Andreas M. Brandmaier

Computer & Data Scientist
in Lifespan Psychology

Find Out More About Me


Andreas Brandmaier is head of the Formal Methods in Lifespan Psychology project at the Max Planck Institute for Human Development in Berlin, Germany. He is also a fellow of the Max Planck UCL Centre for Computational Psychiatry and Ageing Research.

Dr. Brandmaier promotes conceptual and methodological innovation within developmental psychology and in interdisciplinary context. Particularly, he develops methods and computational tools to answer methodological challenges of lifespan psychology. His primary research interests are interindividual differences in behavioral and neural development, brain-behavior relations across the lifespan, and the adaption of datamining and machine learning approaches to challenges of psychological research.

Andreas is interested in exploratory methods to better explain interindividual differences in change such as SEM trees and forests combining structural equation modeling and decision trees; finding alternative and optimal study designs when planning empirical longitudinal studies; and modeling the emergence of individuality and its relationship to brain plasticity. Dr. Brandmaier's recent research has been published in Science, Psychological Methods, Psychology and Aging, Developmental Psychology, Frontiers in Psychology, Neuroscience, and NeuroImage. In 2015, Andreas Brandmaier won the Heinz-Billing-Award for outstanding contributions to Computational Science.



Ωnyx is a free software environment for creating and estimating structural equation models (SEM).

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SEM Trees & Forests

SEM trees combine Structural Equation Models and decision trees to an exploratory method to refine theory-driven models.

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PDC is an R package for clustering time series based on their relative complexity.

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LIFESPAN allows evaluating and deriving optimal longitudinal study designs.

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Selected Literature

Brandmaier, A. M., Wenger, E., Bodammer, N. C., Kühn, S., Raz, N., & Lindenberger, U. (in press). Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED). eLife.

Brandmaier, A. M., von Oertzen, T., Ghisletta, P., Lindenberger, U., & Hertzog, C. (2018). Precision, reliability, and effect size of slope variance in latent growth curve models: Implications for statistical power analysis. Frontiers in Psychology, 9:294. doi:10.3389/fpsyg.2018.00294

Brandmaier, A. M., Ram, N., Wagner, G. G., & Gerstorf, D. (2017). Terminal decline in well-being: The role of multi-indicator constellations of physical health and psychosocial correlates. Developmental Psychology, 53, 996-1012. doi:10.1037/dev0000274

Kievit, R., Brandmaier, A., Ziegler, G., van Harmelen, A.-L., de Mooij, S., Moutoussis, M., Goodyer, I., Bullmore, E., Jones, P., Fonagy, P., the Neuroscience in Psychiatry Network (NSPN) Consortium, Lindenberger, U., & Dolan, R. J. (2017). Developmental cognitive neuroscience using latent change score models: A tutorial and applications. BioRxiv. doi:10.1101/110429

Brandmaier, A. M., Prindle, J. J., McArdle, J. J., & Lindenberger, U. (2016). Theory-guided exploration with structural equation model forests. Psychological Methods, 21, 566-582. doi:10.1037/met0000090

Brandmaier, A. M., Oertzen, T. v., Ghisletta, P., Hertzog, C., & Lindenberger, U. (2015). LIFESPAN: A tool for the computer-aided design of longitudinal studies. Frontiers in Psychology, 6:272. doi:10.3389/fpsyg.2015.00272

Brandmaier, A. M., Oertzen, T. v., McArdle, J. J., & Lindenberger, U. (2013). Structural equation model trees. Psychological Methods, 18, 71-86. doi: 10.1037/a0030001

Freund, J., Brandmaier, A. M., Lewejohann, L., Kirste, I., Kritzler, M., Krüger, A., Sachser, N., Lindenberger, U., & Kempermann, G. (2013). Emergence of individuality in genetically identical mice. Science, 340(6133), 756-759. doi:10.1126/science.1235294

Karch, J. D., Sander, M. C., Oertzen, T. v., Brandmaier, A. M., & Werkle-Bergner, M. (in press). Using within-subject pattern classification to understand lifespan age differences in oscillatory mechanisms of working memory selection and maintenance. NeuroImage. doi:10.1016/j.neuroimage.2015.04.038


The following interactive chart shows my recent collaborations. An edge between two nodes in this graph means that the corresponding two authors have jointly published with me in the last couple of years. By the time you are reading this, this is probably outdated but it shows at least a subset of my valued collaborators. Zoom in and out with the mouse wheel. Click and drag the background to move the graph. Click nodes to highlight individual collaborators and their subnetworks: