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A Robustness Evaluation Method of Brain Function Supernetwork Model

A technology of hypernetwork, brain function, applied in the field of image processing

Active Publication Date: 2022-07-08
TAIYUAN UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

As far as the author knows, there is no relevant report on the robustness analysis of brain function hypernetworks

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  • A Robustness Evaluation Method of Brain Function Supernetwork Model
  • A Robustness Evaluation Method of Brain Function Supernetwork Model
  • A Robustness Evaluation Method of Brain Function Supernetwork Model

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Embodiment Construction

[0055] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

[0056] In the description of the present invention, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", The orientations or positional relationships indicated by "horizontal", "top", "bottom", "inside", "outside", etc. are based on the orientations or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the present invention and simplifying the description, rather than An indication or implication that the referred device or element must have a particular orientati...

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Abstract

The invention relates to an image processing technology, in particular to a robustness evaluation method based on a brain function super-network model. The method is realized by the following steps: Step S1: Preprocessing a resting state functional magnetic resonance image, and extracting The average time series of each brain region; Step S2: solve the sparse linear regression model based on the covering group lasso method, thereby obtaining the brain function supernetwork model; Step S3: calculate the properties of the brain function supernetwork; The network model simulates the failure process of the brain function super-network; Step S5: Calculate the global efficiency and the relative size of the maximum connected subgraph according to the brain function super-network model after the failure; Step S6: Evaluate the robustness of the brain function super-network. This method evaluates the robustness of the brain function super-network model on the basis of truly representing the complex multivariate interactive relationship of the human brain, so as to realize the functional disorder simulation of brain disease states in the complex multivariate interactive environment.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a robustness evaluation method of a brain function super-network model. Background technique [0002] The robustness of a complex network refers to the ability of a system to respond to changes in external conditions or internal organizations while maintaining relatively normal behavior, reflecting the network model's tolerance for failures or attacks. In human brain network research, robustness can simulate the pathogenesis of brain diseases from the perspective of network models. [0003] Existing studies have applied hypergraph theory to brain functional networks to more realistically represent brain interval interactions. Due to the characteristics of super-edges in super-networks, when nodes are damaged, changes in network topology are more sensitive than traditional networks. However, no relevant studies have analyzed the robustness of brain function ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T3/00G06T5/20G06T7/00G06T7/11G06T7/187G06K9/62G06F30/20G06V10/762G06F119/02
CPCG06T7/11G06T7/0012G06T7/187G06T5/20G06F30/20G06T2207/10088G06T2207/30016G06F2119/02G06F18/23G06T3/02
Inventor 李瑶程忱李鹏祖张程瑞闻敏郭浩
Owner TAIYUAN UNIV OF TECH
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