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Cao J, Hu D, Wang Y, Wang J, Lei B. Epileptic Classification with Deep Transfer Learning based Feature Fusion Algorithm. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2021.3064228] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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2
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Hutson TN, Rezaei F, Gautier NM, Indumathy J, Glasscock E, Iasemidis L. Directed Connectivity Analysis of the Neuro-Cardio- and Respiratory Systems Reveals Novel Biomarkers of Susceptibility to SUDEP. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2020; 1:301-311. [PMID: 34223181 PMCID: PMC8249082 DOI: 10.1109/ojemb.2020.3036544] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality and its pathophysiological mechanisms remain unknown. Goal: We set to record and analyze for the first time concurrent electroencephalographic (EEG), electrocardiographic (ECG), and unrestrained whole-body plethysmographic (Pleth) signals from control (WT - wild type) and SUDEP-prone mice (KO- knockout Kcna1 animal model). Methods: Employing multivariate autoregressive models (MVAR) we measured all tri-organ effective directional interactions by the generalized partial directed coherence (GPDC) in the frequency domain over time (hours). Results: When compared to the control (WT) animals, the SUDEP-prone (KO) animals exhibited (p < 0.001) reduced afferent and efferent interactions between the heart and the brain over the full frequency spectrum (0-200Hz), enhanced efferent interactions from the brain to the lungs and from the heart to the lungs at high (>90 Hz) frequencies (especially during periods with seizure activity), and decreased feedback from the lungs to the brain at low (<40 Hz) frequencies. Conclusions: These results show that impairment in the afferent and efferent pathways in the holistic neuro-cardio-respiratory network could lead to SUDEP, and effective connectivity measures and their dynamics could serve as novel biomarkers of susceptibility to SUDEP and seizures respectively.
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Affiliation(s)
- T Noah Hutson
- Department of Biomedical Engineering, Louisiana Tech University, Ruston, LA 71272, USA
| | - Farnaz Rezaei
- Department of Mathematics and Statistics, Louisiana Tech University, Ruston, LA 71272, USA
| | - Nicole M Gautier
- Department of Cellular Biology and Anatomy, Louisiana State University Health Sciences Center, Shreveport, LA 71130, USA
| | | | - Edward Glasscock
- Department of Biological Sciences, Southern Methodist University, Dallas, TX 75275, USA
| | - Leonidas Iasemidis
- Department of Biomedical Engineering, Louisiana Tech University, Ruston, LA 71272, USA
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Yakovleva TV, Kutepov IE, Karas AY, Yakovlev NM, Dobriyan VV, Papkova IV, Zhigalov MV, Saltykova OA, Krysko AV, Yaroshenko TY, Erofeev NP, Krysko VA. EEG Analysis in Structural Focal Epilepsy Using the Methods of Nonlinear Dynamics (Lyapunov Exponents, Lempel-Ziv Complexity, and Multiscale Entropy). ScientificWorldJournal 2020; 2020:8407872. [PMID: 32095119 PMCID: PMC7036140 DOI: 10.1155/2020/8407872] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Revised: 01/04/2020] [Accepted: 01/09/2020] [Indexed: 11/18/2022] Open
Abstract
This paper analyzes a case with the patient having focal structural epilepsy by processing electroencephalogram (EEG) fragments containing the "sharp wave" pattern of brain activity. EEG signals were recorded using 21 channels. Based on the fact that EEG signals are time series, an approach has been developed for their analysis using nonlinear dynamics tools: calculating the Lyapunov exponent's spectrum, multiscale entropy, and Lempel-Ziv complexity. The calculation of the first Lyapunov exponent is carried out by three methods: Wolf, Rosenstein, and Sano-Sawada, to obtain reliable results. The seven Lyapunov exponent spectra are calculated by the Sano-Sawada method. For the observed patient, studies showed that with medical treatment, his condition did not improve, and as a result, it was recommended to switch from conservative treatment to surgical. The obtained results of the patient's EEG study using the indicated nonlinear dynamics methods are in good agreement with the medical report and MRI data. The approach developed for the analysis of EEG signals by nonlinear dynamics methods can be applied for early detection of structural changes.
