Optimising behaviour using individualised closed-loop brain stimulation

  1. Evelyn Kroesbergen

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Description: In this study we will try to answer the following research question: Which neural predictors progress arithmetic performance by means of brain stimulation? A type of machine learning (Bayesian Optimization) will be used in this project together with transcranial alternating current stimulation (tACS). Specific entrainment of oscillations by tACS have been related to cognitive enhancement, i.e. working memory (Jaušovec & Jaušovec, 2014), and perception (Ambrus et al., 2015). Thus, tACS allows direct modulation of brain oscillations that subserve cognitive processes (Dayan, Censor, Buch, Sandrini, & Cohen, 2013), whereby tACS provides an easy tool to causally investigate predictors of numerical skills. Beside these advantages, there are also several limitations of tACS. For instance, the stimulated brain area needs to be verified by combining the design with a neuroimaging technique such as EEG. Moreover, tACS is prone to individual differences such as differences in neuroanatomy, age, and gender (Krause & Cohen Kadosh, 2014). These limitations can be overcome by means of Bayesian Optimization, which is a sampling technique that randomly chooses samples whereby it learns in real-time. This technique is particularly valuable when there is need to explore a large experimental space, which is the case when applying tACS. For instance, it is unclear which stimulation frequency, amplitude, area of stimulation or phase is most advantageous to improve numerical skills since the effects of tACS highly depend on these parameters (Antal & Paulus, 2013; Bergmann, Karabanov, Hartwigsen, Thielscher, & Siebner, 2016). By combining these measurements in one design, monetary and facilitation constraints are evaded since it is not necessary to execute several smaller studies whereby one parameter is manipulated as is currently done in conventional brain stimulation studies. Based upon the results of a Bayesian optimization design, the following step would be to establish a Neuroadaptive Bayesian Optimization design whereby a target brain state is defined that is more efficient and effective in manipulating arithmetic performance (Lorenz et al., 2016).


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