Brain Neuromodulation Targeting Using MRI Connectivity Abnormalities
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Solution Overview
Problem
Existing methods for determining neuromodulation targets in the human brain are inaccurate due to neglecting individual anatomical and functional differences, leading to imprecise target locations and low therapeutic efficacy for neurological and psychiatric disorders.
Innovation Solution
A method and device for target identification using functional magnetic resonance imaging to determine individualized neuromodulation targets by analyzing brain connectivity patterns and identifying abnormal regions of interest, considering both structural and functional brain data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple separate algorithms are used to detect different seizure types (GTCS, ATCS, absence seizures), then detection coverage is improved, but device complexity and computational burden increase
Solution Approach 1:
The patent implements a single unified algorithm that can detect multiple types of seizures (generalized tonic-clonic seizures, absence seizures, and other seizure types) through one integrated processing system. The algorithm analyzes EEG signals using a combination of spectral analysis, entropy calculation, and pattern recognition that universally applies to different seizure types, eliminating the need for separate specialized algorithms for each seizure category.
Solution Approach 2:
The patent merges multiple detection functions into a single integrated algorithm that processes EEG signals through unified computational steps including power spectral density analysis, sample entropy calculation, and threshold-based classification. This consolidation combines what would traditionally require separate algorithms into one cohesive system that handles all seizure types through combined processing.
2Reliability
If EEG analysis is performed continuously to ensure accurate seizure detection, then detection reliability is improved, but energy consumption increases
Solution Approach 1:
The patent implements periodic analysis of EEG signals where the unified algorithm processes data in discrete time windows rather than continuous analysis. The system calculates power spectral density and sample entropy over specified intervals, allowing the processor to enter low-power states between analysis periods while maintaining reliable seizure detection through regular sampling at optimized intervals.
Solution Approach 2:
The algorithm incorporates adaptive thresholding and signal quality assessment that allow the system to automatically adjust analysis intensity based on signal characteristics. When EEG signals show normal patterns, the system reduces processing intensity; when seizure-like patterns are detected, analysis intensity increases, allowing the system to self-regulate energy consumption while maintaining detection reliability.
Data Source
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AI summary
The present disclosure provides a method and a device for target identification, an electronic apparatus, a storage medium and a neuromodulation apparatus. The method for target identification comprises: acquiring scanning data of a subject, wherein the scanning data comprise the data acquired from magnetic resonance imaging of the brain of the subject; determining at least two regions of interest of the subject based on the scanning data; determining at least one abnormal region of interest in the at least two regions of interest in accordance with a predetermined anomaly detection rule; determining a target based on the at least one abnormal region of interest.