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Characterisation of ictal and interictal states of epilepsy: A system dynamic approach of principal dynamic modes analysis. PLoS One 2018; 13:e0191392. [PMID: 29351559 PMCID: PMC5774786 DOI: 10.1371/journal.pone.0191392] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2017] [Accepted: 01/04/2018] [Indexed: 11/19/2022] Open
Abstract
Epilepsy is a brain disorder characterised by the recurrent and unpredictable interruptions of normal brain function, called epileptic seizures. The present study attempts to derive new diagnostic indices which may delineate between ictal and interictal states of epilepsy. To achieve this, the nonlinear modeling approach of global principal dynamic modes (PDMs) is adopted to examine the functional connectivity of the temporal and frontal lobes with the occipital brain segment using an ensemble of paediatric EEGs having the presence of epileptic seizure. The distinct spectral characteristics of global PDMs are found to be in line with the neural rhythms of brain dynamics. Moreover, we find that the linear trends of associated nonlinear functions (ANFs) associated with the 2nd and 4th global PDMs (representing delta, theta and alpha bands) of Fp1–F3 may differentiate between ictal and interictal states of epilepsy. These findings suggest that global PDMs and their associated ANFs may offer potential utility as diagnostic neural measures for ictal and interictal states of epilepsy.
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102
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Lopes MA, Lee KE, Goltsev AV. Neuronal network model of interictal and recurrent ictal activity. Phys Rev E 2017; 96:062412. [PMID: 29347379 DOI: 10.1103/physreve.96.062412] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Indexed: 02/04/2023]
Abstract
We propose a neuronal network model which undergoes a saddle node on an invariant circle bifurcation as the mechanism of the transition from the interictal to the ictal (seizure) state. In the vicinity of this transition, the model captures important dynamical features of both interictal and ictal states. We study the nature of interictal spikes and early warnings of the transition predicted by this model. We further demonstrate that recurrent seizures emerge due to the interaction between two networks.
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Affiliation(s)
- M A Lopes
- Living Systems Institute, University of Exeter, Exeter EX4 4QD, United Kingdom.,Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter EX4 4QD, United Kingdom.,EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter EX4 4QD, United Kingdom.,Department of Physics and I3N, University of Aveiro, 3810-193 Aveiro, Portugal
| | - K-E Lee
- Department of Physics and I3N, University of Aveiro, 3810-193 Aveiro, Portugal.,Department of Anesthesiology and Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA
| | - A V Goltsev
- Department of Physics and I3N, University of Aveiro, 3810-193 Aveiro, Portugal.,A.F. Ioffe Physico-Technical Institue, 194021 St. Petersburg, Russia
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103
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Tieng QM, Anbazhagan A, Chen M, Reutens DC. Mouse epileptic seizure detection with multiple EEG features and simple thresholding technique. J Neural Eng 2017; 14:066006. [DOI: 10.1088/1741-2552/aa8069] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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104
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Sohanian Haghighi H, Markazi AHD. A new description of epileptic seizures based on dynamic analysis of a thalamocortical model. Sci Rep 2017; 7:13615. [PMID: 29051507 PMCID: PMC5648785 DOI: 10.1038/s41598-017-13126-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Accepted: 09/13/2017] [Indexed: 12/11/2022] Open
Abstract
Increasing evidence suggests that the brain dynamics can be interpreted from the viewpoint of nonlinear dynamical systems. The aim of this paper is to investigate the behavior of a thalamocortical model from this perspective. The model includes both cortical and sensory inputs that can affect the dynamic nature of the model. Driving response of the model subjected to various harmonic stimulations is considered to identify the effects of stimulus parameters on the cortical output. Detailed numerical studies including phase portraits, Poincare maps and bifurcation diagrams reveal a wide range of complex dynamics including period doubling and chaos in the output. Transition between different states can occur as the stimulation parameters are changed. In addition, the amplitude jump phenomena and hysteresis are shown to be possible as a result of the bending in the frequency response curve. These results suggest that the jump phenomenon due to the brain nonlinear resonance can be responsible for the transitions between ictal and interictal states.
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Affiliation(s)
- H Sohanian Haghighi
- School of Mechanical Engineering, Iran University of Science and Technology, Tehran, 16844, Iran.
| | - A H D Markazi
- School of Mechanical Engineering, Iran University of Science and Technology, Tehran, 16844, Iran
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105
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Fan D, Wang Q, Su J, Xi H. Stimulus-induced transitions between spike-wave discharges and spindles with the modulation of thalamic reticular nucleus. J Comput Neurosci 2017; 43:203-225. [PMID: 28939929 DOI: 10.1007/s10827-017-0658-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2017] [Revised: 08/11/2017] [Accepted: 09/04/2017] [Indexed: 12/19/2022]
Abstract
It is believed that thalamic reticular nucleus (TRN) controls spindles and spike-wave discharges (SWD) in seizure or sleeping processes. The dynamical mechanisms of spatiotemporal evolutions between these two types of activity, however, are not well understood. In light of this, we first use a single-compartment thalamocortical neural field model to investigate the effects of TRN on occurrence of SWD and its transition. Results show that the increasing inhibition from TRN to specific relay nuclei (SRN) can lead to the transition of system from SWD to slow-wave oscillation. Specially, it is shown that stimulations applied in the cortical neuronal populations can also initiate the SWD and slow-wave oscillation from the resting states under the typical inhibitory intensity from TRN to SRN. Then, we expand into a 3-compartment coupled thalamocortical model network in linear and circular structures, respectively, to explore the spatiotemporal evolutions of wave states in different compartments. The main results are: (i) for the open-ended model network, SWD induced by stimulus in the first compartment can be transformed into sleep-like slow UP-DOWN and spindle states as it propagates into the downstream compartments; (ii) for the close-ended model network, weak stimulations performed in the first compartment can result in the consistent experimentally observed spindle oscillations in all three compartments; in contrast, stronger periodic single-pulse stimulations applied in the first compartment can induce periodic transitions between SWD and spindle oscillations. Detailed investigations reveal that multi-attractor coexistence mechanism composed of SWD, spindles and background state underlies these state evolutions. What's more, in order to demonstrate the state evolution stability with respect to the topological structures of neural network, we further expand the 3-compartment coupled network into 10-compartment coupled one, with linear and circular structures, and nearest-neighbor (NN) coupled network as well as its realization of small-world (SW) topology via random rewiring, respectively. Interestingly, for the cases of linear and circular connetivities, qualitatively similar results were obtained in addition to the more irregularity of firings. However, SWD can be eventually transformed into the consistent low-amplitude oscillations for both NN and SW networks. In particular, SWD evolves into the slow spindling oscillations and background tonic oscillations within the NN and SW network, respectively. Our modeling and simulation studies highlight the effect of network topology in the evolutions of SWD and spindling oscillations, which provides new insights into the mechanisms of cortical seizures development.
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Affiliation(s)
- 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.
| | - Jianzhong Su
- Department of Mathematics, University of Texas at Arlington, Arlington, TX, 76019-0408, USA
| | - Hongguang Xi
- Department of Mathematics, University of Texas at Arlington, Arlington, TX, 76019-0408, USA
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106
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Martínez-González CL, Balankin A, López T, Manjarrez-Marmolejo J, Martínez-Ortiz EJ. Evaluation of dynamic scaling of growing interfaces in EEG fluctuations of seizures in animal model of temporal lobe epilepsy. Comput Biol Med 2017; 88:41-49. [PMID: 28692930 DOI: 10.1016/j.compbiomed.2017.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Revised: 06/26/2017] [Accepted: 07/02/2017] [Indexed: 11/28/2022]
Abstract
Epileptic seizures, as a dynamic phenomenon in brain behavior, obey a scale-free behavior, frequently analyzed by electrical activity recording. This recording can be seen as a surface that roughens with time. Dynamic scaling studies roughening processes or growing interfaces. In this theory, a set of exponents -obtained from scale invariance properties- characterize rough interfaces growth. The aim of the present study was to investigate scaling behavior in EEG time series fluctuations of a chemical animal model of temporal lobe epilepsy, with dynamic scaling to detect changes on seizure onset. We analyzed local variables in different sampling intervals and estimated rough, scaling and dynamic exponents. Results exhibited long-range correlations in interictal activity. Results of renormalization and data collapsing confirmed that each epoch of EEG fluctuations for interictal, preictal and postictal collapse in a curve in different scales, each segment independently; remarkably, we found non-scaling behavior in seizures epochs. Data for the different sampling intervals for ictal period do not collapse in one curve, which implies that ictal activity does not exhibit the same scaling behavior than the other epochs. Statistical significant differences of growth exponent were found between interictal and ictal segment, while for scaling exponent, significant differences were found between interictal and postictal segment. These results confirm the potential of scaling exponents as characteristic parameters to detect changes on seizure onset, which suggests their use as inputs for analysis methods for seizure detection in long-term recordings, while changes in growth exponent are potentially useful for prediction purposes.
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Affiliation(s)
| | - Alexander Balankin
- Instituto Politécnico Nacional, SEPI ESIME-Z, Av. IPN S/N, C.P. 07738, Mexico
| | - Tessy López
- Universidad Autónoma Metropolitana, C.P. 14387, Mexico
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107
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Alsharif MM, El-Fetoh NMA, Ali GY, Alanazi KF, Alanazi AN, FalahAlanazi O, Alshalan MH, Alfuhigi ZD, Alruwaili AE, Alhazmi RS, Alruwaili ASM, Alanizy TMA, Alshammari JH, Altimyat AO, Alshammari MMM. Epilepsy as a health problem among school children in Turaif, Northern Saudi Arabia, 2017. Electron Physician 2017; 9:5036-5042. [PMID: 28979739 PMCID: PMC5614289 DOI: 10.19082/5036] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2017] [Accepted: 07/11/2017] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Epilepsy is the most common serious neurological disorder and is one of the world's most prevalent non-communicable diseases. There are no recently published data on the prevalence of epilepsy in school children in Northern Saudi Arabia. OBJECTIVE This study was conducted to determine the prevalence of epilepsy and to show some of its risk factors in school children and adolescents (6-18 years) in Turaif city, Northern Saudi Arabia. METHODS This, population-based, cross-sectional study was conducted in Turaif city, over a 6-month period, from July 2016 to January 2017. It included pupils aged 6 to18 years in all primary, preparatory and secondary schools in Turaif city. Multi-stage sampling was employed. A designated structured questionnaire was completed for each patient and included the patient's history, clinical examination, investigations and medications. Data were analyzed by SPSS version 16, using Chi-Squared test and descriptive statistics. RESULTS Out of 1,230 children, 66 (5.5%) had epilepsy; 68.2% of them were males and 31.8% females (p=0.000). Consanguinity between parents plays a significant role where 59.1% of cases had parents who were cousins (p=0.000). Family history also had a significant effect as 68.2% of cases had epilepsy cases in their families (p=0.000). CONCLUSION Epilepsy prevalence among school children (6-18 years) in Turaif city is higher in males than females. Consanguinity and positive family history are important factors. Decision makers must take effective steps to limit the causes and risk factors of the problem.
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Affiliation(s)
| | - Nagah Mohamed Abo El-Fetoh
- Associate Professor, Community Medicine Department, Faculty of medicine, Northern Border University, Arar, Saudi Arabia
| | - Gihan Yousef Ali
- Lecture of Pediatric, Pediatric Department, Sohag College of Medicine, Sohag University, Eygpt
| | | | | | - Ohud FalahAlanazi
- Student, Faculty of Medicine, Northern Border University, Arar, Saudi Arabia
| | | | | | - Anwar Eid Alruwaili
- Student, Faculty of Medicine, Northern Border University, Arar, Saudi Arabia
| | - Reem Sebeh Alhazmi
- Student, Faculty of Medicine, Northern Border University, Arar, Saudi Arabia
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108
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Deviations from Critical Dynamics in Interictal Epileptiform Activity. J Neurosci 2017; 36:12276-12292. [PMID: 27903734 DOI: 10.1523/jneurosci.0809-16.2016] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Revised: 10/09/2016] [Accepted: 10/11/2016] [Indexed: 11/21/2022] Open
Abstract
The framework of criticality provides a unifying perspective on neuronal dynamics from in vitro cortical cultures to functioning human brains. Recent findings suggest that a healthy cortex displays critical dynamics, giving rise to scale-free spatiotemporal cascades of activity, termed neuronal avalanches. Pharmacological manipulations of the excitation-inhibition balance (EIB) in cortical cultures were previously shown to result in deviations from criticality and from the power law scaling of avalanche size distribution. To examine the sensitivity of neuronal avalanche metrics to altered EIB in humans, we focused on epilepsy, a neurological disorder characterized by hyperexcitable networks. Using magnetoencephalography, we quantitatively assessed deviations from criticality in the brain dynamics of patients with epilepsy during interictal (between-seizures) activity. Compared with healthy control subjects, epilepsy patients tended to exhibit a higher neural gain and larger avalanches, particularly during interictal epileptiform activity. Moreover, deviations from scale-free behavior were exclusively connected to brief intervals at epileptiform discharges, strengthening the association between deviations from criticality and the instantaneous changes in EIB. The avalanches collected during interictal epileptiform activity had not only a stereotypical size range but also involved particular spatial patterns of activations, as expected for periods of epileptic network dominance. Overall, the neuronal avalanche metrics provide a quantitative novel description of interictal brain activity of patients with epilepsy. SIGNIFICANCE STATEMENT Healthy brain dynamics requires a delicate balance between excitatory and inhibitory processes. Several brain disorders, such as epilepsy, are associated with altered excitation-inhibition balance, but assessing this balance using noninvasive tools is still challenging. In this study, we apply the framework of critical brain dynamics to data from epilepsy patients, which were recorded between seizures. We show that metrics of criticality provide a sensitive tool for noninvasive assessment of changes in the balance. Specifically, brain activity of epilepsy patients deviates from healthy critical brain dynamics, particularly during abnormal epileptiform activity. The study offers a novel quantitative perspective on epilepsy and its relation to healthy brain dynamics.
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109
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Lainscsek C, Weyhenmeyer J, Cash SS, Sejnowski TJ. Delay Differential Analysis of Seizures in Multichannel Electrocorticography Data. Neural Comput 2017; 29:3181-3218. [PMID: 28777720 DOI: 10.1162/neco_a_01009] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
High-density electrocorticogram (ECoG) electrodes are capable of recording neurophysiological data with high temporal resolution with wide spatial coverage. These recordings are a window to understanding how the human brain processes information and subsequently behaves in healthy and pathologic states. Here, we describe and implement delay differential analysis (DDA) for the characterization of ECoG data obtained from human patients with intractable epilepsy. DDA is a time-domain analysis framework based on embedding theory in nonlinear dynamics that reveals the nonlinear invariant properties of an unknown dynamical system. The DDA embedding serves as a low-dimensional nonlinear dynamical basis onto which the data are mapped. This greatly reduces the risk of overfitting and improves the method's ability to fit classes of data. Since the basis is built on the dynamical structure of the data, preprocessing of the data (e.g., filtering) is not necessary. We performed a large-scale search for a DDA model that best fit ECoG recordings using a genetic algorithm to qualitatively discriminate between different cortical states and epileptic events for a set of 13 patients. A single DDA model with only three polynomial terms was identified. Singular value decomposition across the feature space of the model revealed both global and local dynamics that could differentiate electrographic and electroclinical seizures and provided insights into highly localized seizure onsets and diffuse seizure terminations. Other common ECoG features such as interictal periods, artifacts, and exogenous stimuli were also analyzed with DDA. This novel framework for signal processing of seizure information demonstrates an ability to reveal unique characteristics of the underlying dynamics of the seizure and may be useful in better understanding, detecting, and maybe even predicting seizures.
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Affiliation(s)
- Claudia Lainscsek
- Howard Hughes Medical Institute, Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, U.S.A., and Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, U.S.A.
| | - Jonathan Weyhenmeyer
- Goodman Campbell Brain and Spine, Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, IN 46202, U.S.A.
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, U.S.A.
| | - Terrence J Sejnowski
- Howard Hughes Medical Institute, Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, U.S.A., and Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, U.S.A.
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110
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Cocchi L, Gollo LL, Zalesky A, Breakspear M. Criticality in the brain: A synthesis of neurobiology, models and cognition. Prog Neurobiol 2017; 158:132-152. [PMID: 28734836 DOI: 10.1016/j.pneurobio.2017.07.002] [Citation(s) in RCA: 226] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Revised: 06/15/2017] [Accepted: 07/13/2017] [Indexed: 11/26/2022]
Abstract
Cognitive function requires the coordination of neural activity across many scales, from neurons and circuits to large-scale networks. As such, it is unlikely that an explanatory framework focused upon any single scale will yield a comprehensive theory of brain activity and cognitive function. Modelling and analysis methods for neuroscience should aim to accommodate multiscale phenomena. Emerging research now suggests that multi-scale processes in the brain arise from so-called critical phenomena that occur very broadly in the natural world. Criticality arises in complex systems perched between order and disorder, and is marked by fluctuations that do not have any privileged spatial or temporal scale. We review the core nature of criticality, the evidence supporting its role in neural systems and its explanatory potential in brain health and disease.
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Affiliation(s)
- Luca Cocchi
- QIMR Berghofer Medical Research Institute, Brisbane, Australia.
| | | | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Australia
| | - Michael Breakspear
- QIMR Berghofer Medical Research Institute, Brisbane, Australia; Metro North Mental Health Service, Brisbane, Australia
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111
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Spatiotemporal propagation patterns of generalized ictal spikes in childhood absence epilepsy. Clin Neurophysiol 2017; 128:1553-1562. [PMID: 28709121 DOI: 10.1016/j.clinph.2017.05.021] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2017] [Revised: 04/29/2017] [Accepted: 05/31/2017] [Indexed: 11/23/2022]
Abstract
OBJECTIVE This work investigates the spatial distribution in time of generalized ictal spikes in the typical absences of childhood absence epilepsy (CAE). METHODS We studied twelve children with CAE, who had more than two typical absences during their routine video-EEG. Seizures were identified, and ictal spikes were marked over the maximum electronegative peak, clustered, waveform-averaged and spatiotemporaly analyzed in 2D electrode space. RESULTS Consistency of spatiotemporal patterns of ictal spikes was high between the absences of the same child, but low between children. Three main discharge patterns were identified: of anterio-posterior propagation, of posterio-anterior propagation and confined to the frontal/prefrontal regions. In 4 patients, the propagation patterns transformed during the seizure into either a lateralized diminished or a non-lateralized reverse direction form. Most spikes originated fronto-temporaly, all maximized over the frontal/prefrontal electrodes and mostly decayed prefrontaly. In 4 patients, lateralized propagation patterns were identified. CONCLUSIONS Ictal spike propagation patterns suggest that epileptogenic CAE networks are personalized, interconnect distal areas in the brain - not the entire cortex - with a tendency to generate bilateral symmetrical discharges, sometimes unsuccessfully. The transformation of propagation patterns during the seizure indicates the existence of dynamic interplay within epileptogenic networks. SIGNIFICANCE Our results support the revised concept of ictogenesis of ILAE definition in genetic (also known as idiopathic) generalized epilepsies. Understanding the focal features in CAE avoids misdiagnosis as focal epilepsy and inappropriate treatment.
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112
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Zerouali Y, Ghaziri J, Nguyen DK. Multimodal investigation of epileptic networks: The case of insular cortex epilepsy. PROGRESS IN BRAIN RESEARCH 2017; 226:1-33. [PMID: 27323937 DOI: 10.1016/bs.pbr.2016.04.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The insula is a deep cortical structure sharing extensive synaptic connections with a variety of brain regions, including several frontal, temporal, and parietal structures. The identification of the insular connectivity network is obviously valuable for understanding a number of cognitive processes, but also for understanding epilepsy since insular seizures involve a number of remote brain regions. Ultimately, knowledge of the structure and causal relationships within the epileptic networks associated with insular cortex epilepsy can offer deeper insights into this relatively neglected type of epilepsy enabling the refining of the clinical approach in managing patients affected by it. In the present chapter, we first review the multimodal noninvasive tests performed during the presurgical evaluation of epileptic patients with drug refractory focal epilepsy, with particular emphasis on their value for the detection of insular cortex epilepsy. Second, we review the emerging multimodal investigation techniques in the field of epilepsy, that aim to (1) enhance the detection of insular cortex epilepsy and (2) unveil the architecture and causal relationships within epileptic networks. We summarize the results of these approaches with emphasis on the specific case of insular cortex epilepsy.
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Affiliation(s)
- Y Zerouali
- Research Centre, Centre hospitalier de l'Université de Montréal, Montreal, QC, Canada; Ecole Polytechnique de Montréal, Montreal, QC, Canada
| | - J Ghaziri
- Research Centre, Centre hospitalier de l'Université de Montréal, Montreal, QC, Canada
| | - D K Nguyen
- Research Centre, Centre hospitalier de l'Université de Montréal, Montreal, QC, Canada; CHUM-Hôpital Notre-Dame, Montreal, QC, Canada.
