1,599 research outputs found

    Adiabatic dynamic causal modelling

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    This technical note introduces adiabatic dynamic causal modelling, a method for inferring slow changes in biophysical parameters that control fluctuations of fast neuronal states. The application domain we have in mind is inferring slow changes in variables (e.g., extracellular ion concentrations or synaptic efficacy) that underlie phase transitions in brain activity (e.g., paroxysmal seizure activity). The scheme is efficient and yet retains a biophysical interpretation, in virtue of being based on established neural mass models that are equipped with a slow dynamic on the parameters (such as synaptic rate constants or effective connectivity). In brief, we use an adiabatic approximation to summarise fast fluctuations in hidden neuronal states (and their expression in sensors) in terms of their second order statistics; namely, their complex cross spectra. This allows one to specify and compare models of slowly changing parameters (using Bayesian model reduction) that generate a sequence of empirical cross spectra of electrophysiological recordings. Crucially, we use the slow fluctuations in the spectral power of neuronal activity as empirical priors on changes in synaptic parameters. This introduces a circular causality, in which synaptic parameters underwrite fast neuronal activity that, in turn, induces activity-dependent plasticity in synaptic parameters. In this foundational paper, we describe the underlying model, establish its face validity using simulations and provide an illustrative application to a chemoconvulsant animal model of seizure activity

    Optical mapping of neuronal activity during seizures in zebrafish

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    Mapping neuronal activity during the onset and propagation of epileptic seizures can provide a better understanding of the mechanisms underlying this pathology and improve our approaches to the development of new drugs. Recently, zebrafish has become an important model for studying epilepsy both in basic research and in drug discovery. Here, we employed a transgenic line with pan-neuronal expression of the genetically-encoded calcium indicator GCaMP6s to measure neuronal activity in zebrafish larvae during seizures induced by pentylenetretrazole (PTZ). With this approach, we mapped neuronal activity in different areas of the larval brain, demonstrating the high sensitivity of this method to different levels of alteration, as induced by increasing PTZ concentrations, and the rescuing effect of an anti-epileptic drug. We also present simultaneous measurements of brain and locomotor activity, as well as a high-throughput assay, demonstrating that GCaMP measurements can complement behavioural assays for the detection of subclinical epileptic seizures, thus enabling future investigations on human hypomorphic mutations and more effective drug screening methods. Notably, the methodology described here can be easily applied to the study of many human neuropathologies modelled in zebrafish, allowing a simple and yet detailed investigation of brain activity alterations associated with the pathological phenotype

    Investigation of intracranial pharmacotherapy in Genetic Absence Epilepsy Rats from Strasbourg (GAERS): a potential strategy to overcome the limitations of the standard treatment in epilepsy

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    Epilepsy is a chronic disorder of the brain and affects approximately 50 million people worldwide. It is, thus, one of the most common neurological diseases (World Health Organization, 2006). The main approach of epilepsy treatment is systemic drug application. The central nervous system (CNS), however, is a particularly challenging target for drug delivery. Systemic drug therapy is limited by the blood-brain-barrier (BBB), restricting the distribution of pharmaceuticals into the CNS. One approach to by-pass the BBB is intrathecal (IT) administration of anti-epileptic drugs with direct application of substances into the cerebrospinal fluid (CSF). This study aimed to investigate whether IT application of anticonvulsant substances is a reasonable approach to treat epilepsy. The in-depth evaluation of IT drug application was performed in the genetic absence epilepsy rats from Strasbourg (GAERS). Seizures in GAERS rats closely resemble human absence seizures and can be detected as characteristic spike-and-wave discharges (SWDs) in the electroencephalogram (EEG). To quantify seizure occurrence in GAERS and assess the efficacy of anti-epileptic therapy, automated seizure detection based on EEG recordings was implemented. With this method, seizure detection was performed with an F-score of 96 %. The analysis of 12h and 24h recordings in untreated animals revealed circadian undulations of SWD activity with a peak of seizures between 2am and 4am. This observation was in agreement with earlier studies that showed that SWD activity depends on the vigilance level in rats (Drinkenburg et al., 1991). By intracerebroventricular (i.c.v.) injections, drug application into the CSF was archived. To this end, a guide cannula was implanted into the right lateral ventricle. Initially, the standard anti-absence drugs, ethosuximide (ETX), and valproate (VPA) (Manning et al., 2003), were tested with this IT application approach. The treatment caused a substantial and dose-dependent reduction in SWD for both drugs and revealed that localized therapy with ETX is significantly more effective than with VPA. Additionally, the i.c.v. administration of ETX was dramatically more efficient than systemic ETX application of the same dose. The subsequent analysis of substance distributions in the brain after intracranial application suggested that the therapeutic effect was not caused by the indirect entry of ETX into brain parenchyma via the bloodstream but rather mediated by the direct entry from the CSF. Additional experiments to explore the therapeutic efficacy of the neuropeptides Neuropeptide Y (NPY) and Somatostatin (SST) as potential alternative to standard drugs, did not reveal a robust anti-absence effect. Consequently, these neuropeptides were not considered potent substances for localized drug therapy in absence epilepsy

