Toward personalized medicine in connectomic deep brain stimulation

Area of Science:

• Neurology
• Neuroscience
• Biomedical Engineering

Background:

• Deep brain stimulation (DBS) offers group-level therapeutic benefits for neurological and neuropsychiatric disorders.
• Individual patient symptom persistence despite optimal electrode placement highlights limitations of current disease-centric DBS strategies.
• Heterogeneous patient phenotypes and comorbidities necessitate personalized treatment approaches.

Purpose of the Study:

• To propose a three-step, circuit-based framework for developing patient-specific DBS targets.
• To address individual symptom constellations and improve treatment efficacy in DBS.
• To enhance the fraction of top-responding patients in DBS therapy.

Main Methods:

• Establish a symptom network target library by mapping DBS effects to circuits using group-level data.
• Implement template matching to align symptom networks with individual patient circuits.
• Utilize network blending to calculate optimal stimulation targets and parameters based on patient profiles.

Main Results:

• The proposed framework enables the creation of personalized DBS treatment targets.
• It facilitates the integration of individual symptom profiles and priorities into treatment planning.
• The approach offers a strategy to optimize electrode placement and programming for enhanced patient response.

Conclusions:

• A patient-specific, circuit-based framework can refine DBS targeting beyond current disease-centric models.
• Personalized DBS strategies hold promise for improving outcomes in complex neurological and neuropsychiatric conditions.
• Further research is needed to address limitations and challenges in implementing this personalized approach.