应用多模态磁共振影像基于多层感知机的轻度认知障碍分类识别方法OA

Objective Mild cognitive impairment(MCI)is the prodromal stage of Alzheimer's disease(AD).MCI includes stable mild cognitive impairment(sMCI)and progressive mild cognitive impairment(pMCI).It is very important to distinguish sMCI and pMCI accurately for the early diagnosis and intervention of AD.Methods In this paper,a multi-modal and multi-atlas MCI classification recognition method based on multi-layer perceptron was proposed to differentiate pMCI from sMCI.Applying FreeSurfer,the morphological characteristic parameters derived from structural magnetic resonance imaging(sMRI)data were achieved including average cortical thickness,surface area,gray matter volume,sulcal depth,and folding index,as well as the average voxel intensity of specific anatomical regions.Adopting DPABI,the functional characteristic parameters derived from resting state functional MRI(rs-fMRI)data were obtained including amplitude of low frequency fluctuations(ALFF)and regional homogeneity(ReHo).In addition,ridge regression was applied for characteristic parameters selection,and the brain regions with the most distinguishing features were determined based on the coefficients matrix of ridge regression output.Results The proposed method was cross-validated by 10 fold on the ADNI dataset,integrating the characteristic parameters from two modalities of sMRI and rs-fMRI,the accuracy of distinguishing between sMCI and pMCI was 82.7%,with an AUC of 93.0%.In addition,it was found that the thickness and volume of entorhinal cortex and temporal lobe,and the mean value of voxel intensity of hippocampus and parahippocampal gyrus had significant differences between groups.Conclusions The proposed method has the potential to distinguish pMCI from sMCI.The performance of MCI classification recognition can be significantly improved by incorporating modality features of structural MRI and rs-fMRI and integrating multi-atlas.