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Affiliation(s)
- Tatiana V. Yakovleva
- Department of Mathematics and Modelling, Yuri Gagarin State Technical University of Saratov, Saratov 410054, Russia
| | - Ilya E. Kutepov
- Department of Mathematics and Modelling, Yuri Gagarin State Technical University of Saratov, Saratov 410054, Russia
| | - Antonina Yu Karas
- Medical Center of Neurology, Diagnosis and Treatment of Epilepsy “Epineiro”, Saratov 410054, Russia
| | - Nikolai M. Yakovlev
- Medical Center of Neurology, Diagnosis and Treatment of Epilepsy “Epineiro”, Saratov 410054, Russia
| | - Vitalii V. Dobriyan
- Department of Mathematics and Modelling, Yuri Gagarin State Technical University of Saratov, Saratov 410054, Russia
| | - Irina V. Papkova
- Department of Mathematics and Modelling, Yuri Gagarin State Technical University of Saratov, Saratov 410054, Russia
| | - Maxim V. Zhigalov
- Department of Mathematics and Modelling, Yuri Gagarin State Technical University of Saratov, Saratov 410054, Russia
| | - Olga A. Saltykova
- Department of Mathematics and Modelling, Yuri Gagarin State Technical University of Saratov, Saratov 410054, Russia
| | - Anton V. Krysko
- Department of Mathematics and Modelling, Yuri Gagarin State Technical University of Saratov, Saratov 410054, Russia
| | - Tatiana Yu Yaroshenko
- Department of Mathematics and Modelling, Yuri Gagarin State Technical University of Saratov, Saratov 410054, Russia
| | - Nikolai P. Erofeev
- Department of Mathematics and Modelling, Yuri Gagarin State Technical University of Saratov, Saratov 410054, Russia
| | - Vadim A. Krysko
- Department of Mathematics and Modelling, Yuri Gagarin State Technical University of Saratov, Saratov 410054, Russia
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Martínez-González CL, Balankin A, López T, Manjarrez-Marmolejo J, Martínez-Ortiz EJ. Evaluation of dynamic scaling of growing interfaces in EEG fluctuations of seizures in animal model of temporal lobe epilepsy. Comput Biol Med 2017; 88:41-49. [PMID: 28692930 DOI: 10.1016/j.compbiomed.2017.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Revised: 06/26/2017] [Accepted: 07/02/2017] [Indexed: 11/28/2022]
Abstract
Epileptic seizures, as a dynamic phenomenon in brain behavior, obey a scale-free behavior, frequently analyzed by electrical activity recording. This recording can be seen as a surface that roughens with time. Dynamic scaling studies roughening processes or growing interfaces. In this theory, a set of exponents -obtained from scale invariance properties- characterize rough interfaces growth. The aim of the present study was to investigate scaling behavior in EEG time series fluctuations of a chemical animal model of temporal lobe epilepsy, with dynamic scaling to detect changes on seizure onset. We analyzed local variables in different sampling intervals and estimated rough, scaling and dynamic exponents. Results exhibited long-range correlations in interictal activity. Results of renormalization and data collapsing confirmed that each epoch of EEG fluctuations for interictal, preictal and postictal collapse in a curve in different scales, each segment independently; remarkably, we found non-scaling behavior in seizures epochs. Data for the different sampling intervals for ictal period do not collapse in one curve, which implies that ictal activity does not exhibit the same scaling behavior than the other epochs. Statistical significant differences of growth exponent were found between interictal and ictal segment, while for scaling exponent, significant differences were found between interictal and postictal segment. These results confirm the potential of scaling exponents as characteristic parameters to detect changes on seizure onset, which suggests their use as inputs for analysis methods for seizure detection in long-term recordings, while changes in growth exponent are potentially useful for prediction purposes.
