1
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Combining the neural mass model and Hodgkin–Huxley formalism: Neuronal dynamics modelling. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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2
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Zhao Y, Boley M, Pelentritou A, Karoly PJ, Freestone DR, Liu Y, Muthukumaraswamy S, Woods W, Liley D, Kuhlmann L. Space-time resolved inference-based neurophysiological process imaging: application to resting-state alpha rhythm. Neuroimage 2022; 263:119592. [PMID: 36031185 DOI: 10.1016/j.neuroimage.2022.119592] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 07/28/2022] [Accepted: 08/24/2022] [Indexed: 11/28/2022] Open
Abstract
Neural processes are complex and difficult to image. This paper presents a new space-time resolved brain imaging framework, called Neurophysiological Process Imaging (NPI), that identifies neurophysiological processes within cerebral cortex at the macroscopic scale. By fitting uncoupled neural mass models to each electromagnetic source time-series using a novel nonlinear inference method, population averaged membrane potentials and synaptic connection strengths are efficiently and accurately inferred and imaged across the whole cerebral cortex at a resolution afforded by source imaging. The efficiency of the framework enables return of the augmented source imaging results overnight using high performance computing. This suggests it can be used as a practical and novel imaging tool. To demonstrate the framework, it has been applied to resting-state magnetoencephalographic source estimates. The results suggest that endogenous inputs to cingulate, occipital, and inferior frontal cortex are essential modulators of resting-state alpha power. Moreover, endogenous input and inhibitory and excitatory neural populations play varied roles in mediating alpha power in different resting-state sub-networks. The framework can be applied to arbitrary neural mass models and has broad applicability to image neural processes in different brain states.
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Affiliation(s)
- Yun Zhao
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | - Mario Boley
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | - Andria Pelentritou
- Swinburne University of Technology, Hawthorn, Australia; Laboratoire de Recherche en Neuroimagerie (LREN), University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Philippa J Karoly
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia; Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia
| | - Dean R Freestone
- Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia; Seer Medical Pty Ltd, Melbourne, Australia
| | - Yueyang Liu
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | | | - William Woods
- School of Health Sciences, Swinburne University of Technology, Hawthorn, Australia
| | - David Liley
- Swinburne University of Technology, Hawthorn, Australia; Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia; School of Health Sciences, Swinburne University of Technology, Hawthorn, Australia
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia; Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia.
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3
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Chang S, Wang J, Liu C, Yi G, Lu M, Che Y, Wei X. A Data Driven Experimental System for Individualized Brain Stimulation Design and Validation. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1848-1857. [PMID: 34478377 DOI: 10.1109/tnsre.2021.3110275] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Deep brain stimulation (DBS) is an effective clinical treatment for epilepsy. However, the individualized setting and adaptive adjustment of DBS parameters are still facing great challenges. This paper investigates a data-driven hardware-in-the-loop (HIL) experimental system for closed-loop brain stimulation system individualized design and validation. The unscented Kalman filter (UKF) is utilized to estimate critical parameters of neural mass model (NMM) from the electroencephalogram recordings to reconstruct individual neural activity. Based on the reconstructed NMM, we build a digital signal processor (DSP) based virtual brain platform with real time scale and biological signal level scale. Then, the corresponding hardware parts of signal amplification detection and closed-loop controller are designed to form the HIL experimental system. Based on the designed experimental system, the proportional-integral controller for different individual NMM is designed and validated, which proves the effectiveness of the experimental system. This experimental system provides a platform to explore neural activity under brain stimulation and the effects of various closed-loop stimulation paradigms.
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4
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Bahari F, Kimbugwe J, Alloway KD, Gluckman BJ. Model-based analysis and forecast of sleep-wake regulatory dynamics: Tools and applications to data. CHAOS (WOODBURY, N.Y.) 2021; 31:013139. [PMID: 33754773 PMCID: PMC7837756 DOI: 10.1063/5.0024024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Extensive clinical and experimental evidence links sleep-wake regulation and state of vigilance (SOV) to neurological disorders including schizophrenia and epilepsy. To understand the bidirectional coupling between disease severity and sleep disturbances, we need to investigate the underlying neurophysiological interactions of the sleep-wake regulatory system (SWRS) in normal and pathological brains. We utilized unscented Kalman filter based data assimilation (DA) and physiologically based mathematical models of a sleep-wake regulatory network synchronized with experimental measurements to reconstruct and predict the state of SWRS in chronically implanted animals. Critical to applying this technique to real biological systems is the need to estimate the underlying model parameters. We have developed an estimation method capable of simultaneously fitting and tracking multiple model parameters to optimize the reconstructed system state. We add to this fixed-lag smoothing to improve reconstruction of random input to the system and those that have a delayed effect on the observed dynamics. To demonstrate application of our DA framework, we have experimentally recorded brain activity from freely behaving rodents and classified discrete SOV continuously for many-day long recordings. These discretized observations were then used as the "noisy observables" in the implemented framework to estimate time-dependent model parameters and then to forecast future state and state transitions from out-of-sample recordings.
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5
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Aqil M, Atasoy S, Kringelbach ML, Hindriks R. Graph neural fields: A framework for spatiotemporal dynamical models on the human connectome. PLoS Comput Biol 2021; 17:e1008310. [PMID: 33507899 PMCID: PMC7872285 DOI: 10.1371/journal.pcbi.1008310] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 02/09/2021] [Accepted: 12/11/2020] [Indexed: 12/22/2022] Open
Abstract
Tools from the field of graph signal processing, in particular the graph Laplacian operator, have recently been successfully applied to the investigation of structure-function relationships in the human brain. The eigenvectors of the human connectome graph Laplacian, dubbed "connectome harmonics", have been shown to relate to the functionally relevant resting-state networks. Whole-brain modelling of brain activity combines structural connectivity with local dynamical models to provide insight into the large-scale functional organization of the human brain. In this study, we employ the graph Laplacian and its properties to define and implement a large class of neural activity models directly on the human connectome. These models, consisting of systems of stochastic integrodifferential equations on graphs, are dubbed graph neural fields, in analogy with the well-established continuous neural fields. We obtain analytic predictions for harmonic and temporal power spectra, as well as functional connectivity and coherence matrices, of graph neural fields, with a technique dubbed CHAOSS (shorthand for Connectome-Harmonic Analysis Of Spatiotemporal Spectra). Combining graph neural fields with appropriate observation models allows for estimating model parameters from experimental data as obtained from electroencephalography (EEG), magnetoencephalography (MEG), or functional magnetic resonance imaging (fMRI). As an example application, we study a stochastic Wilson-Cowan graph neural field model on a high-resolution connectome graph constructed from diffusion tensor imaging (DTI) and structural MRI data. We show that the model equilibrium fluctuations can reproduce the empirically observed harmonic power spectrum of resting-state fMRI data, and predict its functional connectivity, with a high level of detail. Graph neural fields natively allow the inclusion of important features of cortical anatomy and fast computations of observable quantities for comparison with multimodal empirical data. They thus appear particularly suitable for modelling whole-brain activity at mesoscopic scales, and opening new potential avenues for connectome-graph-based investigations of structure-function relationships.
