1
|
Kuhlmann L, Lehnertz K, Richardson MP, Schelter B, Zaveri HP. Seizure prediction - ready for a new era. Nat Rev Neurol 2019; 14:618-630. [PMID: 30131521 DOI: 10.1038/s41582-018-0055-2] [Citation(s) in RCA: 191] [Impact Index Per Article: 38.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Epilepsy is a common disorder characterized by recurrent seizures. An overwhelming majority of people with epilepsy regard the unpredictability of seizures as a major issue. More than 30 years of international effort have been devoted to the prediction of seizures, aiming to remove the burden of unpredictability and to couple novel, time-specific treatment to seizure prediction technology. A highly influential review published in 2007 concluded that insufficient evidence indicated that seizures could be predicted. Since then, several advances have been made, including successful prospective seizure prediction using intracranial EEG in a small number of people in a trial of a real-time seizure prediction device. In this Review, we examine advances in the field, including EEG databases, seizure prediction competitions, the prospective trial mentioned and advances in our understanding of the mechanisms of seizures. We argue that these advances, together with statistical evaluations, set the stage for a resurgence in efforts towards the development of seizure prediction methodologies. We propose new avenues of investigation involving a synergy between mechanisms, models, data, devices and algorithms and refine the existing guidelines for the development of seizure prediction technology to instigate development of a solution that removes the burden of the unpredictability of seizures.
Collapse
Affiliation(s)
- Levin Kuhlmann
- Centre for Human Psychopharmacology, Swinburne University of Technology, Melbourne, Victoria, Australia.,Department of Medicine - St. Vincent's, The University of Melbourne, Parkville, Victoria, Australia.,Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn, Bonn, Germany. .,Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany.
| | - Mark P Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Björn Schelter
- Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, UK
| | - Hitten P Zaveri
- Department of Neurology, Yale University, New Haven, CT, USA
| |
Collapse
|
2
|
Farah FH, Grigorovsky V, Bardakjian BL. Coupled Oscillators Model of Hyperexcitable Neuroglial Networks. Int J Neural Syst 2018; 29:1850041. [PMID: 30415633 DOI: 10.1142/s0129065718500417] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Glial populations within neuronal networks of the brain have recently gained much interest in the context of hyperexcitability and epilepsy. In this paper, we present an oscillator-based neuroglial model capable of generating Spontaneous Electrical Discharges (SEDs) in hyperexcitable conditions. The network is composed of 16 coupled Cognitive Rhythm Generators (CRGs), which are oscillator-based mathematical constructs previously described by our research team. CRGs are well-suited for modeling assemblies of excitable cells, and in this network, each represents one of the following populations: excitatory pyramidal cells, inhibitory interneurons, astrocytes, and microglia. We investigated various pathways leading to hyperexcitability, and our results suggest an important role for astrocytes and microglia in the generation of SEDs of various durations. Analysis of the resultant SEDs revealed two underlying duration distributions with differing properties. Particularly, short and long SEDs are associated with deterministic and random underlying processes, respectively. The mesoscale of this model makes it well-suited for (a) the elucidation of glia-related hypotheses in hyperexcitable conditions, (b) use as a testing platform for neuromodulation purposes, and (c) a hardware implementation for closed-loop neuromodulation.
