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Human brain effect connection network construction method based on non-stationary dynamic Bayesian network

A Bayesian network and connection network technology, applied in the field of dynamic human brain effect connection network construction, can solve the problems that cannot truly reflect the human brain effect connection network structure, and achieve a reasonable and reliable brain effect connection network structure

Active Publication Date: 2020-03-17
BEIJING UNIV OF TECH
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Problems solved by technology

[0004] However, current research on Bayesian network methods and dynamic Bayesian network methods largely relies on an assumption that the connection pattern of the entire fMRI time series is static or assumes that the time series is stationary, however, this assumption does not Does not really reflect the structure of the human brain effect connection network

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  • Human brain effect connection network construction method based on non-stationary dynamic Bayesian network
  • Human brain effect connection network construction method based on non-stationary dynamic Bayesian network
  • Human brain effect connection network construction method based on non-stationary dynamic Bayesian network

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Embodiment Construction

[0024] Set forth below the specific embodiment of the present invention and detailed steps, the flow chart of the method involved in the present invention is as follows figure 1 shown, including:

[0025] (Step 1) Data Acquisition.

[0026] First, in order to verify the effectiveness of the method of the present invention, an open-source simulation fMRI data generation toolkit (Asimulation toolbox for fMRI data, SimTB) was used to generate a set of event-based simulation data. The toolbox allows flexible generation of fMRI datasets under spatiotemporal separability models and is designed for testing of various analytical methods. In SimTB, data generation is fully controlled, including creating and manipulating spatial sources, implementing block- and event-related experimental designs, including tissue-specific baselines, simulating head movements, and more. Specifically, first determine the number of subjects (20), then determine the scanning time, that is, the number of t...

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Abstract

The invention discloses a dynamic human brain effect connection identification method based on a non-stationary dynamic Bayesian network. The method comprises the steps of obtaining the functional magnetic resonance imaging data; preprocessing the original image data by using a DPABI software package; selecting a brain region needing to identify the effect connection as a region of interest; identifying the brain effect connection of the extracted region of interest by using a non-stationary dynamic Bayesian network method; and observing a dynamic brain effect connection network constructed bythe non-stationary dynamic Bayesian network at different moments, and analyzing the brain effect connection network at different moments so as to further understand the internal mechanism and the operation mode of a brain. According to the method, the dynamic Bayesian network method is used as a basic framework method, and the dynamic human brain effect connection network changing along with timecan be constructed from the fMRI time series data.

Description

technical field [0001] The invention relates to a method for constructing a dynamic human brain effect connection network of functional magnetic resonance imaging data, in particular to a method based on a non-stationary dynamic Bayesian network. Background technique [0002] The human brain is one of the most complex systems in the known universe. In order to systematically and accurately explore the working mechanism of the human brain, brain science research must be carried out at the level of connections and networks. Functional magnetic resonance imaging (fMRI) is a non-invasive in vivo brain functional imaging technique. Because it has a reliable theoretical basis and high spatial and temporal resolution, it provides favorable conditions for experimental research in cognitive neuroscience, and at the same time provides a powerful means for understanding the human brain. In particular, the construction of human brain effect connection networks from fMRI data can help u...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/063
CPCG06N3/063Y02D10/00
Inventor 冀俊忠刘金铎
Owner BEIJING UNIV OF TECH
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