Automatic Parkinson's disease identification method based on multimode hyperlinks network modeling

Abstract

The invention provides an automatic Parkinson's disease identification method based on multimode hyperlinks network modeling. The method includes: the DTI structure connection is used as the constraint and fused into the building process of an fMRI brain function network to build a multimode hyperlinks network model; node degree, edge degree and fit degree are extracted according hypernet featuresto serve as the original feature set, a multitask feature selection method (semi-M2TFS) is used to perform optimal feature subset screening on the original feature set to obtain the feature subset indicating the maximum difference degree between a Parkinson's disease patient and a normal person; a multi-core support vector machine pattern classifier is trained according to the optimal feature setand applied to Parkinson's disease patient classification diagnosis. Compared with an existing single-mode hyperlinks network modeling method, the method has the advantages that the multimode hyperlinks network can truly reflect the brain function connection mechanism and is excellent in classification identification accuracy and significant to the assisting of Parkinson's disease clinical diagnosis and automatic identification.

Description

Technical field [0001] The invention provides a multi-modal brain network modeling method based on Diffusion Tensor Imaging (DTI) and Functional Magnetic Resonance Imaging (fMRI), which relates to pattern classification and machine learning, and belongs to signal processing And the field of pattern recognition technology. Background technique [0002] Parkinson's disease (PD) is a common degenerative disease of the nervous system. Its clinical manifestations include motor retardation, resting tremor, muscle rigidity, abnormal gait and posture, hypoosmia, depression and other symptoms. Its etiology and mechanism have been so far unknown. At present, the clinical diagnosis of Parkinson's mainly depends on physical examination, medical history, and clinical manifestations of patients. However, when most patients are diagnosed, they are already in the advanced stage, making most Parkinson's patients miss the best treatment period. Therefore, the development of Parkinson's early di...

AI Technical Summary

Problems solved by technology

[0004] The above research methods analyze the brain network model of PD patients from the perspective of DTI and fMRI respectively, but DTI only evaluates the microscopic function of the white matter, and cannot measure its macroscopic changes; while BOLD-fMRI mainly examines the functional state of the gray matter cortex , it is impossible to evaluate the functional changes of white matter in the brain, so the single-modal brain network can only reflect one side information of the brain, and cannot integrate the complementary information provided by different modal data

Images