Deep Brain Stimulation Parameter Tuning Using fMRI Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current DBS parameter optimization for neurological disorders lacks real-time feedback mechanisms, relying on subjective patient observation and lengthy trial-and-error processes, which is inefficient and limits access to DBS therapy.
Innovation Solution
A deep learning-based system that utilizes functional MRI data to automatically optimize DBS parameters through unsupervised autoencoders and deep learning models, determining optimal parameters within a single clinical visit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual trial-and-error method is used to optimize DBS parameters, then the system can determine optimal parameters through subjective patient observation, but the optimization period becomes extremely lengthy (6-8 weeks to 1 year or longer)
Solution Approach 1:
The patent implements a feedback mechanism using fMRI imaging to objectively measure brain response to DBS stimulation. The system acquires fMRI data during stimulation, processes the functional MRI response maps, and uses deep learning models to determine whether the current parameters are optimal or to predict better parameters. This closed-loop feedback replaces subjective observation with objective neural activity measurement, enabling rapid parameter optimization without requiring lengthy trial periods.
2Adaptability or versatility
If multiple possible stimulation parameters are tested manually, then the system can find optimal parameters for modern DBS electrodes with stimulation directionality, but the number of required clinic visits increases significantly
Solution Approach 1:
The patent implements a self-service optimization system where the deep learning-based parameter prediction model automatically determines optimal DBS parameters without requiring extensive manual adjustment by clinicians. The system autonomously processes fMRI data, extracts features, classifies current parameters, and predicts optimal parameter sets, thereby eliminating the need for numerous sequential clinic visits and significantly increasing patient throughput while maintaining full adaptability to modern electrode capabilities.
3Measurement precision
If iterative manual adjustment of programming parameters is performed, then the neurologist can determine optimal parameter combination, but the financial burden on the patient increases substantially
Solution Approach 1:
The patent replaces the manual mechanical process of iterative parameter adjustment with an automated computational system. Instead of relying on the neurologist's manual trial-and-error adjustment over extended periods, the system uses fMRI-based objective measurement combined with deep learning algorithms to rapidly identify optimal parameters. This substitution reduces both the time required for optimization and the associated financial burden on patients, while maintaining or improving optimization accuracy through objective neural response measurement.
Data Source
AI summary
A system for optimizing DBS parameters for a subject includes automatically performing actions via a processor. The actions include obtaining functional MRI data of a brain of the subject acquired utilizing an MRI system during DBS of the brain utilizing a first set of DBS parameters. The actions include generating functional MRI response maps from the functional MRI data. The actions include extracting, utilizing an unsupervised autoencoder-based neural network, features from the functional MRI response maps. The actions include determining, utilizing a deep learning-based DBS parameter classification model, whether the first set of DBS parameters are optimal DBS parameters for the subject based on the features. The actions include, when the first set of DBS parameters are not the optimal DBS parameters, predicting, utilizing a deep learning-based DBS parameter prediction model, a second set of DBS parameters that are the optimal DBS parameters for the subject based on the features.