affiliation(s)
- Peter A Robinson
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Natasha C Gabay
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Tara Babaie-Janvier
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
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32
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Progress in modelling of brain dynamics during anaesthesia and the role of sleep-wake circuitry. Biochem Pharmacol 2021; 191:114388. [DOI: 10.1016/j.bcp.2020.114388] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 12/16/2020] [Accepted: 12/17/2020] [Indexed: 12/28/2022]
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33
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Robinson PA, Henderson JA, Gabay NC, Aquino KM, Babaie-Janvier T, Gao X. Determination of Dynamic Brain Connectivity via Spectral Analysis. Front Hum Neurosci 2021; 15:655576. [PMID: 34335207 PMCID: PMC8323754 DOI: 10.3389/fnhum.2021.655576] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 06/03/2021] [Indexed: 11/30/2022] Open
Abstract
Spectral analysis based on neural field theory is used to analyze dynamic connectivity via methods based on the physical eigenmodes that are the building blocks of brain dynamics. These approaches integrate over space instead of averaging over time and thereby greatly reduce or remove the temporal averaging effects, windowing artifacts, and noise at fine spatial scales that have bedeviled the analysis of dynamical functional connectivity (FC). The dependences of FC on dynamics at various timescales, and on windowing, are clarified and the results are demonstrated on simple test cases, demonstrating how modes provide directly interpretable insights that can be related to brain structure and function. It is shown that FC is dynamic even when the brain structure and effective connectivity are fixed, and that the observed patterns of FC are dominated by relatively few eigenmodes. Common artifacts introduced by statistical analyses that do not incorporate the physical nature of the brain are discussed and it is shown that these are avoided by spectral analysis using eigenmodes. Unlike most published artificially discretized “resting state networks” and other statistically-derived patterns, eigenmodes overlap, with every mode extending across the whole brain and every region participating in every mode—just like the vibrations that give rise to notes of a musical instrument. Despite this, modes are independent and do not interact in the linear limit. It is argued that for many purposes the intrinsic limitations of covariance-based FC instead favor the alternative of tracking eigenmode coefficients vs. time, which provide a compact representation that is directly related to biophysical brain dynamics.
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Affiliation(s)
- Peter A Robinson
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - James A Henderson
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Natasha C Gabay
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Kevin M Aquino
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Tara Babaie-Janvier
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Xiao Gao
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia.,Department of Biomedical Engineering, University of Melbourne, Parkville, VIC, Australia
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34
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El-Zghir RK, Gabay NC, Robinson PA. Modal-Polar Representation of Evoked Response Potentials in Multiple Arousal States. Front Hum Neurosci 2021; 15:642479. [PMID: 34163339 PMCID: PMC8215109 DOI: 10.3389/fnhum.2021.642479] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 05/10/2021] [Indexed: 11/13/2022] Open
Abstract
An expansion of the corticothalamic transfer function into eigenmodes and resonant poles is used to derive a simple formula for evoked response potentials (ERPs) in various states of arousal. The transfer function corresponds to the cortical response to an external stimulus, which encodes all the information and properties of the linear system. This approach links experimental observations of resonances and characteristic timescales in brain activity with physically based neural field theory (NFT). The present work greatly simplifies the formula of the analytical ERP, and separates its spatial part (eigenmodes) from the temporal part (poles). Within this framework, calculations involve contour integrations that yield an explicit expression for ERPs. The dominant global mode is considered explicitly in more detail to study how the ERP varies with time in this mode and to illustrate the method. For each arousal state in sleep and wake, the resonances of the system are determined and it is found that five poles are sufficient to study the main dynamics of the system in waking eyes-open and eyes-closed states. Similarly, it is shown that six poles suffice to reproduce ERPs in rapid-eye movement sleep, sleep state 1, and sleep state 2 states, whereas just four poles suffice to reproduce the dynamics in slow wave sleep. Thus, six poles are sufficient to preserve the main global ERP dynamics of the system for all states of arousal. These six poles correspond to the dominant resonances of the system at slow-wave, alpha, and beta frequencies. These results provide the basis for simplified analytic treatment of brain dynamics and link observations more closely to theory.
