Multivariate Biomarker Generation from Imaging and Clinical Data
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Solution Overview
Problem
Current methods for generating biomarkers for neuropsychiatric, neurodevelopmental, and neurobehavioral disorders lack effective integration of functional imaging data and clinical data, limiting their ability to provide comprehensive correlations and accurate diagnostic and therapeutic insights.
Innovation Solution
A computer-implemented method that uses a multivariate classifier to compute correlations between functional imaging data and clinical data, incorporating additional biological measures, to generate biomarkers that indicate neural signatures, treatment targets, and diagnostic information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If functional imaging data and clinical data are analyzed separately using traditional methods, then the analysis process is simple and straightforward, but the diagnostic precision and therapeutic effectiveness are limited
Solution Approach 1:
The patent combines functional imaging data and clinical data into a unified analysis framework using multivariate classification. This merging of previously separate data streams enables comprehensive biomarker generation that integrates multiple levels of biological information, thereby improving diagnostic precision while managing system complexity through integrated processing.
Solution Approach 2:
The multivariate classification system serves multiple functions simultaneously: it processes functional imaging data, integrates clinical data, generates biomarkers, and provides both diagnostic and therapeutic insights. This multi-functional approach allows a single system to address multiple analytical needs without requiring separate specialized systems for each function.
2Reliability
If multiple imaging techniques and data types are integrated to assess multiple levels of function, then comprehensive correlations and accurate diagnostic insights are achieved, but the complexity of measurements, archiving, and processing increases
Solution Approach 1:
The patent introduces multivariate classification as an intermediary processing layer that mediates between multiple data sources (functional imaging, clinical data, biological measures) and the final diagnostic output. This intermediary system standardizes and integrates diverse data types, managing processing complexity while maintaining diagnostic reliability through systematic analysis.
Solution Approach 2:
The system transforms multiple types of input data (imaging, clinical, biological) into standardized parameters and features that can be processed uniformly by the multivariate classifier. This parameter transformation enables integration of diverse data sources with varying formats and scales, reducing processing complexity while preserving diagnostic reliability.
Data Source
AI summary
Systems and methods for generating biomarkers associated with neuropsychiatric disorders, neurodevelopmental disorders, neurobehavioral disorders, or other neurological disorders are described. In general, the biomarkers are generated based on correlations between functional imaging data and clinical acquired from a subject, as computed using a multivariate classifier. Functional imaging data may include functional magnetic resonance images, or activation maps generated from such images. Clinical data generally includes data associated with a clinical or behavioral characterization of the subject. The biomarkers can be used to monitor or otherwise assess a treatment response; to provide diagnostic information, such as subtyping or classifying a disorder; to provide prognostic information, such as a prediction of treatment response or outcome; or to indicate functional or anatomical targets for treatments.