Monopolar network mapping predicts tremor outcomes of deep brain stimulation in Parkinson’s disease

Deep brain stimulation is an effective therapy for tremor in Parkinson’s disease, yet clinical outcomes vary substantially across individuals. This variability highlights the need for continuous and quantitative measures that more accurately capture symptom severity and treatment response. Although precise subthalamic targeting remains fundamental to effective therapy, accounting for network-level modulation of disrupted motor circuits underlying core symptoms may further enhance clinical benefit. Nevertheless, the relative contributions of local and network-level effects to tremor suppression remain unclear, limiting our understanding of the mechanisms driving clinical improvement.

In this study, we identified a patient-specific connectivity fingerprint associated with effective tremor suppression following subthalamic deep brain stimulation. Twenty patients with Parkinson’s disease underwent objective tremor quantification using accelerometry during systematic monopolar reviews. For each stimulation setting, the corresponding volume of tissue activated served as a seed for probabilistic tractography, informed by individual diffusion-weighted imaging and subject-specific parcellation of grey and white matter structures. Subsequently, partial least squares regression was employed to analyse over 1,500 stimulation conditions, aiming to identify brain regions whose connectivity patterns predicted tremor outcomes. Ultimately, the resulting network model was compared with a local-effects model to assess whether network mapping provided superior predictive capability for motor improvement compared to traditional local targeting methods.

Our results demonstrate that (1) modulation of the basal ganglia and the cerebello-thalamo-cortical circuit at the network level predicts clinical improvement more accurately than local stimulation alone ( P < 0.001); and (2) optimal stimulation sites for tremor relief are located in white-matter regions adjacent to the subthalamic nucleus. Model significance was confirmed through permutation testing ( P < 0.001).

Based on these findings, we propose a framework that combines sensor-based measurements with monopolar mapping to investigate the effects of symptom-specific neuromodulation and to guide the selection of connectomic parameters for personalised treatment of motor symptoms.