Method for analyzing influence of gene on multi-modal brain image phenotype
A multimodal and brain imaging technology, applied in the field of image analysis based on image genetics, can solve problems such as ignoring multiple relationships between multiple vertices, and achieve the effect of strong correlation
Pending Publication Date: 2022-06-03
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Finally, the structural information obtained by traditional graph-based feature extraction methods only captures pairwise relationships while ignoring the multiple interrelationships among multiple vertices.
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[0061] The present invention will be further described in detail below in conjunction with the accompanying drawings.
[0062] The present invention provides a method for the effect of genes on multimodal brain imaging phenotypes. First, a graph diffusion method is proposed to enhance the similarity measure between samples given the multimodal phenotype. The method utilizes multiple input similarity graphs and fuses them into a unified graph with valuable geometric structures between different imaging phenotypes; The graph regularization term is used to combine cross-modal information to establish a hypergraph regularization multimodal learning model based on graph diffusion, and an optimization strategy is designed to solve this model and obtain gene-related imaging phenotypes. In the validation part, the above algorithms are tested on the ADNI dataset with MATLAB software. The overall flow chart of the present invention is as follows figure 1 shown, including the following...
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The invention discloses a method for analyzing the influence of a gene on a multi-modal brain image phenotype, and the method comprises the steps: firstly, providing a graph diffusion method which is used for enhancing the similarity measurement between samples under the condition that the multi-modal phenotype is given, and fusing a plurality of input similarity graphs into a unified graph with geometric structures between different imaging phenotypes; secondly, representing a high-order similarity relationship among samples by using a unified graph, establishing a hypergraph regularization multi-modal learning model based on graph diffusion by combining a hypergraph regularization item with cross-modal information, and designing an optimization strategy to solve the model to obtain an imaging phenotype related to a gene; and finally, fusing phenotypic features selected from different modes by adopting a multi-core support vector machine. According to the method, the image phenotype data can be fully utilized, and the influence of the gene on the multi-modal brain image phenotype can be effectively analyzed.
Description
technical field [0001] The invention belongs to the field of image analysis based on imaging genetics, and particularly relates to a method for analyzing the influence of genes on multimodal brain imaging phenotypes. Background technique [0002] Brain imaging genetics (brain imaging genetics or brain imaging genomics) is a combination of multimodal neuroimaging and genetic methods to detect genetic variations in brain structure and function that affect behaviors such as cognition and emotion regulation. It uses brain imaging technology to evaluate the influence of genes on individuals by taking the structure and function of the brain as phenotypes, and explores how genes affect the neural structure and function of the brain. To study the correlation between genetics and brain structure and function, to build a visible bridge between "genes and brain" and "brain and behavior". [0003] In recent research work, some literatures have pointed out that integrating multimodal br...
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Login to View More IPC IPC(8): G06K9/62G06V10/764G06V10/74
CPCG06F18/22G06F18/2411Y02A90/10
Inventor 汪美玲张道强
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS