Neural Network Translates Non-Invasive Brain Data to Cellular Observations
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Solution Overview
Problem
Current methods are inadequate for safely and effectively evaluating brain cell changes and repair processes in living subjects, as they cannot access the human brain in living subjects or animals over long periods in natural settings, limiting understanding of cellular-level contributions to cognitive health.
Innovation Solution
The development of a system integrating non-invasive or minimally invasive data collection with cellular imaging and biomimetic models, using neural networks to translate system-level information into cellular-level observations, enabling the identification of sleep signatures and predicting brain cell changes from non-invasive recordings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive methods are used to access the human brain for cellular-level evaluation, then measurement precision improves, but object-generated harmful factors worsen
Solution Approach 1:
The patent uses an artificial neural network as an intermediary to translate non-invasive system-level recordings into cellular-level observations. The neural network acts as a mediator that bridges the gap between non-invasive macroscopic measurements and cellular-level information, allowing researchers to obtain cellular-level measurement precision without direct invasive access to the brain.
Solution Approach 2:
The patent creates a computational model (copy) of brain cell behavior based on non-invasive recordings. Instead of directly observing cells through invasive means, the system generates a virtual representation of cellular activity from system-level data, enabling cellular-level evaluation without physical intrusion into the brain.
2Object-generated harmful factors
If non-invasive methods are used to evaluate the brain, then object-generated harmful factors are reduced, but measurement precision deteriorates
Solution Approach 1:
The patent replaces the mechanical/invasive approach of directly accessing brain tissue with a computational/information-based approach. Instead of using physical probes or invasive imaging to observe cells, the system uses non-invasive recordings processed through artificial neural networks to infer cellular-level information, substituting mechanical intervention with computational analysis.
Solution Approach 2:
The artificial neural network serves as an intermediary that enhances the information content of non-invasive recordings. By processing system-level data through the neural network, the system extracts cellular-level patterns that would otherwise be inaccessible, maintaining measurement precision while avoiding invasive procedures.
3Measurement precision
If direct observation of brain cells in living subjects is attempted, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent extracts cellular-level information from system-level recordings through computational analysis. Instead of inserting complex devices into the brain to directly observe cells, the system extracts cellular patterns from non-invasive data using artificial neural networks, separating the information extraction process from physical intrusion.
Solution Approach 2:
The system creates a computational copy of cellular behavior from non-invasive recordings. Rather than building complex devices to directly image or measure cells in the living brain, the patent generates a virtual model of cellular activity that replicates what direct observation would reveal, avoiding the need for complex invasive apparatus.
Data Source
AI summary
Methods described herein are directed to solving the problem of safely evaluating organs or tissues in a living subject and assessing and monitoring these living systems. In certain aspects the brain of a living subject is evaluated. The methods described herein integrate behavioral measurements and other non-invasive information gathering (e.g., imaging, EEG, etc.) or minimally invasive information gathering (e.g., biological fluid sampling) with cellular imaging, and biomimetic models to safely evaluate a subject. In vitro models are established that can be manipulated and monitored on the cellular level.