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Affiliation(s)
- Rawan K. El-Zghir
- School of Physics, University of Sydney, Sydney, NSW, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Natasha C. Gabay
- School of Physics, University of Sydney, Sydney, NSW, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Peter A. Robinson
- School of Physics, University of Sydney, Sydney, NSW, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
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35
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Robinson PA. Neural field theory of neural avalanche exponents. BIOLOGICAL CYBERNETICS 2021; 115:237-243. [PMID: 33939016 DOI: 10.1007/s00422-021-00875-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 04/10/2021] [Indexed: 06/12/2023]
Abstract
The power-law exponents of observed size and lifetime distributions of near-critical neural avalanches are calculated from neural field theory using diagrammatic methods. This brings neural avalanches within the ambit of neural field theory, which has also previously explained near-critical 1/f spectra and many other observed features of neural activity. This strengthens the case for near-criticality of the brain and opens the way for these other phenomena to be interrelated with avalanches and their dynamics.
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Affiliation(s)
- P A Robinson
- School of Physics, The University of Sydney, Sydney, New South Wales, 2006, Australia.
- Center of Excellence for Integrative Brain Function, The University of Sydney, Sydney, New South Wales, 2006, Australia.
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36
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Brain dynamics and structure-function relationships via spectral factorization and the transfer function. Neuroimage 2021; 235:117989. [PMID: 33819612 DOI: 10.1016/j.neuroimage.2021.117989] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 02/15/2021] [Accepted: 03/12/2021] [Indexed: 01/07/2023] Open
Abstract
It is shown how the brain's linear transfer function provides a means to systematically analyze brain connectivity and dynamics, and to infer connectivity, eigenmodes, and activity measures such as spectra, evoked responses, coherence, and causality, all of which are widely used in brain monitoring. In particular, the Wilson spectral factorization algorithm is outlined and used to efficiently obtain linear transfer functions from experimental two-point correlation functions. The algorithm is tested on a series of brain-like structures of increasing complexity which include time delays, asymmetry, two-dimensionality, and complex network connectivity. These tests are used to verify the algorithm is suitable for application to brain dynamics, specify sampling requirements for experimental time series, and to verify that its runtime is short enough to obtain accurate results for systems of similar size to current experiments. The results can equally well be applied to inference of the transfer function in complex linear systems other than brains.
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37
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Glomb K, Cabral J, Cattani A, Mazzoni A, Raj A, Franceschiello B. Computational Models in Electroencephalography. Brain Topogr 2021; 35:142-161. [PMID: 33779888 PMCID: PMC8813814 DOI: 10.1007/s10548-021-00828-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 02/19/2021] [Indexed: 12/17/2022]
Abstract
Computational models lie at the intersection of basic neuroscience and healthcare applications because they allow researchers to test hypotheses in silico and predict the outcome of experiments and interactions that are very hard to test in reality. Yet, what is meant by “computational model” is understood in many different ways by researchers in different fields of neuroscience and psychology, hindering communication and collaboration. In this review, we point out the state of the art of computational modeling in Electroencephalography (EEG) and outline how these models can be used to integrate findings from electrophysiology, network-level models, and behavior. On the one hand, computational models serve to investigate the mechanisms that generate brain activity, for example measured with EEG, such as the transient emergence of oscillations at different frequency bands and/or with different spatial topographies. On the other hand, computational models serve to design experiments and test hypotheses in silico. The final purpose of computational models of EEG is to obtain a comprehensive understanding of the mechanisms that underlie the EEG signal. This is crucial for an accurate interpretation of EEG measurements that may ultimately serve in the development of novel clinical applications.