    Loss of neuronal network resilience precedes seizures and determines the ictogenic nature of interictal synaptic perturbations

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    The mechanisms of seizure emergence, and the role of brief interictal epileptiform discharges (IEDs) in seizure generation are two of the most important unresolved issues in modern epilepsy research. Our study shows that the transition to seizure is not a sudden phenomenon,but a slow process characterized by the progressive loss of neuronal network resilience. From a dynamical perspective, the slow transition is governed by the principles of critical slowing, a robust natural phenomenon observable in systems characterized by transitions between dynamical regimes. In epilepsy, this process is modulated by the synchronous synaptic input from IEDs. IEDs are external perturbations that produce phasic changes in the slow transition process and exert opposing effects on the dynamics of a seizure-generating network, causing either anti-seizure or pro-seizure effects. We show that the multifaceted nature of IEDs is defined by the dynamical state of the network at the moment of the discharge occurrence

    Identification of A Neural Mass Model of Burst Suppression

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    Burst suppression includes alternating patterns of silent and fast spike activities in neuronal activities observable (in micro or macro scale) electro-physiological recordings. Biological models of burst suppression are given as dynamical systems with slow and fast states. The aim of this paper is to give a method to identify parameters of a mesoscopic model of burst suppression that can provide insights into study underlying generators of intracranial electroencephalogram (iEEG) data. An optimisation technique based upon a genetic algorithm (GA) is employed to find feasible model parameters to replicate burst patterns in the iEEG data with paroxysmal transitions. Then, a continuous-discrete unscented Kalman filter (CD-UKF) is used to infer hidden states of the model and to enhance the identification results from the GA. The results show promise in finding the model parameters of a partially observed mesoscopic model of burst suppression

    Scale-free bursting in human cortex following hypoxia at birth

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    The human brain is fragile in the face of oxygen deprivation. Even a briefinterruption of metabolic supply at birth challenges an otherwise healthy neonatal cortex, leading to a cascade of homeostatic responses. During recovery from hypoxia, cortical activity exhibits a period of highly irregular electrical fluctuations known as burst suppression. Here we show that these bursts have fractal properties, with power-law scaling of burst sizes across a remarkable 5 orders of magnitude and a scale-free relationship between burst sizes and durations. Although burst waveforms vary greatly, their average shape converges to a simple form that is asymmetric at long time scales. Using a simple computational model, we argue that this asymmetry reflects activity-dependent changes in the excitatory-inhibitory balance of cortical neurons. Bursts become more symmetric following the resumption of normal activity, with a corresponding reorganization of burst scaling relationships. These findings place burst suppression in the broad class of scale-free physical processes termed crackling noise and suggest that the resumption of healthy activity reflects a fundamental reorganization in the relationship between neuronal activity and its underlying metabolic constraints

    Slow-Fast Duffing Neural Mass Model

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    Epileptic seizures may be initiated by random neuronal fluctuations and/or by pathological slow regulatory dynamics of ion currents. This paper presents extensions to the Jansen and Rit neural mass model (JRNMM) to replicate paroxysmal transitions in intracranial electroencephalogram (iEEG) recordings. First, the Duffing NMM (DNMM) is introduced to emulate stochastic generators of seizures. The DNMM is constructed by applying perturbations to linear models of synaptic transmission in each neural population of the JRNMM. Then, the slow-fast DNMM is introduced by considering slow dynamics (relative to membrane potential and firing rate) of some internal parameters of the DNMM to replicate pathological evolution of ion currents. Through simulation, it is illustrated that the slow-fast DNMM exhibits transitions to and from seizures with etiologies that are linked either to random input fluctuations or pathological evolution of slow states. Estimation and optimization of a log likelihood function (LLF) using a continuous-discrete unscented Kalman filter (CD-UKF) and a genetic algorithm (GA) are performed to capture dynamics of iEEG data with paroxysmal transitions

    Primary Cilia in the Pathogenic Mouse Brain & EEG Waveform Analysis of a Mouse Model of Alzheimer\u27s Disease

