Publications of Andreas M. Brandmaier

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