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Affiliation(s)
- Marco Aqil
- Department of Mathematics, Vrije Universiteit, Amsterdam, The Netherlands
| | - Selen Atasoy
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, United Kingdom
- Center for Music in the Brain, University of Aarhus, Aarhus, Denmark
| | - Morten L. Kringelbach
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, United Kingdom
- Center for Music in the Brain, University of Aarhus, Aarhus, Denmark
| | - Rikkert Hindriks
- Department of Mathematics, Vrije Universiteit, Amsterdam, The Netherlands
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6
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O'Neill GC, Tewarie P, Vidaurre D, Liuzzi L, Woolrich MW, Brookes MJ. Dynamics of large-scale electrophysiological networks: A technical review. Neuroimage 2018; 180:559-576. [PMID: 28988134 DOI: 10.1016/j.neuroimage.2017.10.003] [Citation(s) in RCA: 109] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Revised: 09/23/2017] [Accepted: 10/02/2017] [Indexed: 12/12/2022] Open
Abstract
For several years it has been argued that neural synchronisation is crucial for cognition. The idea that synchronised temporal patterns between different neural groups carries information above and beyond the isolated activity of these groups has inspired a shift in focus in the field of functional neuroimaging. Specifically, investigation into the activation elicited within certain regions by some stimulus or task has, in part, given way to analysis of patterns of co-activation or functional connectivity between distal regions. Recently, the functional connectivity community has been looking beyond the assumptions of stationarity that earlier work was based on, and has introduced methods to incorporate temporal dynamics into the analysis of connectivity. In particular, non-invasive electrophysiological data (magnetoencephalography/electroencephalography (MEG/EEG)), which provides direct measurement of whole-brain activity and rich temporal information, offers an exceptional window into such (potentially fast) brain dynamics. In this review, we discuss challenges, solutions, and a collection of analysis tools that have been developed in recent years to facilitate the investigation of dynamic functional connectivity using these imaging modalities. Further, we discuss the applications of these approaches in the study of cognition and neuropsychiatric disorders. Finally, we review some existing developments that, by using realistic computational models, pursue a deeper understanding of the underlying causes of non-stationary connectivity.
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Affiliation(s)
- George C O'Neill
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Prejaas Tewarie
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Diego Vidaurre
- Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Lucrezia Liuzzi
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom.
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7
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Karoly PJ, Kuhlmann L, Soudry D, Grayden DB, Cook MJ, Freestone DR. Seizure pathways: A model-based investigation. PLoS Comput Biol 2018; 14:e1006403. [PMID: 30307937 PMCID: PMC6199000 DOI: 10.1371/journal.pcbi.1006403] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Revised: 10/23/2018] [Accepted: 07/26/2018] [Indexed: 02/06/2023] Open
Abstract
We present the results of a model inversion algorithm for electrocorticography (ECoG) data recorded during epileptic seizures. The states and parameters of neural mass models were tracked during a total of over 3000 seizures from twelve patients with focal epilepsy. These models provide an estimate of the effective connectivity within intracortical circuits over the time course of seizures. Observing the dynamics of effective connectivity provides insight into mechanisms of seizures. Estimation of patients seizure dynamics revealed: 1) a highly stereotyped pattern of evolution for each patient, 2) distinct sub-groups of onset mechanisms amongst patients, and 3) different offset mechanisms for long and short seizures. Stereotypical dynamics suggest that, once initiated, seizures follow a deterministic path through the parameter space of a neural model. Furthermore, distinct sub-populations of patients were identified based on characteristic motifs in the dynamics at seizure onset. There were also distinct patterns between long and short duration seizures that were related to seizure offset. Understanding how these different patterns of seizure evolution arise may provide new insights into brain function and guide treatment for epilepsy, since specific therapies may have preferential effects on the various parameters that could potentially be individualized. Methods that unite computational models with data provide a powerful means to generate testable hypotheses for further experimental research. This work provides a demonstration that the hidden connectivity parameters of a neural mass model can be dynamically inferred from data. Our results underscore the power of theoretical models to inform epilepsy management. It is our hope that this work guides further efforts to apply computational models to clinical data.
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Affiliation(s)
- Philippa J Karoly
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy, Australia
| | - Levin Kuhlmann
- Brain Dynamics Lab, Swinburne University of Technology, Hawthorn, Australia
- Centre for Neural Engineering, The University of Melbourne, Parkville, Australia
| | - Daniel Soudry
- Department of Electrical Engineering, Technion, Haifa, Israel
| | - David B Grayden
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia
- Centre for Neural Engineering, The University of Melbourne, Parkville, Australia
| | - Mark J Cook
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy, Australia
| | - Dean R Freestone
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy, Australia
- Seer Medical Pty, Melbourne, Australia
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8
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Madi MK, Karameh FN. Hybrid Cubature Kalman filtering for identifying nonlinear models from sampled recording: Estimation of neuronal dynamics. PLoS One 2017; 12:e0181513. [PMID: 28727850 PMCID: PMC5519212 DOI: 10.1371/journal.pone.0181513] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Accepted: 07/03/2017] [Indexed: 11/18/2022] Open
Abstract
Kalman filtering methods have long been regarded as efficient adaptive Bayesian techniques for estimating hidden states in models of linear dynamical systems under Gaussian uncertainty. Recent advents of the Cubature Kalman filter (CKF) have extended this efficient estimation property to nonlinear systems, and also to hybrid nonlinear problems where by the processes are continuous and the observations are discrete (continuous-discrete CD-CKF). Employing CKF techniques, therefore, carries high promise for modeling many biological phenomena where the underlying processes exhibit inherently nonlinear, continuous, and noisy dynamics and the associated measurements are uncertain and time-sampled. This paper investigates the performance of cubature filtering (CKF and CD-CKF) in two flagship problems arising in the field of neuroscience upon relating brain functionality to aggregate neurophysiological recordings: (i) estimation of the firing dynamics and the neural circuit model parameters from electric potentials (EP) observations, and (ii) estimation of the hemodynamic model parameters and the underlying neural drive from BOLD (fMRI) signals. First, in simulated neural circuit models, estimation accuracy was investigated under varying levels of observation noise (SNR), process noise structures, and observation sampling intervals (dt). When compared to the CKF, the CD-CKF consistently exhibited better accuracy for a given SNR, sharp accuracy increase with higher SNR, and persistent error reduction with smaller dt. Remarkably, CD-CKF accuracy shows only a mild deterioration for non-Gaussian process noise, specifically with Poisson noise, a commonly assumed form of background fluctuations in neuronal systems. Second, in simulated hemodynamic models, parametric estimates were consistently improved under CD-CKF. Critically, time-localization of the underlying neural drive, a determinant factor in fMRI-based functional connectivity studies, was significantly more accurate under CD-CKF. In conclusion, and with the CKF recently benchmarked against other advanced Bayesian techniques, the CD-CKF framework could provide significant gains in robustness and accuracy when estimating a variety of biological phenomena models where the underlying process dynamics unfold at time scales faster than those seen in collected measurements.
