Management of Epilepsy in People with Intellectual Disabilities

Brodtkorb, Eylert

doi:10.1002/9781118936979.ch15

Cited by 4 publications

(4 citation statements)

References 97 publications

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“…They may not be able to report and describe adverse reactions from AEDs in the form of slowing of central information processing (114). Symptoms of overdosing, such as sedation, ataxia, or blurred vision, may even occur unnoticed by the caregivers [68, 84, 126]. Such unspecific adverse reactions are not uncommon with LEV, PER, and TPM (Table 1) and may be indirectly expressed as disturbed behavior and interpreted as specific pharmacodynamic effects [57, 127, 128].…”

Section: Biological Vulnerabilitymentioning

confidence: 99%

Mechanisms Underlying Aggressive Behavior Induced by Antiepileptic Drugs: Focus on Topiramate, Levetiracetam, and Perampanel

Hansen

Ljung

Brodtkorb

et al. 2018

Behavioural Neurology

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Antiepileptic drugs (AEDs) are effective against seizures, but their use is often limited by adverse effects, among them psychiatric and behavioral ones including aggressive behavior (AB). Knowledge of the incidence, risk factors, and the underlying mechanisms of AB induced by AEDs may help to facilitate management and reduce the risk of such side effects. The exact incidence of AB as an adverse effect of AEDs is difficult to estimate, but frequencies up to 16% have been reported. Primarily, levetiracetam (LEV), perampanel (PER), and topiramate (TPM), which have diverse mechanisms of action, have been associated with AB. Currently, there is no evidence for a specific pharmacological mechanism solely explaining the increased incidence of AB with LEV, PER, and TPM. Serotonin (5-HT) and GABA, and particularly glutamate (via the AMPA receptor), seem to play key roles. Other mechanisms involve hormones, epigenetics, and “alternative psychosis” and related phenomena. Increased individual susceptibility due to an underlying neurological and/or a mental health disorder may further explain why people with epilepsy are at an increased risk of AB when using AEDs. Remarkably, AB may occur with a delay of weeks or months after start of treatment. Information to patients, relatives, and caregivers, as well as sufficient clinical follow-up, is crucial, and there is a need for further research to understand the complex relationship between AED mechanisms of action and the induction/worsening of AB.

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Section: Biological Vulnerabilitymentioning

confidence: 99%

Mechanisms Underlying Aggressive Behavior Induced by Antiepileptic Drugs: Focus on Topiramate, Levetiracetam, and Perampanel

Hansen

Ljung

Brodtkorb

et al. 2018

Behavioural Neurology

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“…(12), i.e.. We thus estimated the variance of the parameter D by numerically evaluating the inverse Hessian of log at its minimum [10], and taking the appropriate diagonal element. We note that such estimate of the error is statistically meaningful only limited to the context of the optimization problem defined in eq.…”

Section: Methodsmentioning

confidence: 99%

Simultaneous photoactivation and high-speed structural tracking reveal diffusion-dominated motion in the endoplasmic reticulum

Dora

Obara

Abel

et al. 2023

Preprint

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The endoplasmic reticulum (ER) is a structurally complex, membrane-enclosed compartment that stretches from the nuclear envelope to the extreme periphery of eukaryotic cells. The organelle is crucial for numerous distinct cellular processes, but how these processes are spatially regulated within the structure is unclear. Traditional imaging-based approaches to understanding protein dynamics within the organelle are limited by the convoluted structure and rapid movement of molecular components. Here, we introduce a combinatorial imaging and machine learning-assisted image analysis approach to track the motion of photoactivated proteins within the ER of live cells. We find that simultaneous knowledge of the underlying ER structure is required to accurately analyze fluorescently-tagged protein redistribution, and after appropriate structural calibration we see all proteins assayed show signatures of Brownian diffusion-dominated motion over micron spatial scales. Remarkably, we find that in some cells the ER structure can be explored in a highly asymmetric manner, likely as a result of uneven connectivity within the organelle. This remains true independently of the size, topology, or folding state of the fluorescently-tagged molecules, suggesting a potential role for ER connectivity in driving spatially regulated biology in eukaryotes.

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“…Hence, we have performed only numerical estimation both of the position of the maxima and of the second derivatives around the maxima. For numerical optimization of objective functions (log-likelihood or log-joint probability) we have used an open-source implementation of the CMA-ES algorithm (Hansen, 2006 , 2015 ), whereas for the numerical estimation of the Hessian (second derivative around the mode) we have used an open-source library for numerical differentiation (Brodtkorb and D'Errico, 2015 ).…”

Section: Methodsmentioning

confidence: 99%

Comparative Analysis of Behavioral Models for Adaptive Learning in Changing Environments

Marković

Kiebel

2016

Front. Comput. Neurosci.

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Probabilistic models of decision making under various forms of uncertainty have been applied in recent years to numerous behavioral and model-based fMRI studies. These studies were highly successful in enabling a better understanding of behavior and delineating the functional properties of brain areas involved in decision making under uncertainty. However, as different studies considered different models of decision making under uncertainty, it is unclear which of these computational models provides the best account of the observed behavioral and neuroimaging data. This is an important issue, as not performing model comparison may tempt researchers to over-interpret results based on a single model. Here we describe how in practice one can compare different behavioral models and test the accuracy of model comparison and parameter estimation of Bayesian and maximum-likelihood based methods. We focus our analysis on two well-established hierarchical probabilistic models that aim at capturing the evolution of beliefs in changing environments: Hierarchical Gaussian Filters and Change Point Models. To our knowledge, these two, well-established models have never been compared on the same data. We demonstrate, using simulated behavioral experiments, that one can accurately disambiguate between these two models, and accurately infer free model parameters and hidden belief trajectories (e.g., posterior expectations, posterior uncertainties, and prediction errors) even when using noisy and highly correlated behavioral measurements. Importantly, we found several advantages of Bayesian inference and Bayesian model comparison compared to often-used Maximum-Likelihood schemes combined with the Bayesian Information Criterion. These results stress the relevance of Bayesian data analysis for model-based neuroimaging studies that investigate human decision making under uncertainty.

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Management of Epilepsy in People with Intellectual Disabilities

Cited by 4 publications

References 97 publications

Mechanisms Underlying Aggressive Behavior Induced by Antiepileptic Drugs: Focus on Topiramate, Levetiracetam, and Perampanel

Mechanisms Underlying Aggressive Behavior Induced by Antiepileptic Drugs: Focus on Topiramate, Levetiracetam, and Perampanel

Simultaneous photoactivation and high-speed structural tracking reveal diffusion-dominated motion in the endoplasmic reticulum

Comparative Analysis of Behavioral Models for Adaptive Learning in Changing Environments

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