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Hoffman C, Cheng J, Morales R, Ji D, Dabaghian Y. Altered patterning of neural activity in a tauopathy mouse model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.23.586417. [PMID: 38585991 PMCID: PMC10996513 DOI: 10.1101/2024.03.23.586417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Alzheimer's disease (AD) is a complex neurodegenerative condition that manifests at multiple levels and involves a spectrum of abnormalities ranging from the cellular to cognitive. Here, we investigate the impact of AD-related tau-pathology on hippocampal circuits in mice engaged in spatial navigation, and study changes of neuronal firing and dynamics of extracellular fields. While most studies are based on analyzing instantaneous or time-averaged characteristics of neuronal activity, we focus on intermediate timescales-spike trains and waveforms of oscillatory potentials, which we consider as single entities. We find that, in healthy mice, spike arrangements and wave patterns (series of crests or troughs) are coupled to the animal's location, speed, and acceleration. In contrast, in tau-mice, neural activity is structurally disarrayed: brainwave cadence is detached from locomotion, spatial selectivity is lost, the spike flow is scrambled. Importantly, these alterations start early and accumulate with age, which exposes progressive disinvolvement the hippocampus circuit in spatial navigation. These features highlight qualitatively different neurodynamics than the ones provided by conventional analyses, and are more salient, thus revealing a new level of the hippocampal circuit disruptions.
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Affiliation(s)
- C Hoffman
- Department of Neurology, The University of Texas McGovern Medical School, 6431 Fannin St, Houston, TX 77030
| | - J Cheng
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030
| | - R Morales
- Department of Neurology, The University of Texas McGovern Medical School, 6431 Fannin St, Houston, TX 77030
| | - D Ji
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030
| | - Y Dabaghian
- Department of Neurology, The University of Texas McGovern Medical School, 6431 Fannin St, Houston, TX 77030
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Lucasius C, Grigorovsky V, Nariai H, Galanopoulou AS, Gursky J, Moshe SL, Bardakjian BL. Biomimetic Deep Learning Networks With Applications to Epileptic Spasms and Seizure Prediction. IEEE Trans Biomed Eng 2024; 71:1056-1067. [PMID: 37851549 PMCID: PMC10979638 DOI: 10.1109/tbme.2023.3325762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2023]
Abstract
OBJECTIVE In this study, we present a novel biomimetic deep learning network for epileptic spasms and seizure prediction and compare its performance with state-of-the-art conventional machine learning models. METHODS Our proposed model incorporates modular Volterra kernel convolutional networks and bidirectional recurrent networks in combination with the phase amplitude cross-frequency coupling features derived from scalp EEG. They are applied to the standard CHB-MIT dataset containing focal epilepsy episodes as well as two other datasets from the Montefiore Medical Center and the University of California Los Angeles that provide data of patients experiencing infantile spasm (IS) syndrome. RESULTS Overall, in this study, the networks can produce accurate predictions (100%) and significant detection latencies (10 min). Furthermore, the biomimetic network outperforms conventional ones by producing no false positives. SIGNIFICANCE Biomimetic neural networks utilize extensive knowledge about processing and learning in the electrical networks of the brain. Predicting seizures in adults can improve their quality of life. Epileptic spasms in infants are part of a particular seizure type that needs identifying when suspicious behaviors are noticed in babies. Predicting epileptic spasms within a given time frame (the prediction horizon) suggests their existence and allows an epileptologist to flag an EEG trace for future review.
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Földi T, Lőrincz ML, Berényi A. Temporally Targeted Interactions With Pathologic Oscillations as Therapeutical Targets in Epilepsy and Beyond. Front Neural Circuits 2021; 15:784085. [PMID: 34955760 PMCID: PMC8693222 DOI: 10.3389/fncir.2021.784085] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 11/10/2021] [Indexed: 11/13/2022] Open
Abstract
Self-organized neuronal oscillations rely on precisely orchestrated ensemble activity in reverberating neuronal networks. Chronic, non-malignant disorders of the brain are often coupled to pathological neuronal activity patterns. In addition to the characteristic behavioral symptoms, these disturbances are giving rise to both transient and persistent changes of various brain rhythms. Increasing evidence support the causal role of these "oscillopathies" in the phenotypic emergence of the disease symptoms, identifying neuronal network oscillations as potential therapeutic targets. While the kinetics of pharmacological therapy is not suitable to compensate the disease related fine-scale disturbances of network oscillations, external biophysical modalities (e.g., electrical stimulation) can alter spike timing in a temporally precise manner. These perturbations can warp rhythmic oscillatory patterns via resonance or entrainment. Properly timed phasic stimuli can even switch between the stable states of networks acting as multistable oscillators, substantially changing the emergent oscillatory patterns. Novel transcranial electric stimulation (TES) approaches offer more reliable neuronal control by allowing higher intensities with tolerable side-effect profiles. This precise temporal steerability combined with the non- or minimally invasive nature of these novel TES interventions make them promising therapeutic candidates for functional disorders of the brain. Here we review the key experimental findings and theoretical background concerning various pathological aspects of neuronal network activity leading to the generation of epileptic seizures. The conceptual and practical state of the art of temporally targeted brain stimulation is discussed focusing on the prevention and early termination of epileptic seizures.