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Affiliation(s)
| | - Alexander Balankin
- Instituto Politécnico Nacional, SEPI ESIME-Z, Av. IPN S/N, C.P. 07738, Mexico
| | - Tessy López
- Universidad Autónoma Metropolitana, C.P. 14387, Mexico
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Zhang X, Yi H, Bai W, Tian X. Dynamic trajectory of multiple single-unit activity during working memory task in rats. Front Comput Neurosci 2015; 9:117. [PMID: 26441626 PMCID: PMC4585230 DOI: 10.3389/fncom.2015.00117] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2015] [Accepted: 09/07/2015] [Indexed: 02/02/2023] Open
Abstract
Working memory plays an important role in complex cognitive tasks. A popular theoretical view is that transient properties of neuronal dynamics underlie cognitive processing. The question raised here as to how the transient dynamics evolve in working memory. To address this issue, we investigated the multiple single-unit activity dynamics in rat medial prefrontal cortex (mPFC) during a Y-maze working memory task. The approach worked by reconstructing state space from delays of the original single-unit firing rate variables, which were further analyzed using kernel principal component analysis (KPCA). Then the neural trajectories were obtained to visualize the multiple single-unit activity. Furthermore, the maximal Lyapunov exponent (MLE) was calculated to quantitatively evaluate the neural trajectories during the working memory task. The results showed that the neuronal activity produced stable and reproducible neural trajectories in the correct trials while showed irregular trajectories in the incorrect trials, which may establish a link between the neurocognitive process and behavioral performance in working memory. The MLEs significantly increased during working memory in the correctly performed trials, indicating an increased divergence of the neural trajectories. In the incorrect trials, the MLEs were nearly zero and remained unchanged during the task. Taken together, the trial-specific neural trajectory provides an effective way to track the instantaneous state of the neuronal population during the working memory task and offers valuable insights into working memory function. The MLE describes the changes of neural dynamics in working memory and may reflect different neuronal population states in working memory.
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Affiliation(s)
- Xiaofan Zhang
- Department of Biomedical Engineering, School of Biomedical Engineering and Technology, Tianjin Medical University Tianjin, China
| | - Hu Yi
- Department of Biomedical Engineering, School of Biomedical Engineering and Technology, Tianjin Medical University Tianjin, China
| | - Wenwen Bai
- Department of Biomedical Engineering, School of Biomedical Engineering and Technology, Tianjin Medical University Tianjin, China
| | - Xin Tian
- Department of Biomedical Engineering, School of Biomedical Engineering and Technology, Tianjin Medical University Tianjin, China
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6
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Sotelo A, Guijarro ED, Trujillo L. Seizure states identification in experimental epilepsy using gabor atom analysis. J Neurosci Methods 2015; 241:121-31. [DOI: 10.1016/j.jneumeth.2014.12.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2014] [Revised: 12/01/2014] [Accepted: 12/03/2014] [Indexed: 11/17/2022]
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Kantorovich S, Astary GW, King MA, Mareci TH, Sarntinoranont M, Carney PR. Influence of neuropathology on convection-enhanced delivery in the rat hippocampus. PLoS One 2013; 8:e80606. [PMID: 24260433 PMCID: PMC3832660 DOI: 10.1371/journal.pone.0080606] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2013] [Accepted: 10/03/2013] [Indexed: 01/08/2023] Open
Abstract
Local drug delivery techniques, such as convention-enhanced delivery (CED), are promising novel strategies for delivering therapeutic agents otherwise limited by systemic toxicity and blood-brain-barrier restrictions. CED uses positive pressure to deliver infusate homogeneously into interstitial space, but its distribution is dependent upon appropriate tissue targeting and underlying neuroarchitecture. To investigate effects of local tissue pathology and associated edema on infusate distribution, CED was applied to the hippocampi of rats that underwent electrically-induced, self-sustaining status epilepticus (SE), a prolonged seizure. Infusion occurred 24 hours post-SE, using a macromolecular tracer, the magnetic resonance (MR) contrast agent gadolinium chelated with diethylene triamine penta-acetic acid and covalently attached to albumin (Gd-albumin). High-resolution T1- and T2-relaxation-weighted MR images were acquired at 11.1 Tesla in vivo prior to infusion to generate baseline contrast enhancement images and visualize morphological changes, respectively. T1-weighted imaging was repeated post-infusion to visualize final contrast-agent distribution profiles. Histological analysis was performed following imaging to characterize injury. Infusions of Gd-albumin into injured hippocampi resulted in larger distribution volumes that correlated with increased injury severity, as measured by hyperintense regions seen in T2-weighted images and corresponding histological assessments of neuronal degeneration, myelin degradation, astrocytosis, and microglial activation. Edematous regions included the CA3 hippocampal subfield, ventral subiculum, piriform and entorhinal cortex, amygdalar nuclei, middle and laterodorsal/lateroposterior thalamic nuclei. This study demonstrates MR-visualized injury processes are reflective of cellular alterations that influence local distribution volume, and provides a quantitative basis for the planning of local therapeutic delivery strategies in pathological brain regions.
