MRI-Guided Targeted Neuromodulation for Personalized Brain Targets

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Solution Overview

Problem

Conventional brain stimulation therapies, such as TMS, face challenges in accurately identifying personalized stimulation targets due to the difficulty in manually locating effective regions like the DLPFC, and existing targeting methods are prone to noise and fail to consider spatial relations between brain voxels, leading to unreliable and impractical results.

Innovation Solution

A targeted neuromodulation system that utilizes structural and functional MRI scans to derive individualized maps of ROI parcellation, calculating target scores based on parcel connectivity, network relationships, and discarding noisy data to identify precise stimulation targets for personalized neuromodulation therapies like TMS.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual methods are used to locate stimulation targets in the brain, then the process is simple to perform, but the accuracy and reliability of target identification deteriorates

Engineering Contradiction:

Improvesimplicity of manual target locationVSAvoidaccuracy of stimulation target identification

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The patent replaces manual mechanical localization methods with automated computational image processing and data analysis systems. The system uses computer algorithms to process MRI scans and identify stimulation targets, substituting human manual operations with automated computational mechanisms that provide both ease of operation and high precision.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces intermediate processing steps including image registration, noise filtering, and data normalization between the raw imaging data and final target identification. These intermediary processes mediate between the input data and output results, improving accuracy while maintaining operational simplicity through automated workflows.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Device complexity

If conventional targeting methods are used, then the device complexity is low, but the reliability of stimulation target identification deteriorates due to noise and failure to consider spatial relations

Engineering Contradiction:

Improvesimplicity of targeting systemVSAvoidreliability of target identification

Core Design Contradiction:

Device complexityVSReliability

Solution Approach 1:

The patent segments the brain imaging data into distinct regions of interest (ROIs) and processes them separately through specialized algorithms. This segmentation allows the system to handle different spatial relationships and noise characteristics in different brain regions, improving reliability while maintaining manageable system complexity through modular processing.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent changes key parameters including noise thresholds, spatial relationship weights, and connectivity metrics to optimize target identification reliability. By dynamically adjusting these parameters based on individual patient data characteristics, the system achieves high reliability without requiring overly complex fixed-structure systems.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If individualized maps of ROI parcellation are derived considering spatial relations and network connectivity, then the accuracy of target identification improves, but the computational complexity and processing time increases

Engineering Contradiction:

Improveaccuracy of individualized target mappingVSAvoidcomputational complexity of processing

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent performs preliminary actions by pre-processing MRI data to reduce noise and pre-identifying potential regions of interest before detailed analysis. This preliminary processing simplifies subsequent complex computations by reducing data volume and focusing computational resources on the most relevant brain regions, thereby improving accuracy while managing computational complexity.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent applies different processing qualities and computational intensities to different brain regions based on their relevance to the clinical condition. High-complexity processing is applied only to critical ROIs with strong network connectivity, while less critical regions receive simplified processing. This local quality approach improves overall accuracy without uniformly increasing computational complexity across the entire brain.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS20250318879A1Systems and Methods for Targeted Neuromodulation

Publication Date: 2025.10.16 THE BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIV

AI summary

Systems and methods for neuronavigation in accordance with embodiments of the invention are illustrated. Targeting systems and methods as described herein can generate personalized stimulation targets for the treatment of mental conditions. In many embodiments, direct stimulation of a personalized the stimulation target indirectly impacts a brain structure that is more difficult to reach via the stimulation modality. In various embodiments, the mental condition is major depressive disorder. In a number of embodiments, the mental condition is suicidal ideation.