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113
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Fan D, Liao F, Wang Q. The pacemaker role of thalamic reticular nucleus in controlling spike-wave discharges and spindles. CHAOS (WOODBURY, N.Y.) 2017; 27:073103. [PMID: 28764392 DOI: 10.1063/1.4991869] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Absence epilepsy, characterized by 2-4 Hz spike-wave discharges (SWDs), can be caused by pathological interactions within the thalamocortical system. Cortical spindling oscillations are also demonstrated to involve the oscillatory thalamocortical rhythms generated by the synaptic circuitry of the thalamus and cortex. This implies that SWDs and spindling oscillations can share the common thalamocortical mechanism. Additionally, the thalamic reticular nucleus (RE) is hypothesized to regulate the onsets and propagations of both the epileptic SWDs and sleep spindles. Based on the proposed single-compartment thalamocortical neural field model, we firstly investigate the stimulation effect of RE on the initiations, terminations, and transitions of SWDs. It is shown that the activations and deactivations of RE triggered by single-pulse stimuli can drive the cortical subsystem to behave as the experimentally observed onsets and self-abatements of SWDs, as well as the transitions from 2-spike and wave discharges (2-SWDs) to SWDs. In particular, with increasing inhibition from RE to the specific relay nucleus (TC), rich transition behaviors in cortex can be obtained through the upstream projection path, RE→TC→Cortex. Although some of the complex dynamical patterns can be expected from the earlier single compartment thalamocortical model, the effect of brain network topology on the emergence of SWDs and spindles, as well as the transitions between them, has not been fully investigated. We thereby develop a spatially extended 3-compartment coupled network model with open-/closed-end connective configurations, to investigate the spatiotemporal effect of RE on the SWDs and spindles. Results show that the degrees of activations of RE1 can induce the rich spatiotemporal evolution properties including the propagations from SWDs to spindles within different compartments and the transitions between them, through the RE1→TC1→Cortex1 and Cortex1→Cortex2→Cortex3 projecting paths, respectively. Overall, those results imply that RE possesses the pacemaker function in controlling SWDs and spindling oscillations, which computationally provide causal support for the involvement of RE in absence seizures and sleep spindles.
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Affiliation(s)
- Denggui Fan
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, People's Republic of China
| | - Fucheng Liao
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, People's Republic of China
| | - Qingyun Wang
- Department of Dynamics and Control, Beihang University, Beijing 100191, People's Republic of China
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114
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Visser S, Nicks R, Faugeras O, Coombes S. Standing and travelling waves in a spherical brain model: The Nunez model revisited. PHYSICA D. NONLINEAR PHENOMENA 2017; 349:27-45. [PMID: 28626276 PMCID: PMC5421190 DOI: 10.1016/j.physd.2017.02.017] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Revised: 02/27/2017] [Accepted: 02/28/2017] [Indexed: 06/07/2023]
Abstract
The Nunez model for the generation of electroencephalogram (EEG) signals is naturally described as a neural field model on a sphere with space-dependent delays. For simplicity, dynamical realisations of this model either as a damped wave equation or an integro-differential equation, have typically been studied in idealised one dimensional or planar settings. Here we revisit the original Nunez model to specifically address the role of spherical topology on spatio-temporal pattern generation. We do this using a mixture of Turing instability analysis, symmetric bifurcation theory, centre manifold reduction and direct simulations with a bespoke numerical scheme. In particular we examine standing and travelling wave solutions using normal form computation of primary and secondary bifurcations from a steady state. Interestingly, we observe spatio-temporal patterns which have counterparts seen in the EEG patterns of both epileptic and schizophrenic brain conditions.
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Affiliation(s)
- S. Visser
- School of Mathematical Sciences, University of Nottingham, NG7 2RD, UK
- Wellcome Trust Centre for Biomedical Modelling and Analysis, RILD Building, University of Exeter, EX2 5DW, UK
| | - R. Nicks
- School of Mathematical Sciences, University of Nottingham, NG7 2RD, UK
| | - O. Faugeras
- INRIA Sophia Antipolis Mediterannee, 2004 Route Des Lucioles, Sophia Antipolis, 06410, France
| | - S. Coombes
- School of Mathematical Sciences, University of Nottingham, NG7 2RD, UK
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115
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Bauer PR, Thijs RD, Lamberts RJ, Velis DN, Visser GH, Tolner EA, Sander JW, Lopes da Silva FH, Kalitzin SN. Dynamics of convulsive seizure termination and postictal generalized EEG suppression. Brain 2017; 140:655-668. [PMID: 28073789 DOI: 10.1093/brain/aww322] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Accepted: 10/31/2016] [Indexed: 12/21/2022] Open
Abstract
It is not fully understood how seizures terminate and why some seizures are followed by a period of complete brain activity suppression, postictal generalized EEG suppression. This is clinically relevant as there is a potential association between postictal generalized EEG suppression, cardiorespiratory arrest and sudden death following a seizure. We combined human encephalographic seizure data with data of a computational model of seizures to elucidate the neuronal network dynamics underlying seizure termination and the postictal generalized EEG suppression state. A multi-unit computational neural mass model of epileptic seizure termination and postictal recovery was developed. The model provided three predictions that were validated in EEG recordings of 48 convulsive seizures from 48 subjects with refractory focal epilepsy (20 females, age range 15-61 years). The duration of ictal and postictal generalized EEG suppression periods in human EEG followed a gamma probability distribution indicative of a deterministic process (shape parameter 2.6 and 1.5, respectively) as predicted by the model. In the model and in humans, the time between two clonic bursts increased exponentially from the start of the clonic phase of the seizure. The terminal interclonic interval, calculated using the projected terminal value of the log-linear fit of the clonic frequency decrease was correlated with the presence and duration of postictal suppression. The projected terminal interclonic interval explained 41% of the variation in postictal generalized EEG suppression duration (P < 0.02). Conversely, postictal generalized EEG suppression duration explained 34% of the variation in the last interclonic interval duration. Our findings suggest that postictal generalized EEG suppression is a separate brain state and that seizure termination is a plastic and autonomous process, reflected in increased duration of interclonic intervals that determine the duration of postictal generalized EEG suppression.
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Affiliation(s)
- Prisca R Bauer
- Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands.,NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Roland D Thijs
- Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands.,NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK.,Department of Neurology, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Robert J Lamberts
- Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands
| | - Demetrios N Velis
- Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands
| | - Gerhard H Visser
- Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands
| | - Else A Tolner
- Department of Neurology, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Josemir W Sander
- Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands.,NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK.,Epilepsy Society, Chalfont St Peter SL9 0RJ, UK
| | - Fernando H Lopes da Silva
- Center of Neurosciences, Swammerdam Institute of Life Sciences, University of Amsterdam, P.O. Box 94215 1090 GE, The Netherlands.,Instituto Superior Técnico, University of Lisbon, 1049-001, Lisbon, Portugal
| | - Stiliyan N Kalitzin
- Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands.,Image Sciences Institute, University Medical Center Utrecht, P.O. Box 85500, 3508 GA Utrecht, The Netherlands
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Chu H, Chung CK, Jeong W, Cho KH. Predicting epileptic seizures from scalp EEG based on attractor state analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 143:75-87. [PMID: 28391821 DOI: 10.1016/j.cmpb.2017.03.002] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2016] [Accepted: 03/01/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Epilepsy is the second most common disease of the brain. Epilepsy makes it difficult for patients to live a normal life because it is difficult to predict when seizures will occur. In this regard, if seizures could be predicted a reasonable period of time before their occurrence, epilepsy patients could take precautions against them and improve their safety and quality of life. In this paper, we investigate a novel seizure precursor based on attractor state analysis for seizure prediction. METHODS We analyze the transition process from normal to seizure attractor state and investigate a precursor phenomenon seen before reaching the seizure attractor state. From the result of an analysis, we define a quantified spectral measure in scalp EEG for seizure prediction. From scalp EEG recordings, the Fourier coefficients of six EEG frequency bands are extracted, and the defined spectral measure is computed based on the coefficients for each half-overlapped 20-second-long window. The computed spectral measure is applied to seizure prediction using a low-complexity methodology. RESULTS Within scalp EEG, we identified an early-warning indicator before an epileptic seizure occurs. Getting closer to the bifurcation point that triggers the transition from normal to seizure state, the power spectral density of low frequency bands of the perturbation of an attractor in the EEG, showed a relative increase. A low-complexity seizure prediction algorithm using this feature was evaluated, using ∼583h of scalp EEG in which 143 seizures in 16 patients were recorded. With the test dataset, the proposed method showed high sensitivity (86.67%) with a false prediction rate of 0.367h-1 and average prediction time of 45.3min. CONCLUSIONS A novel seizure prediction method using scalp EEG, based on attractor state analysis, shows potential for application with real epilepsy patients. This is the first study in which the seizure-precursor phenomenon of an epileptic seizure is investigated based on attractor-based analysis of the macroscopic dynamics of the brain. With the scalp EEG, we first propose use of a spectral feature identified for seizure prediction, in which the dynamics of an attractor are excluded, and only the perturbation dynamics from the attractor are considered.
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Affiliation(s)
- Hyunho Chu
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Daejeon, Republic of Korea
| | - Chun Kee Chung
- Department of Neurosurgery, Seoul National University College of Medicine, Seoul, Republic of Korea; Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Republic of Korea
| | - Woorim Jeong
- Interdisciplinary Program in Neuroscience, Seoul National University College of Natural Science, Seoul, Republic of Korea
| | - Kwang-Hyun Cho
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Daejeon, Republic of Korea.
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117
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Medaglia JD, Pasqualetti F, Hamilton RH, Thompson-Schill SL, Bassett DS. Brain and cognitive reserve: Translation via network control theory. Neurosci Biobehav Rev 2017; 75:53-64. [PMID: 28104411 PMCID: PMC5359115 DOI: 10.1016/j.neubiorev.2017.01.016] [Citation(s) in RCA: 79] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Revised: 01/10/2017] [Accepted: 01/11/2017] [Indexed: 01/01/2023]
Abstract
Traditional approaches to understanding the brain's resilience to neuropathology have identified neurophysiological variables, often described as brain or cognitive "reserve," associated with better outcomes. However, mechanisms of function and resilience in large-scale brain networks remain poorly understood. Dynamic network theory may provide a basis for substantive advances in understanding functional resilience in the human brain. In this perspective, we describe recent theoretical approaches from network control theory as a framework for investigating network level mechanisms underlying cognitive function and the dynamics of neuroplasticity in the human brain. We describe the theoretical opportunities offered by the application of network control theory at the level of the human connectome to understand cognitive resilience and inform translational intervention.
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Affiliation(s)
- John Dominic Medaglia
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California-Riverside, Riverside, CA 92521, United States
| | - Roy H Hamilton
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
| | | | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, PA 19104, United States; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, United States.
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118
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Roberts JA, Friston KJ, Breakspear M. Clinical Applications of Stochastic Dynamic Models of the Brain, Part II: A Review. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2017. [DOI: 10.1016/j.bpsc.2016.12.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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119
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Altered Effective Connectivity Network in Childhood Absence Epilepsy: A Multi-frequency MEG Study. Brain Topogr 2017; 30:673-684. [PMID: 28286918 DOI: 10.1007/s10548-017-0555-1] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2016] [Accepted: 02/07/2017] [Indexed: 12/11/2022]
Abstract
Using multi-frequency magnetoencephalography (MEG) data, we investigated whether the effective connectivity (EC) network of patients with childhood absence epilepsy (CAE) is altered during the inter-ictal period in comparison with healthy controls. MEG data from 13 untreated CAE patients and 10 healthy controls were recorded. Correlation analysis and Granger causality analysis were used to construct an EC network at the source level in eight frequency bands. Alterations in the spatial pattern and topology of the network in CAE were investigated by comparing the patients with the controls. The network pattern was altered mainly in 1-4 Hz, showing strong connections within the frontal cortex and weak connections in the anterior-posterior pathways. The EC involving the precuneus/posterior cingulate cortex (PC/PCC) significantly decreased in low-frequency bands. In addition, the parameters of graph theory were significantly altered in several low- and high-frequency bands. CAE patients display frequency-specific abnormalities in the network pattern even during the inter-ictal period, and the frontal cortex and PC/PCC might play crucial roles in the pathophysiology of CAE. The EC network of CAE patients was over-connective and random during the inter-ictal period. This study is the first to reveal the frequency-specific alteration in the EC network during the inter-ictal period in CAE patients. Multiple-frequency MEG data are useful in investigating the pathophysiology of CAE, which can serve as new biomarkers of this disorder.
