A classification method of fMRI data based on super-network discriminant subgraph

Abstract

The invention discloses a functional magnetic resonance image data classification method based on a supernetwork discriminant subgraph, which preprocesses the resting state functional magnetic resonance image, and extracts the average time series of each segmented brain region; uses sparse linear regression Method and sparse learning to optimize the objective function and generate a hypernetwork; extract each hyperedge in the hypernetwork as a subgraph, calculate the frequency of the subgraph, select the frequency threshold, filter the frequent subgraph, and use the frequent subgraph pattern as a feature ;Use the frequent score feature selection method on the training set, and then get the optimal feature subset and regularization parameter C based on the performance of the test set; use the classification algorithm based on the graph kernel, and use the discriminative subgraph as a feature to classify ; Quantify the importance and redundancy of the selected features. For the diagnosis of brain diseases, it not only retains the integrity of the original network topology, but also does not lose the discriminability of features, and presents higher-level and more complex interactions between 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...

AI Technical Summary

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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