Dincer, Aylin

AbstractBackgroundTriplication of the APP allele in Down syndrome (DS) leads to excess amyloid production and Alzheimer’s disease (AD) related cognitive decline. Key biomarkers (amyloid, tau, neurodegeneration) can identify pathological processes that occur before clinical symptoms and more precisely stage adults with DS along the AD continuum. Previous research has used changes in cortical thickness (CT) as an indicator of neurodegeneration in autosomal‐dominant AD1, but not DS‐related AD. It is unclear when and where cortical thinning occurs along the AD continuum in DS and if these differences are associated with increasing AD pathology.MethodCross‐sectional analysis compared DS participants from the Alzheimer Biomarkers Consortium‐Down Syndrome (106 amyloid‐/cognitively unimpaired, nonclinical, 20% reserved for receiver‐operating characteristic, ROC, analyses; 45 amyloid+/cognitively unimpaired, preclinical; 27 amyloid+/cognitively impaired, clinical) who had amyloid positron emission tomography and magnetic resonance imaging (MRI). Amyloid positivity was defined using standardized uptake value ratio (partial volume corrected) cortical mean values (1.42/1.19 for PIB/AV452) and impairment was based on consensus diagnosis. CT differences between nonclinical and clinical groups were calculated in FreeSurfer v5.3 for multiple thresholds (p<.05/.01/.005/.001/.0005/.0001) using a cluster‐wise false discovery rate of .001, corrected across hemispheres, after controlling for age and sex. CT values were extracted for each threshold and used in ROC analyses (nonclinical reserved participants compared against preclinical and preclinical compared against clinical) to identify key cortical regions that best discriminated amyloid positivity and between unimpaired and impaired.ResultSignificant decreases in parietal and inferior temporal CT were observed between nonclinical and clinical (Figures 1 and 2). ROC analyses identified regions in the left (threshold .05) and right (threshold .005) hemispheres that best differentiated nonclinical from preclinical. Observed area under the curves (AUCs) of .772 and .830 exceeded those previously reported for autosomal‐dominant AD (.547/.540)1. Analyses differentiating preclinical from clinical observed AUCs of .786 for left (threshold .0005) and .770 for right (threshold .0005) hemispheres.ConclusionCT differences primarily within posterior cortical regions differentiated adults with DS by amyloid status and cognitive impairment. CT differences were more diffuse for amyloid positivity but more focal for impairment.1. Dincer (2020). NeuroImage: Clinical, 28, 102491.2. Su (2019). Alzheimer’s Dement, 11, 180‐190.

Abstract Neurofilament light chain, a putative measure of neuronal damage, is measurable in blood and CSF and is predictive of cognitive function in individuals with Alzheimer’s disease. There has been limited prior work linking neurofilament light and functional connectivity, and no prior work has investigated neurofilament light associations with functional connectivity in autosomal dominant Alzheimer’s disease. Here, we assessed relationships between blood neurofilament light, cognition, and functional connectivity in a cross-sectional sample of 106 autosomal dominant Alzheimer’s disease mutation carriers and 76 non-carriers. We employed an innovative network-level enrichment analysis approach to assess connectome-wide associations with neurofilament light. Neurofilament light was positively correlated with deterioration of functional connectivity within the default mode network and negatively correlated with connectivity between default mode network and executive control networks, including the cingulo-opercular, salience, and dorsal attention networks. Further, reduced connectivity within the default mode network and between the default mode network and executive control networks was associated with reduced cognitive function. Hierarchical regression analysis revealed that neurofilament levels and functional connectivity within the default mode network and between the default mode network and the dorsal attention network explained significant variance in cognitive composite scores when controlling for age, sex, and education. A mediation analysis demonstrated that functional connectivity within the default mode network and between the default mode network and dorsal attention network partially mediated the relationship between blood neurofilament light levels and cognitive function. Our novel results indicate that blood estimates of neurofilament levels correspond to direct measurements of brain dysfunction, shedding new light on the underlying biological processes of Alzheimer’s disease. Further, we demonstrate how variation within key brain systems can partially mediate the negative effects of heightened total serum neurofilament levels, suggesting potential regions for targeted interventions. Finally, our results lend further evidence that low-cost and minimally invasive blood measurements of neurofilament may be a useful marker of brain functional connectivity and cognitive decline in Alzheimer’s disease.

