An Automated System for Localizing the Effects of Transcranial Magnetic Stimulation

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Transcranial magnetic stimulation (TMS) is a non-invasive technique to modulate motor and cognitive functions in the human brain and elucidate structure-function relationships. However, there is considerable variability in the observed effects and clinical outcomes, which may be attributed to a complex interplay of interindividual functional-anatomical differences and the variable Response behaviour of neuronal networks. There has been extensive experimental and computational research on the precise way howTMS affects the dynamics of neural circuits. A crucial prerequisite for building and validating such models is to know which cortical locationsare effectively stimulated. Yet, it is still difficult to identify the neural structures that underlie the observed physiological or behavioural effects. This is a serious obstacle to the understanding of the mechanism of stimulation, the interpretation of the induced effects,and the planning of effective stimulation protocols. We recently developed a modelling framework, which we applied to TMS induced motor evoked potentials. This approach combines multiple Stimulation experiments with different coil positions and/or orientations, assumingthat at the site of activation the relationship between electric field and motor evoked potentials is stable across experimental conditions. We demonstrated that our method identifies sharply bounded neural structures located in the gyral crowns as origin of the motor evokedpotentials. Notably, we showed that the proposed approach has high discriminatory power on the individual level. In the current project we aim at the following objectives: (1) we plan to develop a method to automatically optimize the choice of coil positions and orientations.This method will only rely on the individual head geometry and therefore enables the computation of the optimal stimulation scheme prior to the experiment; (2) we will extend potential stimulation sites to subcortical white matter and investigate causal relationships between white matter stimulation and experimental effects; (3) we will extend our algorithm to the identification of networks of neural populations, leading to a large combinatorial problem that requires sophisticated optimization schemes. Our method will identify multivariaterelationships between externally observable effects and stimulation of neural populations; (4) as so far localization techniques have only been applied to motor evoked potentials, we will generalize the method to more complex brain processes and experimentalparadigms in the sensorimotor and language domains; (5) with this knowledge, we will be able to precisely and individually localize language regions in the brain; (6) the method will be integrated with a robotic system, thus allowing for an automated identification of structure-function relationships, excluding potential human error sources. In this line, we seek to establish the benefits and drawbacks of such an automated system.