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Frauke Beyer

About Frauke Beyer:

Max-Planck-Institut für Kognitions- und Neurowissenschaften
Frauke Beyer
Stephanstraße 1a
04103 Leipzig

LHA-ID: 7WATTN7WMF-7

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ASSOCIATION OF HIPPOCAMPAL VOLUMES WITH COGNITIVE TASKS IN A LARGE POPULATION-BASED COHORT
LIFE- Leipzig Research Center for Civilization Diseases

Abstract (Expand)

Background: Hippocampal volume, assessed via high-resolution MRI, is associated with memory and visuospatial performance in humans (Squire, 2004) and specifically prone to develop atrophy with age … (Apostolova,2015). This process has been linked to neurodegenerative diseases, such as Alzheimer’s disease (Apostolova,2015) and a decline of cognitive functions (Bruno,2016). However, due to differences in study-design and characteristics certain heterogeneity in results remains, in particular considering subfieldspecific effects (deFlores,2015). Therefore, we aim to determine the association of volumes of the whole hippocampus and its subfields on cognition in a large population-based cohort. Methods: Subjects: 1956 healthy participants from the Leipzig Research-Center-for-Civilization-Disease, aged 19-82years with MRI and neuropsychological tests (mean-age=57.61,±15.08SD). Exclusion: stroke, major-brain-pathologies, central-nervous-medication. Independent Variables: Volume of hippocampus and its subfields (CornuAmmonis1, 2-3, 4-DentateGyrus,(Pre-)subiculum). Dependent Variables: Verbal word-list learning, verbal-fluency, TrailMakingTask-(TMT)-A&B. Covariates: sex, age, years-of-education, total grey-mattervolume Image Analysis on high-resolution T1-images assessed at 3T. Hippocampal volumes were estimated using automatic segmentation analysis implemented in FreeSurfer (www.freesurfer.net). Statistical Analysis: Independent and dependent variables were first entered into Pearson Correlations. Variables with a correlation coefficient of r>0.1 were entered into multiple linear-regressions and adjusted for potential confounding(forward inclusion-model). Results: According to bivariate correlations, better performance in verbal-learning, verbal-fluency and TMT-A&B correlated moderately with larger whole-hippocampal volume and the volumes of all subfields(all |r|>0.102, all p0.046, all p0.5). Conclusions: Using a large cross-sectional cohort of healthy adults we found that volumes of the whole-hippocampus and subfields covering the CA4/dentate-gyrus region were weakly, yet specifically associated with verbal-learning and spatial processing-speed. Our preliminary results are in line with previous studies presuming a differential involvement of the hippocampus in tasks of verbal-learning and spatial processing (Oosterman,2010). Upcoming analyses implementing parcellation along the anteriorposterior- axis and random-effect-models might help to further disentangle these effects.

Authors: Sebastian Huhn, Rui Zhang, Frauke Beyer, L. Lampe, Tobias Luck, Steffi Gerlinde Riedel-Heller, M. L. Schroeter, Markus Löffler, M. Stumvoll, A. Villringer, A. V. Witte

Date Published: 1st Jul 2017

Journal: Elsevier BV

Human Diseases: cognitive disorder, dementia

Higher body mass index is associated with reduced posterior default mode connectivity in older adults.
LIFE Adult

Abstract (Expand)

Obesity is a complex neurobehavioral disorder that has been linked to changes in brain structure and function. However, the impact of obesity on functional connectivity and cognition in aging humans … is largely unknown. Therefore, the association of body mass index (BMI), resting-state network connectivity, and cognitive performance in 712 healthy, well-characterized older adults of the Leipzig Research Center for Civilization Diseases (LIFE) cohort (60-80 years old, mean BMI 27.6 kg/m(2) +/- 4.2 SD, main sample: n = 521, replication sample: n = 191) was determined. Statistical analyses included a multivariate model selection approach followed by univariate analyses to adjust for possible confounders. Results showed that a higher BMI was significantly associated with lower default mode functional connectivity in the posterior cingulate cortex and precuneus. The effect remained stable after controlling for age, sex, head motion, registration quality, cardiovascular, and genetic factors as well as in replication analyses. Lower functional connectivity in BMI-associated areas correlated with worse executive function. In addition, higher BMI correlated with stronger head motion. Using 3T neuroimaging in a large cohort of healthy older adults, independent negative associations of obesity and functional connectivity in the posterior default mode network were observed. In addition, a subtle link between lower resting-state connectivity in BMI-associated regions and cognitive function was found. The findings might indicate that obesity is associated with patterns of decreased default mode connectivity similar to those seen in populations at risk for Alzheimer's disease. Hum Brain Mapp 38:3502-3515, 2017. (c) 2017 Wiley Periodicals, Inc.

