Neurocognitive Decline Detection Using fMRI and Machine Learning
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Solution Overview
Problem
Current methods for detecting and forecasting neurocognitive decline (NCD) are unreliable, as they fail to effectively capture early structural and functional changes in the brain before overt symptoms appear.
Innovation Solution
The use of functional neuroimaging data, such as fMRI, obtained during naturalistic language-processing tasks, combined with machine-learning classifier models, to detect current NCD status and forecast future NCD development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If functional neuroimaging techniques such as fMRI are used to detect early structural and functional changes in the brain, then early detection capability is improved, but measurement precision and reliability remain insufficient due to lack of reliable protocols
Solution Approach 1:
The patent transforms functional neuroimaging data by extracting quantitative features that characterize brain activation patterns during naturalistic language processing tasks. These transformed parameters serve as reliable inputs for machine learning classifiers, enabling precise detection of neurocognitive decline while maintaining early detection capability
Solution Approach 2:
The patent introduces machine learning classifier models as intermediary systems between raw functional neuroimaging data and clinical diagnosis. These classifiers process and interpret complex brain activation patterns, providing reliable and objective detection of neurocognitive decline stages without requiring manual interpretation
2Measurement precision
If machine learning classifier models are trained on functional neuroimaging data to predict NCD status, then prediction accuracy is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent divides the complex analysis into distinct segments: data collection during naturalistic tasks, feature extraction from functional neuroimaging data, model training with labeled outcomes, and classification prediction. This segmentation manages complexity while maintaining high prediction accuracy through specialized processing at each stage
3Adaptability or versatility
If naturalistic language-processing tasks are used for data collection, then ecological validity is improved, but task complexity and data processing requirements increase
Solution Approach 1:
The patent extracts specific quantitative features from the complex functional neuroimaging data collected during naturalistic language processing tasks. This extraction isolates the most relevant brain activation patterns related to language processing, reducing data complexity while preserving ecological validity
Data Source
AI summary
Predictions of current and/or future neurocognitive decline (NCD) can be based on functional neuroimaging data (e.g., fMRI data) and a machine-learning classifier model. Functional neuroimaging data is obtained while the subject performs a naturalistic language-processing task, such as watching a movie clip, and processed to extract a quantitative feature set. A classifier model is trained using machine learning techniques to predict a subject's NCD status based on the quantitative feature set.