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White Matter Disruption In The Predementia Stages Of Alzheimer's Disease

Posted on:2020-03-10Degree:DoctorType:Dissertation
Country:ChinaCandidate:W ShaoFull Text:PDF
GTID:1364330578983561Subject:Neurology
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BackgroundAlzheimer's disease(AD)is a neurodegenerative,progressive and highly disabled disease.Despite decades of global efforts in the development of AD drugs,at present,there is no effective drug to cure AD or significantly delay the progression of AD.The main reason may be that it is too late to intervene when the clinical symptoms are evident and the neuronal degeneration becomes irreversible.Intervention in the early stage is more likely to achieve modified treatment of AD.The pathophysiological process of AD can occur years or even decades before dementia occur.Amnestic mild cognitive impairment(aMCI)is closely related to the risk of AD,and can be considered to be a precursor clinical stage of AD.Recently,many researchers tend to focus on subjective cognitive decline(SCD).Many studies have shown that SCD can be considered to be the preclinical stage of AD.In view of the advantages of non-invasiveness and reproducibility,magnetic resonance imaging(MRI)has become one of the main means to identify the structural and functional characteristics of the brain in the early stage of AD.A large number of studies have shown that white matter degeneration plays an important role in the occurrence and progression of AD.Even before gray matter atrophy,white matter degeneration can occur.However,the current studies which have obtained consistent results in SCD mainly focus on the exploration of the gray matter degeneration,such as the medial temporal lobe atrophy and so on.There are relatively few studies focused on the alterations of white matter microstructure in SCD,and the results are not consistent.Therefore,in this study,diffusion tensor imaging(DTI)was used to explore the changes of white matter microstructure(white matter integrity and white matter network)in SCD and aMCI.We assume that compared with the normal controls,SCD individuals will show white matter changes similar to the patterns in aMCI,and present an intermediate state between the normal elderly and aMCI.MethodsExperiment 1:22 normal controls,20 SCD patients and 25 aMCI patients were enrolled in this study.DTI images were obtained by 3T magnetic resonance scanning in all subjects.The DTI metrics of each subject were calculated,including fractional anisotropy(FA),average diffusivity(MD),radial diffusivity(DR)and axial diffusivity(DA).The atlas-based segmentation strategy and atlas-based analyses were used to compare the DTI metrics of the main white matter tracts among the three groups.The correlations between the altered diffusion metrics and cognitive performances were calculated in the SCD and aMCI groups respectively.Experiment 2:to construct and analyze the WM structural networks among the 60 participants[including 20 normal controls(NC),20 SCD patients,and 20 aMCI patients],we adopted diffusion-tensor magnetic resonance imaging and graph theory approaches.Network properties were then compared among the three groups.The relationships between the altered network properties and the neuropsychological performances were assessed in the SCD and aMCI groups.ResultsExperiment 1:The diffusion tensor metrics in the SCD group were intermediate between the aMCI group and the normal control(NC)group.Compared with the NC group,the SCD group presented disrupted integrity in the left cingulum of hippocampus(CgH).The white matter integrity of the left anterior thalamic radiation(ATR)and right corticospinal tract(CST)in the SCD group was significantly higher than that in the aMCI group.The disrupted integrity of the left ATR,the right CST and the left CgH was presented in the aMCI group.The receiver operating characteristic curve analyses revealed that the disrupted diffusion metrics exhibited the area under the curve value of 0.829(sensitivity=68%,specificity=81.82%)for the discrimination between aMCI and control participants and 0.784(sensitivity=60%,specificity=86.36%)for discrimination between SMI and control participants.In the aMCI group,the MD of the left CgH was negatively correlated with memory function,while the DR of the right CST was negatively correlated with executive function.However,there was no significant correlation in the SCD group.Experiment 2:Compared with the NC group,in the SCD group,global efficiency and local efficiency were significantly decreased,while the shortest path length was increased.The SCD group had intermediate values between those obtained in the NC and aMCI groups.Moreover,the regions located in the frontal and parietal lobes,limbic system and caudate nucleus had lower nodal efficiency in the SCD group than in the NC group,and this reduction in efficiency was similar to that observed in the aMCI group.The receiver operating characteristic curve analysis revealed that the disrupted WM network properties exhibited the area under the curve value of 0.903(sensitivity=85%,specificity=85%)for discrimination between the SMI and control participants,while 0.905(sensitivity=90%,specificity=85%)for discrimination between the MCI and control participants.In the aMCI group,the altered global efficiency and nodal efficiencies of the right anterior cingulate and paracingulategyri(ACG)and right middle frontal gyrus(MFG)was correlated with episodic memory performance.However,there was no significant relationship between network properties and neuropsychological scores in the SCD group.ConclusionsOur findings suggest that people with SCD exhibit detectable WM integrity changes and topological alterations in WM networks that are similar to those observed in people with aMCI.These results support the notion that SCD may represent an asymptomatic at-risk stage of AD and a transitional state from NC to aMCI.The early identification of disrupted WM properties could facilitate to the early detection of this preclinical stage of AD.
Keywords/Search Tags:Alzheimer's disease, amnestic mild cognitive impairment, subjective cognitive decline, white matter, diffusion tensor imaging
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