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Affiliation(s)
- Katharina Glomb
- Connectomics Lab, Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland.
| | - Joana Cabral
- Life and Health Sciences Research Institute (ICVS), University of Minho, Braga, Portugal
| | - Anna Cattani
- Department of Psychiatry, University of Wisconsin-Madison, Madison, USA.,Department of Biomedical and Clinical Sciences 'Luigi Sacco', University of Milan, Milan, Italy
| | - Alberto Mazzoni
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Ashish Raj
- School of Medicine, UCSF, San Francisco, USA
| | - Benedetta Franceschiello
- Department of Ophthalmology, Hopital Ophthalmic Jules Gonin, FAA, Lausanne, Switzerland.,CIBM Centre for Biomedical Imaging, EEG Section CHUV-UNIL, Lausanne, Switzerland.,Laboratory for Investigative Neurophysiology, Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
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38
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Hindriks R. Relation between the phase-lag index and lagged coherence for assessing interactions in EEG and MEG data. NEUROIMAGE: REPORTS 2021. [DOI: 10.1016/j.ynirp.2021.100007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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39
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Usman SM, Khalid S, Bashir Z. Epileptic seizure prediction using scalp electroencephalogram signals. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.01.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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40
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Griffiths JD, McIntosh AR, Lefebvre J. A Connectome-Based, Corticothalamic Model of State- and Stimulation-Dependent Modulation of Rhythmic Neural Activity and Connectivity. Front Comput Neurosci 2020; 14:575143. [PMID: 33408622 PMCID: PMC7779529 DOI: 10.3389/fncom.2020.575143] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 11/19/2020] [Indexed: 11/13/2022] Open
Abstract
Rhythmic activity in the brain fluctuates with behaviour and cognitive state, through a combination of coexisting and interacting frequencies. At large spatial scales such as those studied in human M/EEG, measured oscillatory dynamics are believed to arise primarily from a combination of cortical (intracolumnar) and corticothalamic rhythmogenic mechanisms. Whilst considerable progress has been made in characterizing these two types of neural circuit separately, relatively little work has been done that attempts to unify them into a single consistent picture. This is the aim of the present paper. We present and examine a whole-brain, connectome-based neural mass model with detailed long-range cortico-cortical connectivity and strong, recurrent corticothalamic circuitry. This system reproduces a variety of known features of human M/EEG recordings, including spectral peaks at canonical frequencies, and functional connectivity structure that is shaped by the underlying anatomical connectivity. Importantly, our model is able to capture state- (e.g., idling/active) dependent fluctuations in oscillatory activity and the coexistence of multiple oscillatory phenomena, as well as frequency-specific modulation of functional connectivity. We find that increasing the level of sensory drive to the thalamus triggers a suppression of the dominant low frequency rhythms generated by corticothalamic loops, and subsequent disinhibition of higher frequency endogenous rhythmic behaviour of intracolumnar microcircuits. These combine to yield simultaneous decreases in lower frequency and increases in higher frequency components of the M/EEG power spectrum during states of high sensory or cognitive drive. Building on this, we also explored the effect of pulsatile brain stimulation on ongoing oscillatory activity, and evaluated the impact of coexistent frequencies and state-dependent fluctuations on the response of cortical networks. Our results provide new insight into the role played by cortical and corticothalamic circuits in shaping intrinsic brain rhythms, and suggest new directions for brain stimulation therapies aimed at state-and frequency-specific control of oscillatory brain activity.
