Predicting TMS-induced activation in human neocortex using concurrent TMS/PET, finite element analysis and computational modeling

Maximizing brain networks engagement via individualized connectome-wide target search

BACKGROUND

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.

OBJECTIVE

Here we implemented a target search algorithm leveraging on mathematical tools from Network Control Theory (NCT) and whole brain connectomics analysis. By means of computational simulations, we aimed to identify the optimal stimulation target(s)- at the individual brain level- capable of reaching maximal engagement of the stimulated networks' nodes.

RESULTS

At the model level, in silico predictions suggest 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. Indeed, NCT allows us to computationally model different stimulation scenarios tailored on the individual structural connectivity profiles and initial brain states.

CONCLUSIONS

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.

Low-frequency rTMS targeting individual self-initiated finger-tapping task activation modulates the amplitude of local neural activity in the putamen

Repetitive transcranial magnetic stimulation (rTMS) has been used in the clinical treatment of Parkinson's disease (PD). Most of rTMS studies on PD used high-frequency stimulation; however, excessive nonvoluntary movement may represent abnormally cortical excitability, which is likely to be suppressed by low-frequency rTMS. Decreased neural activity in the basal ganglia on functional magnetic resonance imaging (fMRI) is a characteristic of PD. In the present study, we found that low-frequency (1 Hz) rTMS targeting individual finger-tapping activation elevated the amplitude of local neural activity (percentage amplitude fluctuation, PerAF) in the putamen as well as the functional connectivity (FC) of the stimulation target and basal ganglia in healthy participants. These results provide evidence for our hypothesis that low-frequency rTMS over the individual task activation site can modulate deep brain functions, and that FC might serve as a bridge transmitting the impact of rTMS to the deep brain regions. It suggested that a precisely localized individual task activation site can act as a target for low-frequency rTMS when it is used as a therapeutic tool for PD.