Individualized Neuromodulation Target Identification via MRI Segmentation
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Solution Overview
Problem
Current methods for identifying neuromodulation targets in the brain are inaccurate due to neglecting individual anatomical and functional differences, leading to imprecise location of neuromodulation sites, which affects the therapeutic efficacy for neurological and psychiatric disorders.
Innovation Solution
A method and device that utilize magnetic resonance imaging (MRI) to determine individualized neuromodulation targets by acquiring scanning data, determining brain regions, and applying a target identification rule to pinpoint specific areas for neuromodulation, considering both structural and functional connectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional neuromodulation target identification methods are used, then the process is simple, but the location accuracy is poor due to neglecting individual anatomical and functional differences
Solution Approach 1:
The patent segments the brain into multiple regions of interest (ROIs) based on anatomical structures and functional networks. It divides the complex brain into manageable segments (e.g., prefrontal cortex, temporal lobe, parietal lobe, occipital lobe) and further subdivides them into functional networks (e.g., default mode network, salience network, executive control network). This segmentation enables precise target location by analyzing each segment's connectivity and function individually, resolving the contradiction between simplicity and accuracy.
Solution Approach 2:
The patent transitions from traditional two-dimensional anatomical mapping to three-dimensional functional connectivity analysis. By incorporating functional connectivity metrics, network topology, and spatial relationships across multiple brain regions, the system adds dimensional complexity to the identification process. This dimensional enhancement enables accurate target localization that accounts for both anatomical structure and functional organization, thereby improving measurement precision.
2Reliability
If individualized target identification based on functional connectivity is implemented, then therapeutic efficacy is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-defining anatomical regions and functional networks based on established neuroanatomical knowledge. It pre-establishes connectivity templates and threshold criteria for identifying target regions. During actual target identification, the system only needs to process individual patient data against these pre-established frameworks, significantly reducing computation time while maintaining personalized accuracy. This preliminary preparation resolves the contradiction between therapeutic efficacy and processing time.
Solution Approach 2:
The patent applies local quality analysis by examining functional connectivity and network properties specific to each brain region and its relationships with other regions. Instead of treating the brain as a homogeneous structure, the system analyzes local connectivity patterns, node degrees, and network centrality metrics for each region of interest. This localized analysis identifies targets based on their specific functional characteristics and connectivity profiles, improving therapeutic efficacy while managing computational complexity through region-specific processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables precise location of individualized neuromodulation targets, improving the accuracy and efficacy of therapeutic interventions for neurological and psychiatric disorders by accounting for individual variability.
Implementation Method 1
acquiring scanning data of a subject, wherein the scanning data comprise data acquired from magnetic resonance imaging of a brain of the subject
Data Source
AI summary
The present disclosure provides a method and a device for target identification, electric device, a storage medium, and a neuromodulation apparatus. The method for target identification includes: acquiring scanning data of a subject, wherein the scanning data include data acquired from magnetic resonance imaging of a brain of the subject; determining, based on the scanning data, at least two brain regions of the subject, each brain region including at least one voxel; determining, based on a disease type of the subject, at least one target brain region corresponding to the type of the disease in the at least two brain regions; and determining, based on a preset target identification rule, the at least one target located in the target brain region.