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Affiliation(s)
- Svetlana Kantorovich
- Department of Neuroscience, University of Florida, Gainesville, Florida, United States of America
- Wilder Center of Excellence for Epilepsy Research, University of Florida, Gainesville, Florida, United States of America
- Department of Pediatrics, Division of Pediatric Neurology, University of Florida, Gainesville, Florida, United States of America
| | - Garrett W. Astary
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, United States of America
| | - Michael A. King
- Department of Pharmacology and Therapeutics, University of Florida, Gainesville, Florida, United States of America
- Malcom Randall Veterans Affairs Medical Center, Gainesville, University of Florida, Gainesville, Florida, United States of America
| | - Thomas H. Mareci
- Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, Florida, United States of America
| | - Malisa Sarntinoranont
- Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, Florida, United States of America
| | - Paul R. Carney
- Department of Neuroscience, University of Florida, Gainesville, Florida, United States of America
- Wilder Center of Excellence for Epilepsy Research, University of Florida, Gainesville, Florida, United States of America
- Department of Pediatrics, Division of Pediatric Neurology, University of Florida, Gainesville, Florida, United States of America
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, United States of America
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8
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Synthesis of high-complexity rhythmic signals for closed-loop electrical neuromodulation. Neural Netw 2013; 42:62-73. [DOI: 10.1016/j.neunet.2013.01.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2012] [Revised: 01/05/2013] [Accepted: 01/06/2013] [Indexed: 11/20/2022]
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Serletis D, Bardakjian BL, Valiante TA, Carlen PL. Complexity and multifractality of neuronal noise in mouse and human hippocampal epileptiform dynamics. J Neural Eng 2012; 9:056008. [PMID: 22929878 DOI: 10.1088/1741-2560/9/5/056008] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Fractal methods offer an invaluable means of investigating turbulent nonlinearity in non-stationary biomedical recordings from the brain. Here, we investigate properties of complexity (i.e. the correlation dimension, maximum Lyapunov exponent, 1/f(γ) noise and approximate entropy) and multifractality in background neuronal noise-like activity underlying epileptiform transitions recorded at the intracellular and local network scales from two in vitro models: the whole-intact mouse hippocampus and lesional human hippocampal slices. Our results show evidence for reduced dynamical complexity and multifractal signal features following transition to the ictal epileptiform state. These findings suggest that pathological breakdown in multifractal complexity coincides with loss of signal variability or heterogeneity, consistent with an unhealthy ictal state that is far from the equilibrium of turbulent yet healthy fractal dynamics in the brain. Thus, it appears that background noise-like activity successfully captures complex and multifractal signal features that may, at least in part, be used to classify and identify brain state transitions in the healthy and epileptic brain, offering potential promise for therapeutic neuromodulatory strategies for afflicted patients suffering from epilepsy and other related neurological disorders.
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Affiliation(s)
- Demitre Serletis
- Neurological Institute, Epilepsy Center, Cleveland Clinic, OH 44195, USA.
