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A proof-of-principle simulation for closed-loop control based on preexisting experimental thalamic DBS-enhanced
Ching-Fu Wang1, Shih-Hung Yang2, Sheng-Huang Lin3
1Department of Biomedical Engineering, National Yang Ming University, No.155, Sec.2, Linong St., Taipei 112, Taiwan, ROC.
Brain Stimulation
|March 17, 2017
Summary
This study introduces cognitive-enhancing deep brain stimulation (ceDBS), a closed-loop system that improves skill learning by using neural signals. The system reduces power consumption and optimizes stimulation for cognitive enhancement.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Cognitive Science
Background:
- Deep brain stimulation (DBS) is used for Parkinson's disease and essential tremor, but open-loop systems have limitations.
- Closed-loop DBS aims to improve efficiency and reduce power consumption by using neural feedback.
- Optimal control schemes for DBS in cognitive enhancement, like instrumental learning, are challenging due to unclear biomarker associations.
Purpose of the Study:
- To propose and validate a closed-loop DBS system (ceDBS) for enhancing instrumental skill learning.
- To identify a physiological marker for cognitive enhancement during skill learning.
- To develop a predictive model and controller for adaptive DBS.
Main Methods:
- Acquired local field potential (LFP) signals from the CL thalamus in animal models.
- Identified theta oscillation power ratio as a marker for skill learning.
- Implemented on-line marker extraction using FPGA and developed an ARX-based predictor.
- Designed a fuzzy expert system-based controller for adaptive DBS.
Main Results:
- Demonstrated a strong coupling between theta oscillation and learning in a lever-pressing task.
- Successfully extracted the theta-band power ratio in real-time.
- The closed-loop ceDBS system reduced power consumption and achieved target physiological marker levels.
- Validated the effectiveness of the predictive model and fuzzy controller.
Conclusions:
- The proposed ceDBS system effectively enhances instrumental skill learning.
- Theta-band power ratio serves as a reliable physiological marker for cognitive enhancement.
- Closed-loop control with predictive modeling optimizes DBS for cognitive functions and reduces energy usage.
