MEG Brain Function Inventory Using SQUID Sensors
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Solution Overview
Problem
Current methods for detecting and monitoring neurodegenerative disorders like Alzheimer's disease are inadequate due to the inability of existing imaging techniques to non-invasively measure brain cortical function with sufficient temporal resolution and accuracy, as they either fail to directly image neural function or are hindered by signal attenuation and mismatched temporal resolution.
Innovation Solution
Development of models using magnetoencephalography (MEG) data collected with superconducting quantum interference device (SQUID) sensors to detect and stage cognitive impairment by analyzing the relative presence and intensity of evoked responses, employing data science techniques such as regression and machine learning to differentiate between normal and cognitively impaired patients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If fMRI is used to image brain function, then blood flow can be visualized, but the temporal resolution is too slow (seconds) to capture millisecond-scale neural events
Solution Approach 1:
The patent replaces the mechanical/blood-flow-based measurement system of fMRI with a magnetic field detection system (MEG) that directly measures neural electrical activity. This substitution enables millisecond temporal resolution while maintaining measurement precision for neural function detection.
2Illumination intensity
If EEG is used to detect brain activity with millisecond resolution, then temporal resolution is improved, but signal attenuation by surrounding tissues causes near and far signals to be comingled
Solution Approach 1:
The patent introduces magnetic field detection as an intermediary measurement approach. Instead of detecting electrical potentials directly through attenuating tissues (EEG), the system detects magnetic fields generated by neural currents, which penetrate tissues without attenuation. This intermediary approach preserves signal clarity while maintaining millisecond temporal resolution.
3Measurement precision
If PET is used to detect glucose consumption, then metabolism can be imaged, but it only indirectly measures cognitive function rather than directly imaging neural activity
Solution Approach 1:
The patent replaces indirect metabolic measurement systems (PET measuring glucose consumption) with a direct neural activity measurement system (MEG detecting magnetic fields from neural currents). This substitution provides direct information about neural function while maintaining measurement precision.
4Measurement precision
If multiple SQUID sensors are used to detect brain signals, then measurement coverage is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent extracts and focuses measurement on specific, localized brain regions of interest rather than attempting to measure the entire brain simultaneously. By placing a small array of SQUID sensors over targeted cortical areas, the system achieves high measurement precision for specific functions while minimizing device complexity and sensor count.
Solution Approach 2:
The patent applies local quality by concentrating measurement resources (SQUID sensors) on specific cortical regions where particular neural functions are located. This localized approach optimizes measurement precision for targeted functions while avoiding the complexity of whole-brain coverage.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The approach enables non-invasive detection and monitoring of neurodegenerative disorders with statistically significant classification accuracy, distinguishing between normal and cognitively impaired patients based on subtle differences in brain activation patterns, thereby improving the assessment and progression tracking of neurodegenerative diseases.
Implementation Method 1
magnetoencephalography (MEG) data collected with superconducting quantum interference device (SQUID) sensors to detect and stage cognitive impairment
Implementation Method 2
superconducting quantum interference device (SQUID) sensors
Data Source
AI summary
Techniques are described for determining cognitive impairment, an example of which includes accessing a set of epochs of magnetoencephalography (MEG) data of responses of a brain of a test patient to a plurality of auditory stimulus events; processing the set of epochs to identify parameter values one or more of which is based on information from the individual epochs without averaging or otherwise collapsing the epoch data. The parameter values are input into a model that is trained based on the parameters to determine whether the test patient is cognitively impaired.