Vidal-Pineiro, D., Wang, Y., Krogsrud, S. K., Amlien, I. K., Baaré, W. F. C., Bartres-Faz, D., Bertram, L., Brandmaier, A. M., Drevon, C. A., Düzel, S., Ebmeier, K., Henson, R. N., Junqué, C., Kievit, R. A., Kühn, S., Leonardsen, E., Lindenberger, U., Madsen, K. S., Magnussen, F., Mowinckel, A. M., Nyberg, L., Roe, J. M., Segura, B., Smith, S. M., Sørensen, Ø., Suri, S., Westerhausen, R., Zalesky, A., Zsoldos, E., Walhovd, K. B., & Fjell, A. (2021). Individual variations in 'brain age' relate to early-life factors more than to longitudinal brain change. eLife, 10, Article e69995. https://doi.org/10.7554/eLife.69995

Arnold, M., Oberski, D. L., Brandmaier, A. M., & Voelkle, M. C. (2020). Identifying heterogeneity in dynamic panel models with individual parameter contribution regression. Structural Equation Modeling, 27(4), 613–628. https://doi.org/10.1080/10705511.2019.1667240

Bender, A. R., Brandmaier, A. M., Düzel, S., Keresztes, A., Pasternak, O., Lindenberger, U., & Kühn, S. (2020). Hippocampal subfields and limbic white matter jointly predict learning rate in older adults. Cerebral Cortex, 30(4), 2465–2477. https://doi.org/10.1093/cercor/bhz252

Brandmaier, A. M., Ghisletta, P., & von Oertzen, T. (2020). Optimal planned missing data design for linear latent growth curve models. Behavior Research Methods, 52, 1445–1458. https://doi.org/10.3758/s13428-019-01325-y

Budin-Ljøsne, I., Friedman, B. B., Suri, S., Solé-Padullés, C., Düzel, S., Drevon, C. A., Baaré, W. F., Mowinckel, A. M., Zsoldos, E., Madsen, K. S., Carver, R. B., Ghisletta, P., Arnesen, M., Bartrés-Faz, D., Brandmaier, A. M., Fjell, A. M., Kvalbein, A., Henson, R. N., Kievit, R., Nawijn, L., Pochet, R., Schnitzler, A., Walhovd, K. B., & Zasiekina, L. (2020). The Global Brain Health Survey: Development of a multi-language survey of public views on brain health. Frontiers in Public Health, 8, Article 387. https://doi.org/10.3389/fpubh.2020.00387

Gorbach, T., Pudas, S., Bartrés-Faz, D., Brandmaier, A. M., Düzel, S., Henson, R. N., Idland, A.-V., Lindenberger, U., Bros, D. M., Mowinckel, A. M., Solé-Padullés, C., Sørensen, Ø., Walhovd, K. B., Watne, L. O., Westerhausen, R., Fjell, A. M., & Nyberg, L. (2020). Longitudinal association between hippocampus atrophy and episodic-memory decline in non-demented APOE-ε4 carriers. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 12(1), Article e12110. https://doi.org/10.1002/dad2.12110

Karch, J. D., Brandmaier, A. M., & Voelkle, M. C. (2020). Gaussian Process Panel Modeling: Machine learning inspired analysis of longitudinal panel data. Frontiers in Psychology, 11, Article 351. https://doi.org/10.3389/fpsyg.2020.00351

Anand, C., Brandmaier, A. M., Arshad, M., Lynn, J., Stanley, J. A., & Raz, N. (2019). White-matter microstructural properties of the corpus callosum: Test-retest and repositioning effects in two parcellation schemes. Brain Structure and Function, 224(9), 3373–3385. https://doi.org/10.1007/s00429-019-01981-y

Jacobucci, R., Brandmaier, A. M., & Kievit, R. A. (2019). A practical guide to variable selection in structural equation modeling by using regularized multiple-indicators, multiple-causes models. Advances in Methods and Practices in Psychological Science, 2(1), 55–76. https://doi.org/10.1177/2515245919826527

Karalija, N., Wåhlin, A., Ek, J., Rieckmann, A., Papenberg, G., Salami, A., Brandmaier, A. M., Köhncke, Y., Johansson, J., Andersson, M., Axelsson, J., Orädd, G., Riklund, K., Lövdén, M., Lindenberger, U., Bäckman, L., & Nyberg, L. (2019). Cardiovascular factors are related to dopamine integrity and cognition in aging. Annals of Clinical and Translational Neurology, 6(11), 2291–2303. https://doi.org/10.1002/acn3.50927

Karch, J. D., Filevich, E., Wenger, E., Lisofsky, N., Becker, M., Butler, O., Mårtensson, J., Lindenberger, U., Brandmaier, A. M., & Kühn, S. (2019). Identifying predictors of within-person variance in MRI-based brain volume estimates. NeuroImage, 200, 575–589. https://doi.org/10.1016/j.neuroimage.2019.05.030

Kühn, S., Düzel, S., Colzato, L., Norman, K., Gallinat, J., Brandmaier, A. M., Lindenberger, U., & Widaman, K. F. (2019). Food for thought: Association between dietary tyrosine and cognitive performance in younger and older adults. Psychological Research, 83(6), 1097–1106. https://doi.org/10.1007/s00426-017-0957-4

Tucker-Drob, E. M., Brandmaier, A. M., & Lindenberger, U. (2019). Coupled cognitive changes in adulthood: A meta-analysis. Psychological Bulletin, 145(3), 273–301. https://doi.org/10.1037/bul0000179

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, Article 294. https://doi.org/10.3389/fpsyg.2018.00294

Brandmaier, A. M., Wenger, E., Bodammer, N. C., Kühn, S., Raz, N., & Lindenberger, U. (2018). Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED). eLife, 7, Article e35718. https://doi.org/10.7554/eLife.35718

Kievit, R., Brandmaier, A. M., 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. (2018). Developmental cognitive neuroscience using latent change score models: A tutorial and applications. Developmental Cognitive Neuroscience, 33, 99–117. https://doi.org/10.1016/j.dcn.2017.11.007

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(5), 996–1012. https://doi.org/10.1037/dev0000274

Bender, A. R., Prindle, J. J., Brandmaier, A. M., & Raz, N. (2016). White matter and memory in healthy adults: Coupled changes over two years. NeuroImage, 131, 193–204. https://doi.org/10.1016/j.neuroimage.2015.10.085

Brandmaier, A. M., Prindle, J. J., McArdle, J. J., & Lindenberger, U. (2016). Theory-guided exploration with structural equation model forests. Psychological Methods, 21(4), 566–582. https://doi.org/10.1037/met0000090

Brandmaier, A. M. (2015). pdc: An R package for complexity-based clustering of time series. Journal of Statistical Software, 67(5), 1–23. https://doi.org/10.18637/jss.v067.i05