Spatial heterogeneity in the accumulation of amyloid-β plaques throughout the brain during asymptomatic as well as clinical stages of Alzheimer disease calls for precise localization and quantification of this protein using PET imaging. To address this need, we have developed and evaluated a technique that quantifies the extent of amyloid-β pathology on a millimeter-by-millimeter scale in the brain with unprecedented precision using data from PET scans. Methods: An intermodal and intrasubject registration with normalized mutual information as the cost function was used to transform all FreeSurfer neuroanatomic labels into PET image space, which were subsequently used to compute regional SUV ratio (SUVR). We have evaluated our technique using postmortem histopathologic staining data from 52 older participants as the standard-of-truth measurement. Results: Our method resulted in consistently and significantly higher SUVRs in comparison to the conventional method in almost all regions of interest. A 2-way ANOVA revealed a significant main effect of method as well as a significant interaction effect of method on the relationship between computed SUVR and histopathologic staining score. Conclusion: These findings suggest that processing the amyloid-β PET data in subjects' native space can improve the accuracy of the computed SUVRs, as they are more closely associated with the histopathologic staining data than are the results of the conventional approach.
Non-linear relations of brain amyloid beta (Aβ) with task- based functional connectivity (tbFC) measured with functional magnetic resonance imaging (fMRI) have been reported in late middle age. Our objective was to examine the association between brain Aβ and resting-state functional connectivity (rsFC) in late middle-aged adults. Global brain Aβ burden was ascertained with
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F-Florbetaben Positron Emission Tomography (PET); rsFC was ascertained on 3T Magnetic Resonance Imaging (MRI) among 333 late middle-aged Hispanics adults without dementia in four major brain functional connectivity networks: default mode network (DMN), fronto-parietal control network (FPC), salience network (SAL) and dorsal attention network (DAN). We examined the relationship of global brain Aβ with rsFC using multivariable linear regression adjusted for age, sex, education, and APOE-ε4 genotype. We quantified the non-linear associations both with quadratic terms and by categorizing Aβ into three groups: low Aβ, intermediate Aβ, and positive Aβ. We found no significant linear or non-linear associations between Aβ, measured either continuously or categorically, with rsFC in the examined networks. Our null findings may be explained by the younger age of our participants in whom amyloid burden is relatively low. It is also possible that the recently reported non-linear relationship is exclusive to task fMRI and not rsfMRI.
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