Dynamic Brain Model Using Temporal Measures for Neural Pattern Analysis
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Solution Overview
Problem
Current methods for assessing brain function are inadequate, particularly for neurological diseases like Alzheimer's, as they lack sensitivity and reliability in detecting subtle neural patterns, and existing techniques such as EEG and MEG are limited in their ability to measure overall brain activity and deeper brain regions.
Innovation Solution
A system and method for analyzing neurophysiologic activity using a multiplicity of spatially distributed sensors to acquire time series data, processing it to create a dynamic model representing temporal measures among neural populations, and comparing this model with pre-stored templates to classify brain conditions, enabling accurate and differential diagnosis of various neurological conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If EEG measurements are aggregated during ERP trials to represent overall subject response, then a single vector representing activated brain regions is produced, but brain regions that remain relatively inactive are not represented and overall brain activity cannot be measured
Solution Approach 1:
The patent segments the brain activity measurement into multiple independent components rather than aggregating into a single vector. Each sensor channel maintains its own time series data, allowing separate analysis of activated and non-activated brain regions. This segmentation preserves information from all brain regions while enabling focused analysis of specific regions of interest.
Solution Approach 2:
The patent transitions from analyzing data in the time domain alone to incorporating frequency domain analysis through spectral decomposition. By examining brain activity across multiple frequency bands (delta, theta, alpha, beta, gamma), the system captures neural patterns that are not visible in raw time-domain signals, thereby detecting subtle neural patterns from less active regions.
2Measurement precision
If conventional EEG instrumentation is used, then electrical activity near the outer surface of the brain is detected, but sensitivity at deeper brain regions is substantially reduced
Solution Approach 1:
The patent uses signal processing techniques as intermediaries to enhance the detection capability of conventional EEG instrumentation. Spectral analysis and pattern recognition algorithms act as intermediaries that extract information from deep brain regions that is otherwise obscured by noise and signal attenuation, without requiring complex hardware modifications.
Solution Approach 2:
The patent changes the analysis parameters from raw voltage measurements to spectral power distributions across multiple frequency bands. This parameter transformation allows the system to detect subtle neural patterns from deep brain regions by analyzing frequency-specific oscillations that penetrate through tissue more effectively than raw electrical signals.
3Adaptability or versatility
If spectral analysis is used to cluster EEG/QEEG/MEG data according to treatment outcome, then data can be grouped by treatment response, but subtle characteristic indicia of certain diseases or conditions cannot be recognized
Solution Approach 1:
The patent performs preliminary spectral decomposition and feature extraction before classification. By pre-processing the data to identify spectral power distributions, coherence patterns, and other frequency-domain features, the system prepares the data in a form that enables detection of subtle neural patterns associated with specific brain conditions, which then can be accurately classified.
Solution Approach 2:
The patent employs dynamic pattern recognition that adapts to individual subject characteristics. Rather than using fixed spectral thresholds, the system dynamically adjusts classification parameters based on each subject's baseline brain activity patterns, enabling detection of subtle deviations that indicate specific neurological conditions while maintaining versatility across different patient populations.
Data Source
AI summary
A set of brain data representing a time series of neurophysiologic activity acquired by spatially distributed sensors arranged to detect neural signaling of a brain (such as by the use of magnetoencephalography) is obtained. The set of brain data is processed to obtain a dynamic brain model based on a set of statistically-independent temporal measures, such as partial cross correlations, among groupings of different time series within the set of brain data. The dynamic brain model represents interactions between neural populations of the brain occurring close in time, such as with zero lag, for example. The dynamic brain model can be analyzed to obtain the neurophysiologic assessment of the brain. Data processing techniques may be used to assess structural or neurochemical brain pathologies.