Study of individual brain topology for the personalization of interventions via transcranial magnetic stimulation: a network modulation approach

COEX 

In complex networks, like the brain, information transfer is ensured by an efficient topological architecture. A methodology to causally validate this structure-function relationship is represented by Transcranial Magnetic Stimulation (TMS), a form of noninvasive brain stimulation that can act on the neural mechanisms underpinning cognition. However, the vast majority of approved interventions and approaches still rely on anatomical landmarks and rarely on the individual structure of networks in the brain, drastically reducing the potential efficacy of neuromodulation. In trying to fill this gap, we employed an approach to study the topographical properties of the individual connectome, determine its response to external perturbations and use this information to tailor the selection of stimulation targets in the brain by relying on a target search algorithm leveraging on mathematical tools from Network Control Theory (NCT) and whole brain connectomics analysis. By means of computational simulations, we identified the optimal stimulation target(s)d at the individual brain level capable of reaching maximal engagement of the stimulated networks’ nodes. At the model level, in silico predictions suggested that stimulation of NCT-derived cerebral sites might induce significantly higher network engagement, compared to traditionally employed neuromodulation sites, demonstrating NCT to be a useful tool in guiding brain stimulation. In conclusion, the use of NCT to computationally predict TMS pulse propagation suggests that individualized targeting is crucial for more successful network engagement. Future studies will be needed to verify such prediction in real stimulation scenarios.