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Affiliation(s)
- Mahmoud K. Madi
- Department of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon
| | - Fadi N. Karameh
- Department of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon
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9
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On memories, neural ensembles and mental flexibility. Neuroimage 2017; 157:297-313. [PMID: 28602817 DOI: 10.1016/j.neuroimage.2017.05.068] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2017] [Revised: 05/30/2017] [Accepted: 05/31/2017] [Indexed: 12/18/2022] Open
Abstract
Memories are assumed to be represented by groups of co-activated neurons, called neural ensembles. Describing ensembles is a challenge: complexity of the underlying micro-circuitry is immense. Current approaches use a piecemeal fashion, focusing on single neurons and employing local measures like pairwise correlations. We introduce an alternative approach that identifies ensembles and describes the effective connectivity between them in a holistic fashion. It also links the oscillatory frequencies observed in ensembles with the spatial scales at which activity is expressed. Using unsupervised learning, biophysical modeling and graph theory, we analyze multi-electrode LFPs from frontal cortex during a spatial delayed response task. We find distinct ensembles for different cues and more parsimonious connectivity for cues on the horizontal axis, which may explain the oblique effect in psychophysics. Our approach paves the way for biophysical models with learned parameters that can guide future Brain Computer Interface development.
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Kameneva T, Ying T, Guo B, Freestone DR. Neural mass models as a tool to investigate neural dynamics during seizures. J Comput Neurosci 2017; 42:203-215. [PMID: 28102460 DOI: 10.1007/s10827-017-0636-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2015] [Revised: 09/14/2016] [Accepted: 01/02/2017] [Indexed: 11/30/2022]
Abstract
Epilepsy is one of the most common neurological disorders and is characterized by recurrent seizures. We use theoretical neuroscience tools to study brain dynamics during seizures. We derive and simulate a computational model of a network of hippocampal neuronal populations. Each population within the network is based on a model that has been shown to replicate the electrophysiological dynamics observed during seizures. The results provide insights into possible mechanisms for seizure spread. We observe that epileptiform activity remains localized to a pathological region when a global connectivity parameter is less than a critical value. After establishing the critical value for seizure spread, we explored how to correct the effect by altering particular synaptic gains. The spreading of seizures is quantified using numerical methods for seizure detection. The results from this study provide a new avenue of exploration for seizure control.
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Affiliation(s)
- Tatiana Kameneva
- Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, Australia.
| | - Tianlin Ying
- Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, Australia
| | - Ben Guo
- Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, Australia
| | - Dean R Freestone
- Department of Medicine, St. Vincent's Hospital, The University of Melbourne, Melbourne, Australia
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11
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Freestone DR, Layton KJ, Kuhlmann L, Cook MJ. Statistical Performance Analysis of Data-Driven Neural Models. Int J Neural Syst 2016; 27:1650045. [DOI: 10.1142/s0129065716500453] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Data-driven model-based analysis of electrophysiological data is an emerging technique for understanding the mechanisms of seizures. Model-based analysis enables tracking of hidden brain states that are represented by the dynamics of neural mass models. Neural mass models describe the mean firing rates and mean membrane potentials of populations of neurons. Various neural mass models exist with different levels of complexity and realism. An ideal data-driven model-based analysis framework will incorporate the most realistic model possible, enabling accurate imaging of the physiological variables. However, models must be sufficiently parsimonious to enable tracking of important variables using data. This paper provides tools to inform the realism versus parsimony trade-off, the Bayesian Cramer-Rao (lower) Bound (BCRB). We demonstrate how the BCRB can be used to assess the feasibility of using various popular neural mass models to track epilepsy-related dynamics via stochastic filtering methods. A series of simulations show how optimal state estimates relate to measurement noise, model error and initial state uncertainty. We also demonstrate that state estimation accuracy will vary between seizure-like and normal rhythms. The performance of the extended Kalman filter (EKF) is assessed against the BCRB. This work lays a foundation for assessing feasibility of model-based analysis. We discuss how the framework can be used to design experiments to better understand epilepsy.
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Affiliation(s)
- Dean R. Freestone
- Department of Medicine St Vincent’s Hospital Melbourne, The University of Melbourne, Fitzroy, Victoria, 3065, Australia
- Department of Statistics, Columbia University, New York, New York, 10027, United States
| | - Kelvin J. Layton
- Institute for Telecommunications Research, University of South Australia, Mawson Lakes, SA 5095, Australia
| | - Levin Kuhlmann
- Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, Victoria, 3010, Australia
| | - Mark J. Cook
- Department of Medicine St Vincent’s Hospital Melbourne, The University of Melbourne, Fitzroy, Victoria, 3065, Australia
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12
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Proix T, Spiegler A, Schirner M, Rothmeier S, Ritter P, Jirsa VK. How do parcellation size and short-range connectivity affect dynamics in large-scale brain network models? Neuroimage 2016; 142:135-149. [PMID: 27480624 DOI: 10.1016/j.neuroimage.2016.06.016] [Citation(s) in RCA: 70] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2015] [Revised: 06/07/2016] [Accepted: 06/09/2016] [Indexed: 01/25/2023] Open
Abstract
Recent efforts to model human brain activity on the scale of the whole brain rest on connectivity estimates of large-scale networks derived from diffusion magnetic resonance imaging (dMRI). This type of connectivity describes white matter fiber tracts. The number of short-range cortico-cortical white-matter connections is, however, underrepresented in such large-scale brain models. It is still unclear on the one hand, which scale of representation of white matter fibers is optimal to describe brain activity on a large-scale such as recorded with magneto- or electroencephalography (M/EEG) or functional magnetic resonance imaging (fMRI), and on the other hand, to which extent short-range connections that are typically local should be taken into account. In this article we quantified the effect of connectivity upon large-scale brain network dynamics by (i) systematically varying the number of brain regions before computing the connectivity matrix, and by (ii) adding generic short-range connections. We used dMRI data from the Human Connectome Project. We developed a suite of preprocessing modules called SCRIPTS to prepare these imaging data for The Virtual Brain, a neuroinformatics platform for large-scale brain modeling and simulations. We performed simulations under different connectivity conditions and quantified the spatiotemporal dynamics in terms of Shannon Entropy, dwell time and Principal Component Analysis. For the reconstructed connectivity, our results show that the major white matter fiber bundles play an important role in shaping slow dynamics in large-scale brain networks (e.g. in fMRI). Faster dynamics such as gamma oscillations (around 40 Hz) are sensitive to the short-range connectivity if transmission delays are considered.
