Primate Brain Modelling via Segmented Dynamical Nodes
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Solution Overview
Problem
Current technologies face challenges in modeling and simulating brain function effectively, particularly in reproducing the complex interactions of neurons and integrating neuroimaging data to understand brain dynamics.
Innovation Solution
A computing system is developed that integrates dynamical models of the brain with measured neuroimaging data, using a decision engine to compare and fit the model outputs to the imaging data, thereby enabling model-based inference of neurophysiological mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a detailed mathematical model of brain dynamics is created to accurately represent neuronal interactions, then the model can provide deeper insights into brain function mechanisms, but the model complexity and computational requirements increase significantly
Solution Approach 1:
The brain model is divided into multiple nodes representing different brain regions, with each node containing a local dynamic model. This segmentation allows the complex brain system to be modeled as interconnected simpler units, managing complexity while maintaining overall accuracy.
Solution Approach 2:
Each node in the brain model has its own local dynamic model with specific parameters tailored to represent the unique characteristics of that brain region. This local quality approach enables accurate representation of regional differences without requiring the entire model to be uniformly complex.
2Measurement precision
If the brain model is fitted to individual neuroimaging data to create personalized models, then the model can capture individual brain dynamics more accurately, but the time and computational resources required for data processing and model fitting increase
Solution Approach 1:
The system performs preliminary processing of neuroimaging data and pre-fitting of model parameters before final individualization. By preparing the model framework and processing data in advance, the actual individualized fitting process is accelerated, reducing the time required to create personalized brain models.
3Adaptability or versatility
If multiple neuroimaging modalities are integrated into the model to comprehensively capture brain activity, then the model can represent more aspects of brain function, but the difficulty of detecting and measuring and data integration increases
Solution Approach 1:
The decision engine is designed to handle multiple neuroimaging modalities (fMRI, EEG, MEG, PET) through a unified framework. This universal approach allows the system to process and integrate different types of neuroimaging data using the same model structure, simplifying the integration process while maintaining comprehensive coverage of brain function.
Data Source
AI summary
In one aspect the application relates to a computing system for providing data for modelling a human brain comprises a database including a plurality of datasets (or allow access to a plurality of datasets), each dataset including at least a dynamical model of the brain including at least one node and a neurodataset of a neuroimaging modality input. The at least one node include a representation of a local dynamic model and a parameter set of the local dynamic model.