Robustness evaluation method for brain function super-network model

Abstract

The invention relates to an image processing technology, in particular to a robustness evaluation method based on a brain function super-network model, which is realized by adopting the following steps of: S1, preprocessing a resting state functional magnetic resonance image, and extracting an average time sequence of each brain region; s2, solving the sparse linear regression model based on a coverage group lasso method to obtain a brain function super-network model; s3, calculating attributes of the brain function super network; s4, simulating a failure process of the brain function super-network through the constructed super-network model; s5, calculating the global efficiency and the relative size of the maximum connected sub-graph according to the brain function super-network model after the fault; and S6, evaluating the robustness of the brain function super network. According to the method, on the basis of truly representing the complex multi-element interaction relationship ofthe human brain, robustness evaluation is carried out on a brain function super-network model so as to realize functional disorder simulation of the brain disease state in a complex multi-element interaction environment.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a method for evaluating the robustness of a brain function hypernetwork 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 organization while maintaining relatively normal behavior, reflecting the tolerance of the network model to failure or attack. In the study of human brain networks, 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 hyperedges in hypernetworks, when nodes are damaged, changes in network topology are more sensitive than traditional networks. However, no related studies have analyzed the robustness of brain functional ...

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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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