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Affiliation(s)
- Timothée Proix
- Aix-Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France.
| | - Andreas Spiegler
- Aix-Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Michael Schirner
- Department of Neurology, Charité University Medicine Berlin, Germany
| | - Simon Rothmeier
- Department of Neurology, Charité University Medicine Berlin, Germany
| | - Petra Ritter
- Department of Neurology, Charité University Medicine Berlin, Germany; BrainModes Research Group, Max-Planck Institute for Cognitive and Brain Sciences, Leipzig, Germany
| | - Viktor K Jirsa
- Aix-Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France.
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13
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Kuhlmann L, Freestone DR, Manton JH, Heyse B, Vereecke HE, Lipping T, Struys MM, Liley DT. Neural mass model-based tracking of anesthetic brain states. Neuroimage 2016; 133:438-456. [DOI: 10.1016/j.neuroimage.2016.03.039] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2015] [Revised: 02/26/2016] [Accepted: 03/18/2016] [Indexed: 01/22/2023] Open
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14
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Freestone DR, Karoly PJ, Peterson ADH, Kuhlmann L, Lai A, Goodarzy F, Cook MJ. Seizure Prediction: Science Fiction or Soon to Become Reality? Curr Neurol Neurosci Rep 2016; 15:73. [PMID: 26404726 DOI: 10.1007/s11910-015-0596-3] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
This review highlights recent developments in the field of epileptic seizure prediction. We argue that seizure prediction is possible; however, most previous attempts have used data with an insufficient amount of information to solve the problem. The review discusses four methods for gaining more information above standard clinical electrophysiological recordings. We first discuss developments in obtaining long-term data that enables better characterisation of signal features and trends. Then, we discuss the usage of electrical stimulation to probe neural circuits to obtain robust information regarding excitability. Following this, we present a review of developments in high-resolution micro-electrode technologies that enable neuroimaging across spatial scales. Finally, we present recent results from data-driven model-based analyses, which enable imaging of seizure generating mechanisms from clinical electrophysiological measurements. It is foreseeable that the field of seizure prediction will shift focus to a more probabilistic forecasting approach leading to improvements in the quality of life for the millions of people who suffer uncontrolled seizures. However, a missing piece of the puzzle is devices to acquire long-term high-quality data. When this void is filled, seizure prediction will become a reality.
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Affiliation(s)
- Dean R Freestone
- Department of Medicine, St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia, 3065. .,Department of Statistics, Columbia University, New York, NY, 10027, USA.
| | - Philippa J Karoly
- Department of Medicine, St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia, 3065.,Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC, Australia, 3000
| | - Andre D H Peterson
- Department of Medicine, St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia, 3065
| | - Levin Kuhlmann
- Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC, Australia, 3000
| | - Alan Lai
- Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC, Australia, 3000
| | - Farhad Goodarzy
- Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC, Australia, 3000
| | - Mark J Cook
- Department of Medicine, St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia, 3065.
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15
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Aram P, Kadirkamanathan V, Anderson SR. Spatiotemporal System Identification With Continuous Spatial Maps and Sparse Estimation. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2015; 26:2978-2983. [PMID: 25647667 DOI: 10.1109/tnnls.2015.2392563] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
We present a framework for the identification of spatiotemporal linear dynamical systems. We use a state-space model representation that has the following attributes: 1) the number of spatial observation locations are decoupled from the model order; 2) the model allows for spatial heterogeneity; 3) the model representation is continuous over space; and 4) the model parameters can be identified in a simple and sparse estimation procedure. The model identification procedure we propose has four steps: 1) decomposition of the continuous spatial field using a finite set of basis functions where spatial frequency analysis is used to determine basis function width and spacing, such that the main spatial frequency contents of the underlying field can be captured; 2) initialization of states in closed form; 3) initialization of state-transition and input matrix model parameters using sparse regression-the least absolute shrinkage and selection operator method; and 4) joint state and parameter estimation using an iterative Kalman-filter/sparse-regression algorithm. To investigate the performance of the proposed algorithm we use data generated by the Kuramoto model of spatiotemporal cortical dynamics. The identification algorithm performs successfully, predicting the spatiotemporal field with high accuracy, whilst the sparse regression leads to a compact model.
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Zhao X, Robinson PA. Generalized seizures in a neural field model with bursting dynamics. J Comput Neurosci 2015; 39:197-216. [PMID: 26282528 DOI: 10.1007/s10827-015-0571-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2014] [Revised: 07/02/2015] [Accepted: 07/26/2015] [Indexed: 11/27/2022]
Abstract
The mechanisms underlying generalized seizures are explored with neural field theory. A corticothalamic neural field model that has accounted for multiple brain activity phenomena and states is used to explore changes leading to pathological seizure states. It is found that absence seizures arise from instabilities in the system and replicate experimental studies in numerous animal models and clinical studies.
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Affiliation(s)
- X Zhao
- School of Physics, The University of Sydney, Sydney, New South Wales, 2006, Australia.
- Center for Integrative Brain Function, University of Sydney, NSW, 2006, Australia.
- Neurosleep, 431 Glebe Point Rd, Glebe, New South Wales, 2037, Australia.
