Personalization and network specificity of cerebellar TMS in schizophrenia

BackgroundCerebellar transcranial magnetic stimulation (TMS) may serve as an adjuvant therapy for psychosis symptoms, most recently we have shown improvements in negative symptoms. Historically, cerebellum TMS has not utilized functional neuroanatomy for targeting, and the precision of TMS to the cerebellum is unclear. A classical view of the cerebellum as solely involved in motor computations has been updated with the discovery of rich non-motor connectivity including the default, dorsal attention, frontoparietal control and ventral attention networks. We sought to assess cerebellar TMS magnetic field effect within individually defined networks of the cerebellum. MethodsImaging data from schizophrenia and schizoaffective participants (n=27) in a double-blinded trial of cerebellar TMS (NCT05343598) was used. Individualized resting-state connectivity fMRI maps of the cerebellum was computed for 7 canonical networks (Yeo et al 2011; Buckner et al 2011). Individualized TMS simulations were computed in SimNIBS with real-world participant-specific coil placement and intensity determination. ResultsThe peak stimulation effect (99th percentile) for each network in each participant was computed. The electric field induced by cerebellar TMS predominantly engaged specific functional networks more than others (p<0.001), indicating selective targeting of these networks. The strongest effects were found on default (44.4%), limbic (37%) and frontoparietal control (11.1%) networks. Cerebellar brain network organization was found to be similar in the patient sample to previously published large-sample organization. ConclusionsFor personalized TMS, it is important to consider the targeted network, as well as the potential off-target network effects. Our findings demonstrate that cerebellar TMS has the strongest field effect on non-motor, cognitive and affective networks within the cerebellum. These results suggest cerebellar TMS may be ideal for schizophrenia symptoms unaddressed by pharmacological treatments, and effects may vary by individual network topology.