Studying variability in human brain aging in a population-based German cohort—rationale and design of 1000BRAINS

Caspers, Svenja; Moebus, Susanne; Lux, Silke; Pundt, Noreen; Schütz, Holger; Mühleisen, Thomas W.; Gras, Vincent; Eickhoff, Simon B.; Romanzetti, Sandro; Stöcker, Tony; Stirnberg, Rüdiger; Kirlangic, Mehmet Eylem; Minnerop, Martina; Pieperhoff, Peter; Mödder, Ulrich; Das, Samir; Evans, Alan C.; Jöckel, Karl Heinz; Erbel, Raimund; Cichon, Sven; Nöthen, Markus M.; Sturma, Dieter; Bauer, Andreas; Shah, N. Jon; Zilles, Karl; Amunts, Katrin

doi:10.3389/fnagi.2014.00149

Cited by 110 publications

(152 citation statements)

References 83 publications

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“…LHAB has its focus on healthy older adults excluding participants with a history of neurological diseases and cognitive impairment (Zollig et al (). On the other hand, 1000BRAINS is conducted as a population‐based epidemiological cohort study, excluding subjects only if they do not meet the eligibility requirements for the MR acquisition based on the MR safety guidelines (Caspers et al (). Thus, although in the current study, we assured that the two samples would not differ in their age ranges and gender distribution, the two samples differed in several sample characteristics.…”

Section: Discussionmentioning

confidence: 99%

“…LHAB has its focus on healthy older adults excluding participants with a history of neurological diseases and cognitive impairment (Zollig et al (2011). On the other hand, 1000BRAINS is conducted as a population-based epidemiological cohort study, excluding subjects only if they do not meet the eligibility requirements for the MR acquisition based on the MR safety guidelines (Caspers et al (2014).…”

Section: Comparability Of Independent Samples Of Older Adultsmentioning

confidence: 99%

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Generalizing age effects on brain structure and cognition: A two‐study comparison approach

Jockwitz

Mérillat

Liem

et al. 2019

Human Brain Mapping

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Normal aging is accompanied by an interindividually variable decline in cognitive abilities and brain structure. This variability, in combination with methodical differences and differences in sample characteristics across studies, pose a major challenge for generalizability of results from different studies. Therefore, the current study aimed at cross‐validating age‐related differences in cognitive abilities and brain structure (measured using cortical thickness [CT]) in two large independent samples, each consisting of 228 healthy older adults aged between 65 and 85 years: the Longitudinal Healthy Aging Brain (LHAB) database (University of Zurich, Switzerland) and the 1000BRAINS (Research Centre Jülich, Germany). Participants from LHAB showed significantly higher education, physical well‐being, and cognitive abilities (processing speed, concept shifting, reasoning, semantic verbal fluency, and vocabulary). In contrast, CT values were larger for participants of 1000BRAINS. Though, both samples showed highly similar age‐related differences in both, cognitive abilities and CT. These effects were in accordance with functional aging theories, for example, posterior to anterior shift in aging as was shown for the default mode network. Thus, the current two‐study approach provides evidence that independently on heterogeneous metrics of brain structure or cognition across studies, age‐related effects on cognitive ability and brain structure can be generalized over different samples, assuming the same methodology is used.

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Section: Discussionmentioning

confidence: 99%

Section: Comparability Of Independent Samples Of Older Adultsmentioning

confidence: 99%

Generalizing age effects on brain structure and cognition: A two‐study comparison approach

Jockwitz

Mérillat

Liem

et al. 2019

Human Brain Mapping

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“…Sample 3, a fully independent sample covering a different age range, was obtained from the population-based 1000BRAINS study (Caspers S et al 2014 acquisition, participants kept their eyes closed and were instructed to let the mind wander without thinking of anything in particular (Caspers S et al 2014).…”

