MRI-Based Personalized Target Selection for Non-Invasive Neuromodulation
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Solution Overview
Problem
Current non-invasive neuromodulation treatments for mental disorders, such as depression, have low effectiveness due to the use of fixed therapeutic targets that do not account for individual patient variability, resulting in treatment rates ranging from 10% to 60%.
Innovation Solution
A personalized target selection method using brain MRI data and machine learning algorithms to identify optimal therapeutic targets by analyzing patient-specific brain region connections and differences from a healthy population, employing preprocessing, dimensionality reduction, clustering, and coordinate transformation to select candidate targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed therapeutic targets are used for non-invasive neuromodulation treatment, then the treatment protocol is simple and easy to implement, but the effective rate is low (10%-60%) due to lack of personalization
Solution Approach 1:
The patent changes the parameter of target selection from fixed anatomical locations to dynamic, patient-specific targets determined by machine learning analysis of individual brain imaging data. This transforms the treatment approach from one-size-fits-all to personalized medicine, thereby improving effectiveness without requiring complex procedural changes
Solution Approach 2:
The patent uses machine learning models trained on population data to generate personalized treatment targets for individual patients. The model copies successful treatment patterns from healthy control subjects and adapts them to each patient's specific brain anatomy and functional connectivity, achieving personalization while maintaining evidence-based treatment protocols
2Measurement precision
If personalized target selection using MRI data and machine learning is implemented, then treatment accuracy and personalization are improved, but the complexity of the treatment protocol increases
Solution Approach 1:
The patent performs preliminary processing of MRI data including skull stripping, normalization, and feature extraction before machine learning analysis. By preparing the data in advance with standardized preprocessing steps, the system reduces the complexity of real-time analysis and enables accurate target selection without requiring complex computational resources during treatment planning
Solution Approach 2:
The patent segments the complex target selection process into distinct modules: data preprocessing, feature extraction, machine learning classification, and coordinate transformation. This segmentation allows each module to be optimized independently and simplifies the overall system by breaking down the complex task into manageable, well-defined steps
Data Source
AI summary
The provided is a personalized target selection method for a non-invasive neuromodulation technology, including: preprocessing functional magnetic resonance imaging (fMRI) data from MRI scan data of a current patient to acquire fMRI brain image feature data; inputting the fMRI brain image feature data into a pre-trained inter-subtype classification model to acquire a subtype label of the current patient and all feature voxels of the subtype label; preprocessing T1-weighted MRI data of structural magnetic resonance imaging (sMRI) data from the MRI scan data of the current patient to acquire a skull outline and a transformation matrix between the sMRI and fMRI data; performing coordinate transformation on the feature voxels, calculating a distance between each voxel on the skull outline and each feature voxel, marking response feature voxels, and counting a number of response feature voxels; and sorting the number of response feature voxels.