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120
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Direito B, Teixeira CA, Sales F, Castelo-Branco M, Dourado A. A Realistic Seizure Prediction Study Based on Multiclass SVM. Int J Neural Syst 2017; 27:1750006. [DOI: 10.1142/s012906571750006x] [Citation(s) in RCA: 58] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensional feature sets, is presented. The feature sets, combined with multiclass classification and post-processing schemes aim at the generation of alarms and reduced influence of false positives. This study considers 216 patients from the European Epilepsy Database, and includes 185 patients with scalp EEG recordings and 31 with intracranial data. The strategy was tested over a total of 16,729.80[Formula: see text]h of inter-ictal data, including 1206 seizures. We found an overall sensitivity of 38.47% and a false positive rate per hour of 0.20. The performance of the method achieved statistical significance in 24 patients (11% of the patients). Despite the encouraging results previously reported in specific datasets, the prospective demonstration on long-term EEG recording has been limited. Our study presents a prospective analysis of a large heterogeneous, multicentric dataset. The statistical framework based on conservative assumptions, reflects a realistic approach compared to constrained datasets, and/or in-sample evaluations. The improvement of these results, with the definition of an appropriate set of features able to improve the distinction between the pre-ictal and nonpre-ictal states, hence minimizing the effect of confounding variables, remains a key aspect.
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Affiliation(s)
- Bruno Direito
- Institute for Biomedical Imaging and Life Sciences, Faculty of Medicine, University of Coimbra Coimbra, Portugal
| | - César A. Teixeira
- Department of Informatics Engineering, University of Coimbra, Portugal
| | | | - Miguel Castelo-Branco
- Institute for Biomedical Imaging and Life Sciences, Faculty of Medicine, University of Coimbra Coimbra, Portugal
| | - António Dourado
- Department of Informatics Engineering, University of Coimbra, Portugal
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121
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Dynamic models of large-scale brain activity. Nat Neurosci 2017; 20:340-352. [PMID: 28230845 DOI: 10.1038/nn.4497] [Citation(s) in RCA: 482] [Impact Index Per Article: 68.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2016] [Accepted: 01/06/2017] [Indexed: 12/14/2022]
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122
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Pinto HPP, Carvalho VR, Medeiros DDC, Almeida AFS, Mendes EMAM, Moraes MFD. Auditory processing assessment suggests that Wistar audiogenic rat neural networks are prone to entrainment. Neuroscience 2017; 347:48-56. [PMID: 28188855 DOI: 10.1016/j.neuroscience.2017.01.043] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Revised: 01/24/2017] [Accepted: 01/26/2017] [Indexed: 12/17/2022]
Abstract
Epilepsy is a neurological disease related to the occurrence of pathological oscillatory activity, but the basic physiological mechanisms of seizure remain to be understood. Our working hypothesis is that specific sensory processing circuits may present abnormally enhanced predisposition for coordinated firing in the dysfunctional brain. Such facilitated entrainment could share a similar mechanistic process as those expediting the propagation of epileptiform activity throughout the brain. To test this hypothesis, we employed the Wistar audiogenic rat (WAR) reflex animal model, which is characterized by having seizures triggered reliably by sound. Sound stimulation was modulated in amplitude to produce an auditory steady-state-evoked response (ASSR; -53.71Hz) that covers bottom-up and top-down processing in a time scale compatible with the dynamics of the epileptic condition. Data from inferior colliculus (IC) c-Fos immunohistochemistry and electrographic recordings were gathered for both the control Wistar group and WARs. Under 85-dB SLP auditory stimulation, compared to controls, the WARs presented higher number of Fos-positive cells (at IC and auditory temporal lobe) and a significant increase in ASSR-normalized energy. Similarly, the 110-dB SLP sound stimulation also statistically increased ASSR-normalized energy during ictal and post-ictal periods. However, at the transition from the physiological to pathological state (pre-ictal period), the WAR ASSR analysis demonstrated a decline in normalized energy and a significant increase in circular variance values compared to that of controls. These results indicate an enhanced coordinated firing state for WARs, except immediately before seizure onset (suggesting pre-ictal neuronal desynchronization with external sensory drive). These results suggest a competing myriad of interferences among different networks that after seizure onset converge to a massive oscillatory circuit.
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Affiliation(s)
- Hyorrana Priscila Pereira Pinto
- Núcleo de Neurociências (NNC), Departamento de Fisiologia e Biofísica, Instituto de Ciências Biológicas - Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais CEP 31270-901, Brazil
| | - Vinícius Rezende Carvalho
- Núcleo de Neurociências (NNC), Departamento de Fisiologia e Biofísica, Instituto de Ciências Biológicas - Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais CEP 31270-901, Brazil; Programa de Pós-Graduação em Engenharia Elétrica - Escola de Engenharia - Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais CEP 31270-901, Brazil
| | - Daniel de Castro Medeiros
- Núcleo de Neurociências (NNC), Departamento de Fisiologia e Biofísica, Instituto de Ciências Biológicas - Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais CEP 31270-901, Brazil; Programa de Pós-Graduação em Engenharia Elétrica - Escola de Engenharia - Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais CEP 31270-901, Brazil; Centro de Tecnologia e Pesquisa em Magneto Ressonância - CTPMAG - Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, 31270-901 Belo Horizonte, MG, Brazil
| | - Ana Flávia Santos Almeida
- Núcleo de Neurociências (NNC), Departamento de Fisiologia e Biofísica, Instituto de Ciências Biológicas - Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais CEP 31270-901, Brazil
| | - Eduardo Mazoni Andrade Marçal Mendes
- Núcleo de Neurociências (NNC), Departamento de Fisiologia e Biofísica, Instituto de Ciências Biológicas - Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais CEP 31270-901, Brazil; Programa de Pós-Graduação em Engenharia Elétrica - Escola de Engenharia - Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais CEP 31270-901, Brazil; Centro de Tecnologia e Pesquisa em Magneto Ressonância - CTPMAG - Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, 31270-901 Belo Horizonte, MG, Brazil
| | - Márcio Flávio Dutra Moraes
- Núcleo de Neurociências (NNC), Departamento de Fisiologia e Biofísica, Instituto de Ciências Biológicas - Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais CEP 31270-901, Brazil; Programa de Pós-Graduação em Engenharia Elétrica - Escola de Engenharia - Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais CEP 31270-901, Brazil; Centro de Tecnologia e Pesquisa em Magneto Ressonância - CTPMAG - Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, 31270-901 Belo Horizonte, MG, Brazil.
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Clinical Applications of Stochastic Dynamic Models of the Brain, Part I: A Primer. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2017. [PMID: 29528293 DOI: 10.1016/j.bpsc.2017.01.010] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Biological phenomena arise through interactions between an organism's intrinsic dynamics and stochastic forces-random fluctuations due to external inputs, thermal energy, or other exogenous influences. Dynamic processes in the brain derive from neurophysiology and anatomical connectivity; stochastic effects arise through sensory fluctuations, brainstem discharges, and random microscopic states such as thermal noise. The dynamic evolution of systems composed of both dynamic and random effects can be studied with stochastic dynamic models (SDMs). This article, Part I of a two-part series, offers a primer of SDMs and their application to large-scale neural systems in health and disease. The companion article, Part II, reviews the application of SDMs to brain disorders. SDMs generate a distribution of dynamic states, which (we argue) represent ideal candidates for modeling how the brain represents states of the world. When augmented with variational methods for model inversion, SDMs represent a powerful means of inferring neuronal dynamics from functional neuroimaging data in health and disease. Together with deeper theoretical considerations, this work suggests that SDMs will play a unique and influential role in computational psychiatry, unifying empirical observations with models of perception and behavior.
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124
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Milton J, Wu J, Campbell SA, Bélair J. Outgrowing Neurological Diseases: Microcircuits, Conduction Delay and Childhood Absence Epilepsy. COMPUTATIONAL NEUROLOGY AND PSYCHIATRY 2017. [DOI: 10.1007/978-3-319-49959-8_2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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125
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Sinha N, Dauwels J, Kaiser M, Cash SS, Brandon Westover M, Wang Y, Taylor PN. Predicting neurosurgical outcomes in focal epilepsy patients using computational modelling. Brain 2016; 140:319-332. [PMID: 28011454 PMCID: PMC5278304 DOI: 10.1093/brain/aww299] [Citation(s) in RCA: 161] [Impact Index Per Article: 20.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Revised: 10/08/2016] [Accepted: 10/10/2016] [Indexed: 01/03/2023] Open
Abstract
See Eissa and Schevon (doi:10.1093/aww332) for a scientific commentary on this article. Surgery can be a last resort for patients with intractable, medically refractory epilepsy. For many of these patients, however, there is substantial risk that the surgery will be ineffective. The prediction of who is likely to benefit from a surgical approach is crucial for being able to inform patients better, conduct principled prospective clinical trials, and ultimately tailor therapeutic approaches to these patients more effectively. Dynamical computational models, informed with patient data, can be used to make predictions and give mechanistic insight. In this study, we develop patient-specific dynamical network models of epileptogenic cortex. We infer the network connectivity matrix from non-seizure electrographic recordings of patients and use these connectivity matrices as the network structure in our model. The model simulates the dynamics of a bi-stable switch at every node in this network, meaning that every node starts in a background state, but has the ability to transit to a co-existing seizure state. Whether a transition happens in a node is partly determined by the stochastic nature of the input to the node, but also by the input the node receives from other connected nodes in the network. By conducting simulations with such a model, we can detect the average transition time for nodes in a given network, and therefore define nodes with a short transition time as highly epileptogenic. In a retrospective study, we found that in some patients the regions with high epileptogenicity in the model overlap with those identified clinically as the seizure onset zone. Moreover, it was found that the resection of these regions in the model reduces the overall likelihood of a seizure. Following removal of these regions in the model, we predicted surgical outcomes and compared these to actual patient outcomes. Our predictions were found to be 81.3% accurate on a dataset of 16 patients with intractable epilepsy. Intriguingly, in patients with unsuccessful outcomes, the proposed computational approach is able to suggest alternative resection sites. The model presented here gives mechanistic insight as to why surgery may be unsuccessful in some patients. This may aid clinicians in presurgical evaluation by providing a tool to explore various surgical options, offering complementary information to existing clinical techniques.