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10
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Chen W, Cahoy DO, Tasker JG, Chiu AWL. Kernel duration and modulation gain in a coupled oscillator model and their implications on the progression of seizures. NETWORK (BRISTOL, ENGLAND) 2012; 23:59-75. [PMID: 22571251 DOI: 10.3109/0954898x.2012.678463] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
The coupled oscillator model has previously been used for the simulation of neuronal activities in in vitro rat hippocampal slice seizure data and the evaluation of seizure suppression algorithms. Each model unit can be described as either an oscillator which can generate action potential spike trains without inputs, or a threshold-based unit. With the change of only one parameter, each unit can either be an oscillator or a threshold-based spiking unit. This would eliminate the need of a new set of equations for each type of unit. Previous analysis has suggested that long kernel duration and imbalance of inhibitory feedback can cause the system to intermittently transition into and out of ictal activities. The state transitions of seizure-like events were investigated here; specifically, how the system excitability may change when the system underwent transitions in the preictal and postictal processes. Analysis showed that the area of the excitation kernel is positively correlated with the mean firing rate of ictal activity. The kernel duration is also correlated to the amount of ictal activity. The transition into ictal involved the escape from the saddle point foci in the state space trajectory identified using Newton's method.
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Affiliation(s)
- Wu Chen
- Biomedical Engineering, Louisiana Tech University, Ruston, LA, United States
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COLIC SINISA, ZALAY OSBERTC, BARDAKJIAN BERJL. RESPONSIVE NEUROMODULATORS BASED ON ARTIFICIAL NEURAL NETWORKS USED TO CONTROL SEIZURE-LIKE EVENTS IN A COMPUTATIONAL MODEL OF EPILEPSY. Int J Neural Syst 2011; 21:367-83. [DOI: 10.1142/s0129065711002894] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Deep brain stimulation (DBS) has been noted for its potential to suppress epileptic seizures. To date, DBS has achieved mixed results as a therapeutic approach to seizure control. Using a computational model, we demonstrate that high-complexity, biologically-inspired responsive neuromodulation is superior to periodic forms of neuromodulation (responsive and non-responsive) such as those implemented in DBS, as well as neuromodulation using random and random repetitive-interval stimulation. We configured radial basis function (RBF) networks to generate outputs modeling interictal time series recorded from rodent hippocampal slices that were perfused with low Mg2+/high K+solution. We then compared the performance of RBF-based interictal modulation, periodic biphasic-pulse modulation, random modulation and random repetitive modulation on a cognitive rhythm generator (CRG) model of spontaneous seizure-like events (SLEs), testing efficacy of SLE control. A statistically significant improvement in SLE mitigation for the RBF interictal modulation case versus the periodic and random cases was observed, suggesting that the use of biologically-inspired neuromodulators may achieve better results for the purpose of electrical control of seizures in a clinical setting.
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Affiliation(s)
- SINISA COLIC
- Edwards S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, M5S 3G4, Canada
| | - OSBERT C. ZALAY
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, M5S 3G9, Canada
| | - BERJ L. BARDAKJIAN
- Edwards S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, M5S 3G4, Canada
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, M5S 3G9, Canada
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Abstract
Epilepsy is characterized by intermittent, paroxysmal, hypersynchronous electrical activity that may remain localized and/or spread and severely disrupt the brain's normal multitask and multiprocessing function. Epileptic seizures are the hallmarks of such activity. The ability to issue warnings in real time of impending seizures may lead to novel diagnostic tools and treatments for epilepsy. Applications may range from a warning to the patient to avert seizure-associated injuries, to automatic timely administration of an appropriate stimulus. Seizure prediction could become an integral part of the treatment of epilepsy through neuromodulation, especially in the new generation of closed-loop seizure control systems.
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Affiliation(s)
- Leon D Iasemidis
- The Harrington Department of Biomedical Engineering, School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85287-9709, USA.
