Functional magnetic resonance image data classification method based on super-network discriminant subgraphs

Abstract

The invention discloses a functional magnetic resonance image data classification method based on super-network discriminant subgraphs. The method includes the steps of preprocessing a resting-state functional magnetic resonance image, and extracting the average time series of divided brain regions; using a sparse linear regression method and sparse learning to optimize an objective function to generate a super-network; extracting each super-edge in the super-network as a sub-graph, calculating the frequency of each sub-graph, selecting a frequency threshold, filtering the frequent sub-graphs, and taking a frequent sub-graph mode as a feature; in a training set, adopting a frequent score feature selection method, and then obtaining an optimal feature subset and a regularization parameter C based on the performance of a test set; adopting a classification algorithm based on a graph kernel and using discriminant subgraphs as features for classification; and quantifying the importance and redundancy of the selected features. For the diagnosis of brain diseases, the method of the invention retains the integrity of an original network topology, without the loss of discrimination of the features, and shows a higher level and more complex interaction among the brain regions.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a method for classifying functional magnetic resonance image data based on a supernetwork discriminant subgraph. Background technique [0002] The combination of functional magnetic resonance imaging technology and complex network theory has become one of the hot spots in the field of brain science and is widely used in various researches. This method realizes the mining and modeling of the underlying working mechanism of the human brain, and has achieved many surprising results. However, due to the limitations of its own principles, there are currently methodological limitations. [0003] The traditional functional connection network method is based on the pairwise correlation between different brain regions, so the correlation-based method can only capture the information between pairs of brain regions, so it cannot fully reflect the interaction between multiple brain r...

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Problems solved by technology

The features extracted in this way lose the topological structure information between brain regions, and cannot effectively explain the structural changes of the brain network.

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