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Affiliation(s)
- John D. Griffiths
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Anthony Randal McIntosh
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
| | - Jeremie Lefebvre
- Department of Biology, University of Ottawa, Ottawa, ON, Canada
- Krembil Research Institute, University Health Network, Toronto, ON, Canada
- Department of Mathematics, University of Toronto, Toronto, ON, Canada
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41
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Noroozbabaee L, Steyn-Ross DA, Steyn-Ross ML, Sleigh JW. Analysis of the Hindriks and van Putten model for propofol anesthesia: Limitations and extensions. Neuroimage 2020; 227:117633. [PMID: 33316393 DOI: 10.1016/j.neuroimage.2020.117633] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Revised: 12/02/2020] [Accepted: 12/04/2020] [Indexed: 11/28/2022] Open
Abstract
We present a detailed analysis of the Hindriks and van Putten thalamocortical mean-field model for propofol anesthesia [NeuroImage 60(23), 2012]. The Hindriks and van Putten (HvP) model predicts increases in delta and alpha power for moderate (up to 130%) prolongation of GABAA inhibitory response, corresponding to light anesthetic sedation. Our analysis reveals that, for deeper anesthetic effect, the model exhibits an unexpected abrupt jump in cortical activity from a low-firing state to an extremely high-firing stable state (∼250 spikes/s), and remains locked there even at GABAA prolongations as high as 300% which would be expected to induce full comatose suppression of all firing activity. We demonstrate that this unphysiological behavior can be completely suppressed with appropriate tuning of the parameters controlling the sigmoidal functions that map soma voltage to firing rate for the excitatory and inhibitory neural populations, coupled with elimination of the putative population-dependent anesthetic efficacies introduced in the HvP model. The modifications reported here constrain the anesthetized brain activity into a biologically plausible range in which the cortex now has access to a moderate-firing state ("awake") and a low-firing ("anesthetized") state such that the brain can transition from "awake" to "anesthetized" states at a critical level of drug concentration. The modified HvP model predicts a drug-effect hysteresis in which the drug concentration required for induction is larger than that at emergence. In addition, the revised model shows a decrease in the intensity and frequency of alpha-band fluctuations, transitioning to delta-band dominance, with deepening anesthesia. These predicted drug concentration-dependent changes in EEG dynamics are consistent with clinical reports.
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Affiliation(s)
- Leyla Noroozbabaee
- School of Engineering, University of Waikato, Hamilton 3240, New Zealand
| | - D A Steyn-Ross
- School of Engineering, University of Waikato, Hamilton 3240, New Zealand.
| | - Moira L Steyn-Ross
- School of Engineering, University of Waikato, Hamilton 3240, New Zealand
| | - J W Sleigh
- Waikato Clinical School, University of Auckland, Waikato Hospital, Hamilton 3204, New Zealand
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Diffuse neural coupling mediates complex network dynamics through the formation of quasi-critical brain states. Nat Commun 2020; 11:6337. [PMID: 33303766 PMCID: PMC7729877 DOI: 10.1038/s41467-020-19716-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 10/28/2020] [Indexed: 01/17/2023] Open
Abstract
The biological mechanisms that allow the brain to balance flexibility and integration remain poorly understood. A potential solution may lie in a unique aspect of neurobiology, which is that numerous brain systems contain diffuse synaptic connectivity. Here, we demonstrate that increasing diffuse cortical coupling within a validated biophysical corticothalamic model traverses the system through a quasi-critical regime in which spatial heterogeneities in input noise support transient critical dynamics in distributed subregions. The presence of quasi-critical states coincides with known signatures of complex, adaptive brain network dynamics. Finally, we demonstrate the presence of similar dynamic signatures in empirical whole-brain human neuroimaging data. Together, our results establish that modulating the balance between local and diffuse synaptic coupling in a thalamocortical model subtends the emergence of quasi-critical brain states that act to flexibly transition the brain between unique modes of information processing.
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43
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Mukta KN, Robinson PA, Pagès JC, Gabay NC, Gao X. Evoked response activity eigenmode analysis in a convoluted cortex via neural field theory. Phys Rev E 2020; 102:062303. [PMID: 33466049 DOI: 10.1103/physreve.102.062303] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 07/15/2020] [Indexed: 11/07/2022]
Abstract
Neural field theory of the corticothalamic system is used to explore evoked response potentials (ERPs) caused by spatially localized impulse stimuli on the convoluted cortex and on a spherical cortex. Eigenfunctions are calculated analytically on the spherical cortex and numerically on the convoluted cortex via eigenfunction expansions. Eigenmodes on a convoluted cortex are similar to those of the spherical cortex, and a few such modes are found to be sufficient to reproduce the main ERP features. It is found that the ERP peak is stronger in spherical cortex than convoluted cortex, but in both cases the peak decreases monotonically with increasing distance from the stimulus point. In the convoluted case, cortical folding causes ERPs to differ between locations at the same distance from the stimulus point and spherical symmetries are only approximately preserved.
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Affiliation(s)
- K N Mukta
- School of Physics, University of Sydney, New South Wales 2006, Australia
- Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
| | - P A Robinson
- School of Physics, University of Sydney, New South Wales 2006, Australia
- Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
| | - J C Pagès
- School of Physics, University of Sydney, New South Wales 2006, Australia
- Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
- School of Physics, University of Zurich, Zürich, Canton of Zürich, Switzerland
| | - N C Gabay
- School of Physics, University of Sydney, New South Wales 2006, Australia
- Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
| | - Xiao Gao
- School of Physics, University of Sydney, New South Wales 2006, Australia
- Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
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Frassineti L, Parente A, Manfredi C. Multiparametric EEG analysis of brain network dynamics during neonatal seizures. J Neurosci Methods 2020; 348:109003. [PMID: 33249182 DOI: 10.1016/j.jneumeth.2020.109003] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 11/06/2020] [Accepted: 11/15/2020] [Indexed: 11/30/2022]
Abstract
BACKGROUND One of the most challenging issues in paediatric neurology is the diagnosis of neonatal seizures, whose delayed treatment may affect the neurodevelopment of the newborn. Formulation of the correct diagnosis is conditioned by the high number of perceptually or automatically detected false positives. NEW METHOD New methodologies are proposed to assess neonatal seizures trend over time. Our approach is based on the analysis of standardized trends of two properties of the brain network: the Synchronizabilty (S) and the degree of phase synchronicity given by the Circular Omega Complexity (COC). Qualitative and quantitative methods based on network dynamics allow differentiating seizure events from interictal periods and seizure-free patients. RESULTS The methods were tested on a public dataset of labelled neonatal seizures. COC shows significant differences among seizure and non-seizure events (p-value <0.001, Cohen's d 0.86). Combining S and COC in standardized temporal instants provided a reliable description of the physiological behaviour of the brain's network during neonatal seizures. COMPARISON WITH EXISTING METHOD(S) Few of the existing network methods propose an operative way for carrying their analytical approach into the diagnostic process of neonatal seizures. Our methods offer a simple representation of brain network dynamics easily implementable and understandable also by less experienced staff. CONCLUSIONS Our findings confirm the usefulness of the evaluation of brain network dynamics over time for a better understanding and interpretation of the complex mechanisms behind neonatal seizures. The proposed methods could also reliably support existing seizure detectors as a post-processing step in doubtful cases.
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Affiliation(s)
- Lorenzo Frassineti
- Department of Information Engineering, Universita' degli Studi di Firenze, Firenze, Italy; Department of Medical Biotechnologies, Universita' degli Studi di Siena, Siena, Italy.
| | - Angela Parente
- School of Engineering, Universita' degli Studi di Firenze, Firenze, Italy.
| | - Claudia Manfredi
- Department of Information Engineering, Universita' degli Studi di Firenze, Firenze, Italy.
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Tewarie P, Hunt BAE, O'Neill GC, Byrne A, Aquino K, Bauer M, Mullinger KJ, Coombes S, Brookes MJ. Relationships Between Neuronal Oscillatory Amplitude and Dynamic Functional Connectivity. Cereb Cortex 2020; 29:2668-2681. [PMID: 29897408 DOI: 10.1093/cercor/bhy136] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Revised: 05/12/2018] [Accepted: 05/16/2018] [Indexed: 12/15/2022] Open
Abstract
Event-related fluctuations of neural oscillatory amplitude are reported widely in the context of cognitive processing and are typically interpreted as a marker of brain "activity". However, the precise nature of these effects remains unclear; in particular, whether such fluctuations reflect local dynamics, integration between regions, or both, is unknown. Here, using magnetoencephalography, we show that movement induced oscillatory modulation is associated with transient connectivity between sensorimotor regions. Further, in resting-state data, we demonstrate a significant association between oscillatory modulation and dynamic connectivity. A confound with such empirical measurements is that increased amplitude necessarily means increased signal-to-noise ratio (SNR): this means that the question of whether amplitude and connectivity are genuinely coupled, or whether increased connectivity is observed purely due to increased SNR is unanswered. Here, we counter this problem by analogy with computational models which show that, in the presence of global network coupling and local multistability, the link between oscillatory modulation and long-range connectivity is a natural consequence of neural networks. Our results provide evidence for the notion that connectivity is mediated by neural oscillations, and suggest that time-frequency spectrograms are not merely a description of local synchrony but also reflect fluctuations in long-range connectivity.