- Cooperative Research Center for Alertness, Safety, and Productivity, University of Sydney, NSW, 2006, Australia.
| | - P A Robinson
- School of Physics, The University of Sydney, Sydney, New South Wales, 2006, Australia
- Center for Integrative Brain Function, University of Sydney, NSW, 2006, Australia
- Neurosleep, 431 Glebe Point Rd, Glebe, New South Wales, 2037, Australia
- Cooperative Research Center for Alertness, Safety, and Productivity, University of Sydney, NSW, 2006, Australia
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17
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How the cortico-thalamic feedback affects the EEG power spectrum over frontal and occipital regions during propofol-induced sedation. J Comput Neurosci 2015; 39:155-79. [PMID: 26256583 DOI: 10.1007/s10827-015-0569-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2015] [Revised: 07/05/2015] [Accepted: 07/13/2015] [Indexed: 12/16/2022]
Abstract
Increasing concentrations of the anaesthetic agent propofol initially induces sedation before achieving full general anaesthesia. During this state of anaesthesia, the observed specific changes in electroencephalographic (EEG) rhythms comprise increased activity in the δ- (0.5-4 Hz) and α- (8-13 Hz) frequency bands over the frontal region, but increased δ- and decreased α-activity over the occipital region. It is known that the cortex, the thalamus, and the thalamo-cortical feedback loop contribute to some degree to the propofol-induced changes in the EEG power spectrum. However the precise role of each structure to the dynamics of the EEG is unknown. In this paper we apply a thalamo-cortical neuronal population model to reproduce the power spectrum changes in EEG during propofol-induced anaesthesia sedation. The model reproduces the power spectrum features observed experimentally both in frontal and occipital electrodes. Moreover, a detailed analysis of the model indicates the importance of multiple resting states in brain activity. The work suggests that the α-activity originates from the cortico-thalamic relay interaction, whereas the emergence of δ-activity results from the full cortico-reticular-relay-cortical feedback loop with a prominent enforced thalamic reticular-relay interaction. This model suggests an important role for synaptic GABAergic receptors at relay neurons and, more generally, for the thalamus in the generation of both the δ- and the α- EEG patterns that are seen during propofol anaesthesia sedation.
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Abstract
Neurostimulation as a therapeutic tool has been developed and used for a range of different diseases such as Parkinson's disease, epilepsy, and migraine. However, it is not known why the efficacy of the stimulation varies dramatically across patients or why some patients suffer from severe side effects. This is largely due to the lack of mechanistic understanding of neurostimulation. Hence, theoretical computational approaches to address this issue are in demand. This chapter provides a review of mechanistic computational modeling of brain stimulation. In particular, we will focus on brain diseases, where mechanistic models (e.g., neural population models or detailed neuronal models) have been used to bridge the gap between cellular-level processes of affected neural circuits and the symptomatic expression of disease dynamics. We show how such models have been, and can be, used to investigate the effects of neurostimulation in the diseased brain. We argue that these models are crucial for the mechanistic understanding of the effect of stimulation, allowing for a rational design of stimulation protocols. Based on mechanistic models, we argue that the development of closed-loop stimulation is essential in order to avoid inference with healthy ongoing brain activity. Furthermore, patient-specific data, such as neuroanatomic information and connectivity profiles obtainable from neuroimaging, can be readily incorporated to address the clinical issue of variability in efficacy between subjects. We conclude that mechanistic computational models can and should play a key role in the rational design of effective, fully integrated, patient-specific therapeutic brain stimulation.
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Aram P, Freestone DR, Cook MJ, Kadirkamanathan V, Grayden DB. Model-based estimation of intra-cortical connectivity using electrophysiological data. Neuroimage 2015; 118:563-75. [PMID: 26116963 DOI: 10.1016/j.neuroimage.2015.06.048] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2014] [Revised: 06/03/2015] [Accepted: 06/16/2015] [Indexed: 11/17/2022] Open
Abstract
This paper provides a new method for model-based estimation of intra-cortical connectivity from electrophysiological measurements. A novel closed-form solution for the connectivity function of the Amari neural field equations is derived as a function of electrophysiological observations. The resultant intra-cortical connectivity estimate is driven from experimental data, but constrained by the mesoscopic neurodynamics that are encoded in the computational model. A demonstration is provided to show how the method can be used to image physiological mechanisms that govern cortical dynamics, which are normally hidden in clinical data from epilepsy patients. Accurate estimation performance is demonstrated using synthetic data. Following the computational testing, results from patient data are obtained that indicate a dominant increase in surround inhibition prior to seizure onset that subsides in the cases when the seizures spread.
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Affiliation(s)
- P Aram
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK; Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield, UK.
| | - D R Freestone
- NeuroEngineering Laboratory, Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, VIC, Australia; Department of Medicine, St. Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, VIC, Australia; Department of Statistics, Columbia University, New York, NY 10027, USA.
| | - M J Cook
- Department of Medicine, St. Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, VIC, Australia.
| | - V Kadirkamanathan
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK.
| | - D B Grayden
- NeuroEngineering Laboratory, Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, VIC, Australia; Department of Medicine, St. Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, VIC, Australia; Centre for Neural Engineering, The University of Melbourne, Melbourne, VIC, Australia.
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20
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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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Sanz-Leon P, Knock SA, Spiegler A, Jirsa VK. Mathematical framework for large-scale brain network modeling in The Virtual Brain. Neuroimage 2015; 111:385-430. [PMID: 25592995 DOI: 10.1016/j.neuroimage.2015.01.002] [Citation(s) in RCA: 157] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2014] [Revised: 12/29/2014] [Accepted: 01/01/2015] [Indexed: 12/19/2022] Open
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A probabilistic method for determining cortical dynamics during seizures. J Comput Neurosci 2015; 38:559-75. [DOI: 10.1007/s10827-015-0554-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2014] [Revised: 03/08/2015] [Accepted: 03/12/2015] [Indexed: 11/26/2022]
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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]
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Tracking slow modulations in synaptic gain using dynamic causal modelling: validation in epilepsy. Neuroimage 2014; 107:117-126. [PMID: 25498428 PMCID: PMC4306529 DOI: 10.1016/j.neuroimage.2014.12.007] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2014] [Revised: 11/23/2014] [Accepted: 12/03/2014] [Indexed: 01/24/2023] Open
Abstract
In this work we propose a proof of principle that dynamic causal modelling can identify plausible mechanisms at the synaptic level underlying brain state changes over a timescale of seconds. As a benchmark example for validation we used intracranial electroencephalographic signals in a human subject. These data were used to infer the (effective connectivity) architecture of synaptic connections among neural populations assumed to generate seizure activity. Dynamic causal modelling allowed us to quantify empirical changes in spectral activity in terms of a trajectory in parameter space — identifying key synaptic parameters or connections that cause observed signals. Using recordings from three seizures in one patient, we considered a network of two sources (within and just outside the putative ictal zone). Bayesian model selection was used to identify the intrinsic (within-source) and extrinsic (between-source) connectivity. Having established the underlying architecture, we were able to track the evolution of key connectivity parameters (e.g., inhibitory connections to superficial pyramidal cells) and test specific hypotheses about the synaptic mechanisms involved in ictogenesis. Our key finding was that intrinsic synaptic changes were sufficient to explain seizure onset, where these changes showed dissociable time courses over several seconds. Crucially, these changes spoke to an increase in the sensitivity of principal cells to intrinsic inhibitory afferents and a transient loss of excitatory–inhibitory balance. We propose a framework to characterise slow dynamical changes in the brain. Dynamical causal modelling finds the most likely connectivity among two brain areas. The synaptic weights defining these connections are tracked in time. We analyse brain activity of an epileptic subject, at the focus and just outside it. We point to modulations of synaptic connections as responsible of the seizure.