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Affiliation(s)
- Tamás Földi
- MTA-SZTE "Momentum" Oscillatory Neuronal Networks Research Group, Department of Physiology, University of Szeged, Szeged, Hungary.,Neurocybernetics Excellence Center, University of Szeged, Szeged, Hungary.,HCEMM-USZ Magnetotherapeutics Research Group, University of Szeged, Szeged, Hungary.,Child and Adolescent Psychiatry, Department of the Child Health Center, University of Szeged, Szeged, Hungary
| | - Magor L Lőrincz
- MTA-SZTE "Momentum" Oscillatory Neuronal Networks Research Group, Department of Physiology, University of Szeged, Szeged, Hungary.,Neurocybernetics Excellence Center, University of Szeged, Szeged, Hungary.,Department of Physiology, Anatomy and Neuroscience, Faculty of Sciences University of Szeged, Szeged, Hungary.,Neuroscience Division, Cardiff University, Cardiff, United Kingdom
| | - Antal Berényi
- MTA-SZTE "Momentum" Oscillatory Neuronal Networks Research Group, Department of Physiology, University of Szeged, Szeged, Hungary.,Neurocybernetics Excellence Center, University of Szeged, Szeged, Hungary.,HCEMM-USZ Magnetotherapeutics Research Group, University of Szeged, Szeged, Hungary.,Neuroscience Institute, New York University, New York, NY, United States
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Kalitzin S, Petkov G, Suffczynski P, Grigorovsky V, Bardakjian BL, Lopes da Silva F, Carlen PL. Epilepsy as a manifestation of a multistate network of oscillatory systems. Neurobiol Dis 2019; 130:104488. [DOI: 10.1016/j.nbd.2019.104488] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 06/03/2019] [Accepted: 06/04/2019] [Indexed: 12/18/2022] Open
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Farah FH, Grigorovsky V, Bardakjian BL. Coupled Oscillators Model of Hyperexcitable Neuroglial Networks. Int J Neural Syst 2018; 29:1850041. [PMID: 30415633 DOI: 10.1142/s0129065718500417] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Glial populations within neuronal networks of the brain have recently gained much interest in the context of hyperexcitability and epilepsy. In this paper, we present an oscillator-based neuroglial model capable of generating Spontaneous Electrical Discharges (SEDs) in hyperexcitable conditions. The network is composed of 16 coupled Cognitive Rhythm Generators (CRGs), which are oscillator-based mathematical constructs previously described by our research team. CRGs are well-suited for modeling assemblies of excitable cells, and in this network, each represents one of the following populations: excitatory pyramidal cells, inhibitory interneurons, astrocytes, and microglia. We investigated various pathways leading to hyperexcitability, and our results suggest an important role for astrocytes and microglia in the generation of SEDs of various durations. Analysis of the resultant SEDs revealed two underlying duration distributions with differing properties. Particularly, short and long SEDs are associated with deterministic and random underlying processes, respectively. The mesoscale of this model makes it well-suited for (a) the elucidation of glia-related hypotheses in hyperexcitable conditions, (b) use as a testing platform for neuromodulation purposes, and (c) a hardware implementation for closed-loop neuromodulation.
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Affiliation(s)
- Firas H Farah
- 1 Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S3G4, Canada
| | - Vasily Grigorovsky
- 2 Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario M5S3G9, Canada
| | - Berj L Bardakjian
- 3 Institute of Biomaterials and Biomedical Engineering, University of Toronto, 164 College Street, Room 407, Toronto, Ontario M5S3G9, Canada
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The origin of segmentation motor activity in the intestine. Nat Commun 2015; 5:3326. [PMID: 24561718 PMCID: PMC4885742 DOI: 10.1038/ncomms4326] [Citation(s) in RCA: 112] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2013] [Accepted: 01/27/2014] [Indexed: 12/19/2022] Open
Abstract
The segmentation motor activity of the gut that facilitates absorption of nutrients, was first described in the late 19th century but the fundamental mechanisms underlying it remain poorly understood. The dominant theory suggests alternate excitation and inhibition from the enteric nervous system. Here we demonstrate that typical segmentation can occur after total nerve blockade. The segmentation motor pattern emerges when the amplitude of the dominant pacemaker, the slow wave generated by ICC associated with the myenteric plexus (ICC-MP), is modulated by the phase of induced lower frequency rhythmic transient depolarizations, generated by ICC associated with the deep muscular plexus (ICC-DMP), resulting in a waxing and waning of the amplitude of the slow wave and a rhythmic checkered pattern of segmentation motor activity. Phase amplitude modulation of the slow waves points to an underlying system of coupled nonlinear oscillators originating in ICC.
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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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Kang EE, Zalay OC, Serletis D, Carlen PL, Bardakjian BL. Markers of pathological excitability derived from principal dynamic modes of hippocampal neurons. J Neural Eng 2012; 9:056004. [PMID: 22871606 DOI: 10.1088/1741-2560/9/5/056004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Transformation of principal dynamic modes (PDMs) under epileptogenic conditions was investigated by computing the Volterra kernels in a rodent epilepsy model derived from a mouse whole hippocampal preparation, where epileptogenesis was induced by altering the concentrations of Mg(2 +) and K(+) of the perfusate for different levels of excitability. Both integrating and differentiating PDMs were present in the neuronal dynamics, and both of them increased in absolute magnitude for increased excitability levels. However, the integrating PDMs dominated at all levels of excitability in terms of their relative contributions to the overall response, whereas the dominant frequency responses of the differentiating PDMs were shifted to higher ranges under epileptogenic conditions, from ripple activities (75-200 Hz) to fast ripple activities (200-500 Hz).
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Affiliation(s)
- Eunji E Kang
- Department of Electrical and Computer Engineering, University of Toronto, M5S 3G4 ON, Canada.
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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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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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