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Integrating electric field modeling and neuroimaging to explain inter-individual variability of tACS effects.

ABSTRACT: Transcranial electrical stimulation (tES) of the brain can have variable effects, plausibly driven by individual differences in neuroanatomy and resulting differences of the electric fields inside the brain. Here, we integrated individual simulations of electric fields during tES with source localization to predict variability of transcranial alternating current stimulation (tACS) aftereffects on ?-oscillations. In two experiments, participants received 20-min of either ?-tACS (1?mA) or sham stimulation. Magnetoencephalogram (MEG) was recorded for 10-min before and after stimulation. tACS caused a larger power increase in the ?-band compared to sham. The variability of this effect was significantly predicted by measures derived from individual electric field modeling. Our results directly link electric field variability to variability of tACS outcomes, underline the importance of individualizing stimulation protocols, and provide a novel approach to analyze tACS effects in terms of dose-response relationships.

SUBMITTER: Kasten FH

PROVIDER: S-EPMC6882891 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Integrating electric field modeling and neuroimaging to explain inter-individual variability of tACS effects.

Kasten Florian H FH Duecker Katharina K Maack Marike C MC Meiser Arnd A Herrmann Christoph S CS

Nature communications 20191128 1

Transcranial electrical stimulation (tES) of the brain can have variable effects, plausibly driven by individual differences in neuroanatomy and resulting differences of the electric fields inside the brain. Here, we integrated individual simulations of electric fields during tES with source localization to predict variability of transcranial alternating current stimulation (tACS) aftereffects on α-oscillations. In two experiments, participants received 20-min of either α-tACS (1 mA) or sham sti ...[more]

PMID: 31780668

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Project description:PurposeInter-individual variability in clinical endpoints and occurrence of potentially severe adverse effects represent an enormous challenge in drug development at all phases of (pre-)clinical research. To ensure patient safety it is important to identify adverse events or critical subgroups within the population as early as possible. Hence, a comprehensive understanding of the processes governing pharmacokinetics and pharmacodynamics is of utmost importance. In this paper we combine Bayesian statistics with detailed mechanistic physiologically-based pharmacokinetic (PBPK) models. On the example of pravastatin we demonstrate that this combination provides a powerful tool to investigate inter-individual variability in groups of patients and to identify clinically relevant homogenous subgroups in an unsupervised approach. Since PBPK models allow the identification of physiological, drug-specific and genotype-specific knowledge separately, our approach supports knowledge-based extrapolation to other drugs or populations.MethodsPBPK models are based on generic distribution models and extensive collections of physiological parameters and allow a mechanistic investigation of drug distribution and drug action. To systematically account for parameter variability within patient populations, a Bayesian-PBPK approach is developed rigorously quantifying the probability of a parameter given the amount of information contained in the measured data. Since these parameter distributions are high-dimensional, a Markov chain Monte Carlo algorithm is used, where the physiological and drug-specific parameters are considered in separate blocks.ResultsConsidering pravastatin pharmacokinetics as an application example, Bayesian-PBPK is used to investigate inter-individual variability in a cohort of 10 patients. Correlation analyses infer structural information about the PBPK model. Moreover, homogeneous subpopulations are identified a posteriori by examining the parameter distributions, which can even be assigned to a polymorphism in the hepatic organ anion transporter OATP1B1.ConclusionsThe presented Bayesian-PBPK approach systematically characterizes inter-individual variability within a population by updating prior knowledge about physiological parameters with new experimental data. Moreover, clinically relevant homogeneous subpopulations can be mechanistically identified. The large scale PBPK model separates physiological and drug-specific knowledge which allows, in combination with Bayesian approaches, the iterative assessment of specific populations by integrating information from several drugs.

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Integrating electric field modeling and neuroimaging to explain inter-individual variability of tACS effects.

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Integrating electric field modeling and neuroimaging to explain inter-individual variability of tACS effects.

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