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Affiliation(s)
- Prejaas Tewarie
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Benjamin A E Hunt
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - George C O'Neill
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Aine Byrne
- School of Mathematical Sciences, University of Nottingham, Nottingham, UK
| | - Kevin Aquino
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Markus Bauer
- School of Psychology, University of Nottingham, University Park, Nottingham, UK
| | - Karen J Mullinger
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Stephen Coombes
- School of Mathematical Sciences, University of Nottingham, Nottingham, UK
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
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Ferdousi M, Babaie-Janvier T, Robinson PA. Nonlinear wave-wave interactions in the brain. J Theor Biol 2020; 500:110308. [PMID: 32389568 DOI: 10.1016/j.jtbi.2020.110308] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2019] [Revised: 03/30/2020] [Accepted: 04/27/2020] [Indexed: 11/24/2022]
Abstract
Neural field theory of the corticothalamic system is used to analyze nonlinear wave-wave interactions in steady state visual evoked potential responses. The nonlinear power spectrum is analytically calculated by convolving the linear power spectrum with itself and other factors. Periodic sine and square wave stimuli are used to generate steady state visual evoked potential responses and to study stimulus-driven nonlinear corticothalamic dynamic interactions. Moreover, we use dual sine drives to analyze the driven dynamics. Numerical analysis shows that the nonlinear power spectrum embodies key nonlinear features, including harmonic and subharmonic generation, entrainment of the alpha rhythm to periodic stimuli at the drive frequency, sum and difference frequencies due to wave-wave coalescence and decay. Further, the scaling properties of the key phenomena observed in nonlinear interactions are studied, verifying some of the theoretical predictions for these being generated by three-wave processes.
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Affiliation(s)
- M Ferdousi
- School of Physics, University of Sydney, New South Wales 2006, Australia; Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia.
| | - T Babaie-Janvier
- School of Physics, University of Sydney, New South Wales 2006, Australia; Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
| | - P A Robinson
- School of Physics, University of Sydney, New South Wales 2006, Australia; Center for Integrative Brain Function, University of Sydney, New South Wales 2006, Australia
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47
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Babaie-Janvier T, Robinson PA. Neural Field Theory of Evoked Response Potentials With Attentional Gain Dynamics. Front Hum Neurosci 2020; 14:293. [PMID: 32848668 PMCID: PMC7426978 DOI: 10.3389/fnhum.2020.00293] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 06/29/2020] [Indexed: 11/30/2022] Open
Abstract
A generalized neural field model of large-scale activity in the corticothalamic system is used to predict standard evoked potentials. This model embodies local feedbacks that modulate the gains of neural activity as part of the response to incoming stimuli and thus enables both activity changes and effective connectivity changes to be calculated as parts of a generalized evoked response, and their relative contributions to be determined. The results show that incorporation of gain modulations enables a compact and physically justifiable description of the differences in gain between background-EEG and standard-ERP conditions, with the latter able to be initiated from the background state, rather than requiring distinct parameters as in earlier work. In particular, top-down gains are found to be reduced during an ERP, consistent with recent theoretical suggestions that the role of internal models is diminished in favor of external inputs when the latter change suddenly. The static-gain and modulated-gain system transfer functions are analyzed via control theory in terms of system resonances that were recently shown to implement data filtering whose gain adjustments can be interpreted as attention. These filters are shown to govern early and late features in standard evoked responses and their gain parameters are shown to be dynamically adjusted in a way that implements a form of attention. The results show that dynamically modulated resonant filters responsible for the low-frequency oscillations in an evoked potential response have different parameters than those responsible for low-frequency resting EEG responses, while both responses share similar mid- and high-frequency resonant filters. These results provide a biophysical mechanism by which oscillatory activity in the theta, alpha, and beta frequency ranges of an evoked response are modulated as reflections of attention; notably theta is enhanced and alpha suppressed during the latter parts of the ERP. Furthermore, the model enables the part of the ERP response induced by gain modulations to be estimated and interpreted in terms of attention.
