Spatiotemporal relationship between subthreshold amyloid accumulation and aerobic glycolysis in the human brain.

In Alzheimer's disease, brain amyloid deposition has a distinct topography that correlates with aerobic glycolysis (AG), that is, the use of glucose beyond that predicted by oxygen consumption. The causes for this relationship remain unclear but might provide crucialinsight into how amyloid deposition begins. Here we develop methods to study the earliest topography of amyloid deposition based on amyloid imaging and investigate its spatiotemporal evolution with respect to the topography of AG in adults. We find that the spatiotemporal dynamics of amyloid deposition are largely explained by 1 factor, defined here as the amyloid topography dissimilarity index (ATDI). ATDI is bimodal, more highly dynamic during early amyloid accumulation, and predicts which individuals will cross a conservative quantitative threshold at least 3-5 years in advance. Using ATDI, we demonstrate that subthreshold amyloid accumulates primarily in regions that have high AG during early adulthood. Our findings suggest that early on-target subthreshold amyloid deposition mirrors its later regional pattern, which best corresponds to the topography of young adult brain AG.

Comparing cortical signatures of atrophy between late-onset and autosomal dominant Alzheimer disease.

Defining a signature of cortical regions of interest preferentially affected by Alzheimer disease (AD) pathology may offer improved sensitivity to early AD compared to hippocampal volume or mesial temporal lobe alone. Since late-onset Alzheimer disease (LOAD) participants tend to have age-related comorbidities, the younger-onset age in autosomal dominant AD (ADAD) may provide a more idealized model of cortical thinning in AD. To test this, the goals of this study were to compare the degree of overlap between the ADAD and LOAD cortical thinning maps and to evaluate the ability of the ADAD cortical signature regions to predict early pathological changes in cognitively normal individuals. We defined and analyzed the LOAD cortical maps of cortical thickness in 588 participants from the Knight Alzheimer Disease Research Center (Knight ADRC) and the ADAD cortical maps in 269 participants from the Dominantly Inherited Alzheimer Network (DIAN) observational study. Both cohorts were divided into three groups: cognitively normal controls (nADRC = 381; nDIAN = 145), preclinical (nADRC = 153; nDIAN = 76), and cognitively impaired (nADRC = 54; nDIAN = 48). Both cohorts underwent clinical assessments, 3T MRI, and amyloid PET imaging with either 11C-Pittsburgh compound B or 18F-florbetapir. To generate cortical signature maps of cortical thickness, we performed a vertex-wise analysis between the cognitively normal controls and impaired groups within each cohort using six increasingly conservative statistical thresholds to determine significance. The optimal cortical map among the six statistical thresholds was determined from a receiver operating characteristic analysis testing the performance of each map in discriminating between the cognitively normal controls and preclinical groups. We then performed within-cohort and cross-cohort (e.g. ADAD maps evaluated in the Knight ADRC cohort) analyses to examine the sensitivity of the optimal cortical signature maps to the amyloid levels using only the cognitively normal individuals (cognitively normal controls and preclinical groups) in comparison to hippocampal volume. We found the optimal cortical signature maps were sensitive to early increases in amyloid for the asymptomatic individuals within their respective cohorts and were significant beyond the inclusion of hippocampus volume, but the cortical signature maps performed poorly when analyzing across cohorts. These results suggest the cortical signature maps are a useful MRI biomarker of early AD-related neurodegeneration in preclinical individuals and the pattern of decline differs between LOAD and ADAD.