Collapse
Affiliation(s)
- Firas H Farah
- 1 Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S3G4, Canada
| | - Vasily Grigorovsky
- 2 Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario M5S3G9, Canada
| | - Berj L Bardakjian
- 3 Institute of Biomaterials and Biomedical Engineering, University of Toronto, 164 College Street, Room 407, Toronto, Ontario M5S3G9, Canada
| |
Collapse
|
3
|
Eissa TL, Dijkstra K, Brune C, Emerson RG, van Putten MJAM, Goodman RR, McKhann GM, Schevon CA, van Drongelen W, van Gils SA. Cross-scale effects of neural interactions during human neocortical seizure activity. Proc Natl Acad Sci U S A 2017; 114:10761-10766. [PMID: 28923948 PMCID: PMC5635869 DOI: 10.1073/pnas.1702490114] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Small-scale neuronal networks may impose widespread effects on large network dynamics. To unravel this relationship, we analyzed eight multiscale recordings of spontaneous seizures from four patients with epilepsy. During seizures, multiunit spike activity organizes into a submillimeter-sized wavefront, and this activity correlates significantly with low-frequency rhythms from electrocorticographic recordings across a 10-cm-sized neocortical network. Notably, this correlation effect is specific to the ictal wavefront and is absent interictally or from action potential activity outside the wavefront territory. To examine the multiscale interactions, we created a model using a multiscale, nonlinear system and found evidence for a dual role for feedforward inhibition in seizures: while inhibition at the wavefront fails, allowing seizure propagation, feedforward inhibition of the surrounding centimeter-scale networks is activated via long-range excitatory connections. Bifurcation analysis revealed that distinct dynamical pathways for seizure termination depend on the surrounding inhibition strength. Using our model, we found that the mesoscopic, local wavefront acts as the forcing term of the ictal process, while the macroscopic, centimeter-sized network modulates the oscillatory seizure activity.
Collapse
Affiliation(s)
- Tahra L Eissa
- Department of Pediatrics, University of Chicago, Chicago, IL 60637;
| | - Koen Dijkstra
- Department of Applied Mathematics, MIRA Institute for Biomedical Engineering and Technical Medicine, University of Twente, Enschede 7500AE, The Netherlands;
| | - Christoph Brune
- Department of Applied Mathematics, MIRA Institute for Biomedical Engineering and Technical Medicine, University of Twente, Enschede 7500AE, The Netherlands
| | - Ronald G Emerson
- Department of Neurology, Columbia University, New York, NY 10032
| | - Michel J A M van Putten
- Deptartment of Neurology and Clinical Neurophysiolgy, Medisch Spectrum Twente, Enschede 7500AE, The Netherlands
- Clinical Neurophysiology Group, MIRA Institute for Biomedical Engineering and Technical Medicine, University of Twente, Enschede 7500AE, The Netherlands
| | - Robert R Goodman
- Department of Neurological Surgery, Columbia University, New York, NY 10032
| | - Guy M McKhann
- Department of Neurological Surgery, Columbia University, New York, NY 10032
| | | | | | - Stephan A van Gils
- Department of Applied Mathematics, MIRA Institute for Biomedical Engineering and Technical Medicine, University of Twente, Enschede 7500AE, The Netherlands
| |
Collapse
|
4
|
Boothe DL, Yu AB, Kudela P, Anderson WS, Vettel JM, Franaszczuk PJ. Impact of Neuronal Membrane Damage on the Local Field Potential in a Large-Scale Simulation of Cerebral Cortex. Front Neurol 2017. [PMID: 28638364 PMCID: PMC5461262 DOI: 10.3389/fneur.2017.00236] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
Within multiscale brain dynamics, the structure–function relationship between cellular changes at a lower scale and coordinated oscillations at a higher scale is not well understood. This relationship may be particularly relevant for understanding functional impairments after a mild traumatic brain injury (mTBI) when current neuroimaging methods do not reveal morphological changes to the brain common in moderate to severe TBI such as diffuse axonal injury or gray matter lesions. Here, we created a physiology-based model of cerebral cortex using a publicly released modeling framework (GEneral NEural SImulation System) to explore the possibility that performance deficits characteristic of blast-induced mTBI may reflect dysfunctional, local network activity influenced by microscale neuronal damage at the cellular level. We operationalized microscale damage to neurons as the formation of pores on the neuronal membrane based on research using blast paradigms, and in our model, pores were simulated by a change in membrane conductance. We then tracked changes in simulated electrical activity. Our model contained 585 simulated neurons, comprised of 14 types of cortical and thalamic neurons each with its own compartmental morphology and electrophysiological properties. Comparing the functional activity of neurons before and after simulated damage, we found that simulated pores in the membrane reduced both action potential generation and local field potential (LFP) power in the 1–40 Hz range of the power spectrum. Furthermore, the location of damage modulated the strength of these effects: pore formation on simulated axons reduced LFP power more strongly than did pore formation on the soma and the dendrites. These results indicate that even small amounts of cellular damage can negatively impact functional activity of larger scale oscillations, and our findings suggest that multiscale modeling provides a promising avenue to elucidate these relationships.