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25
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Freestone DR, Karoly PJ, Nešić D, Aram P, Cook MJ, Grayden DB. Estimation of effective connectivity via data-driven neural modeling. Front Neurosci 2014; 8:383. [PMID: 25506315 PMCID: PMC4246673 DOI: 10.3389/fnins.2014.00383] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2014] [Accepted: 11/09/2014] [Indexed: 01/12/2023] Open
Abstract
This research introduces a new method for functional brain imaging via a process of model inversion. By estimating parameters of a computational model, we are able to track effective connectivity and mean membrane potential dynamics that cannot be directly measured using electrophysiological measurements alone. The ability to track the hidden aspects of neurophysiology will have a profound impact on the way we understand and treat epilepsy. For example, under the assumption the model captures the key features of the cortical circuits of interest, the framework will provide insights into seizure initiation and termination on a patient-specific basis. It will enable investigation into the effect a particular drug has on specific neural populations and connectivity structures using minimally invasive measurements. The method is based on approximating brain networks using an interconnected neural population model. The neural population model is based on a neural mass model that describes the functional activity of the brain, capturing the mesoscopic biophysics and anatomical structure. The model is made subject-specific by estimating the strength of intra-cortical connections within a region and inter-cortical connections between regions using a novel Kalman filtering method. We demonstrate through simulation how the framework can be used to track the mechanisms involved in seizure initiation and termination.
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Affiliation(s)
- Dean R Freestone
- Department of Medicine, St. Vincent's Hospital Melbourne, The University of Melbourne Fitzroy, VIC, Australia ; NeuroEngineering Laboratory, Department of Electrical and Electronic Engineering, The University of Melbourne Parkville, VIC, Australia
| | - Philippa J Karoly
- Department of Medicine, St. Vincent's Hospital Melbourne, The University of Melbourne Fitzroy, VIC, Australia ; NeuroEngineering Laboratory, Department of Electrical and Electronic Engineering, The University of Melbourne Parkville, VIC, Australia
| | - Dragan Nešić
- NeuroEngineering Laboratory, Department of Electrical and Electronic Engineering, The University of Melbourne Parkville, VIC, Australia
| | - Parham Aram
- Department of Automatic Control and Systems Engineering, University of Sheffield Sheffield, UK
| | - Mark J Cook
- Department of Medicine, St. Vincent's Hospital Melbourne, The University of Melbourne Fitzroy, VIC, Australia
| | - David B Grayden
- NeuroEngineering Laboratory, Department of Electrical and Electronic Engineering, The University of Melbourne Parkville, VIC, Australia ; Centre for Neural Engineering, The University of Melbourne Parkville, VIC, Australia
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26
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Aram P, Shen L, Pugh JA, Vaidyanathan S, Kadirkamanathan V. An efficient TOF-SIMS image analysis with spatial correlation and alternating non-negativity-constrained least squares. ACTA ACUST UNITED AC 2014; 31:753-60. [PMID: 25452330 DOI: 10.1093/bioinformatics/btu734] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
MOTIVATION Advances in analytical instrumentation towards acquiring high-resolution images of mass spectrometry constantly demand efficient approaches for data analysis. This is particularly true of time-of-flight secondary ion mass spectrometry imaging where recent advances enable acquisition of high-resolution data in multiple dimensions. In many applications, the distribution of different species from a sampled surface is spatially continuous in nature and a model that incorporates the spatial correlation across the surface would be preferable to estimations at discrete spatial locations. A key challenge here is the capability to analyse the high-resolution multidimensional data to extract relevant information reliably and efficiently. RESULTS We propose a framework based on alternating non-negativity-constrained least squares which accounts for the spatial correlation across the sample surface. The proposed method also decouples the computational complexity of the estimation procedure from the image resolution, which significantly reduces the processing time. We evaluate the performance of the algorithm with biochemical image datasets generated from mixture of metabolites.
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Affiliation(s)
- Parham Aram
- Department of Automatic Control and Systems Engineering and Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, UK
| | - Lingli Shen
- Department of Automatic Control and Systems Engineering and Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, UK
| | - John A Pugh
- Department of Automatic Control and Systems Engineering and Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, UK
| | - Seetharaman Vaidyanathan
- Department of Automatic Control and Systems Engineering and Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, UK
| | - Visakan Kadirkamanathan
- Department of Automatic Control and Systems Engineering and Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, UK
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27
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Balson RS, Freestone DR, Cook MJ, Burkitt AN, Grayden DB. Seizure dynamics: a computational model based approach demonstrating variability in seizure mechanisms. BMC Neurosci 2014. [PMCID: PMC4125068 DOI: 10.1186/1471-2202-15-s1-p152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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28
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Pnevmatikakis EA, Rad KR, Huggins J, Paninski L. Fast Kalman Filtering and Forward–Backward Smoothing via a Low-Rank Perturbative Approach. J Comput Graph Stat 2014. [DOI: 10.1080/10618600.2012.760461] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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29
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Aarabi A, He B. Seizure prediction in hippocampal and neocortical epilepsy using a model-based approach. Clin Neurophysiol 2013; 125:930-40. [PMID: 24374087 DOI: 10.1016/j.clinph.2013.10.051] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2012] [Revised: 10/18/2013] [Accepted: 10/20/2013] [Indexed: 12/17/2022]
Abstract
OBJECTIVES The aim of this study is to develop a model based seizure prediction method. METHODS A neural mass model was used to simulate the macro-scale dynamics of intracranial EEG data. The model was composed of pyramidal cells, excitatory and inhibitory interneurons described through state equations. Twelve model's parameters were estimated by fitting the model to the power spectral density of intracranial EEG signals and then integrated based on information obtained by investigating changes in the parameters prior to seizures. Twenty-one patients with medically intractable hippocampal and neocortical focal epilepsy were studied. RESULTS Tuned to obtain maximum sensitivity, an average sensitivity of 87.07% and 92.6% with an average false prediction rate of 0.2 and 0.15/h were achieved using maximum seizure occurrence periods of 30 and 50 min and a minimum seizure prediction horizon of 10s, respectively. Under maximum specificity conditions, the system sensitivity decreased to 82.9% and 90.05% and the false prediction rates were reduced to 0.16 and 0.12/h using maximum seizure occurrence periods of 30 and 50 min, respectively. CONCLUSIONS The spatio-temporal changes in the parameters demonstrated patient-specific preictal signatures that could be used for seizure prediction. SIGNIFICANCE The present findings suggest that the model-based approach may aid prediction of seizures.