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Affiliation(s)
- Nishant Sinha
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
| | - Justin Dauwels
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
| | - Marcus Kaiser
- Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, UK.,Institute of Neuroscience, Faculty of Medical Science, Newcastle University, Newcastle upon Tyne, UK
| | - Sydney S Cash
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - M Brandon Westover
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Yujiang Wang
- Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, UK
| | - Peter N Taylor
- Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, UK .,Institute of Neuroscience, Faculty of Medical Science, Newcastle University, Newcastle upon Tyne, UK.,Institute of Neurology, University College London, UK
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127
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Insufficient developmental excitatory neuronal activity fails to foster establishment of normal levels of inhibitory neuronal activity. Int J Dev Neurosci 2016; 55:66-71. [PMID: 27686511 DOI: 10.1016/j.ijdevneu.2016.09.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Revised: 09/16/2016] [Accepted: 09/17/2016] [Indexed: 11/22/2022] Open
Abstract
The nervous system is composed of excitatory and inhibitory neurons. One major class of inhibitory neurons release the neurotransmitter γ-Aminobutyric acid (GABA). GABAergic inhibitory activity maintains the balance that is disrupted in conditions such as epilepsy. At least some GABAergic neurons are initially excitatory and undergo a developmental conversion to convert to inhibitory neurons. The mechanism(s) behind this conversion are thought to include a critical developmental increase in excitatory activity. To test this hypothesis, we subjected ex vivo developing neuronal networks on multi-electrode arrays to various stimulation and pharmacological regimens. Synaptic activity of networks initially consists of epileptiform-like high-amplitude individual "spikes", which convert to organized bursts of activity over the course of approximately 1 month. Stimulation of networks with a digitized synaptic signal for 5days hastened the decrease of epileptiform activity. By contrast, stimulation for a single day delayed the appearance of bursts and instead increased epileptiform signaling. GABA treatment reduced total signals in unstimulated networks and networks stimulated for 5days, but instead increased signaling in networks stimulated for 1day. This increase was prevented by co-treatment with (2R)-amino-5-phosphonopentanoate and 6-cyano-7-nitroquinoxaline-2,3-dione, confirming that GABA invoked excitatory activity in networks stimulated for 1day. Glutamate increased signals in networks subjected to all stimulation regimens; the GABA receptor antagonist bicuculline prevented this increase only in networks stimulated for 1day. These latter findings are consistent with the induction of so-called "mixed" synapses (which release a combination of excitatory and inhibitory neurotransmitters) in networks stimulated for 1day, and support the hypothesis that a critical level of excitatory activity fosters the developmental transition of GABAergic neurons from excitatory to inhibitory.
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128
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Yuan S, Zhou W, Li J, Wu Q. Sparse representation-based EMD and BLDA for automatic seizure detection. Med Biol Eng Comput 2016; 55:1227-1238. [DOI: 10.1007/s11517-016-1587-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2015] [Accepted: 10/11/2016] [Indexed: 11/28/2022]
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129
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Classifying amygdala kindling stages using quantitative assessments of extracellular recording of EEG in rats. Brain Res Bull 2016; 127:148-155. [PMID: 27659238 DOI: 10.1016/j.brainresbull.2016.09.012] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Revised: 09/13/2016] [Accepted: 09/19/2016] [Indexed: 11/23/2022]
Abstract
PURPOSE Determining different seizure stage specific features in a kindling model is a crucial step in developing efficient objective techniques for early prediction and treatment of seizures. This study identified and categorized kindling stages based on their electrophysiological features through processing extracellular field potentials of Amygdala rapid kindling. METHODS Thirteen Wistar rats (200±10g) were divided into 2 groups including kindle (n=7) and sham (n=6) and respectively underwent an amygdala rapid kindling and placebo stimulation. EEG signals in each stage were classified into 7 bands: delta (0-4Hz), theta (4-8Hz), alpha (8-12Hz), low beta (12-16Hz), mid beta (16-20Hz), high beta (20-28Hz) and gamma (28-40Hz). Spectral power and power of sub bands of stage 3 (localized seizure stage (SS)) and stages 4 and 5 (generalized SSs) were compared between kindling and sham groups. RESULT Spectral analyses showed larger spikes in delta and theta subbands in the stages of 3, 4, and 5 of kindling, compared with sham animals. Generalized SSs contained more spikes than the localized SS in the kindling. Kindling process was accompanied by reduction in high beta and gamma oscillations and increase in delta sub band power which were significant in the generalized SSs. The theta/alpha ratio in the localized SS was higher than the generalized SSs and sham group, but the difference with the sham group was statistically significant. CONCLUSION Our results showed that reduced high beta and gamma and increased delta oscillations power are associated with behavioral seizure progression.
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130
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Goodfellow M, Rummel C, Abela E, Richardson MP, Schindler K, Terry JR. Estimation of brain network ictogenicity predicts outcome from epilepsy surgery. Sci Rep 2016; 6:29215. [PMID: 27384316 PMCID: PMC4935897 DOI: 10.1038/srep29215] [Citation(s) in RCA: 104] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 06/13/2016] [Indexed: 02/01/2023] Open
Abstract
Surgery is a valuable option for pharmacologically intractable epilepsy. However, significant post-operative improvements are not always attained. This is due in part to our incomplete understanding of the seizure generating (ictogenic) capabilities of brain networks. Here we introduce an in silico, model-based framework to study the effects of surgery within ictogenic brain networks. We find that factors conventionally determining the region of tissue to resect, such as the location of focal brain lesions or the presence of epileptiform rhythms, do not necessarily predict the best resection strategy. We validate our framework by analysing electrocorticogram (ECoG) recordings from patients who have undergone epilepsy surgery. We find that when post-operative outcome is good, model predictions for optimal strategies align better with the actual surgery undertaken than when post-operative outcome is poor. Crucially, this allows the prediction of optimal surgical strategies and the provision of quantitative prognoses for patients undergoing epilepsy surgery.
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Affiliation(s)
- M Goodfellow
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK.,Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, UK.,EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK
| | - C Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Switzerland
| | - E Abela
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Switzerland
| | - M P Richardson
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK.,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - K Schindler
- Department of Neurology, University of Bern, Switzerland
| | - J R Terry
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK.,Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, UK.,EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK
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131
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Wang J, Niebur E, Hu J, Li X. Suppressing epileptic activity in a neural mass model using a closed-loop proportional-integral controller. Sci Rep 2016; 6:27344. [PMID: 27273563 PMCID: PMC4895166 DOI: 10.1038/srep27344] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Accepted: 05/18/2016] [Indexed: 11/09/2022] Open
Abstract
Closed-loop control is a promising deep brain stimulation (DBS) strategy that could be used to suppress high-amplitude epileptic activity. However, there are currently no analytical approaches to determine the stimulation parameters for effective and safe treatment protocols. Proportional-integral (PI) control is the most extensively used closed-loop control scheme in the field of control engineering because of its simple implementation and perfect performance. In this study, we took Jansen's neural mass model (NMM) as a test bed to develop a PI-type closed-loop controller for suppressing epileptic activity. A graphical stability analysis method was employed to determine the stabilizing region of the PI controller in the control parameter space, which provided a theoretical guideline for the choice of the PI control parameters. Furthermore, we established the relationship between the parameters of the PI controller and the parameters of the NMM in the form of a stabilizing region, which provided insights into the mechanisms that may suppress epileptic activity in the NMM. The simulation results demonstrated the validity and effectiveness of the proposed closed-loop PI control scheme.
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Affiliation(s)
- Junsong Wang
- School of Biomedical Engineering, Tianjin Medical University, Tianjin 300070, China
| | - Ernst Niebur
- Zanvyl Krieger Mind/Brain Institute and Solomon Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Jinyu Hu
- Division of Immunology and Rheumatology, Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Xiaoli Li
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
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132
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Wulsin AC, Solomon MB, Privitera MD, Danzer SC, Herman JP. Hypothalamic-pituitary-adrenocortical axis dysfunction in epilepsy. Physiol Behav 2016; 166:22-31. [PMID: 27195458 DOI: 10.1016/j.physbeh.2016.05.015] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Revised: 04/04/2016] [Accepted: 05/11/2016] [Indexed: 12/27/2022]
Abstract
Epilepsy is a common neurological disease, affecting 2.4million people in the US. Among the many different forms of the disease, temporal lobe epilepsy (TLE) is one of the most frequent in adults. Recent studies indicate the presence of a hyperactive hypothalamopituitary- adrenocortical (HPA) axis and elevated levels of glucocorticoids in TLE patients. Moreover, in these patients, stress is a commonly reported trigger of seizures, and stress-related psychopathologies, including depression and anxiety, are highly prevalent. Elevated glucocorticoids have been implicated in the development of stress-related psychopathologies. Similarly, excess glucocorticoids have been found to increase neuronal excitability, epileptiform activity and seizure susceptibility. Thus, patients with TLE may generate abnormal stress responses that both facilitate ictal discharges and increase vulnerability for the development of comorbid psychopathologies. Here, we will examine the evidence that the HPA axis is disrupted in TLE, consider potential mechanisms by which this might occur, and discuss the implications of HPA dysfunction for seizuretriggering and psychiatric comorbidities.
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Affiliation(s)
- Aynara C Wulsin
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, College of Medicine, Cincinnati, OH, United States; Neuroscience Program, University of Cincinnati, College of Medicine, Cincinnati, OH, United States; Department of Anesthesia, Cincinnati Childrens Hospital Medical Center, Cincinnati, OH, United States; Department of Pediatrics, Cincinnati Childrens Hospital Medical Center, Cincinnati, OH, United States.
| | - Matia B Solomon
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, College of Medicine, Cincinnati, OH, United States; Neuroscience Program, University of Cincinnati, College of Medicine, Cincinnati, OH, United States
| | - Michael D Privitera
- Department of Neurology, Neuroscience Institute, University of Cincinnati, Cincinnati, OH, United States
| | - Steve C Danzer
- Neuroscience Program, University of Cincinnati, College of Medicine, Cincinnati, OH, United States; Department of Anesthesia, Cincinnati Childrens Hospital Medical Center, Cincinnati, OH, United States; Department of Pediatrics, Cincinnati Childrens Hospital Medical Center, Cincinnati, OH, United States
| | - James P Herman
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, College of Medicine, Cincinnati, OH, United States; Neuroscience Program, University of Cincinnati, College of Medicine, Cincinnati, OH, United States.
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133
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Chvátal V, Goldsmith M, Yang N. McCulloch-Pitts Brains and Pseudorandom Functions. Neural Comput 2016; 28:1042-50. [PMID: 27136970 DOI: 10.1162/neco_a_00841] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
In a pioneering classic, Warren McCulloch and Walter Pitts proposed a model of the central nervous system. Motivated by EEG recordings of normal brain activity, Chvátal and Goldsmith asked whether these dynamical systems can be engineered to produce trajectories that are irregular, disorderly, and apparently unpredictable. We show that they cannot build weak pseudorandom functions.
