Deep brain stimulation: is it time to change gears by closing the loop?

Area of Science:

• Neuroscience
• Biomedical Engineering
• Neurological Disorders

Background:

• Traditional deep brain stimulation (DBS) delivers continuous stimulation, lacking real-time patient responsiveness.
• Adaptive deep brain stimulation (aDBS) was developed to address these limitations by providing on-demand, contingency-based stimulation.
• aDBS has shown promise in various neurological conditions, with ongoing efforts towards clinical implementation.

Purpose of the Study:

• To provide a comprehensive technical overview of adaptive deep brain stimulation (aDBS).
• To summarize current and future aDBS approaches and control algorithms.
• To serve as a reference for understanding aDBS models and facilitating clinical translation.

Main Methods:

• Review of existing literature on adaptive deep brain stimulation (aDBS) techniques and control algorithms.
• Discussion of the conceptual and functional model of aDBS, including its core modules.
• Analysis of historical development and state-of-the-art biomarkers for aDBS.

Main Results:

• The aDBS model comprises a sensing module for feedback variable assessment, a control module for parameter adjustment, and a stimulation module for delivery.
• The review covers the evolution and current state of biomarkers utilized in aDBS.
• Technical aspects and control strategies for aDBS are detailed.

Conclusions:

• aDBS modulates neural circuits in real-time using biofeedback, representing an advancement over traditional DBS.
• Significant progress has been made in closed-loop DBS technology, paving the way for large-scale clinical trials.
• Understanding the technical intricacies of aDBS is vital as brain-computer interfaces become more integrated into patient care.