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Affiliation(s)
- Ardalan Aarabi
- University of Minnesota, Minneapolis, MN 55455, USA; University of Picardie-Jules Verne, France
| | - Bin He
- University of Minnesota, Minneapolis, MN 55455, USA.
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30
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Spiegler A, Jirsa V. Systematic approximations of neural fields through networks of neural masses in the virtual brain. Neuroimage 2013; 83:704-25. [PMID: 23774395 DOI: 10.1016/j.neuroimage.2013.06.018] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2013] [Revised: 05/03/2013] [Accepted: 06/03/2013] [Indexed: 11/30/2022] Open
Abstract
Full brain network models comprise a large-scale connectivity (the connectome) and neural mass models as the network's nodes. Neural mass models absorb implicitly a variety of properties in their constant parameters to achieve a reduction in complexity. In situations, where the local network connectivity undergoes major changes, such as in development or epilepsy, it becomes crucial to model local connectivity explicitly. This leads naturally to a description of neural fields on folded cortical sheets with local and global connectivities. The numerical approximation of neural fields in biologically realistic situations as addressed in Virtual Brain simulations (see http://thevirtualbrain.org/app/ (version 1.0)) is challenging and requires a thorough evaluation if the Virtual Brain approach is to be adapted for systematic studies of disease and disorders. Here we analyze the sampling problem of neural fields for arbitrary dimensions and provide explicit results for one, two and three dimensions relevant to realistically folded cortical surfaces. We characterize (i) the error due to sampling of spatial distribution functions; (ii) useful sampling parameter ranges in the context of encephalographic (EEG, MEG, ECoG and functional MRI) signals; (iii) guidelines for choosing the right spatial distribution function for given anatomical and geometrical constraints.
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Affiliation(s)
- A Spiegler
- Institut de Neurosciences des Systèmes, UMR INSERM 1106, Aix-Marseille Université, Faculté de Médecine, 27, Boulevard Jean Moulin, 13005 Marseille, France.
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31
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Sanz Leon P, Knock SA, Woodman MM, Domide L, Mersmann J, McIntosh AR, Jirsa V. The Virtual Brain: a simulator of primate brain network dynamics. Front Neuroinform 2013; 7:10. [PMID: 23781198 PMCID: PMC3678125 DOI: 10.3389/fninf.2013.00010] [Citation(s) in RCA: 205] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2013] [Accepted: 05/22/2013] [Indexed: 01/21/2023] Open
Abstract
We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications.
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Affiliation(s)
- Paula Sanz Leon
- Institut de Neurosciences des Systèmes, Aix Marseille Université Marseille, France
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32
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Gorzelic P, Schiff SJ, Sinha A. Model-based rational feedback controller design for closed-loop deep brain stimulation of Parkinson's disease. J Neural Eng 2013; 10:026016. [DOI: 10.1088/1741-2560/10/2/026016] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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33
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Sedigh-Sarvestani M, Schiff SJ, Gluckman BJ. Reconstructing mammalian sleep dynamics with data assimilation. PLoS Comput Biol 2012; 8:e1002788. [PMID: 23209396 PMCID: PMC3510073 DOI: 10.1371/journal.pcbi.1002788] [Citation(s) in RCA: 28] [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: 05/21/2012] [Accepted: 10/03/2012] [Indexed: 01/14/2023] Open
Abstract
Data assimilation is a valuable tool in the study of any complex system, where measurements are incomplete, uncertain, or both. It enables the user to take advantage of all available information including experimental measurements and short-term model forecasts of a system. Although data assimilation has been used to study other biological systems, the study of the sleep-wake regulatory network has yet to benefit from this toolset. We present a data assimilation framework based on the unscented Kalman filter (UKF) for combining sparse measurements together with a relatively high-dimensional nonlinear computational model to estimate the state of a model of the sleep-wake regulatory system. We demonstrate with simulation studies that a few noisy variables can be used to accurately reconstruct the remaining hidden variables. We introduce a metric for ranking relative partial observability of computational models, within the UKF framework, that allows us to choose the optimal variables for measurement and also provides a methodology for optimizing framework parameters such as UKF covariance inflation. In addition, we demonstrate a parameter estimation method that allows us to track non-stationary model parameters and accommodate slow dynamics not included in the UKF filter model. Finally, we show that we can even use observed discretized sleep-state, which is not one of the model variables, to reconstruct model state and estimate unknown parameters. Sleep is implicated in many neurological disorders from epilepsy to schizophrenia, but simultaneous observation of the many brain components that regulate this behavior is difficult. We anticipate that this data assimilation framework will enable better understanding of the detailed interactions governing sleep and wake behavior and provide for better, more targeted, therapies. Mathematical models are developed to better understand interactions between components of a system that together govern the overall behavior. Mathematical models of sleep have helped to elucidate the neuronal cell groups that are involved in promoting sleep and wake behavior and the transitions between them. However, to be able to take full advantage of these models one must be able to estimate the value of all included variables accurately. Data assimilation refers to methods that allow the user to combine noisy measurements of just a few system variables with the mathematical model of that system to estimate all variables, including those originally inaccessible for measurement. Using these techniques we show that we can reconstruct the unmeasured variables and parameters of a mathematical model of the sleep-wake network. These reconstructed estimates can then be used to better understand the underlying neuronal behavior that results in sleep and wake activity. Because sleep is implicated in a wide array of neurological disorders from epilepsy to schizophrenia, we anticipate that this framework will enable better understanding of the link between sleep and the rest of the brain and provide for better, more targeted, therapies.