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Affiliation(s)
- Vašek Chvátal
- Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada
| | - Mark Goldsmith
- Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada
| | - Nan Yang
- Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada
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134
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Kim LU, D'Orsogna MR, Chou T. Onset, timing, and exposure therapy of stress disorders: mechanistic insight from a mathematical model of oscillating neuroendocrine dynamics. Biol Direct 2016; 11:13. [PMID: 27013324 PMCID: PMC4807591 DOI: 10.1186/s13062-016-0117-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2015] [Accepted: 03/17/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The hypothalamic-pituitary-adrenal (HPA) axis is a neuroendocrine system that regulates numerous physiological processes. Disruptions in the activity of the HPA axis are correlated with stress-related diseases such as post-traumatic stress disorder (PTSD) and major depressive disorder. In this paper, we characterize "normal" and "diseased" states of the HPA axis as basins of attraction of a dynamical system describing the inhibition of peptide hormones such as corticotropin-releasing hormone (CRH) and adrenocorticotropic hormone (ACTH) by circulating glucocorticoids such as cortisol (CORT). RESULTS In addition to including key physiological features such as ultradian oscillations in cortisol levels and self-upregulation of CRH neuron activity, our model distinguishes the relatively slow process of cortisol-mediated CRH biosynthesis from rapid trans-synaptic effects that regulate the CRH secretion process. We show that the slow component of the negative feedback allows external stress-induced reversible transitions between "normal" and "diseased" states in novel intensity-, duration-, and timing-dependent ways. CONCLUSION Our two-step negative feedback model suggests a mechanism whereby exposure therapy of stress disorders such as PTSD may act to normalize downstream dysregulation of the HPA axis. Our analysis provides a causative rationale for improving treatments and guiding the design of new protocols.
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Affiliation(s)
- Lae U Kim
- Department of Biomathematics, Univ of California, Los Angeles, 5109 Life Sciences 621 Charles E. Young Dr. South, Los Angeles, USA
| | - Maria R D'Orsogna
- Department of Mathematics, CalState-Northridge, 18111 Nordhoff St., Los Angeles, USA
| | - Tom Chou
- Department of Biomathematics and Department of Mathematics, University of California, Los Angeles, 5209 Life Sciences 621 Charles E. Young Dr. South, Los Angeles, USA.
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135
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Computational models of epileptiform activity. J Neurosci Methods 2016; 260:233-51. [DOI: 10.1016/j.jneumeth.2015.03.027] [Citation(s) in RCA: 123] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2015] [Revised: 03/23/2015] [Accepted: 03/24/2015] [Indexed: 12/24/2022]
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136
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Stephan KE, Binder EB, Breakspear M, Dayan P, Johnstone EC, Meyer-Lindenberg A, Schnyder U, Wang XJ, Bach DR, Fletcher PC, Flint J, Frank MJ, Heinz A, Huys QJM, Montague PR, Owen MJ, Friston KJ. Charting the landscape of priority problems in psychiatry, part 2: pathogenesis and aetiology. Lancet Psychiatry 2016; 3:84-90. [PMID: 26573969 DOI: 10.1016/s2215-0366(15)00360-0] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2015] [Revised: 07/20/2015] [Accepted: 07/20/2015] [Indexed: 12/11/2022]
Abstract
This is the second of two companion papers proposing priority problems for research on mental disorders. Whereas the first paper focuses on questions of nosology and diagnosis, this Personal View concerns pathogenesis and aetiology of psychiatric diseases. We hope that this (non-exhaustive and subjective) list of problems, nominated by scientists and clinicians from different fields and institutions, provides guidance and perspectives for choosing future directions in psychiatric science.
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Affiliation(s)
- Klaas E Stephan
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich, Zurich, Switzerland; ETH Zurich, Zurich, Switzerland; The Wellcome Trust Centre for Neuroimaging, University College London, London, UK; Max Planck Institute for Metabolism Research, Cologne, Germany.
| | - Elisabeth B Binder
- Deptartment of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany; Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Michael Breakspear
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; Metro North Mental Health Service, Brisbane, Australia
| | - Peter Dayan
- Gatsby Computational Neuroscience Unit, University College London, London, UK
| | - Eve C Johnstone
- Department of Psychiatry, University of Edinburgh, Edinburgh, UK
| | | | - Ulrich Schnyder
- Department of Psychiatry and Psychotherapy, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Xiao-Jing Wang
- Center for Neural Science, New York University, New York, NY, USA; Institute of Brain and Cognitive Science, NYU Shanghai, Shanghai, China
| | - Dominik R Bach
- Department of Psychiatry, Psychotherapy and Psychosomatics, Hospital of Psychiatry, University of Zurich, Zurich, Switzerland; The Wellcome Trust Centre for Neuroimaging, University College London, London, UK
| | - Paul C Fletcher
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Jonathan Flint
- The Wellcome Trust Centre for Human Genetics, Oxford University, Oxford, UK
| | - Michael J Frank
- Brown Institute for Brain Science, Brown University, Providence, RI, USA
| | - Andreas Heinz
- Department of Psychiatry, Humboldt University Berlin, Berlin, Germany
| | - Quentin J M Huys
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich, Zurich, Switzerland; Department of Psychiatry, Psychotherapy and Psychosomatics, Hospital of Psychiatry, University of Zurich, Zurich, Switzerland; ETH Zurich, Zurich, Switzerland
| | - P Read Montague
- The Wellcome Trust Centre for Neuroimaging, University College London, London, UK; Computational Psychiatry Unit, Virginia Tech Carilion Research Institute, Roanoke, VA, USA
| | - Michael J Owen
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK; Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Karl J Friston
- The Wellcome Trust Centre for Neuroimaging, University College London, London, UK
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137
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Hutchings F, Han CE, Keller SS, Weber B, Taylor PN, Kaiser M. Predicting Surgery Targets in Temporal Lobe Epilepsy through Structural Connectome Based Simulations. PLoS Comput Biol 2015; 11:e1004642. [PMID: 26657566 PMCID: PMC4675531 DOI: 10.1371/journal.pcbi.1004642] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2015] [Accepted: 10/29/2015] [Indexed: 02/03/2023] Open
Abstract
Temporal lobe epilepsy (TLE) is a prevalent neurological disorder resulting in disruptive seizures. In the case of drug resistant epilepsy resective surgery is often considered. This is a procedure hampered by unpredictable success rates, with many patients continuing to have seizures even after surgery. In this study we apply a computational model of epilepsy to patient specific structural connectivity derived from diffusion tensor imaging (DTI) of 22 individuals with left TLE and 39 healthy controls. We validate the model by examining patient-control differences in simulated seizure onset time and network location. We then investigate the potential of the model for surgery prediction by performing in silico surgical resections, removing nodes from patient networks and comparing seizure likelihood post-surgery to pre-surgery simulations. We find that, first, patients tend to transit from non-epileptic to epileptic states more often than controls in the model. Second, regions in the left hemisphere (particularly within temporal and subcortical regions) that are known to be involved in TLE are the most frequent starting points for seizures in patients in the model. In addition, our analysis also implicates regions in the contralateral and frontal locations which may play a role in seizure spreading or surgery resistance. Finally, the model predicts that patient-specific surgery (resection areas chosen on an individual, model-prompted, basis and not following a predefined procedure) may lead to better outcomes than the currently used routine clinical procedure. Taken together this work provides a first step towards patient specific computational modelling of epilepsy surgery in order to inform treatment strategies in individuals. Temporal lobe epilepsy (TLE) is a disorder characterised by unpredictable seizures, where surgical removal of brain tissue is often the final treatment option. In roughly 30% of cases surgery procedures are unsuccessful at preventing future seizures. This paper shows the application of a computational model which uses patient derived brain connectivity to predict the success rates of surgery in people with TLE. We consider the brains of 22 patients as networks, with brain regions as nodes and the white matter connections between them as edges. The brain network is unique to each subject and produced from brain imaging scans of 22 patients and 39 controls. Seizures are simulated before and after surgery, where surgery in the model is the removal of nodes from the network. The model successfully identifies regions known to be involved in TLE, and its predicted success rates for surgery are close to the results found in reality. The model additionally provides patient specific recommendations for surgical procedures, which in simulations show improved results compared to standard surgery in every case. This is a first step towards designing personalised surgery procedures in order to improve surgery success rates.
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Affiliation(s)
- Frances Hutchings
- Interdisciplinary Computing and Complex BioSystems, School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom
- * E-mail:
| | - Cheol E. Han
- Department of Biomedical Engineering, Korea University, Seoul, Republic of Korea
- Department of Brain Cognitive Sciences, Seoul National University, Seoul, Republic of Korea
| | - Simon S. Keller
- Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
| | - Bernd Weber
- Center for Economics and Neuroscience, University of Bonn, Bonn, Germany
- Department of Epileptology, University of Bonn, Bonn, Germany
| | - Peter N. Taylor
- Interdisciplinary Computing and Complex BioSystems, School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Marcus Kaiser
- Interdisciplinary Computing and Complex BioSystems, School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom
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138
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Sysoeva MV, Lüttjohann A, van Luijtelaar G, Sysoev IV. Dynamics of directional coupling underlying spike-wave discharges. Neuroscience 2015; 314:75-89. [PMID: 26633265 DOI: 10.1016/j.neuroscience.2015.11.044] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2015] [Revised: 10/31/2015] [Accepted: 11/18/2015] [Indexed: 11/24/2022]
Abstract
PURPOSE Spike and wave discharges (SWDs), generated within cortico-thalamo-cortical networks, are the electroencephalographic biomarker of absence epilepsy. The current work aims to identify mechanisms of SWD initiation, maintenance and termination by the analyses of dynamics and directionality of mutual interactions between the neocortex and various functionally different thalamic nuclei. METHODS Local-field potential recordings of 16 male Wistar Albino Glaxo from Rijswijk (WAG/Rij) rats, equipped with electrodes targeting layer 4-6 of the somatosensory cortex, rostral and caudal reticular thalamic nuclei (rRTN and cRTN), ventro-posteromedial (VPM), anterior (ATN) and posterior (PO) thalamic nuclei, were obtained. 3s epochs prior to SWD onset, after SWD onset, prior to SWD offset and after SWD offset were analyzed with newly developed time-variant adapted nonlinear Granger causality. RESULTS A gradual increase in coupling toward SWD onset between cortico-cortical pairs appears as early as 2s preictally. Next first unidirectional increase in coupling is noticed in a restricted number of cortico-thalamic and thalamo-cortical channel pairs, which turn into bidirectional coupling approaching SWD onset, and a gradual increase of intrathalamic coupling. Seizure onset is characterized by a coupling decrease for more than a second in a majority of channel pairs, only the cortex kept driving the cRTN. Intrathalamically the cRTN drives the PO, VPM and ATN. Most channel pairs no longer show differences in coupling with baseline during SWD maintenance, a major exception is the unidirectional coupling between cortex and cRTN. Toward the end of SWDs, more and more channel pairs show an increase in often bidirectional coupling, this increase suddenly vanishes at SWD offset. CONCLUSION The initiation of SWD is due to a gradual increase in intracortical coupling, followed by a selective increase in first unidirectional and later bidirectional coupling between the cortex and thalamus and also intrathalamically. Once the network is oscillating, coupling decreases in most of the channel pairs, although the cortex keeps its influence on the cRTN. The SWD is dampened by a gradual increase in coupling strength and in the number of channel pairs that influence each other; the latter might represent an endogenous brake of SWDs.
