Dataset Information

The role that choice of model plays in predictions for epilepsy surgery.

ABSTRACT: Mathematical modelling has been widely used to predict the effects of perturbations to brain networks. An important example is epilepsy surgery, where the perturbation in question is the removal of brain tissue in order to render the patient free of seizures. Different dynamical models have been proposed to represent transitions to ictal states in this context. However, our choice of which mathematical model to use to address this question relies on making assumptions regarding the mechanism that defines the transition from background to the seizure state. Since these mechanisms are unknown, it is important to understand how predictions from alternative dynamical descriptions compare. Herein we evaluate to what extent three different dynamical models provide consistent predictions for the effect of removing nodes from networks. We show that for small, directed, connected networks the three considered models provide consistent predictions. For larger networks, predictions are shown to be less consistent. However consistency is higher in networks that have sufficiently large differences in ictogenicity between nodes. We further demonstrate that heterogeneity in ictogenicity across nodes correlates with variability in the number of connections for each node.

SUBMITTER: Junges L

PROVIDER: S-EPMC6517411 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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The role that choice of model plays in predictions for epilepsy surgery.

Junges Leandro L Lopes Marinho A MA Terry John R JR Goodfellow Marc M

Scientific reports 20190514 1

Mathematical modelling has been widely used to predict the effects of perturbations to brain networks. An important example is epilepsy surgery, where the perturbation in question is the removal of brain tissue in order to render the patient free of seizures. Different dynamical models have been proposed to represent transitions to ictal states in this context. However, our choice of which mathematical model to use to address this question relies on making assumptions regarding the mechanism tha ...[more]

PMID: 31089190

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Project description:BackgroundPreclinical studies into drug vs. nondrug choice have emerged to better model and investigate the neurobehavioral mechanisms underlying drug preference. Current literature has suggested that drugs of abuse have inherently low value, thus promoting food preference. Herein, we examined remifentanil vs. food choice to test both the relative value hypothesis and the 'direct effects' (pharmacological effects of drugs on alternative reinforcers) hypothesis of opioid preference.MethodsAdult male rats were trained under two choice procedures (controlled vs. uncontrolled reinforcer frequency) for remifentanil vs. food choice. Furthermore, a series of procedural manipulations known to affect drug reinforcement were tested under both choice procedures. Using remifentanil self-administration data, pharmacokinetic profiles were calculated and analyzed to determine if opioid intake was related to opioid preference.ResultsBoth choice procedures produced dose-dependent preference. Moreover, procedural manipulations produced comparable changes in remifentanil preference under both choice procedures. In addition, calculated pharmacokinetic data revealed that preference was dissociable from intake under the controlled reinforcer frequency choice procedure.ConclusionsWhen compared to the 'direct effects' hypothesis, remifentanil preference was better predicted by the relative value hypothesis, formalized in generalized matching. Use of a controlled reinforcer frequency schedule successfully removed the drug preference-intake confound found in most drug-choice procedures. Importantly, drug preference under the controlled reinforcer frequency schedule remained sensitive to procedural manipulations known to affect drug reinforcement. Thus, given that differential drug intake itself affects neurobiological measurements, future use of controlled reinforcer frequency schedules may help to better isolate the neurobehavioral mechanisms that mediate opioid preference.

Dataset Information

The role that choice of model plays in predictions for epilepsy surgery.

Publications

The role that choice of model plays in predictions for epilepsy surgery.

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