Cross-Species Brain Function Mapping for Mental Illness Prediction
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Solution Overview
Problem
Current clinical diagnosis of mental illnesses lacks objective and reliable diagnostic criteria, relying heavily on subjective behavioral assessments, and existing methods fail to accurately and objectively predict individualized mental illnesses due to the complexity of symptoms and high heterogeneity among patients.
Innovation Solution
A method and system utilizing non-human primate models to construct whole-brain functional connectivity maps, applying feature selection algorithms like group lasso and least absolute shrinkage and selection operator (lasso) to identify characteristic encephalic regions, followed by sparse logistic regression for predicting mental illnesses, providing a more accurate and individualized prediction model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If behavioral scales are used for clinical diagnosis of mental illness, then the diagnosis process is simple and easy to implement, but the diagnostic accuracy and objectivity are insufficient due to subjectivity and symptom overlap
Solution Approach 1:
The patent introduces functional connectivity maps as an intermediary between behavioral symptoms and diagnostic conclusions. These maps serve as objective biomarkers that translate complex behavioral observations into quantifiable neural network patterns, reducing subjectivity while maintaining diagnostic comprehensiveness
Solution Approach 2:
The patent replaces the mechanical/subjective behavioral assessment system with an objective neuroimaging-based system. By substituting behavioral scales with functional connectivity measurements from MRI data, the system achieves higher objectivity and precision in diagnosing mental illnesses
2Reliability
If non-human primate models are used to study human brain diseases, then the pathogenic mechanism becomes simpler and more controllable, but the translation to human clinical applications requires complex cross-species mapping
Solution Approach 1:
The patent establishes universal mapping relationships between macaque and human brain functional networks. The same analytical framework and feature extraction methods can be applied across species, allowing the system to handle both macaque model data and human clinical data through a unified approach
Solution Approach 2:
The patent creates a copied representation of human brain disease mechanisms in macaque models, then uses cross-species functional connectivity mapping to transfer insights back to human applications. The macaque functional connectivity maps serve as simplified copies that preserve essential disease mechanisms while eliminating confounding human-specific variables
3Measurement precision
If whole-brain functional connectivity maps are constructed for mental illness prediction, then the prediction accuracy improves, but the data processing complexity and computational requirements increase
Solution Approach 1:
The patent extracts only the most relevant features from whole-brain functional connectivity maps using feature selection algorithms. Instead of analyzing all possible connectivity patterns, the system identifies and extracts key features that are most predictive of mental illness, significantly reducing computational complexity while maintaining high accuracy
Solution Approach 2:
The patent segments the complex whole-brain functional connectivity data into manageable components through feature selection. By dividing the large-scale connectivity matrix into relevant feature subsets, the system makes the data processing task more tractable while preserving the essential information needed for accurate prediction
Data Source
AI summary
Provided are a method and system for individualized prediction of mental illness on the basis of brain function map monkey-human cross-species migration, and a method for determining a mental illness prediction model. The method for determining a mental illness prediction model comprises the steps: (a) data acquisition; (b) preprocessing; (c) brain region selection; (d) feature construction; (e) feature screening; and (f) modeling prediction. The method and system are non-invasive and have high accuracy, high sensitivity, good specificity, and are convenient to popularize.