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13
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Complexity in neuronal noise depends on network interconnectivity. Ann Biomed Eng 2011; 39:1768-78. [PMID: 21347547 DOI: 10.1007/s10439-011-0281-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2010] [Accepted: 02/13/2011] [Indexed: 12/31/2022]
Abstract
"Noise," or noise-like activity (NLA), defines background electrical membrane potential fluctuations at the cellular level of the nervous system, comprising an important aspect of brain dynamics. Using whole-cell voltage recordings from fast-spiking stratum oriens interneurons and stratum pyramidale neurons located in the CA3 region of the intact mouse hippocampus, we applied complexity measures from dynamical systems theory (i.e., 1/f(γ) noise and correlation dimension) and found evidence for complexity in neuronal NLA, ranging from high- to low-complexity dynamics. Importantly, these high- and low-complexity signal features were largely dependent on gap junction and chemical synaptic transmission. Progressive neuronal isolation from the surrounding local network via gap junction blockade (abolishing gap junction-dependent spikelets) and then chemical synaptic blockade (abolishing excitatory and inhibitory post-synaptic potentials), or the reverse order of these treatments, resulted in emergence of high-complexity NLA dynamics. Restoring local network interconnectivity via blockade washout resulted in resolution to low-complexity behavior. These results suggest that the observed increase in background NLA complexity is the result of reduced network interconnectivity, thereby highlighting the potential importance of the NLA signal to the study of network state transitions arising in normal and abnormal brain dynamics (such as in epilepsy, for example).
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Zalay OC, Serletis D, Carlen PL, Bardakjian BL. System characterization of neuronal excitability in the hippocampus and its relevance to observed dynamics of spontaneous seizure-like transitions. J Neural Eng 2010; 7:036002. [DOI: 10.1088/1741-2560/7/3/036002] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Parekh MB, Carney PR, Sepulveda H, Norman W, King M, Mareci TH. Early MR diffusion and relaxation changes in the parahippocampal gyrus precede the onset of spontaneous seizures in an animal model of chronic limbic epilepsy. Exp Neurol 2010; 224:258-70. [PMID: 20394745 DOI: 10.1016/j.expneurol.2010.03.031] [Citation(s) in RCA: 39] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2009] [Revised: 03/15/2010] [Accepted: 03/30/2010] [Indexed: 10/19/2022]
Abstract
Structural changes in limbic regions are often observed in individuals with temporal lobe epilepsy (TLE) and in animal models. However, the brain structural changes during the evolution into epilepsy remain largely unknown. Therefore, the purpose of this study was to define the temporal changes in limbic structures after experimental status epilepticus (SE) during the latency period of epileptogenesis in vivo, with quantitative diffusion tensor imaging (DTI) and T2 relaxometry in an animal model of chronic TLE. A pair of fifty micron electrodes was implanted into the ventral hippocampus in twelve male adult rats. Self-sustaining SE was induced with electrical stimulation in eleven rats. Three rats served as age-matched controls. In vivo diffusion tensor and T2 magnetic resonance imaging (MRI) was performed at 11.1 Tesla, pre- and post-implantation of electrodes and 3, 5, 7, 10, 20, 40 and 60 days post-SE to assess structural changes. Spontaneous seizures were identified with continuous time-locked video-monitoring. Following imaging in vivo, fixed, excised brains were MR imaged at 17.6 Tesla. Subsequently, histological analysis was correlated with MRI results. Following SE, 8/11 injured rats developed spontaneous seizures. Unique to these 8 rats, early T2, diffusivity and anisotropy changes were observed in vivo within the parahippocampal gyrus (contralateral) and fimbria (bilateral). In excised brains, bilateral increase in anisotropy was observed in the dentate gyrus, corresponding to mossy fiber sprouting as determined by Timm staining. Using T2 relaxometry and DTI, specific transient and long-term structural changes were observed only in rats that developed spontaneous limbic seizures.
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Affiliation(s)
- Mansi B Parekh
- Department of Neuroscience, University of Florida, Gainesville, FL, USA
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