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Affiliation(s)
- M V Sysoeva
- Yuri Gagarin State Technical University of Saratov, Saratov, Russia; Saratov Branch of Kotel'nikov Institute of Radio Engineering and Electronics of RAS, Saratov, Russia.
| | - A Lüttjohann
- Institute of Physiology I, Westfälische Wilhelms University, Münster, Germany
| | - G van Luijtelaar
- Biological Psychology, Donders Centre for Cognition, Radboud University, Nijmegen, The Netherlands
| | - I V Sysoev
- Saratov State University, Saratov, Russia; Saratov Branch of Kotel'nikov Institute of Radio Engineering and Electronics of RAS, Saratov, Russia
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139
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Slow Spatial Recruitment of Neocortex during Secondarily Generalized Seizures and Its Relation to Surgical Outcome. J Neurosci 2015; 35:9477-90. [PMID: 26109670 DOI: 10.1523/jneurosci.0049-15.2015] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Understanding the spatiotemporal dynamics of brain activity is crucial for inferring the underlying synaptic and nonsynaptic mechanisms of brain dysfunction. Focal seizures with secondary generalization are traditionally considered to begin in a limited spatial region and spread to connected areas, which can include both pathological and normal brain tissue. The mechanisms underlying this spread are important to our understanding of seizures and to improve therapies for surgical intervention. Here we study the properties of seizure recruitment-how electrical brain activity transitions to large voltage fluctuations characteristic of spike-and-wave seizures. We do so using invasive subdural electrode arrays from a population of 16 patients with pharmacoresistant epilepsy. We find an average delay of ∼30 s for a broad area of cortex (8 × 8 cm) to be recruited into the seizure, at an estimated speed of ∼4 mm/s. The spatiotemporal characteristics of recruitment reveal two categories of patients: one in which seizure recruitment of neighboring cortical regions follows a spatially organized pattern consistent from seizure to seizure, and a second group without consistent spatial organization of activity during recruitment. The consistent, organized recruitment correlates with a more regular, compared with small-world, connectivity pattern in simulation and successful surgical treatment of epilepsy. We propose that an improved understanding of how the seizure recruits brain regions into large amplitude voltage fluctuations provides novel information to improve surgical treatment of epilepsy and highlights the slow spread of massive local activity across a vast extent of cortex during seizure.
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140
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Sato Y, Doesburg SM, Wong SM, Okanishi T, Anderson R, Nita DA, Ochi A, Otsubo H. Dynamic changes of interictal post-spike slow waves toward seizure onset in focal cortical dysplasia type II. Clin Neurophysiol 2015; 126:1670-6. [DOI: 10.1016/j.clinph.2014.11.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2014] [Revised: 11/06/2014] [Accepted: 11/15/2014] [Indexed: 12/01/2022]
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141
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Papasavvas CA, Wang Y, Trevelyan AJ, Kaiser M. Gain control through divisive inhibition prevents abrupt transition to chaos in a neural mass model. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2015; 92:032723. [PMID: 26465514 PMCID: PMC4789501 DOI: 10.1103/physreve.92.032723] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2015] [Indexed: 06/05/2023]
Abstract
Experimental results suggest that there are two distinct mechanisms of inhibition in cortical neuronal networks: subtractive and divisive inhibition. They modulate the input-output function of their target neurons either by increasing the input that is needed to reach maximum output or by reducing the gain and the value of maximum output itself, respectively. However, the role of these mechanisms on the dynamics of the network is poorly understood. We introduce a novel population model and numerically investigate the influence of divisive inhibition on network dynamics. Specifically, we focus on the transitions from a state of regular oscillations to a state of chaotic dynamics via period-doubling bifurcations. The model with divisive inhibition exhibits a universal transition rate to chaos (Feigenbaum behavior). In contrast, in an equivalent model without divisive inhibition, transition rates to chaos are not bounded by the universal constant (non-Feigenbaum behavior). This non-Feigenbaum behavior, when only subtractive inhibition is present, is linked to the interaction of bifurcation curves in the parameter space. Indeed, searching the parameter space showed that such interactions are impossible when divisive inhibition is included. Therefore, divisive inhibition prevents non-Feigenbaum behavior and, consequently, any abrupt transition to chaos. The results suggest that the divisive inhibition in neuronal networks could play a crucial role in keeping the states of order and chaos well separated and in preventing the onset of pathological neural dynamics.
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Affiliation(s)
- Christoforos A. Papasavvas
- Institute of Neuroscience, Newcastle University, Framlington Place, Newcastle upon Tyne NE2 4HH, United Kingdom
| | - Yujiang Wang
- Interdisciplinary Computing and Complex Biosystems (ICOS) Research Group, School of Computing Science, Newcastle University, Claremont Tower, Newcastle upon Tyne NE1 7RU, United Kingdom
| | - Andrew J. Trevelyan
- Institute of Neuroscience, Newcastle University, Framlington Place, Newcastle upon Tyne NE2 4HH, United Kingdom
| | - Marcus Kaiser
- Institute of Neuroscience, Newcastle University, Framlington Place, Newcastle upon Tyne NE2 4HH, United Kingdom
- Interdisciplinary Computing and Complex Biosystems (ICOS) Research Group, School of Computing Science, Newcastle University, Claremont Tower, Newcastle upon Tyne NE1 7RU, United Kingdom
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142
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Salam MT, Kassiri H, Genov R, Perez Velazquez JL. Rapid brief feedback intracerebral stimulation based on real-time desynchronization detection preceding seizures stops the generation of convulsive paroxysms. Epilepsia 2015; 56:1227-38. [PMID: 26119887 DOI: 10.1111/epi.13064] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/22/2015] [Indexed: 01/20/2023]
Abstract
OBJECTIVE To investigate the abortion of seizure generation using "minimal" intervention in hippocampi using two rat models of human temporal lobe epilepsy. METHODS The recording or stimulation electrodes were implanted into both hippocampi (CA1 area). Using the kainic acid (chronic: experiment duration 24 days) and the 4-aminopyridine (acute: experiment duration 2 h) models of paroxysms in rats, a real-time feedback stimulation paradigm was implemented, which triggered a short periodic electrical stimulus (5 Hz for 5 s) upon detecting a seizure precursor. Our seizure precursor detection algorithm relied on the monitoring of the real-time phase synchronization analysis, and detected/anticipated electrographic seizures as early as a few seconds to a few minutes before the behavioral and electrographic seizure onset, with a very low false-positive rate of the detection. RESULTS The baseline mean seizure frequencies were 5.39 seizures per day (chronic) and 13.2 seizures per hour (acute). The phase synchrony analysis detected 88% (434 of 494) of seizures with a mean false alarm of 0.67 per day (chronic) and 83% (86 of 104) of seizures with a mean false alarm of 0.47 per hour (acute). The feedback stimulation reduced the seizure frequencies to 0.41 seizures per day (chronic) and 2.4 seizures per hour (acute). Overall, the feedback stimulation paradigm reduced seizure frequency by a minimum of 80% to a maximum of 100% in 10 rats, with 83% of the animals rendered seizure-free. SIGNIFICANCE This approach represents a simple and efficient manner for stopping seizure development. Because of the short on-demand stimuli, few or no associated side effects are expected in clinical application in patients with epilepsy. Abnormal synchrony patterns are common features in epilepsy and other neurologic and psychiatric syndromes; therefore, this type of feedback stimulation paradigm could be a novel therapeutic modality for use in various neurologic and psychiatric disorders.
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Affiliation(s)
- Muhammad T. Salam
- Department of Electrical and Computer Engineering; University of Toronto; Toronto Ontario Canada
| | - Hossein Kassiri
- Department of Electrical and Computer Engineering; University of Toronto; Toronto Ontario Canada
| | - Roman Genov
- Department of Electrical and Computer Engineering; University of Toronto; Toronto Ontario Canada
| | - Jose L. Perez Velazquez
- Neuroscience & Mental Health Programme and Division of Neurology; Hospital for Sick Children; Institute of Medical Science and Department of Paediatrics; University of Toronto; Toronto Ontario Canada
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143
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Kuhlmann L, Grayden DB, Wendling F, Schiff SJ. Role of multiple-scale modeling of epilepsy in seizure forecasting. J Clin Neurophysiol 2015; 32:220-6. [PMID: 26035674 PMCID: PMC4455036 DOI: 10.1097/wnp.0000000000000149] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Over the past three decades, a number of seizure prediction, or forecasting, methods have been developed. Although major achievements were accomplished regarding the statistical evaluation of proposed algorithms, it is recognized that further progress is still necessary for clinical application in patients. The lack of physiological motivation can partly explain this limitation. Therefore, a natural question is raised: can computational models of epilepsy be used to improve these methods? Here, we review the literature on the multiple-scale neural modeling of epilepsy and the use of such models to infer physiologic changes underlying epilepsy and epileptic seizures. The authors argue how these methods can be applied to advance the state-of-the-art in seizure forecasting.
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Affiliation(s)
- Levin Kuhlmann
- NeuroEngineering Laboratory, Department of Electrical & Electronic Engineering, The University of Melbourne, VIC 3010, Australia
- Brain Dynamics Unit, Brain and Psychological Sciences Research Centre, Swinburne University of Technology, Hawthorn VIC 3122, Australia
| | - David B. Grayden
- NeuroEngineering Laboratory, Department of Electrical & Electronic Engineering, The University of Melbourne, VIC 3010, Australia
- Centre for Neural Engineering, The University of Melbourne, VIC 3010, Australia
- Bionics Institute, 384 Albert St, East Melbourne, VIC 3002, Australia
- St. Vincent’s Hospital Melbourne, Fitzroy, VIC 3002, Australia
| | - Fabrice Wendling
- INSERM, U1099, Rennes, F-35000, France
- Université de Rennes, LTSI, F-35000, France
| | - Steven J. Schiff
- Center for Neural Engineering, Departments of Engineering Science and Mechanics, Neurosurgery, and Physics, The Pennsylvania State University, University Park, PA 16802, USA
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144
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Naze S, Bernard C, Jirsa V. Computational modeling of seizure dynamics using coupled neuronal networks: factors shaping epileptiform activity. PLoS Comput Biol 2015; 11:e1004209. [PMID: 25970348 PMCID: PMC4430284 DOI: 10.1371/journal.pcbi.1004209] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2014] [Accepted: 02/24/2015] [Indexed: 11/21/2022] Open
Abstract
Epileptic seizure dynamics span multiple scales in space and time. Understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. Mathematical models have been developed to reproduce seizure dynamics across scales ranging from the single neuron to the neural population. In this study, we develop a network model of spiking neurons and systematically investigate the conditions, under which the network displays the emergent dynamic behaviors known from the Epileptor, which is a well-investigated abstract model of epileptic neural activity. This approach allows us to study the biophysical parameters and variables leading to epileptiform discharges at cellular and network levels. Our network model is composed of two neuronal populations, characterized by fast excitatory bursting neurons and regular spiking inhibitory neurons, embedded in a common extracellular environment represented by a slow variable. By systematically analyzing the parameter landscape offered by the simulation framework, we reproduce typical sequences of neural activity observed during status epilepticus. We find that exogenous fluctuations from extracellular environment and electro-tonic couplings play a major role in the progression of the seizure, which supports previous studies and further validates our model. We also investigate the influence of chemical synaptic coupling in the generation of spontaneous seizure-like events. Our results argue towards a temporal shift of typical spike waves with fast discharges as synaptic strengths are varied. We demonstrate that spike waves, including interictal spikes, are generated primarily by inhibitory neurons, whereas fast discharges during the wave part are due to excitatory neurons. Simulated traces are compared with in vivo experimental data from rodents at different stages of the disorder. We draw the conclusion that slow variations of global excitability, due to exogenous fluctuations from extracellular environment, and gap junction communication push the system into paroxysmal regimes. We discuss potential mechanisms underlying such machinery and the relevance of our approach, supporting previous detailed modeling studies and reflecting on the limitations of our methodology. Neurons communicate via different types of synapses on very fast time scales. The combination of hundred thousand of such interconnected cells within a fluctuating extracellular environment forms a complex network that gives rise to function and behavior via the formation of dynamical patterns of activity. In the context of epilepsy, the functional properties of the network at the source of a seizure are disrupted by a possibly large set of factors at the cellular and molecular levels. It is therefore needed to sacrifice some biological accuracy to model seizure dynamics in favor of macroscopic realizations. Here, we present a neuronal network model that convenes both neuronal and network representations with the goal to describe brain dynamics involved in the development of epilepsy. We compare our modeling results with animal in vivo recordings to validate our approach in the context of seizures. Such system-level methodology has significant bearing in understanding neuronal network dynamics that entangle multiple synaptic and extracellular modalities.