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Affiliation(s)
- Tara Babaie-Janvier
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - Peter A Robinson
- School of Physics, University of Sydney, Sydney, NSW, Australia.,Center of Excellence for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
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48
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Liu X, Sanz-Leon P, Robinson PA. Gamma-band correlations in the primary visual cortex. Phys Rev E 2020; 101:042406. [PMID: 32422743 DOI: 10.1103/physreve.101.042406] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 02/25/2020] [Indexed: 11/07/2022]
Abstract
This paper generalizes and extends previous work on using neural field theory to quantitatively analyze the two-dimensional (2D) spatiotemporal correlation properties of gamma-band (30-70 Hz) oscillations evoked by stimuli arriving at the primary visual cortex, and modulated by patchy connectivities that depend on orientation preference (OP). Correlation functions are derived analytically for general stimulus and measurement conditions. The theoretical results reproduce a range of published experimental results. These include (i) the existence of two-point oscillatory temporal cross correlations with zero time lag between neurons with similar OP; (ii) the influence of spatial separation of neurons on the strength of the correlations; and (iii) the effects of differing stimulus orientations. They go beyond prior work by incorporating experimentally observed patchy projection patterns to predict the 2D correlation structure including both OP and ocular dominance effects, thereby relaxing assumptions of translational invariance implicit in prior one-dimensional analysis.
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Affiliation(s)
- X Liu
- School of Physics, University of Sydney, Sydney, New South Wales 2006, Australia and Center for Integrative Brain Function, University of Sydney, Sydney, New South Wales 2006, Australia
| | - P Sanz-Leon
- School of Physics, University of Sydney, Sydney, New South Wales 2006, Australia and Center for Integrative Brain Function, University of Sydney, Sydney, New South Wales 2006, Australia
| | - P A Robinson
- School of Physics, University of Sydney, Sydney, New South Wales 2006, Australia and Center for Integrative Brain Function, University of Sydney, Sydney, New South Wales 2006, Australia
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49
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Hartoyo A, Cadusch PJ, Liley DTJ, Hicks DG. Inferring a simple mechanism for alpha-blocking by fitting a neural population model to EEG spectra. PLoS Comput Biol 2020; 16:e1007662. [PMID: 32352973 PMCID: PMC7217488 DOI: 10.1371/journal.pcbi.1007662] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 05/12/2020] [Accepted: 04/07/2020] [Indexed: 11/18/2022] Open
Abstract
Alpha blocking, a phenomenon where the alpha rhythm is reduced by attention to a visual, auditory, tactile or cognitive stimulus, is one of the most prominent features of human electroencephalography (EEG) signals. Here we identify a simple physiological mechanism by which opening of the eyes causes attenuation of the alpha rhythm. We fit a neural population model to EEG spectra from 82 subjects, each showing a different degree of alpha blocking upon opening of their eyes. Though it has been notoriously difficult to estimate parameters by fitting such models, we show how, by regularizing the differences in parameter estimates between eyes-closed and eyes-open states, we can reduce the uncertainties in these differences without significantly compromising fit quality. From this emerges a parsimonious explanation for the spectral differences between states: Changes to just a single parameter, pei, corresponding to the strength of a tonic excitatory input to the inhibitory cortical population, are sufficient to explain the reduction in alpha rhythm upon opening of the eyes. We detect this by comparing the shift in each model parameter between eyes-closed and eyes-open states. Whereas changes in most parameters are weak or negligible and do not scale with the degree of alpha attenuation across subjects, the change in pei increases monotonically with the degree of alpha blocking observed. These results indicate that opening of the eyes reduces alpha activity by increasing external input to the inhibitory cortical population. One of the most striking features of the human electroencephalogram (EEG) is the presence of neural oscillations in the range of 8-13 Hz. It is well known that attenuation of these alpha oscillations, a process known as alpha blocking, arises from opening of the eyes, though the cause has remained obscure. In this study we infer the mechanism underlying alpha blocking by fitting a neural population model to EEG spectra from 82 different individuals. Although such models have long held the promise of being able to relate macroscopic recordings of brain activity to microscopic neural parameters, their utility has been limited by the difficulty of inferring these parameters from fits to data. Our approach involves fitting eyes-open and eyes-closed EEG spectra in a way that minimizes unnecessary differences in model parameters between the two states. Surprisingly, we find that changes in just one parameter, the level of external input to the inhibitory neurons in cortex, is sufficient to explain the attenuation of alpha oscillations. This indicates that opening of the eyes reduces alpha activity simply by increasing external inputs to the inhibitory neurons in the cortex.