Collapse
Affiliation(s)
- David L Boothe
- U.S. Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, United States.,Altus Engineering, Churchville, MD, United States
| | - Alfred B Yu
- U.S. Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, United States
| | - Pawel Kudela
- Department of Neurosurgery, The Johns Hopkins University School of Medicine, Baltimore, MD, United States.,The Johns Hopkins Institute for Clinical and Translational Research, Baltimore, MD, United States
| | - William S Anderson
- Department of Neurosurgery, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Jean M Vettel
- U.S. Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, United States.,Psychological & Brain Sciences, University of California, Santa Barbara, CA, United States.,Department of Engineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Piotr J Franaszczuk
- U.S. Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, United States.,Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States
| |
Collapse
|
5
|
Abstract
Most seizure forecasting employs statistical learning techniques that lack a representation of the network interactions that give rise to seizures. We present an epilepsy network emulator (ENE) that uses a network of interconnected phase-locked loops (PLLs) to model synchronous, circuit-level oscillations between electrocorticography (ECoG) electrodes. Using ECoG data from a canine-epilepsy model (Davis et al. 2011) and a physiological entropy measure (approximate entropy or ApEn, Pincus 1995), we demonstrate the entropy of the emulator phases increases dramatically during ictal periods across all ECoG recording sites and across all animals in the sample. Further, this increase precedes the observable voltage spikes that characterize seizure activity in the ECoG data. These results suggest that the ENE is sensitive to phase-domain information in the neural circuits measured by ECoG and that an increase in the entropy of this measure coincides with increasing likelihood of seizure activity. Understanding this unpredictable phase-domain electrical activity present in ECoG recordings may provide a target for seizure detection and feedback control.
Collapse
Affiliation(s)
- P.D. Watson
- Beckman Institute of Science and Technology, UIUC, IL, USA
- Neuroscience Program, UIUC, IL, USA
| | - K. M. Horecka
- Beckman Institute of Science and Technology, UIUC, IL, USA
- Neuroscience Program, UIUC, IL, USA
| | - N.J. Cohen
- Beckman Institute of Science and Technology, UIUC, IL, USA
- Neuroscience Program, UIUC, IL, USA
- Department of Psychology, UIUC, IL, USA
| | - R. Ratnam
- Beckman Institute of Science and Technology, UIUC, IL, USA
- Coordinated Science Laboratory, UIUC, Urbana, IL, USA
- Advanced Digital Sciences Center, Illinois at Singapore Pte. Ltd., Singapore
| |
Collapse
|
6
|
Y Ho EC, Truccolo W. Interaction between synaptic inhibition and glial-potassium dynamics leads to diverse seizure transition modes in biophysical models of human focal seizures. J Comput Neurosci 2016; 41:225-44. [PMID: 27488433 PMCID: PMC5002283 DOI: 10.1007/s10827-016-0615-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2015] [Revised: 06/18/2016] [Accepted: 07/06/2016] [Indexed: 11/10/2022]
Abstract
How focal seizures initiate and evolve in human neocortex remains a fundamental problem in neuroscience. Here, we use biophysical neuronal network models of neocortical patches to study how the interaction between inhibition and extracellular potassium ([K (+)] o ) dynamics may contribute to different types of focal seizures. Three main types of propagated focal seizures observed in recent intracortical microelectrode recordings in humans were modelled: seizures characterized by sustained (∼30-60 Hz) gamma local field potential (LFP) oscillations; seizures where the onset in the propagated site consisted of LFP spikes that later evolved into rhythmic (∼2-3 Hz) spike-wave complexes (SWCs); and seizures where a brief stage of low-amplitude fast-oscillation (∼10-20 Hz) LFPs preceded the SWC activity. Our findings are fourfold: (1) The interaction between elevated [K (+)] o (due to abnormal potassium buffering by glial cells) and the strength of synaptic inhibition plays a predominant role in shaping these three types of seizures. (2) Strengthening of inhibition leads to the onset of sustained narrowband gamma seizures. (3) Transition into SWC seizures is obtained either by the weakening of inhibitory synapses, or by a transient strengthening followed by an inhibitory breakdown (e.g. GABA depletion). This reduction or breakdown of inhibition among fast-spiking (FS) inhibitory interneurons increases their spiking activity and leads them eventually into depolarization block. Ictal spike-wave discharges in the model are then sustained solely by pyramidal neurons. (4) FS cell dynamics are also critical for seizures where the evolution into SWC activity is preceded by low-amplitude fast oscillations. Different levels of elevated [K (+)] o were important for transitions into and maintenance of sustained gamma oscillations and SWC discharges. Overall, our modelling study predicts that the interaction between inhibitory interneurons and [K (+)] o glial buffering under abnormal conditions may explain different types of ictal transitions and dynamics during propagated seizures in human focal epilepsy.
