Structural Brain Connectivity in Aging and Neurodegeneration

As our life expectancy rises, the prevalence of common age-related brain diseases such as cognitive decline, dementia and neurovascular disease will increase. Effective preventive and curative interventions are scarce, whilst causative factors remain largely unknown. The role of cerebral white matter in age-related diseases has been established. However, macrostructural white matter changes, which are visible on a conventional MRI, constitute only the tip of the iceberg of the white matter pathology that have occurre

Predicting Global Cognitive Decline in the General Population Using the Disease State Index

Background: Identifying persons at risk for cognitive decline may aid in early detection of persons at risk of dementia and to select those that would benefit most from therapeutic or preventive measures for dementia. Objective: In this study we aimed to validate whether cognitive decline in the general population can be predicted with multivariate data using a previously proposed supervised classification method: Disease State Index (DSI). Methods: We included 2,542 participants, non-demented and without mild cognitive impairment at baseline, from the population-based Rotterdam Study (mean age 60.9 ± 9.1 years). Participants with significant global cognitive decline were defined as the 5% of participants with the largest cognitive decline per year. We trained DSI to predict occurrence of significant global cognitive decline using a large variety of baseline features, including magnetic resonance imaging (MRI) features, cardiovascular risk factors, APOE-ε4 allele carriership, gait features, education, and baseline cognitive function as predictors. The prediction performance was assessed as area under the receiver operating characteristic curve (AUC), using 500 repetitions of 2-fold cross-validation experiments, in which (a randomly selected) half of the data was used for training and the other half for testing. Results: A mean AUC (95% confidence interval) for DSI prediction was 0.78 (0.77–0.79) using only age as input feature. When using all available features, a mean AUC of 0.77 (0.75–0.78) was obtained. Without age, and with age-corrected features and feature selection on MRI features, a mean AUC of 0.70 (0.63–0.76) was obtained, showing the potential of other features besides age. Conclusion: The best performance in the prediction of global cognitive decline in the general population by DSI was obtained using only age as input feature. Other features showed potential, but did not improve prediction. Future studies should evaluate whether the performance could be improved by new features, e.g., longitudinal features, and other prediction methods

Genetic variation underlying cognition and its relation with neurological outcomes and brain imaging

Cognition in adults shows variation due to developmental and degenerative components. A recent genome‐ wide association study identified genetic variants for general cognitive function in 148 independent loci. Here, we aimed to elucidate possible developmental and neurodegenerative pathways underlying these genetic variants by relating them to functional, clinical and neuroimaging outcomes. This study was conducted within the population‐based Rotterdam Study (N=11,496, mean age 65.3±9.9 years, 58.0% female). We used lead variants for general cognitive function to construct a polygenic score (PGS), and additionally excluded developmental variants at multiple significance thresholds. A higher PGS was related to more years of education (β=0.29, p=4.3x10‐7 ) and a larger intracranial volume (β=0.05, p=7.5x10‐4 ). To a smaller extent, the PGS was associated with less cognitive decline (βΔG‐factor=0.03, p=1.3x10‐3 ), which became non‐significant after adjusting for education (p=1.6x10‐2 ). No associations were found with daily functioning, dementia, parkinsonism, stroke or microstructural white matter integrity. Excluding developmental variants attenuated nearly all associations. In conclusion, this study suggests that the genetic variants identified for general cognitive function are acting mainly through the developmental pathway of cognition. Therefore, cognition, assessed cross‐sectionally, seems to have limited value as a biomarker for neurodegeneration

Structural disconnectivity and the risk of dementia in the general population

Objective The disconnectivity hypothesis postulates that partial loss of connecting white matter fibers between brain regions contributes to the development of dementia. Using diffusion MRI to quantify global and tract-specific white matter microstructural integrity, we tested this hypothesis in a longitudinal population-based study. Methods Global and tract-specific fractional anisotropy (FA) and mean diffusivity (MD) were obtained in 4,415 people without dementia (mean age 63.9 years, 55.0% women) from the prospective population-based Rotterdam Study with brain MRI between 2005 and 2011. We modeled the association of these diffusion measures with risk of dementia (follow-up until 2016) and with changes on repeated cognitive assessment after on average 5.4 years, adjusting for age, sex, education, macrostructural MRI markers, depressive symptoms, cardiovascular risk factors, and APOE genotype. Results During a median follow-up of 6.8 years, 101 participants had incident dementia, of whom 83 had clinical Alzheimer disease (AD). Lower global values of FA and higher values of MD were associated with an increased risk of dementia (adjusted hazard ratio [95% confidence interval (CI)] per SD increase for MD 1.79 [1.44–2.23] and FA 0.65 [0.52–0.80]). Similarly, lower global values of FA and higher values of MD related to more cognitive decline in people without dementia (difference in global cognition per SD increase in MD [95% CI] was −0.04 [−0.07 to −0.01]). Associations were most profound in the projection, association, and limbic system tracts. Conclusions Structural disconnectivity is associated with an increased risk of dementia and more pronounced cognitive decline in the general population

Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification

Multivariate analysis of psychological dat

Hemoglobin and anemia in relation to dementia risk and accompanying changes on brain MRI

ObjectiveTo determine the long-term association of hemoglobin levels and anemia with risk of dementia, and explore underlying substrates on brain MRI in the general population.MethodsSerum hemoglobin was measured in 12,305 participants without dementia of the population-based Rotterdam Study (mean age 64.6 years, 57.7% women). We determined risk of dementia and Alzheimer disease (AD) (until 2016) in relation to hemoglobin and anemia. Among 5,267 participants without dementia with brain MRI, we assessed hemoglobin in relation to vascular brain disease, structural connectivity, and global cerebral perfusion.ResultsDuring a mean follow-up of 12.1 years, 1,520 individuals developed dementia, 1,194 of whom had AD. We observed a U-shaped association between hemoglobin levels and dementia (p = 0.005), such that both low and high hemoglobin levels were associated with increased dementia risk (hazard ratio [95% confidence interval (CI)], lowest vs middle quintile 1.29 [1.09-1.52]; highest vs middle quintile 1.20 [1.00-1.44]). Overall prevalence of anemia was 6.1%, and anemia was associated with a 34% increased risk of dementia (95% CI 11%-62%) and 41% (15%-74%) for AD. Among individuals without dementia with brain MRI, similar U-shaped associations were seen of hemoglobin with white matter hyperintensity volume (p = 0.03), and structural connectivity (for mean diffusivity, p < 0.0001), but not with presence of cortical and lacunar infarcts. Cerebral microbleeds were more common with anemia. Hemoglobin levels inversely correlated to cerebral perfusion (p < 0.0001).ConclusionLow and high levels of hemoglobin are associated with an increased risk of dementia, including AD, which may relate to differences in white matter integrity and cerebral perfusion