Individualized TMS Target Localization Using sgACC-DLPFC Connectivity
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Solution Overview
Problem
Current methods for TMS target localization in depression treatment using group-level averaged whole-brain functional connectivity from healthy individuals are not effective for patients with depression due to significant differences in brain connectivity patterns, leading to unstable and non-individualized treatment outcomes.
Innovation Solution
A method and system for individualized TMS target localization using group-level difference statistical maps, utilizing brain imaging data from thousands of depression samples, involving data preprocessing, functional connectivity calculation, two-sample t-tests, and a dual regression algorithm to derive precise individualized targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If group-level averaged whole-brain functional connectivity from healthy individuals is used for TMS target localization, then the method is simple and widely applicable, but the treatment effectiveness for depression patients is reduced due to significant differences in brain connectivity patterns
Solution Approach 1:
The patent applies local quality by transitioning from a uniform group-level approach to an individualized approach. Specifically, it calculates subject-specific functional connectivity maps and identifies individual peak coordinates within the DLPFC region, allowing each patient to receive treatment tailored to their unique brain connectivity pattern while still targeting the same general anatomical region.
Solution Approach 2:
The patent implements preliminary action by pre-defining the DLPFC mask region based on group-level anatomical standards before individualized functional connectivity analysis. This allows the method to efficiently narrow down the search space to the relevant brain region while still capturing individual variability in functional connectivity patterns within that region.
2Measurement precision
If single-subject imaging is used to calculate TMS targets based on sgACC functional connectivity, then individualized localization is achieved, but the results are unstable due to low signal-to-noise ratio of functional MRI imaging
Solution Approach 1:
The patent merges individual subject functional connectivity data with group-level anatomical constraints by intersecting subject-specific peak coordinates with the pre-defined DLPFC mask. This combination allows the method to capture individual variability while benefiting from the stability and anatomical accuracy of group-level definitions, thereby improving both precision and reliability.
Solution Approach 2:
The DLPFC mask serves as an intermediary between group-level anatomical standards and individual-level functional connectivity findings. By constraining the search for peak coordinates within this mask, the method filters out noise from individual scans while preserving meaningful individual variability, thus stabilizing the localization results.
3Stability of the object's composition
If healthy control data is used to establish functional connectivity patterns, then a robust reference framework is available, but the patterns do not reflect disease-specific brain variability in depression patients
Solution Approach 1:
The patent segments the analysis into two distinct components: a stable group-level anatomical framework (DLPFC mask) and variable individual-level functional connectivity patterns. This segmentation allows the method to maintain the stability of anatomical reference while adapting to disease-specific functional variations by analyzing individual patient data within the constrained anatomical region.
Data Source
AI summary
The invention disclosed herein presents a method and system for individualized target localization for transcranial magnetic stimulation (TMS) in treating depression based on group-level differential statistical maps. The system acquires resting-state functional MRI (R-fMRI) brain imaging data from subjects in both a major depressive disorder (MDD) group and a matching normal control group, followed by data preprocessing. Taking the spherical subgenual anterior cingulate cortex (sgACC) as the seed point, functional connectivity calculations are performed for each subject, and sgACC functional connectivity maps within the mask of the dorsolateral prefrontal cortex (DLPFC) region are extracted. A two-sample t-test is conducted on the sgACC functional connectivity maps of the MDD and normal control groups to identify clusters within the DLPFC mask that show significant differences between the two groups, which are used as group-level localization targets. By integrating the obtained group-level localization targets with preprocessed individual MRI brain imaging data, individualized TMS targets are derived using a dual regression algorithm. This invention thoroughly considers the overall abnormal brain activities and individual functional variations of patients with depression, achieving precise individualized localization for TMS treatment in MDD patients.