ECT Response Prediction Using Brain Functional Connectivity
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Solution Overview
Problem
Current methods for predicting the response to electroconvulsive therapy (ECT) are inaccurate, missing approximately 15% of true responders and discouraging over 20% of individuals who might benefit from the treatment, due to lack of reliable biomarkers and personalized approaches.
Innovation Solution
A system and method using functional and anatomical connectivity patterns between brain regions, specifically the subgenual Anterior Cingulate Cortex, dorsolateral prefrontal cortex, and visual networks, as determined by fMRI and diffusion tensor imaging, to predict the effectiveness of ECT and guide transcranial magnetic stimulation targeting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard functional brain imaging approaches are used to predict ECT response, then the measurement process is simple and widely available, but the prediction accuracy is insufficient (missing 15% of true responders and discouraging 20% of potential beneficiaries)
Solution Approach 1:
The patent segments the brain into specific functional networks (visual network, subgenual Anterior Cingulate Cortex network, dorsolateral prefrontal cortex network) and analyzes connectivity patterns between these segmented regions. This segmentation approach transforms the complex whole-brain analysis into manageable network-level assessments, improving prediction accuracy while maintaining analytical tractability
Solution Approach 2:
The patent introduces functional connectivity patterns as an intermediary measure between standard fMRI imaging and ECT response prediction. By measuring connectivity strength between specific brain networks rather than relying on raw imaging data alone, the system achieves higher prediction accuracy (over 90%) while building upon existing fMRI technology
2Reliability
If ECT is prescribed based on current diagnostic and clinical indicators, then treatment can be administered using established guidelines, but the remission rate is limited to approximately 50%
Solution Approach 1:
The patent performs preliminary assessment of functional connectivity patterns before ECT treatment to identify patients most likely to respond. By conducting this personalized evaluation in advance, the system predicts which patients will achieve remission, allowing clinicians to optimize treatment selection and potentially improve overall remission rates beyond the current 50%
Solution Approach 2:
The patent changes the assessment parameter from standard clinical indicators to functional connectivity metrics between specific brain networks. This parameter change enables more precise prediction of treatment response, allowing for personalized treatment decisions that could enhance reliability of ECT outcomes
3Measurement precision
If transcranial magnetic stimulation uses standard 5-cm rule targeting, then the treatment procedure is simple and standardized, but the targeting precision of L-DLPFC is approximate with no consistent differentiation among DLPFC subregions
Solution Approach 1:
The patent applies local quality by differentiating among specific subregions of the dorsolateral prefrontal cortex based on their functional connectivity characteristics. Instead of treating L-DLPFC as a uniform target, the system identifies and targets specific DLPFC subregions that show abnormal connectivity patterns, thereby improving targeting precision while maintaining procedural feasibility
Data Source
AI summary
An exemplary system, method, and computer-accessible medium for determining a position or a characteristic of a target(s) for a transcranial magnetic stimulation (TMS) treatment of a patient(s) can be provided, which can include, for example, receiving imaging information of a portion(s) of a head of the patient(s), and determining the position or the characteristic of the target(s) for the TMS treatment of the patient(s) based on the imaging information. The imaging information can be magnetic resonance imaging information. The imaging information can include information regarding a brain and a skull of the patient(s). The position or the characteristic of the target(s) can be determined by identifying (i) the skull, and (ii) a parcel in a section(s) of a brain of the patient(s). The parcel can a dorsolateral prefrontal cortex (DLPFC) parcel. The DLPFC parcel can be identified using a parcellation procedure, which can be a human connectome pipeline procedure.