Collapse
Affiliation(s)
- E C Y Ho
- Department of Neuroscience & Institute for Brain Science, Brown University, Providence, RI, USA.
- U.S. Department of Veterans Affairs, Center for Neurorestoration and Neurotechnology, Providence, RI, USA.
| | - Wilson Truccolo
- Department of Neuroscience & Institute for Brain Science, Brown University, Providence, RI, USA.
- U.S. Department of Veterans Affairs, Center for Neurorestoration and Neurotechnology, Providence, RI, USA.
| |
Collapse
|
7
|
Plic-1, a new target in repressing epileptic seizure by regulation of GABAAR function in patients and a rat model of epilepsy. Clin Sci (Lond) 2015; 129:1207-23. [PMID: 26415648 DOI: 10.1042/cs20150202] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Accepted: 09/25/2015] [Indexed: 12/27/2022]
Abstract
Plic-1 regulates GABAAR expression at synaptic sites during epileptic seizure. Plic-1 prolongs the seizure latency and reduces the seizure severity in epileptic rats. Plic-1 affects the inhibitory function by changing the mIPSCs and evoked IPSCs of the phasic GABA-ergic synaptic current.
Collapse
|
8
|
Woldman W, Terry JR. Multilevel Computational Modelling in Epilepsy: Classical Studies and Recent Advances. VALIDATING NEURO-COMPUTATIONAL MODELS OF NEUROLOGICAL AND PSYCHIATRIC DISORDERS 2015. [DOI: 10.1007/978-3-319-20037-8_7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
|
9
|
Classifying normal and abnormal status based on video recordings of epileptic patients. ScientificWorldJournal 2014; 2014:459636. [PMID: 24977196 PMCID: PMC4000972 DOI: 10.1155/2014/459636] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2014] [Accepted: 03/27/2014] [Indexed: 11/19/2022] Open
Abstract
Based on video recordings of the movement of the patients with epilepsy, this paper proposed a human action recognition scheme to detect distinct motion patterns and to distinguish the normal status from the abnormal status of epileptic patients. The scheme first extracts local features and holistic features, which are complementary to each other. Afterwards, a support vector machine is applied to classification. Based on the experimental results, this scheme obtains a satisfactory classification result and provides a fundamental analysis towards the human-robot interaction with socially assistive robots in caring the patients with epilepsy (or other patients with brain disorders) in order to protect them from injury.
