N-BRIDGE Neuro-Behavioral Mapping for Patient Stratification
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Solution Overview
Problem
Current psychiatric treatments often have limited efficacy due to the heterogeneity of patients with the same diagnosis, making it challenging to match patients with effective treatments, and existing approaches fail to capture the complex multi-dimensional relationships between neural and behavioral features.
Innovation Solution
The development of a data-driven analytic framework, N-BRIDGE, which maps multi-dimensional relationships between neural and behavioral features, allowing for the identification of robust therapeutic targets by projecting behavioral data into a neural feature space and vice versa, enabling individualized treatment approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional categorical psychiatric diagnoses are used to group patients, then treatment standardization is achieved, but treatment efficacy deteriorates due to patient heterogeneity within diagnostic groups
Solution Approach 1:
The patent segments patients into subgroups based on neural circuitry profiles and behavioral dimensions rather than using broad categorical diagnoses. This fine-grained segmentation identifies homogeneous patient subsets within diagnostic categories, enabling matched treatments for each subgroup while maintaining overall treatment standardization through systematic classification.
Solution Approach 2:
The patent applies local quality by tailoring treatments to specific neural-behavioral profiles of patient subgroups rather than applying uniform treatments to all patients with the same diagnosis. Each patient subgroup receives customized intervention based on their unique neural circuitry characteristics and behavioral dimension patterns.
2Ease of operation
If single-dimensional clinical scales are used to assess patients, then assessment simplicity is maintained, but mapping accuracy to neural features deteriorates
Solution Approach 1:
The patent transitions from single-dimensional clinical scales to multi-dimensional behavioral assessments that capture complex behavioral phenotypes across multiple dimensions. This dimensional expansion enables accurate mapping to multi-dimensional neural feature spaces, improving measurement precision while maintaining systematic assessment through structured dimensional frameworks.
Solution Approach 2:
The patent creates composite behavioral assessments by integrating multiple clinical scales and behavioral measures into unified multi-dimensional profiles. These composite assessments combine information from various sources to generate comprehensive behavioral phenotypes that accurately reflect complex patient presentations and map precisely to neural features.
3Measurement precision
If comprehensive multi-dimensional neural and behavioral data are collected, then mapping accuracy is improved, but data analysis complexity increases
Solution Approach 1:
The patent introduces computational algorithms and statistical models as intermediaries between comprehensive multi-dimensional data collection and clinical interpretation. These intermediary tools process complex neural and behavioral data, identify patterns, and translate them into clinically actionable insights, reducing analysis complexity while preserving mapping accuracy.
Solution Approach 2:
The patent creates simplified representations or models of complex neural-behavioral relationships that can be used for prediction and classification without requiring direct analysis of the full complexity of原始 data. These copied models enable efficient data analysis while maintaining the predictive power and mapping accuracy of comprehensive assessments.
Data Source
AI summary
Described herein are example methods and systems for neuro-behavioral relationships in dimensional geometric bedding (N-BRIDGE), which includes a comprehensive, data-driven analytic framework for mapping the multi-dimensional relationships between neural and behavioral features in humans N-BRIDGE allows mapping of variations along newly-defined data-driven behavioral dimensions that capture the geometry of behavioral/symptom variation to variation in specific neural features. A method for treating a patient based on neuro-behavioral mapping includes receiving, from a user interface of a computing device, behavioral data of a patient corresponding to mental health or cognitive status of the patient, predicting, by at least one processor of the computing device, a neural feature map for the patient representative of neural data based on the behavioral data, determining, by the at least one processor, a therapeutic associated with the neural feature map, and treating the patient with the therapeutic associated with the neural feature map.