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    In the last two decades, primary cilia have become recognized as tiny sensory organelles with considerable physiological function including regulation of cell division and modulation of transduction pathways. Scientists build on former discoveries, identifying new connections and accentuating the value of primary cilia in disease and intercellular signaling. Astrocytes, the most numerous cell type within the brain, act in support of neurons and maintain neurological health by providing neurotrophic factors and removing synaptic debris. These cells are particularly significant in reparations following neural injury and diseases, altering shape and function to protect healthy tissue. Astrocytes display primary cilia, yet little is understood about the function of this organelle within these cells. My dissertation research sought to determine the morphological changes of astrocytic primary cilia under neuropathological conditions, such as brain injury and epilepsy, and explore electroencephalogram activity under anesthesia in a mouse model of Alzheimer’s disease (AD). This research comprised of three major projects: (1) identifying morphological and condition-based differences between neuronal and astrocytic primary cilia, (2) evaluating the implications of Arl13B during cortical injury, and (3) quantifying EEG waveform pattern in an AD mouse model for distinctions indicative of the presence of the disease prior to the phenotypic onset. Immunohistochemistry was used to explore morphological differences and alterations in primary cilia associated with cell-type and the development of astrocytic reactivity. Cortical injury procedures were used to form a localized astrocytic response in Arl13B loss-of-function and gain-of-function strains, as well as an IFT88 knockout strain to compare the role of Arl13B in glial scarring. Electroencephalogram/electromyogram (EEG/EMG) recordings were furthermore used to substantiate seizure activity in a strain of mice prone to epileptic behavior. Finally, the APP23 mouse, a murine model of AD, was exposed to isoflurane anesthesia while EEG waveform was recorded. These recordings were filtered and processed via Brainstorm, a MATLAB graphical interface, and analyzed for Power Spectral Density, Burst Suppression Density, and Phase Amplitude Coupling. Results of morphological changes in cilia were consistent with the hypothesis that similarly to neuronal primary cilia, astrocytic cilia are implicated under reactive conditions assessed by morphological changes. Immunohistochemical analysis also revealed regional and strain differences of primary cilia. However, comparison of Loss-of-Function and Gain-of-Function studies did not collectively support the hypothesis that the ciliary protein Arl13B is a functionally relevant component of GFAP-based neurological repair one week following injury, although injured tissue did consistently show heightened Arl13B close to cortical lesion. Finally, EEG waveform analysis of a mouse model of AD during exposure to anesthesia revealed 3-4-month-old mice show a statistically significant difference in levels of Burst Suppression Density during anesthesia and Power Spectral Densities of Delta, Theta, and Alpha. These results support the potential foundation of waveform analysis during anesthesia as a prudent diagnostic tool to allow for early diagnosis of the disease. This research elucidates (1) the role of astrocytic primary cilia in the pathogenic brain, (2) relevance of Arl13B in brain injury, and (3) provides a possible basis for quantifiable hallmarks of AD in EEG waveform under isoflurane-induced anesthesia

    Clinical Applications of Stochastic Dynamic Models of the Brain, Part II: A Review

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    Brain activity derives from intrinsic dynamics (due to neurophysiology and anatomical connectivity) in concert with stochastic effects that arise from sensory fluctuations, brainstem discharges, and random microscopic states such as thermal noise. The dynamic evolution of systems composed of both dynamic and random fluctuations can be studied with stochastic dynamic models (SDMs). This article, Part II of a two-part series, reviews applications of SDMs to large-scale neural systems in health and disease. Stochastic models have already elucidated a number of pathophysiological phenomena, such as epilepsy and hypoxic ischemic encephalopathy, although their use in biological psychiatry remains rather nascent. Emerging research in this field includes phenomenological models of mood fluctuations in bipolar disorder and biophysical models of functional imaging data in psychotic and affective disorders. Together with deeper theoretical considerations, this work suggests that SDMs will play a unique and influential role in computational psychiatry, unifying empirical observations with models of perception and behavior

    Epilepsy

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    Epilepsy is the most common neurological disorder globally, affecting approximately 50 million people of all ages. It is one of the oldest diseases described in literature from remote ancient civilizations 2000-3000 years ago. Despite its long history and wide spread, epilepsy is still surrounded by myth and prejudice, which can only be overcome with great difficulty. The term epilepsy is derived from the Greek verb epilambanein, which by itself means to be seized and to be overwhelmed by surprise or attack. Therefore, epilepsy is a condition of getting over, seized, or attacked. The twelve very interesting chapters of this book cover various aspects of epileptology from the history and milestones of epilepsy as a disease entity, to the most recent advances in understanding and diagnosing epilepsy
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