AbstractBackgroundTau PET imaging is an established tool for studying Alzheimer Disease but its sensitivity to primary age‐related tauopathy (PART) as well as sex effects in individuals without abnormal levels of amyloid are unknown.MethodsCross‐sectional data were collected from 2014 to 2019 in 144 cognitively normal younger, middle‐aged, and older adults free from amyloid pathology as quantified using PET. Regional values of tau PET binding were measured using flortaucipir. Statistical models examined the main effects of increasing age, sex, race, and sub‐threshold levels of amyloid. Secondary analyses also examined the relationship between tau PET binding and iron as measured using T2* weighted imaging and calcification as measured using CT.ResultsThere were significant positive associations between tau binding and age in 19 regions, with the largest effects seen in the inferior temporal cortex (t=4.59), caudate (t=9.58), and putamen (t=12.57). Iron as measured using T2* imaging mediated only a modest (11.9%) amount of the association between age and tau binding. Elevated tracer uptake in females was present in 23 regions in frontal, lateral parietal, and temporal regions including, most prominently the rostral middle frontal gyrus (t=7.48), superior temporal gyrus (t=5.12) and the inferior temporal gyrus (t=4.22). There were no significant effects of race or sub‐threshold levels of amyloid.ConclusionsTau PET is sensitive to primary effects of age in regions consistent with the neuropathological definition of PART although strong age effects in regions of off‐target binding were also present. There was a robust effect of sex, suggesting prior observations of elevated binding in women is not solely a potentiation of Alzheimer pathology but instead represents an ubiquitous phenomenon. Understand how both sex and age impact tracer binding is critical to understanding the utility of PET tracers as well as interpreting tracer values in the context of disease.

AbstractBackgroundTau PET imaging is an established tool for studying Alzheimer Disease but its sensitivity to primary age‐related tauopathy (PART) as well as sex effects in individuals without abnormal levels of amyloid are unknown.MethodsCross‐sectional data were collected from 2014 to 2019 in 144 cognitively normal younger, middle‐aged, and older adults free from amyloid pathology as quantified using PET. Regional values of tau PET binding were measured using flortaucipir. Statistical models examined the main effects of increasing age, sex, race, and subthreshold levels of amyloid. Secondary analyses also examined the relationship between tau PET binding and iron as measured using T2* weighted imaging and calcification as measured using CT.ResultsThere were significant positive associations between tau binding and age in 19 regions, with the largest effects seen in the inferior temporal cortex (t=4.59), caudate (t=9.58), and putamen (t=12.57). Iron as measured using T2* imaging mediated only a modest (11.9%) amount of the association between age and tau binding. Elevated tracer uptake in females was present in 23 regions in frontal, lateral parietal, and temporal regions including, most prominently the rostral middle frontal gyrus (t=7.48), superior temporal gyrus (t=5.12) and the inferior temporal gyrus (t=4.22). There were no significant effects of race or subthreshold levels of amyloid.ConclusionsTau PET is sensitive to primary effects of age in regions consistent with the neuropathological definition of PART although strong age effects in regions of off‐target binding were also present. There was a robust effect of sex, suggesting prior observations of elevated binding in women is not solely a potentiation of Alzheimer pathology but instead represents an ubiquitous phenomenon. Understand how both sex and age impact tracer binding is critical to understanding the utility of PET tracers as well as interpreting tracer values in the context of disease.

AbstractIntroductionMachine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease.MethodsLongitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non‐carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein ε4 (APOE ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status.ResultsThe Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R2 = 0.95), fluorodeoxyglucose (R2 = 0.93), and atrophy (R2 = 0.95) in mutation carriers compared to non‐carriers.DiscussionResults suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions.

Abstract Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer’s disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers. However, it remains unclear when and how grey matter network integrity changes with disease progression. We investigated these questions in autosomal dominant Alzheimer’s disease mutation carriers, whose conserved age at dementia onset allows individual staging based upon their estimated years to symptom onset. From the Dominantly Inherited Alzheimer Network observational cohort, we selected T1-weighted MRI scans from 269 mutation carriers and 170 non-carriers (mean age 38 ± 15 years, mean estimated years to symptom onset −9 ± 11), of whom 237 had longitudinal scans with a mean follow-up of 3.0 years. Single-subject grey matter networks were extracted, and we calculated for each individual the network properties which describe the network topology, including the size, clustering, path length and small worldness. We determined at which time point mutation carriers and non-carriers diverged for global and regional grey matter network metrics, both cross-sectionally and for rate of change over time. Based on cross-sectional data, the earliest difference was observed in normalized path length, which was decreased for mutation carriers in the precuneus area at 13 years and on a global level 12 years before estimated symptom onset. Based on longitudinal data, we found the earliest difference between groups on a global level 6 years before symptom onset, with a greater rate of decline of network size for mutation carriers. We further compared grey matter network small worldness with established biomarkers for Alzheimer disease (i.e. amyloid accumulation, cortical thickness, brain metabolism and cognitive function). We found that greater amyloid accumulation at baseline was associated with faster decline of small worldness over time, and decline in grey matter network measures over time was accompanied by decline in brain metabolism, cortical thinning and cognitive decline. In summary, network measures decline in autosomal dominant Alzheimer’s disease, which is alike sporadic Alzheimer’s disease, and the properties show decline over time prior to estimated symptom onset. These data suggest that single-subject networks properties obtained from structural MRI scans form an additional non-invasive tool for understanding the substrate of cognitive decline and measuring progression from preclinical to severe clinical stages of Alzheimer’s disease.