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Affiliation(s)
- Agus Hartoyo
- Optical Sciences Centre, Swinburne University of Technology, Hawthorn, Victoria, Australia
- * E-mail: (AH); (DGH)
| | - Peter J. Cadusch
- Department of Physics and Astronomy, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - David T. J. Liley
- Centre for Human Psychopharmacology, School of Health Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
- Department of Medicine, University of Melbourne, Parkville, Victoria, Australia
| | - Damien G. Hicks
- Optical Sciences Centre, Swinburne University of Technology, Hawthorn, Victoria, Australia
- Department of Physics and Astronomy, Swinburne University of Technology, Hawthorn, Victoria, Australia
- Bioinformatics Division, Walter & Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- * E-mail: (AH); (DGH)
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50
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Deeba F, Sanz-Leon P, Robinson PA. Effects of physiological parameter evolution on the dynamics of tonic-clonic seizures. PLoS One 2020; 15:e0230510. [PMID: 32240175 PMCID: PMC7117716 DOI: 10.1371/journal.pone.0230510] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 03/03/2020] [Indexed: 02/02/2023] Open
Abstract
The temporal and spectral characteristics of tonic-clonic seizures are investigated using a neural field model of the corticothalamic system in the presence of a temporally varying connection strength between the cerebral cortex and thalamus. Increasing connection strength drives the system into ∼ 10 Hz seizure oscillations once a threshold is passed and a subcritical Hopf bifurcation occurs. In this study, the spectral and temporal characteristics of tonic-clonic seizures are explored as functions of the relevant properties of physiological connection strengths, such as maximum strength, time above threshold, and the ramp rate at which the strength increases or decreases. Analysis shows that the seizure onset time decreases with the maximum connection strength and time above threshold, but increases with the ramp rate. Seizure duration and offset time increase with maximum connection strength, time above threshold, and rate of change. Spectral analysis reveals that the power of nonlinear harmonics and the duration of the oscillations increase as the maximum connection strength and the time above threshold increase. A secondary limit cycle at ∼ 18 Hz, termed a saddle-cycle, is also seen during seizure onset and becomes more prominent and robust with increasing ramp rate. If the time above the threshold is too small, the system does not reach the 10 Hz limit cycle, and only exhibits 18 Hz saddle-cycle oscillations. It is also seen that the time to reach the saturated large amplitude limit-cycle seizure oscillation from both the instability threshold and from the end of the saddle-cycle oscillations is inversely proportional to the square root of the ramp rate.
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Affiliation(s)
- F. Deeba
- School of Physics, University of Sydney, Sydney, NSW, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
- * E-mail: ,
| | - P. Sanz-Leon
- School of Physics, University of Sydney, Sydney, NSW, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
| | - P. A. Robinson
- School of Physics, University of Sydney, Sydney, NSW, Australia
- Center for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia
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