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Affiliation(s)
- Madineh Sedigh-Sarvestani
- Center for Neural Engineering, Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, Pennsylvania, United States of America.
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34
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Aram P, Freestone DR, Dewar M, Scerri K, Jirsa V, Grayden DB, Kadirkamanathan V. Spatiotemporal multi-resolution approximation of the Amari type neural field model. Neuroimage 2012; 66:88-102. [PMID: 23116813 DOI: 10.1016/j.neuroimage.2012.10.039] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2012] [Revised: 08/23/2012] [Accepted: 10/13/2012] [Indexed: 10/27/2022] Open
Abstract
Neural fields are spatially continuous state variables described by integro-differential equations, which are well suited to describe the spatiotemporal evolution of cortical activations on multiple scales. Here we develop a multi-resolution approximation (MRA) framework for the integro-difference equation (IDE) neural field model based on semi-orthogonal cardinal B-spline wavelets. In this way, a flexible framework is created, whereby both macroscopic and microscopic behavior of the system can be represented simultaneously. State and parameter estimation is performed using the expectation maximization (EM) algorithm. A synthetic example is provided to demonstrate the framework.
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Affiliation(s)
- P Aram
- Theoretical Neuroscience Group, UMR 1106, Institut de Neurosciences des Systemes, 13385 Marseille, France; Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK.
| | - D R Freestone
- NeuroEngineering Laboratory, Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, VIC, Australia; The Bionics Institute, East Melbourne, VIC, Australia.
| | | | - K Scerri
- Department of Systems and Control Engineering, University of Malta, Msida, MSD, Malta.
| | - V Jirsa
- Theoretical Neuroscience Group, UMR 1106, Institut de Neurosciences des Systemes, 13385 Marseille, France.
| | - D B Grayden
- NeuroEngineering Laboratory, Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, VIC, Australia; The Bionics Institute, East Melbourne, VIC, Australia.
| | - V Kadirkamanathan
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK.
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Abstract
Modern conflicts are characterized by an ever increasing use of information and sensing technology, resulting in vast amounts of high resolution data. Modelling and prediction of conflict, however, remain challenging tasks due to the heterogeneous and dynamic nature of the data typically available. Here we propose the use of dynamic spatiotemporal modelling tools for the identification of complex underlying processes in conflict, such as diffusion, relocation, heterogeneous escalation, and volatility. Using ideas from statistics, signal processing, and ecology, we provide a predictive framework able to assimilate data and give confidence estimates on the predictions. We demonstrate our methods on the WikiLeaks Afghan War Diary. Our results show that the approach allows deeper insights into conflict dynamics and allows a strikingly statistically accurate forward prediction of armed opposition group activity in 2010, based solely on data from previous years.
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36
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Kadirkamanathan V, Anderson SR, Billings SA, Zhang X, Holmes GR, Reyes-Aldasoro CC, Elks PM, Renshaw SA. The neutrophil's eye-view: inference and visualisation of the chemoattractant field driving cell chemotaxis in vivo. PLoS One 2012; 7:e35182. [PMID: 22563379 PMCID: PMC3338515 DOI: 10.1371/journal.pone.0035182] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2011] [Accepted: 03/09/2012] [Indexed: 12/26/2022] Open
Abstract
As we begin to understand the signals that drive chemotaxis in vivo, it is becoming clear that there is a complex interplay of chemotactic factors, which changes over time as the inflammatory response evolves. New animal models such as transgenic lines of zebrafish, which are near transparent and where the neutrophils express a green fluorescent protein, have the potential to greatly increase our understanding of the chemotactic process under conditions of wounding and infection from video microscopy data. Measurement of the chemoattractants over space (and their evolution over time) is a key objective for understanding the signals driving neutrophil chemotaxis. However, it is not possible to measure and visualise the most important contributors to in vivo chemotaxis, and in fact the understanding of the main contributors at any particular time is incomplete. The key insight that we make in this investigation is that the neutrophils themselves are sensing the underlying field that is driving their action and we can use the observations of neutrophil movement to infer the hidden net chemoattractant field by use of a novel computational framework. We apply the methodology to multiple in vivo neutrophil recruitment data sets to demonstrate this new technique and find that the method provides consistent estimates of the chemoattractant field across the majority of experiments. The framework that we derive represents an important new methodology for cell biologists investigating the signalling processes driving cell chemotaxis, which we label the neutrophils eye-view of the chemoattractant field.
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Affiliation(s)
- Visakan Kadirkamanathan
- Complex Systems and Signal Processing Group, Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom.
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37
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Chong M, Postoyan R, Nešić D, Kuhlmann L, Varsavsky A. Estimating the unmeasured membrane potential of neuronal populations from the EEG using a class of deterministic nonlinear filters. J Neural Eng 2012; 9:026001. [PMID: 22306591 DOI: 10.1088/1741-2560/9/2/026001] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
We present a model-based estimation method to reconstruct the unmeasured membrane potential of neuronal populations from a single-channel electroencephalographic (EEG) measurement. We consider a class of neural mass models that share a general structure, specifically the models by Stam et al (1999 Clin. Neurophysiol. 110 1801-13), Jansen and Rit (1995 Biol. Cybern. 73 357-66) and Wendling et al (2005 J. Clin. Neurophysiol. 22 343). Under idealized assumptions, we prove the global exponential convergence of our filter. Then, under more realistic assumptions, we investigate the robustness of our filter against model uncertainties and disturbances. Analytic proofs are provided for all results and our analyses are further illustrated via simulations.
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Affiliation(s)
- Michelle Chong
- Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, Australia
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38
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Freestone DR, Burkitt AN, Lai A, Nelson TS, Grayden DB, Vogrin S, Murphy M, D'Souza W, Badawy R, Kuhlmann L, Cook MJ. Probing for cortical excitability. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:1644-7. [PMID: 22254639 DOI: 10.1109/iembs.2011.6090474] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This paper introduces a new method for measuring cortical excitability using an electrical probing stimulus via intracranial electroencephalography (iEEG). Stimuli consisted of 100 single bi-phasic pulses, delivered every 10 minutes. Neural excitability is estimated by extracting a feature from the iEEG responses to the stimuli, which we dub the mean phase variance (PV). We show that the mean PV increases with the rate of inter-ictal discharges in one patient. In another patient, we show that the mean PV changes with sleep and an epileptic seizure. The results demonstrate a proof-of-principal for the method to be applied in a seizure anticipation framework.
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Affiliation(s)
- Dean R Freestone
- Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC, Australia
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