Toward Personalized Medicine in Connectomic Deep Brain Stimulation

TL;DR

This paper proposes a personalized, circuit-based framework for deep brain stimulation that tailors electrode placement and programming to individual patients' symptom profiles, aiming to improve therapeutic outcomes.

Contribution

It introduces a three-step approach involving symptom network mapping, template matching, and network blending for patient-specific DBS targeting.

Findings

Framework integrates group-level data with individual symptom profiles.

Method enables personalized DBS target selection and parameter optimization.

Discussion of limitations and future directions for personalized deep brain stimulation.

Abstract

At the group-level, deep brain stimulation leads to significant therapeutic benefit in a multitude of neurological and neuropsychiatric disorders. At the single-patient level, however, symptoms may sometimes persist despite "optimal" electrode placement at established treatment coordinates. This may be partly explained by limitations of disease-centric strategies that are unable to account for heterogeneous phenotypes and comorbidities observed in clinical practice. Instead, tailoring electrode placement and programming to individual patients' symptom profiles may increase the fraction of top responding patients. Here, we propose a three-step, circuit-based framework that aims to develop patient-specific treatment targets that address the unique symptom constellation prevalent in each patient. First, we describe how a symptom network target library could be established by mapping beneficial or undesirable DBS effects to distinct circuits based on (retrospective) group-level data. Second, we suggest ways of matching the resulting symptom networks to circuits defined in the individual patient (template matching). Third, we introduce network blending as a strategy to calculate optimal stimulation targets and parameters by selecting and weighting a set of symptom-specific networks based on the symptom profile and subjective priorities of the individual patient. We integrate the approach with published literature and conclude by discussing limitations and future challenges.