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Affiliation(s)
- Sebastien Naze
- UMR1106 Inserm, Institut de Neurosciences des Systèmes, Marseille, France
- Aix-Marseille University, Marseille, France
- * E-mail: (SN); (VJ)
| | - Christophe Bernard
- UMR1106 Inserm, Institut de Neurosciences des Systèmes, Marseille, France
- Aix-Marseille University, Marseille, France
| | - Viktor Jirsa
- UMR1106 Inserm, Institut de Neurosciences des Systèmes, Marseille, France
- Aix-Marseille University, Marseille, France
- * E-mail: (SN); (VJ)
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145
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Gadhoumi K, Gotman J, Lina JM. Scale invariance properties of intracerebral EEG improve seizure prediction in mesial temporal lobe epilepsy. PLoS One 2015; 10:e0121182. [PMID: 25867083 PMCID: PMC4395084 DOI: 10.1371/journal.pone.0121182] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2014] [Accepted: 01/28/2015] [Indexed: 11/19/2022] Open
Abstract
Although treatment for epilepsy is available and effective for nearly 70 percent of patients, many remain in need of new therapeutic approaches. Predicting the impending seizures in these patients could significantly enhance their quality of life if the prediction performance is clinically practical. In this study, we investigate the improvement of the performance of a seizure prediction algorithm in 17 patients with mesial temporal lobe epilepsy by means of a novel measure. Scale-free dynamics of the intracerebral EEG are quantified through robust estimates of the scaling exponents--the first cumulants--derived from a wavelet leader and bootstrap based multifractal analysis. The cumulants are investigated for the discriminability between preictal and interictal epochs. The performance of our recently published patient-specific seizure prediction algorithm is then out-of-sample tested on long-lasting data using combinations of cumulants and state similarity measures previously introduced. By using the first cumulant in combination with state similarity measures, up to 13 of 17 patients had seizures predicted above chance with clinically practical levels of sensitivity (80.5%) and specificity (25.1% of total time under warning) for prediction horizons above 25 min. These results indicate that the scale-free dynamics of the preictal state are different from those of the interictal state. Quantifiers of these dynamics may carry a predictive power that can be used to improve seizure prediction performance.
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Affiliation(s)
- Kais Gadhoumi
- Montreal Neurological Institute, McGill University, Montréal, Québec, Canada
- * E-mail:
| | - Jean Gotman
- Montreal Neurological Institute, McGill University, Montréal, Québec, Canada
| | - Jean Marc Lina
- Département de Génie Électrique, École De Technologie Supérieure, Montréal, Québec, Canada
- Centre de recherches mathématiques, Université de Montréal, Montréal, Québec, Canada
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146
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Mofakham S, Zochowski M. Measuring predictability of autonomous network transitions into bursting dynamics. PLoS One 2015; 10:e0122225. [PMID: 25855975 PMCID: PMC4391948 DOI: 10.1371/journal.pone.0122225] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2014] [Accepted: 02/19/2015] [Indexed: 11/24/2022] Open
Abstract
Understanding spontaneous transitions between dynamical modes in a network is of significant importance. These transitions may separate pathological and normal functions of the brain. In this paper, we develop a set of measures that, based on spatio-temporal features of network activity, predict autonomous network transitions from asynchronous to synchronous dynamics under various conditions. These metrics quantify spike-timing distributions within a narrow time window as a function of the relative location of the active neurons. We applied these metrics to investigate the properties of these transitions in excitatory-only and excitatory-and-inhibitory networks and elucidate how network topology, noise level, and cellular heterogeneity affect both the reliability and the timeliness of the predictions. The developed measures can be calculated in real time and therefore potentially applied in clinical situations.
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Affiliation(s)
- Sima Mofakham
- Biophysics Program, University of Michigan, 930N University, Ann Arbor, Michigan, United States of America
| | - Michal Zochowski
- Biophysics Program, University of Michigan, 930N University, Ann Arbor, Michigan, United States of America
- Department of Physics, University of Michigan, 450 Church St, Ann Arbor, Michigan, United States of America
- The R.B. Zajonc Institute for Social Studies, Stawki 5/7, 00–183 Warsaw, Poland
- * E-mail:
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147
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Kimiskidis VK, Koutlis C, Tsimpiris A, Kälviäinen R, Ryvlin P, Kugiumtzis D. Transcranial Magnetic Stimulation Combined with EEG Reveals Covert States of Elevated Excitability in the Human Epileptic Brain. Int J Neural Syst 2015; 25:1550018. [PMID: 25986753 DOI: 10.1142/s0129065715500185] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
BACKGROUND Transcranial magnetic stimulation combined with electroencephalogram (TMS-EEG) can be used to explore the dynamical state of neuronal networks. In patients with epilepsy, TMS can induce epileptiform discharges (EDs) with a stochastic occurrence despite constant stimulation parameters. This observation raises the possibility that the pre-stimulation period contains multiple covert states of brain excitability some of which are associated with the generation of EDs. OBJECTIVE To investigate whether the interictal period contains "high excitability" states that upon brain stimulation produce EDs and can be differentiated from "low excitability" states producing normal appearing TMS-EEG responses. METHODS In a cohort of 25 patients with Genetic Generalized Epilepsies (GGE) we identified two subjects characterized by the intermittent development of TMS-induced EDs. The high-excitability in the pre-stimulation period was assessed using multiple measures of univariate time series analysis. Measures providing optimal discrimination were identified by feature selection techniques. The "high excitability" states emerged in multiple loci (indicating diffuse cortical hyperexcitability) and were clearly differentiated on the basis of 14 measures from "low excitability" states (accuracy = 0.7). CONCLUSION In GGE, the interictal period contains multiple, quasi-stable covert states of excitability a class of which is associated with the generation of TMS-induced EDs. The relevance of these findings to theoretical models of ictogenesis is discussed.
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Affiliation(s)
- Vasilios K Kimiskidis
- Laboratory of Clinical Neurophysiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Christos Koutlis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Alkiviadis Tsimpiris
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Reetta Kälviäinen
- Kuopio Epilepsy Center, Department of Neurology, Kuopio University Hospital, Kuopio, Finland.,Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland
| | - Philippe Ryvlin
- Department of Functional Neurology and Epileptology, Hospices Civils de Lyon, Lyon, France.,Department of Clinical Neurosciences, CHUV, Lausanne, Switzerland
| | - Dimitris Kugiumtzis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
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148
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Esneault E, Peyon G, Froger-Colléaux C, Castagné V. Evaluation of pro-convulsant risk in the rat: Spontaneous and provoked convulsions. J Pharmacol Toxicol Methods 2015; 72:59-66. [DOI: 10.1016/j.vascn.2014.09.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2014] [Revised: 09/04/2014] [Accepted: 09/30/2014] [Indexed: 11/30/2022]
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149
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Popovych OV, Xenakis MN, Tass PA. The spacing principle for unlearning abnormal neuronal synchrony. PLoS One 2015; 10:e0117205. [PMID: 25714553 PMCID: PMC4340932 DOI: 10.1371/journal.pone.0117205] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2014] [Accepted: 12/20/2014] [Indexed: 01/14/2023] Open
Abstract
Desynchronizing stimulation techniques were developed to specifically counteract abnormal neuronal synchronization relevant to several neurological and psychiatric disorders. The goal of our approach is to achieve an anti-kindling, where the affected neural networks unlearn abnormal synaptic connectivity and, hence, abnormal neuronal synchrony, by means of desynchronizing stimulation, in particular, Coordinated Reset (CR) stimulation. As known from neuroscience, psychology and education, learning effects can be enhanced by means of the spacing principle, i.e. by delivering repeated stimuli spaced by pauses as opposed to delivering a massed stimulus (in a single long stimulation session). To illustrate that the spacing principle may boost the anti-kindling effect of CR neuromodulation, in this computational study we carry this approach to extremes. To this end, we deliver spaced CR neuromodulation at particularly weak intensities which render permanently delivered CR neuromodulation ineffective. Intriguingly, spaced CR neuromodulation at these particularly weak intensities effectively induces an anti-kindling. In fact, the spacing principle enables the neuronal population to successively hop from one attractor to another one, finally approaching attractors characterized by down-regulated synaptic connectivity and synchrony. Our computational results might open up novel opportunities to effectively induce sustained desynchronization at particularly weak stimulation intensities, thereby avoiding side effects, e.g., in the case of deep brain stimulation.
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Affiliation(s)
- Oleksandr V. Popovych
- Institute of Neuroscience and Medicine—Neuromodulation, Jülich Research Center, Jülich, Germany
- * E-mail:
| | - Markos N. Xenakis
- Institute of Neuroscience and Medicine—Neuromodulation, Jülich Research Center, Jülich, Germany
| | - Peter A. Tass
- Institute of Neuroscience and Medicine—Neuromodulation, Jülich Research Center, Jülich, Germany
- Department of Neurosurgery, Stanford University, Stanford, California, United States of America
- Department of Neuromodulation, University of Cologne, Cologne, Germany
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150
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Abstract
Decades of experimental work have established an imbalance of excitation and inhibition as the leading mechanism of the transition from normal brain function to seizure. In epilepsy, these transitions are rare and abrupt. Transition processes incorporating positive feedback, such as activity-dependent disinhibition, could provide these uncommon timing features. A rapidly expanding array of genetic etiologies will help delineate the molecular mechanism(s). This delineation will entail quite a bit of cell biology. The genes discovered so far are more remarkable for their diversity than their similarities.
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