Collapse
|
10
|
Using Permutation Entropy to Measure the Changes in EEG Signals During Absence Seizures. ENTROPY 2014. [DOI: 10.3390/e16063049] [Citation(s) in RCA: 74] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
|
11
|
Neurostimulation in the treatment of epilepsy. Exp Neurol 2013; 244:87-95. [DOI: 10.1016/j.expneurol.2013.04.004] [Citation(s) in RCA: 76] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2012] [Revised: 04/04/2013] [Accepted: 04/08/2013] [Indexed: 11/24/2022]
|
12
|
Colic S, Wither RG, Zhang L, Eubanks JH, Bardakjian BL. Characterization of seizure-like events recorded in vivo in a mouse model of Rett syndrome. Neural Netw 2013; 46:109-15. [PMID: 23727441 DOI: 10.1016/j.neunet.2013.05.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2013] [Revised: 05/01/2013] [Accepted: 05/05/2013] [Indexed: 10/26/2022]
Abstract
Rett syndrome is a neurodevelopmental disorder caused by mutations in the X-linked gene encoding methyl-CpG-binding protein 2 (MECP2). Spontaneous recurrent discharge episodes are displayed in Rett-related seizures as in other types of epilepsies. The aim of this paper is to investigate the seizure-like event (SLE) and inter-SLE states in a female MeCP2-deficient mouse model of Rett syndrome and compare them to those found in other spontaneous recurrent epilepsy models. The study was performed on a small population of female MeCP2-deficient mice using telemetric local field potential (LFP) recordings over a 24 h period. Durations of SLEs and inter-SLEs were extracted using a rule-based automated SLE detection system for both daytime and nighttime, as well as high and low power levels of the delta frequency range (0.5-4 Hz) of the recorded LFPs. The results suggest SLE occurrences are not influenced by circadian rhythms, but had a significantly greater association with delta power. Investigating inter-SLE and SLE states by fitting duration histograms to the gamma distribution showed that SLE initiation and termination were associated with random and deterministic mechanisms, respectively. These findings when compared to reported studies on epilepsy suggest that Rett-related seizures share many similarities with absence epilepsy.
Collapse
Affiliation(s)
- Sinisa Colic
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada.
| | | | | | | | | |
Collapse
|
13
|
Panas D, Malinowska U, Piotrowski T, Żygierewicz J, Suffczyński P. Statistical analysis of sleep spindle occurrences. PLoS One 2013; 8:e59318. [PMID: 23560045 PMCID: PMC3613364 DOI: 10.1371/journal.pone.0059318] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2012] [Accepted: 02/13/2013] [Indexed: 11/19/2022] Open
Abstract
Spindles - a hallmark of stage II sleep - are a transient oscillatory phenomenon in the EEG believed to reflect thalamocortical activity contributing to unresponsiveness during sleep. Currently spindles are often classified into two classes: fast spindles, with a frequency of around 14 Hz, occurring in the centro-parietal region; and slow spindles, with a frequency of around 12 Hz, prevalent in the frontal region. Here we aim to establish whether the spindle generation process also exhibits spatial heterogeneity. Electroencephalographic recordings from 20 subjects were automatically scanned to detect spindles and the time occurrences of spindles were used for statistical analysis. Gamma distribution parameters were fit to each inter-spindle interval distribution, and a modified Wald-Wolfowitz lag-1 correlation test was applied. Results indicate that not all spindles are generated by the same statistical process, but this dissociation is not spindle-type specific. Although this dissociation is not topographically specific, a single generator for all spindle types appears unlikely.
Collapse
Affiliation(s)
- Dagmara Panas
- Institute for Adaptive and Neural Computation, School of Informatics, The University of Edinburgh, Edinburgh, United Kingdom.
| | | | | | | | | |
Collapse
|
14
|
Azhar F, Anderson WS. Predicting single-neuron activity in locally connected networks. Neural Comput 2012; 24:2655-77. [PMID: 22845824 DOI: 10.1162/neco_a_00343] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
The characterization of coordinated activity in neuronal populations has received renewed interest in the light of advancing experimental techniques that allow recordings from multiple units simultaneously. Across both in vitro and in vivo preparations, nearby neurons show coordinated responses when spontaneously active and when subject to external stimuli. Recent work (Truccolo, Hochberg, & Donoghue, 2010 ) has connected these coordinated responses to behavior, showing that small ensembles of neurons in arm-related areas of sensorimotor cortex can reliably predict single-neuron spikes in behaving monkeys and humans. We investigate this phenomenon using an analogous point process model, showing that in the case of a computational model of cortex responding to random background inputs, one is similarly able to predict the future state of a single neuron by considering its own spiking history, together with the spiking histories of randomly sampled ensembles of nearby neurons. This model exhibits realistic cortical architecture and displays bursting episodes in the two distinct connectivity schemes studied. We conjecture that the baseline predictability we find in these instances is characteristic of locally connected networks more broadly considered.
Collapse
Affiliation(s)
- Feraz Azhar
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
| | | |
Collapse
|