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A Study Of Predicting Disease Progression In Patients With Subjective Cognitive Decline Based On Brain Network And APOEε4 Allele

Posted on:2023-06-12Degree:DoctorType:Dissertation
Country:ChinaCandidate:S M DengFull Text:PDF
GTID:1524306902489424Subject:Rehabilitation medicine and physical therapy
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OBJECTIVE:1.To determine the differences of brain functional network and white matter structual networrk at baseline between patients with progressive subjective cognitive decline(P-SCD)and patients with stable SCD(S-SCD),whether potential indicators for predicting SCD progression would be detected.2.To explore the effect of APOEε4 allele on brain functional network and white matter structural network,identifying whether the APOEε4 allele would be used as a potential predictor of SCD progression.METHODS:1.The rs-fMRI image data of 39 cases of S-SCD,15 cases of P-SCD and 45 cases of cognitive normal(CN)from Alzheimer’s Disease Neuroimaging Initiative(ADNI)were studied.The anatomical automatic labeling(AAL)atlas constructed a functional network and analyzed the topological properties by graph theory.2.The diffusion tensor imaging(DTI)image data of 35 cases of S-SCD,13 cases of P-SCD and 44 cases of CN from ADNI were studied The deterministic fiber tracking method constructed the brain white matter structural network based on the fractional anisotropy(FA)value.The graph theory was used to analyze the topological properties,and the atlas segmentation method was used to study diffusion tensor imaging parameters of fiber bundles.3.The rs-fMRI image data of 19 cases of SCD with APOEs4(SCD+),29 cases of SCD without APOEε4(SCD-)and 30 cases of CN without APOEε4(CN-)from ADNI were studied The AAL atlas constructed a functional network and used graph theory to analyze the topological properties.4.The DTI image data of 21 cases of SCD+,33 cases of SCD-and 36 cases of CN-from ADNI were studied.The deterministic fiber tracking method constructed the brain white matter structural network based on FA value.The graph theory was used to analyze the topological properties.RESULTS:1.The S-SCD group showed a more substantial small-worldness,the interconnection of adjacent nodes was reduced,and compensatory information transmission was increased.Compared with the CN group,the local efficiency of S-SCD showed an increase in bilateral posterior cingulate connections.Regarding the local efficiency of brain nodes,the local efficiency of right cerebellar Crus I connections in the P-SCD group was significantly lower than that in the S-SCD group.2.There was no significant difference in the white matter structural network at the baseline in P-SCD patients.However,the axial diffusion(Da)in the right cingulate of the hippocampus,the bilateral superior longitudinal fasciculus and the right frontal minor was lower than that in the CN group.3.The SCD+group had increased compensatory information transfer speed and enhanced integration capability for the brain functional network.This group also had high heterogeneity for intracerebral node characteristics,mainly in the default mode network,left superior occipital gyrus,and bilateral putamen.4.For brain structural network,the clustering coefficient and local efficiency of SCD+and SCD-groups were significantly lower than the CN-group.There were significant differences in some brain regions for intracerebral node characteristics,mainly in the default mode network,occipital lobe,temporal lobe,and subcortical regions.CONCLUSION:Compensatory brain functional network changes are found in SCD patients,especially in the bilateral posterior cingulate gyrus,right cerebellar Crus I area may be a potential recognition area of SCD progression;P-SCD patients have white matter microstructure damage at baseline,Da may be potential imaging biomarkers to predict the progress of SCD;APOEε4 allele has effects on brain functional and structural network in SCD patients,may be used as a potential indicator to predict the progress of SCD.
Keywords/Search Tags:SCD, graph theory, brain functional network, brain structural network, APOEε4 allele, diffusion tensor imaging parameters
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