Different psychiatric and neurologic illnesses are a great burden to our society. These kinds of disorders are often treated with pharmaceuticals, regardless of a wide variety of side-effects, poor suitability for many patients and high costs. During the recent decades, non-invasive brain stimulation (NIBS) has risen as a viable treatment alternative to the use of drugs. In NIBS, the state of the brain is affected via electric currents either induced by magnetic fields, or applied directly via electrodes on the scalp. One such method is called transcranial direct current stimulation (tDCS), where a small direct current is applied non-invasively to the brain via electrodes placed onto the scalp. This has, for instance, been shown to be a potential treatment for depression. There is, however, a significant flaw with tDCS in terms of variable efficacy between different patients (inter-individual variability). This arises partially from the dosimetry of tDCS. The tDCS dose is commonly estimated based on the input current, which can be easily set to be the same for a group of subjects. However, multiple studies have pointed out that although the ingoing current is kept the same, the electric field experienced by the brain varies between subjects due to anatomic factors. As it is highly impractical to measure the electric fields in the brain during the stimulation in order to use them as a dose measure, computational modeling therefore remains the only viable way of studying them.In this doctoral thesis comprising five peer-reviewed journal articles, the inter-individual variability of tDCS electric fields is studied using anatomically realistic head models in finite element analysis (FEA). The aim of this thesis is to shed light onto the causes of this variation, as well as to provide evidence to support the viability of using these predicted electric fields as a dosimetric parameter for tDCS. Publication I presents an approach that lowers the computational costs of tDCS electric field predictions using the finite element method. In Publication II, we present a connection between transcranial magnetic stimulation (TMS) motor thresholds and the predicted tDCS electric fields, and in Publication V a connection between predicted electric field normal components and the outcome of tDCS. Publication III and Publication IV study the determinants of the inter-individual variability in tDCS electric fields, and show that body position affects the tDCS electric fields and the focality of the electric field montage used has an effect on the inter-individual variability of those tDCS electric fields. These results provide new information on the causes of inter-individual variability and offer possible approaches to better take it into account with tDCS. Additionally, these results provide further links to connect the FEA-predicted electric fields into physiologically measurable quantities related to NIBS thus giving further support for the value of using these models in the study of tDCS.
|Publication status||Published - 2020|
|MoE publication type||G5 Doctoral dissertation (article)|
- non-invasive brain stimulation
- transcranial direct current stimulation
- interindividual variability
- individualized models
- finite element method