Brain Network Study Of Alzheimer Disease Based On Multimodal MRI

With the aging of the world's population,Alzheimer's disease(AD)has become a major threat to the elderly.At present,AD diagnosis mainly depends on psychology scales and subjective judgment of doctors,the diagnostic accuracy is insufficient.MRI provides us with a non-invasive brain disease examination.DTI can reflect the density and distribution of nerve fibers in the brain,while fMRI can reflect the brain activity of human brain.At the same time,the generation of AD is closely related to the abnormality of the brain network.However,there are currently few studies on brain networks.This paper can use multimodal MRI data to construct and explore brain networks,then find out the differences in brain networks between normal people and AD patients,and use this result to improve the diagnostic accuracy of AD.This paper completed the modeling of brain network and explored the brain network.According to the different MRI data,this paper constructed the functional connectivity network(FCN)and the DTI structural connectivity network(DTISCN).The FCN can reflect the synchrony between brain functions,and the DTISCN can reflect the actual fiber connection in the brain region.This paper linked MRI with brain network research by constructing brain network models.This paper analyzed the constructed brain network by graphical method,and extracted the parameters of nodal degree,nodal efficiency and nodal betweenness centrality of each brain region.The T-test method was used to analyze the graphical parameters of normal people and AD patients,and corresponding parameters with significant differences were used as brain network features.In addition,the support vector machine was used to verify the rationality of the selected brain network features.The two types of brain network features were selected as the standard and the K-fold cross validation method was used to train and classify the sample data.Both types of samples achieved high classification accuracy.This paper proposed the idea of constructing an AD-assisted diagnostic model based on this method.This paper proposed a method for analyzing brain networks using deep learning.This method can find features by autonomous learning,and can perform joint analysis on FCN and DTISCN.This paper constructed a new convolutional neural network model for analyzing brain networks based on the characteristics of processing data.We used the FCN and DTISCN as input to train and test the model.Through the verification of the test set,the network analysis with the model has a high accuracy.The model has the highest accuracy when combined with multimodal brain networks as input,and works better than a single network as input.This paper proposed another idea for constructing an AD-assisted diagnostic model based on this method.