Abstract

More than one billion people worldwide suffer from neurological and neuropsychiatric disorders. Neuromodulation systems that use closed-loop brain stimulation to control brain states can provide new therapies. Current closed-loop brain stimulation has largely used linear time-invariant (LTI) controllers. However, nonlinear brain network dynamics and noise can appear during real-time stimulation, collectively leading to real-time model uncertainty, which degrades the performance or even causes instability of LTI controllers. Three problems need to be resolved to enable accurate and stable control under model uncertainty. First, an adaptive controller is needed to track the model uncertainty. Second, the adaptive controller additionally needs to be robust to noise. Third, theoretical analyses of stability and robustness are needed as prerequisites for applications. We develop a robust adaptive neuromodulation algorithm that solves the above three problems. First, we develop a state-space brain network model that explicitly includes nonlinear terms of real-time model uncertainty and designs an adaptive controller to track and cancel the model uncertainty. Second, to improve the robustness of the adaptive controller, we design linear filters to reduce sensitivity to high-frequency noise. Third, we conduct theoretical analyses to prove the stability of the neuromodulation algorithm. We test the algorithm using comprehensive Monte Carlo simulations spanning a broad range of model nonlinearity, uncertainty, and complexity. We further test the proposed algorithm using nonlinear cortex-basal ganglia-thalamus network models in Parkinson's disease and nonlinear neural mass models in Major depressive disorder. Our results showed that the proposed algorithm accurately tracks various types of target brain state trajectories, enables stable and robust control, and significantly outperforms current neuromodulation algorithms. Our algorithm has implications for future designs of precise and stable closed-loop neuromodulation systems to treat neurological and neuropsychiatric disorders.

Notes

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Graduation Date

2023

Semester

Summer

Advisor

Sundaram, Kalpathy

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Electrical and Computer Engineering

Degree Program

Electrical Engineering

Identifier

CFE0009879; DP0028412

URL

https://purls.library.ucf.edu/go/DP0028412

Language

English

Release Date

February 2029

Length of Campus-only Access

5 years

Access Status

Doctoral Dissertation (Campus-only Access)

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