Brain Network Co-Activation Monitoring for Alzheimer's Disease
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Solution Overview
Problem
Current methods for tracking Alzheimer's disease progression are subjective, inconsistent, and fail to objectively quantify symptoms, often missing pre- and early stages of the disease due to reliance on observational techniques that are difficult to quantify and are influenced by fluctuations in patient condition.
Innovation Solution
Monitoring the co-activation of the default mode network and salience network in the brain using bioelectrical signals and imaging techniques like fMRI, MEG, and PET to identify episodes of temporal co-activation, which can be used to objectively track the progression of Alzheimer's disease and adjust therapy accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If observational techniques are used to track Alzheimer's disease progression, then the monitoring can be performed without complex equipment, but the measurement precision and objectivity deteriorate due to subjective and inconsistent assessments
Solution Approach 1:
The patent replaces subjective observational techniques with objective neuroimaging measurements (fMRI, PET, SPECT) to detect and quantify brain network dysfunction. This substitution transforms the monitoring process from qualitative clinical assessment to quantitative physiological measurement, eliminating observer bias and improving measurement precision while maintaining clinical accessibility
Solution Approach 2:
The patent introduces brain network co-activation patterns as an intermediary biomarker that objectively reflects disease progression. By measuring the functional connectivity between brain regions (default mode network and salience network), the system creates an objective intermediate metric that bridges clinical observation and molecular pathology, enabling precise tracking without direct subjective assessment
2Ease of operation
If traditional observational methods are used, then the monitoring approach remains simple and accessible, but the ability to detect pre- and early-stage disease deteriorates due to lack of sensitive biomarkers
Solution Approach 1:
The patent enables preliminary detection of Alzheimer's disease by identifying brain network dysfunction patterns before clinical symptoms manifest. By monitoring co-activation patterns in the default mode and salience networks, the system can detect pre-clinical disease stages, allowing early intervention while maintaining a relatively simple neuroimaging-based approach
Solution Approach 2:
The patent changes the monitoring parameters from behavioral observations to physiological brain activity measurements. By quantifying functional connectivity parameters (co-activation frequency, duration, and intensity) using neuroimaging, the system achieves high detection sensitivity for early stages while preserving operational simplicity through standardized imaging protocols and automated analysis
3Measurement precision
If neuroimaging techniques like fMRI, MEG, and PET are used to monitor brain activation, then the measurement precision and objectivity improve, but the device complexity and cost increase
Solution Approach 1:
The patent segments the brain into functionally distinct networks (default mode network and salience network) and monitors their co-activation patterns separately. This segmentation allows the use of multiple neuroimaging modalities (fMRI, PET, MEG) to target specific networks with appropriate techniques, optimizing measurement precision while managing system complexity through specialized rather than universal monitoring approaches
Solution Approach 2:
The patent employs multiple neuroimaging modalities (fMRI, PET, MEG) that can serve universal purposes in detecting brain network dysfunction. Each modality provides complementary information about brain activation patterns, and their results can be integrated to achieve comprehensive monitoring. This multi-functionality approach improves measurement precision while the shared analytical framework for assessing co-activation patterns helps manage the inherent complexity
4Measurement precision
If frequent and detailed brain monitoring is performed to accurately track disease progression, then the measurement precision improves, but the loss of time and resources increases
Solution Approach 1:
The patent enables continuous monitoring of brain network co-activation patterns through neuroimaging techniques, allowing repeated assessments over time to track disease progression accurately. By establishing baseline patterns and comparing them across multiple time points, the system achieves high measurement precision while the continuous nature of monitoring reduces the need for extensive retrospective analysis, optimizing time utilization
Data Source
AI summary
A brain condition can be tracked based on identification of co-activation of two antagonistic networks of a patient's brain. Various embodiments concerns methods and devices for sensing one or more signals indicative of brain activity, detecting one or more episodes of default mode network activation based on the one or more signals, detecting one or more episodes of salience network activation based on the one or more signals, and identifying one or more episodes of temporal co-activation of the default mode network and the salience network based on the detected one or more episodes of default mode network activation and the one or more episodes of salience network activation. The brain condition can be tracked and treated based on the identification of the one or more episodes of co-activation.