Section: Samplesmentioning

confidence: 99%

Sex classification by resting state brain connectivity

Weis

Patil²,

Hoffstaedter³

et al. 2019

Preprint

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A large amount of brain imaging research has focused on group studies delineating differences between males and females with respect to both cognitive performance as well as structural and functional brain organization. To supplement existing findings, the present study employed a machine learning approach to assess how accurately participants' sex can be classified based on spatially specific resting state (RS) brainconnectivity, using two samples from the Human Connectome Project (n1 = 434, n2 = 310) and one fully independent sample from the 1000BRAINS study (n=941). The classifier, which was trained on one sample and tested on the other two, was able to reliably classify sex, both within sample and across independent samples, differing both with respect to imaging parameters and sample characteristics. Brain regions displaying highest sex classification accuracies were mainly located along the cingulate cortex, medial and lateral frontal cortex, temporo-parietal regions, insula and precuneus. These areas were stable across samples and match well with previously described sex differences in functional brain organization. While our data show a clear link between sex and regionally specific brain connectivity, they do not support a clear-cut dimorphism in functional brain organization that is driven by sex alone. Keywordsclassification, functional magnetic resonance imaging, machine learning, resting state brain connectivity, sex differences,

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“…The entire 1000BRAINS study protocol, from which our data were drawn, was approved by the Ethics Committee of the University of Duisburg-Essen. 13 All subjects gave prior, written informed consent in accordance with institutional guidelines. The study population included 42 elderly subjects (58-80 years, mean age: 69 AE 6 years) drawn from the 1000BRAINS population-based cohort study, assessing the influence of environmental and genetic factors on variability in the structure and function of the aging brain.…”

Section: Subjectsmentioning

confidence: 99%

Increasing body mass index in an elderly cohort: Effects on the quantitative MR parameters of the brain

Schall¹,

Iordanishvili²,

Mauler³

et al. 2019

Magnetic Resonance Imaging

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Background Body mass index (BMI) is increasing in a large number of elderly persons. This increase in BMI is known to put one at risk for many "diseases of aging," although less is known about how a change in BMI may affect the brains of the elderly. Purpose To investigate the relationship between BMI and quantitative water content, T1, T2*, and the semi‐quantitative magnetization transfer ratio (MTR) of various structures in elderly brains. Study Type Cross‐sectional. Subjects Forty‐two adults (BMI range: 19.1–33.5 kg/m2, age range: 58–80 years). Field Strength 3T MRI (two multi‐echo gradient echoes, actual flip angle imaging, magnetization prepared rapid gradient echo, fluid attenuated inversion recovery). Assessment The 3D two‐point method was used to derive (semi‐)quantitative parameters in global white (WM) and gray matter (GM) and their regions as defined by the Johns Hopkins University and the Montreal Neurological Institute atlases. Statistical Tests Multivariate linear regression with BMI as principal regressor, corrected for the additional regressors age, gender, and glycated hemoglobin. Spearman correlation between quantitative parameters of the regions showing significant changes and the lipid spectra / C‐reactive protein (CRP). Voxel‐based morphometry and analysis of covariance (ANCOVA) to explore changes in the GM volume. Results T1 increased significantly (P < 0.05) in the frontal, temporal, and parietal cortices, while the bilateral corona radiata, right superior longitudinal fasciculus, as well as the corpus callosum showed significant changes in the WM regions. T2* increased significantly in the global WM and left corona radiata. Changes in MTR and the free water content did not reach significance. No significant correlation between any quantitative parameter and the lipid spectra or CRP could be identified. Data Conclusion These results suggest that an elevated BMI predominantly affects T1 in WM as well as GM structures in the elderly human brain. Level of Evidence: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:514–523.

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Studying variability in human brain aging in a population-based German cohort—rationale and design of 1000BRAINS

Cited by 110 publications

References 83 publications

Generalizing age effects on brain structure and cognition: A two‐study comparison approach

Generalizing age effects on brain structure and cognition: A two‐study comparison approach

Sex classification by resting state brain connectivity

Increasing body mass index in an elderly cohort: Effects on the quantitative MR parameters of the brain

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