DBS Configuration via Neural Feedback for Cerebellar Disorders
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Solution Overview
Problem
Deep brain stimulation (DBS) systems face challenges in configuring optimal stimulation settings for treating neurological disorders beyond movement disorders, particularly when stimulating cerebellar pathways connecting to brainstem, diencephalon, or cerebrum, as conventional observation-based methods are ineffective.
Innovation Solution
A system and method that utilize electrophysiology data from implanted DBS electrodes and EEG data from scalp electrodes to identify optimal stimulation electrodes and parameters for DBS, based on changes in electrophysiological and EEG data during motor tasks, to effectively treat neurological disorders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If observation-based methods are used to configure DBS settings, then the configuration process is straightforward for movement disorders, but the method is ineffective for treating neurological disorders in cerebellar pathways
Solution Approach 1:
The system uses real-time electrophysiology data and EEG data as feedback signals to automatically adjust DBS configuration parameters. The feedback mechanism compares recorded neural activity against target patterns and iteratively optimizes electrode selection and stimulation parameters, making the system effective for cerebellar pathway disorders where observation-based methods fail.
Solution Approach 2:
The DBS configuration system performs self-optimization by automatically analyzing electrophysiology data and EEG data to identify optimal electrodes and parameters. The system eliminates the need for manual observation-based tuning by autonomously determining the configuration that produces desired changes in neural activity patterns, thereby achieving both ease of operation and reliability.
2Adaptability or versatility
If multiple DBS electrodes are implanted in cerebellar pathways, then the potential for effective stimulation increases, but the difficulty of identifying optimal stimulation electrodes increases
Solution Approach 1:
The system records electrophysiology data from all implanted DBS electrodes and uses this feedback to automatically identify which electrodes produce the desired therapeutic effect. The feedback loop analyzes the electrical signals from each electrode and determines optimal stimulation parameters, eliminating the difficulty of manual electrode selection while maintaining versatility across multiple electrodes.
Solution Approach 2:
The patent replaces manual mechanical electrode selection with an automated computational system that analyzes electrophysiology data and EEG data. The automated system substitutes the mechanical process of physically testing each electrode with an electronic data-driven approach that identifies optimal electrodes based on recorded neural activity patterns, thereby reducing detection difficulty while preserving adaptability.
3Adaptability or versatility
If DBS is used to treat neurological conditions beyond movement disorders, then the therapeutic application scope expands, but conventional configuration methods become inadequate
Solution Approach 1:
The patent implements a universal DBS configuration system that uses the same electrophysiology data analysis and automated optimization approach for both movement disorders and neurological disorders in cerebellar pathways. The multi-functional system adapts to different therapeutic applications by analyzing disease-specific patterns in the recorded data, thereby expanding application scope without proportionally increasing configuration complexity.
Solution Approach 2:
The patent replaces complex manual configuration procedures with an automated computational system that handles the complexity of configuring DBS for diverse neurological conditions. The electronic system processes electrophysiology data and EEG data to automatically determine optimal parameters, reducing the perceived complexity for clinicians while enabling broader therapeutic applications through data-driven customization.
Data Source
AI summary
Deep brain stimulation (DBS) can be used to treat many neurological conditions beyond traditional movement disorders. When patients do not suffer from traditional movement disorders, medical professionals cannot use traditional observation-based methods to configure the DBS system. A new method for selecting stimulation configurations can include recording internal data and external data as the patient performs (or attempts to perform) a motor task. The internal data is electrophysiology data recorded by a plurality of DBS electrodes, used to identify at least one of the plurality of electrodes closest to a neuronal population involved in control of the at least one motor task. The external data is electroencephalogram (EEG) data recorded by scalp electrodes, which is used to select at least one of the potential stimulation electrodes to deliver the DBS. When the electrode(s) delivering the DBS are selected, optimal parameters for the DBS are then chosen.