Maximizing Brain Networks Engagement via Individualized Connectome-wide Target Search

In recent years, the possibility to noninvasively interact with the human brain has led to unprecedented diagnostic and therapeutic opportunities. 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. Here we implemented a target search algorithm allowing one to identify brain regions inducing maximal network engagement, leveraging on mathematical tools from Network Control Theory (NCT) and whole brain connectomics analysis. Results suggest that stimulation of NCT-derived cerebral sites might induce significantly higher network engagement, compared to traditionally employed neuromodulation sites, proving NCT to be a useful tool in guiding brain stimulation. Indeed, NCT allows to model different stimulation scenarios tailored on the individual structural connectivity profiles and initial brain state, hence suggesting that individualized targeting is crucial for more successful network engagement.