Authors: Frauke Beyer, S. Kharabian Masouleh, J. M. Huntenburg, L. Lampe, Tobias Luck, Steffi Gerlinde Riedel-Heller, Markus Löffler, M. L. Schroeter, M. Stumvoll, A. Villringer, A. V. Witte

Date Published: 12th Apr 2017

Journal: Hum Brain Mapp

Human Diseases: obesity

Predicting brain-age from multimodal imaging data captures cognitive impairment.
LIFE Adult

Abstract (Expand)

The disparity between the chronological age of an individual and their brain-age measured based on biological information has the potential to offer clinically relevant biomarkers of neurological … syndromes that emerge late in the lifespan. While prior brain-age prediction studies have relied exclusively on either structural or functional brain data, here we investigate how multimodal brain-imaging data improves age prediction. Using cortical anatomy and whole-brain functional connectivity on a large adult lifespan sample (N=2354, age 19-82), we found that multimodal data improves brain-based age prediction, resulting in a mean absolute prediction error of 4.29 years. Furthermore, we found that the discrepancy between predicted age and chronological age captures cognitive impairment. Importantly, the brain-age measure was robust to confounding effects: head motion did not drive brain-based age prediction and our models generalized reasonably to an independent dataset acquired at a different site (N=475). Generalization performance was increased by training models on a larger and more heterogeneous dataset. The robustness of multimodal brain-age prediction to confounds, generalizability across sites, and sensitivity to clinically-relevant impairments, suggests promising future application to the early prediction of neurocognitive disorders.

Authors: F. Liem, G. Varoquaux, J. Kynast, Frauke Beyer, S. Kharabian Masouleh, J. M. Huntenburg, L. Lampe, M. Rahim, A. Abraham, R. C. Craddock, Steffi Gerlinde Riedel-Heller, Tobias Luck, Markus Löffler, M. L. Schroeter, A. V. Witte, A. Villringer, D. S. Margulies

Date Published: 1st Mar 2017

Journal: Neuroimage

Human Diseases: Not specified

Diabetes and hypertension contribute to normal-appearing white matter microstructural variability in the brain
LIFE- Leipzig Research Center for Civilization Diseases

Abstract (Expand)

Background and objectives: Obesity has been associated with increased risk of dementia. Grey and white matter (WM) of the brain are commonly used as biomarkers for early detection of dementia. However, … considering WM, available neuroimaging studies had mainly small sample size and yielded less conclusive results (Kullmann et al., 2015). Recently, a positive correlation between obesity and fractional anisotropy (FA) in a middle age group was reported (Birdsill et al. 2017). Furthermore, obesity is related to many medical problems such as diabetes and hypertension. Diabetes and hypertension were found to be correlated with brain structures independently (de Leeuw et al., 2002; Weinstein et al., 2015). Yet, studies rarely investigated non-lesion WM microstructure and its association with diabetes and blood pressure. Therefore we aim to investigate the relation between abdominal obesity, diabetes, blood pressure and WM microstructural variability in a large cohort of community-dwelling healthy adults. Methods: The sample included dementia-free participants (mean age 55 ± 16 years; 50.7% women) of the LIFE cohort with brain MRI scans (n = 1255). WM microstructure was measured with diffusion tensor imaging (DTI). Mean FA was derived from the individual WM skeleton processed by tract-based-spatial-statistic method. Linear regression models were used to assess the relationships between diabetes, blood pressure, waist to hip ratio (WHR) and DTI parameters. Adjustments were made for age, sex, education and Apoe4. Results: The preliminary result indicated diabetes, systolic blood pressure and WHR were independently associated with lower FA, and diabetes explained the most variance besides age. Subgroup analysis revealed both systolic blood pressure and WHR were negatively associated with mean FA in the non-diabetes group (n=1101). Conclusions: The preliminary result of our study indicates that diabetes accelerated brain aging on directional diffusion of WM. Abdominal fat and blood pressure were associated with WM variabilities independently from age, sex and diabetes. With subsequent analysis of additional DTI measures, blood parameters, WM hyperintensity maps and voxel-based microstructural WM “integrity”, we aim to further characterize the associations between obesity, diabetes, blood pressure and WM microstructure. This will contribute to the existing literature and help to disentangle the underlying mechanism.

Authors: Rui Zhang, Frauke Beyer, L. Lampe, Tobias Luck, Steffi Gerlinde Riedel-Heller, M. Stumvoll, Markus Löffler, M. L. Schroeter, A. Villringer, A. V. Witte

Date Published: 2017

Journal: IUNS 21st International Congress of Nutrition

Human Diseases: diabetes mellitus, obesity, hypertension

Obesity is Associated with White Matter Changes and Cognitions among Healthy Elderly
LIFE- Leipzig Research Center for Civilization Diseases

Abstract (Expand)

Midlife obesity has often been associated with accelerated cognitive decline during aging. Obesity leads to changes in multiple physiological factors that could impact neuronal tissue. Numerous studies … have linked obesity and higher body mass index (BMI) with differences in cognitive functions and brain structure, including total brain volume, regional gray matter volume and white matter (WM) microstructure. However, regarding to WM, the available neuroimaging studies incorporated mainly small sample sizes that yielded less conclusive results. Thus, we investigated the association of obesity, measured using BMI and waist to hip ratio (WHR), with changes in WM microstructure, as well as variance in cognitive test scores in a large cohort of community-dwelling healthy individuals older than 60 years.

Authors: Rui Zhang, L. Lampe, Frauke Beyer, Sebastian Huhn, S. K. Masouleh, Tobias Luck, Steffi Gerlinde Riedel-Heller, Markus Löffler, M. L. Schroeter

Date Published: 28th Nov 2016

Journal: SFN 2016

Human Diseases: obesity

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