| Brain aging refers to the phenomenon that the structure and function of brain tissue gradually decline with age,and its physiological mechanism is still unclear.Studies have shown that degenerative brain diseases,such as Alzheimer’s disease(AD)and Parkinson’s disease(PD),are closely related to brain aging.The study of brain aging is expected to better reveal the mechanism of brain degeneration in clinics.In recent years,with the development of neuroimaging technology,structural magnetic resonance imaging(s MRI),positron emission computed tomography(PET),etc.have been widely used in brain aging research.Previous studies have shown that the development of brain aging follows their own specific patterns,reflecting the change trajectory of brain structure and function.These specific patterns can be used as biomarkers of brain aging.In turn,researchers use data-driven methods to explore specific patterns.Common data-driven methods include Statistical parametric mapping(SPM)analysis,The Scaled Subprofile Model/Principal Component Analysis(SSM/PCA)analysis and Brain network analysis,etc.Among them,SPM analysis is a voxel-by-voxel univariate analysis method and good at extracting detailed features;As a multivariate analysis method,SSMPCA can consider the interaction between brain regions;Brain network analysis can intuitively reflect the connections between brain regions from the perspective of the whole brain.These three methods can explore the changes of brain metabolic function/structural from different scales of voxel,regional and whole brain.Therefore,combining the three methods can better understand the metabolic function/structural patterns of brain aging.This study includes three parts of research:(1)Research on Age-related Pattern of Aβ(Aβ-ARP).This research collected the 18Fflorbetapir(18F-AV-45)PET image data of 207 cognitively normal(CN)subjects from the Alzheimer’s Disease Neuroimaging Initiative(ADNI)database and 76 CN subjects from Xuanwu Hospital.SPM analysis was conducted based on the AV-45 standardized uptake value ratio(SUVR)images of ADNI subjects to construct Aβ-ARP.Briefly,with gray matter volume,gender,and years of education as covariates,partial correlation analysis was used to calculate the correlation coefficient between SUVR and age voxel by voxel to construct Aβ-ARP.Then,the Aβ-ARP was verified by Xuanwu subjects.The results revealed that there was a healthy aging pattern characterized by age-associated longitudinal changes of Aβ deposition,which was mainly distributed in the right middle and inferior temporal gyrus,the right temporal pole: middle temporal gyrus,the right inferior occipital gyrus,the right inferior frontal gyrus(triangular portion),and the right precentral gyrus.(2)Research on Age-related Pattern of Glucose metabolism(GM-ARP).We collected the 18Ffluorodeoxyglucose(18F-FDG)PET image data of 93 CN subjects from ADNI database and 41 CN subjects from Xuanwu Hospital.SSM/PCA analysis was conducted on the PET data of the combined 134 CN subjects,and multiple linear regression analysis was used to pick out the principal components with the greatest correlation with their age to construct GM-ARP.Then,the repeatability of the pattern was verified by CN groups of two independent databases.The results showed that GM-ARP was characterized by hypometabolism with the aging in the left supplementary motor area,medial superior frontal gyrus,left anterior cingulate and paracingulate gyri,caudate nucleus,dorsal thalamus and part of superior cerebellum,and hypermetabolism with the aging in the orbital part of superior frontal gyrus,ventral thalamus and part of inferior cerebellum.These findings reveal the characteristics of changes in glucose metabolism during brain aging and can be used to distinguish brain aging from brain pathological aging.(3)Research on PD-related Pattern of Glucose metabolism(PET-PDRP)and PD-related Pattern of Gray matter(MRI-PDRP).PD is one of the important pathological changes of abnormal brain aging.Studying the specific patterns of PD can help us better understand brain aging.We collected the 18F-FDG PET and T1 MRI image data of 20 PD patients and 20 age,sex-matched healthy control(HC)subjects from Huashan Hospital.SSM/PCA analysis was conducted on their PET and MRI data,and multiple logistic regression analysis was used to pick out the principal components that can distinguish PD patients from HC subjects to the greatest extent,to construct PET-PDRP and MRI-PDRP,respectively.Then,PET-PDRP and MRI-PDRP was verified by other 15 PD subjects of Huashan Hospital.The results showed that PET-PDRP was characterized by relative increased metabolic activity in pallidothalamic,pons,putamen,and cerebellum,associated with metabolic decreased in parietal-occipital areas.MRI-PDRP was characterized by relative decreased gray matter volume in pons,transverse temporal gyrus,left cuneus,right superior occipital gyrus and right superior parietal lobule,associated with preservation in gray matter volume in pallidum and putamen.In addition,both PDRPs were verified by the connectome analysis.In summary,based on the three methods,this study proved the existence of Aβ-ARP and GMARP,and verified the reproducibility of these patterns in Chinese and Western populations;proved the existence of PET-PDRP and MRI-PDRP,discovered the correlation between the two PDRPs,and verified their effectiveness at the functional network and structural network levels.These studies provided a new perspective for the subsequent study of brain aging. |
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