Effect of Magnetic Resonance Image Quality on Structural and Functional Brain Connectivity: The Maastricht Study.

In population-based cohort studies, magnetic resonance imaging (MRI) is vital for examining brain structure and function. Advanced MRI techniques, such as diffusion-weighted MRI (dMRI) and resting-state functional MRI (rs-fMRI), provide insights into brain connectivity. However, biases in MRI data acquisition and processing can impact brain connectivity measures and their associations with demographic and clinical variables. This study, conducted with 5110 participants from The Maastricht Study, explored the relationship between brain connectivity and various image quality metrics (e.g., signal-to-noise ratio, head motion, and atlas-template mismatches) that were obtained from dMRI and rs-fMRI scans. Results revealed that in particular increased head motion (R2 up to 0.169, p < 0.001) and reduced signal-to-noise ratio (R2 up to 0.013, p < 0.001) negatively impacted structural and functional brain connectivity, respectively. These image quality metrics significantly affected associations of overall brain connectivity with age (up to -59%), sex (up to -25%), and body mass index (BMI) (up to +14%). Associations with diabetes status, educational level, history of cardiovascular disease, and white matter hyperintensities were generally less affected. This emphasizes the potential confounding effects of image quality in large population-based neuroimaging studies on brain connectivity and underscores the importance of accounting for it.

Association of physical activity and sedentary time with structural brain networks-The Maastricht Study.

We assessed whether objectively measured low- and high-intensity physical activity (LPA and HPA) and sedentary time (ST) were associated with white matter connectivity, both throughout the whole brain and in brain regions involved in motor function. In the large population-based Maastricht Study (n = 1715, age 59.6 ± 8.1 (mean ± standard deviation) years, and 48% women), the amounts of LPA, HPA, and ST were objectively measured during 7 days by an activPAL accelerometer. In addition, using 3T structural and diffusion MRI, we calculated whole brain node degree and node degree of the basal ganglia and primary motor cortex. Multivariable linear regression analysis was performed, and we report standardized regression coefficients (stß) adjusted for age, sex, education level, wake time, diabetes status, BMI, office systolic blood pressure, antihypertensive medication, total-cholesterol-to-HDL-cholesterol ratio, lipid-modifying medication, alcohol use, smoking status, and history of cardiovascular disease. Lower HPA was associated with lower whole brain node degree after full adjustment (stß [95%CI] = - 0.062 [- 0.101, - 0.013]; p = 0.014), whereas lower LPA (stß [95%CI] = - 0.013 [- 0.061, 0.034]; p = 0.580) and higher ST (stß [95%CI] = - 0.030 [- 0.081, 0.021]; p = 0.250) was not. In addition, lower HPA was associated with lower node degree of the basal ganglia after full adjustment (stß [95%CI] = - 0.070 [- 0.121, - 0.018]; p = 0.009). Objectively measured lower HPA, but not lower LPA and higher ST, was associated with lower whole brain node degree and node degree in specific brain regions highly specialized in motor function. Further research is needed to establish whether more HPA may preserve structural brain connectivity.

Interplay of White Matter Hyperintensities, Cerebral Networks, and Cognitive Function in an Adult Population: Diffusion-Tensor Imaging in the Maastricht Study.

Background Lesions of cerebral small vessel disease, such as white matter hyperintensities (WMHs) in individuals with cardiometabolic risk factors, interfere with the trajectories of the white matter and eventually contribute to cognitive decline. However, there is no consensus yet about the precise underlying topological mechanism. Purpose To examine whether WMH and cognitive function are associated and whether any such association is mediated or explained by structural connectivity measures in an adult population. In addition, to investigate underlying local abnormalities in white matter by assessing the tract-specific WMH volumes and their tract-specific association with cognitive function. Materials and Methods In the prospective type 2 diabetes-enriched population-based Maastricht Study, structural and diffusion-tensor MRI was performed (December 2013 to February 2017). Total and tract-specific WMH volumes; network measures; cognition scores; and demographic, cardiovascular, and lifestyle characteristics were determined. Multivariable linear regression and mediation analyses were used to investigate the association of WMH volume, tract-specific WMH volumes, and network measures with cognitive function. Associations were adjusted for age, sex, education, diabetes status, and cardiovascular risk factors. Results A total of 5083 participants (mean age, 59 years ± 9 [standard deviation]; 2592 men; 1027 with diabetes) were evaluated. Larger WMH volumes were associated with stronger local (standardized ß coefficient, 0.065; P < .001), but not global, network efficiency and lower information processing speed (standardized ß coefficient, -0.073; P < .001). Moreover, lower local efficiency (standardized ß coefficient, -0.084; P < .001) was associated with lower information processing speed. In particular, the relationship between WMHs and information processing speed was mediated (percentage mediated, 7.2% [95% CI: 3.5, 10.9]; P < .05) by the local network efficiency. Finally, WMH load was larger in the white matter tracts important for information processing speed. Conclusion White matter hyperintensity volume, local network efficiency, and information processing speed scores are interrelated, and local network properties explain lower cognitive performance due to white matter network alterations. © RSNA, 2020 Online supplemental material is available for this article.

Cardiometabolic determinants of early and advanced brain alterations: Insights from conventional and novel MRI techniques.

Cardiometabolic risk factors may be of key importance in the development of future brain diseases like dementia or depression. However, it remains unclear how these risk factors exactly affect the brain. Advanced MR imaging methods such as, diffusion weighted and functional MRI, can provide detailed insights into subtle brain changes, and potentially into early development of disease. In this narrative review, we summarize the available evidence on the associations of cardiometabolic risk factors with subtle changes in brain MRI measures. We found clear evidence that hyperglycemia, physical inactivity, central obesity, and hypertension are associated with both structural and functional brain alterations, while the role of dyslipidemia is far less clear. However, longitudinal evidence that assesses temporality of the associations with more advanced and thus more precise brain imaging methods is needed to improve our insights into the complex etiology of brain diseases.