Andreas M. Brandmaier

Prof. Dr. Andreas M. Brandmaier

orcid

Research Focus & Metrics

Academic Career Length

15Years (2012-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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10 May 2013 in Science
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Journal Article
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JFJulia Freund
Julia Freund
ABAndreas M. Brandmaier
Andreas M. Brandmaier
Lars LewejohannLars Lewejohann
Lars Lewejohann
IKImke Kirste
Imke Kirste
MKMareike Kritzler
Mareike Kritzler
GKGerd Kempermann
Gerd Kempermann
Identical and Still Different: Even in monozygotic twins reared together, there are always observable differences reflecting the influence of individual responses. Freund et al. (p. 756 ; see the Perspective by Bergmann and Frisén ) developed an inbred mouse model for studying the environmental influences on genetically identical animals and examined their effects on behavioral and neural development.

10 November 2021 in eLife
Verified
Journal Article
Open access
DVDidac Vidal-Pineiro
Didac Vidal-Pineiro
YWYunpeng Wang
Yunpeng Wang
SKStine K Krogsrud
Stine K Krogsrud
IAInge K Amlien
Inge K Amlien
WBWilliam Fc Baaré
William Fc Baaré
AFAnders Fjell
Anders Fjell
Brain age is a widely used index for quantifying individuals’ brain health as deviation from a normative brain aging trajectory. Higher-than-expected brain age is thought partially to reflect above-average rate of brain aging. Here, we explicitly tested this assumption in two independent large test datasets (UK Biobank [main] and Lifebrain [replication]; longitudinal observations ≈ 2750 and 4200) by assessing the relationship between cross-sectional and longitudinal estimates of brain age. Brain age models were estimated in two different training datasets (n ≈ 38,000 [main] and 1800 individuals [replication]) based on brain structural features. The results showed no association between cross-sectional brain age and the rate of brain change measured longitudinally. Rather, brain age in adulthood was associated with the congenital factors of birth weight and polygenic scores of brain age, assumed to reflect a constant, lifelong influence on brain structure from early life. The results call for nuanced interpretations of cross-sectional indices of the aging brain and question their validity as markers of ongoing within-person changes of the aging brain. Longitudinal imaging data should be preferred whenever the goal is to understand individual change trajectories of brain and cognition in aging.

1 February 2021 in Nature Communications
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Journal Article
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JRJames M. Roe
James M. Roe
DVDidac Vidal-Piñeiro
Didac Vidal-Piñeiro
ØSØystein Sørensen
Øystein Sørensen
ABAndreas M. Brandmaier
Andreas M. Brandmaier
SDSandra Düzel
Sandra Düzel
RWRené Westerhausen
René Westerhausen
Aging and Alzheimer’s disease (AD) are associated with progressive brain disorganization. Although structural asymmetry is an organizing feature of the cerebral cortex it is unknown whether continuous age- and AD-related cortical degradation alters cortical asymmetry. Here, in multiple longitudinal adult lifespan cohorts we show that higher-order cortical regions exhibiting pronounced asymmetry at age ~20 also show progressive asymmetry-loss across the adult lifespan. Hence, accelerated thinning of the (previously) thicker homotopic hemisphere is a feature of aging. This organizational principle showed high consistency across cohorts in the Lifebrain consortium, and both the topological patterns and temporal dynamics of asymmetry-loss were markedly similar across replicating samples. Asymmetry-change was further accelerated in AD. Results suggest a system-wide dedifferentiation of the adaptive asymmetric organization of heteromodal cortex in aging and AD.

4 September 2014 in Structural Equation Modeling: A Multidisciplinary Journal
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Journal Article
Closed access
TOTimo von Oertzen
Timo von Oertzen
ABAndreas M. Brandmaier
Andreas M. Brandmaier
STSiny Tsang
Siny Tsang

1 January 2013 in Psychological Methods
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Journal Article
Closed access
ABAndreas M. Brandmaier
Andreas M. Brandmaier
TOTimo von Oertzen
Timo von Oertzen
JMJohn J. McArdle
John J. McArdle
ULUlman Lindenberger
Ulman Lindenberger
In the behavioral and social sciences, structural equation models (SEMs) have become widely accepted as a modeling tool for the relation between latent and observed variables. SEMs can be seen as a unification of several multivariate analysis techniques. SEM Trees combine the strengths of SEMs and the decision tree paradigm by building tree structures that separate a data set recursively into subsets with significantly different parameter estimates in a SEM. SEM Trees provide means for finding covariates and covariate interactions that predict differences in structural parameters in observed as well as in latent space and facilitate theory-guided exploration of empirical data. We describe the methodology, discuss theoretical and practical implications, and demonstrate applications to a factor model and a linear growth curve model.

26 April 2021 in Proceedings of the National Academy of Sciences
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Journal Article
Open access
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LNLars Nyberg
Lars Nyberg
FMFredrik Magnussen
Fredrik Magnussen
ALAnders Lundquist
Anders Lundquist
WBWilliam Baaré
William Baaré
DBDavid Bartrés-Faz
David Bartrés-Faz
AFAnders M. Fjell
Anders M. Fjell
Education has been related to various advantageous lifetime outcomes. Here, using longitudinal structural MRI data (4,422 observations), we tested the influential hypothesis that higher education translates into slower rates of brain aging. Cross-sectionally, education was modestly associated with regional cortical volume. However, despite marked mean atrophy in the cortex and hippocampus, education did not influence rates of change. The results were replicated across two independent samples. Our findings challenge the view that higher education slows brain aging.