  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.
