Concurrent stimulation and sensing in bi-directional brain interfaces: a multi-site translational experience

Area of Science:

• Biomedical Engineering
• Neuroscience
• Medical Devices

Background:

• Adaptive deep brain stimulation (aDBS) offers personalized treatment but faces challenges with artifacts from concurrent stimulation and sensing.
• Artifacts can disrupt aDBS algorithm performance, leading to suboptimal or unstable stimulation.
• Optimizing device design and configuration is crucial for reliable aDBS implementation.

Purpose of the Study:

• To develop a design analysis and guidance framework for concurrent stimulation and sensing in aDBS.
• To identify and mitigate artifacts in aDBS systems.
• To provide a validated checklist for improving aDBS system performance.

Main Methods:

• Defined a general architecture for feedback-enabled aDBS devices.
• Identified signal chain components contributing to artifacts.
• Collected data from subjects with investigational aDBS systems (Summit RC + S) and used a prototype device (DyNeuMo) for verification.
• Validated artifact mitigation strategies through benchtop and chronic implant studies.

Main Results:

• Derived and validated a 'checklist' of configuration settings for enhanced aDBS system performance.
• Key settings include active recharge, sense-channel blanking, high-pass filters, impedance management, and algorithm parameter tuning.
• Optimized system configuration prevented algorithm limit-cycles and demonstrated feasibility of a 'fast' aDBS prototype with suitable noise performance.

Conclusions:

• A framework for studying and mitigating artifacts in chronic aDBS devices has been presented.
• Translating novel sensing devices requires careful balancing of performance constraints.
• Successful clinical translation of aDBS therapies depends on optimizing system-level performance and artifact management.