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    Dynamic reconfiguration of brain coactivation states associated with active and lecture-based learning of university physics

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    ; London Vol. 10, Iss. 1,  (2025): 55.     DOI:10.1038/s41539-025-00348-9
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    References (131)

    • 1.
      Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. stat. softw. 67, 1–48 (2015).
    • 2.
      Gorgolewski, K. et al. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. Front. Neuroinform. 5, 13 (2011).
    • 3.
      Hill, N. M. & Schneider, W. Brain changes in the development of expertise: neuroanatomical and neurophysiological evidence about skill-based adaptations. in The Cambridge Handbook of Expertise and Expert Performance (eds. Ericsson, K. A., Charness, N., Feltovich, P. J. & Hoffman, R. R.) 653–682. https://doi.org/10.1017/CBO9780511816796.037
    • 4.
      Jeon, H.-A. & Friederici, A. D. What Does “Being an Expert” mean to the brain? Functional specificity and connectivity in expertise. Cereb. Cortex https://doi.org/10.1093/cercor/bhw329
    • 5.
      Kong, R. Q. et al. A network correspondence toolbox for quantitative evaluation of novel neuroimaging results. Nat. Commun. 16, 2930 (2025).
    • 6.
      Lenth, R. emmeans: Estimated marginal means, aka least-squares means. R package version 1.10.2. https://CRAN.Rproject.org/package=emmeans (2024).
    • 7.
      Li, L., Zheng, Q., Xue, Y., Bai, M. & Mu, Y. Coactivation pattern analysis reveals altered whole-brain functional transient dynamics in autism spectrum disorder. Eur. Child Adolesc. Psychiatry https://doi.org/10.1007/s00787-024-02474-y
    • 8.
      Singer, S. R., Nielsen, N., & Schweingruber, H. A. Discipline-Based Education Research: Understanding and Improving Learning in Undergraduate Science and Engineering 13362. https://doi.org/10.17226/13362
    • 9.
      Smith, D. NeuroCAPs: a Python package for performing co-activation patterns analyses on resting-state and task-based fMRI data. Zenodo, https://doi.org/10.5281/ZENODO.15061878
    • 10.
      Wang, S. et al. Evaluation of field map and nonlinear registration methods for correction of susceptibility artifacts in diffusion MRI. Front. Neuroinform. 11, 17 (2017).
    • 11.
      Weisberg, S. M. & Newcombe, N. S. Embodied cognition and STEM learning: overview of a topical collection in CR:PI. Cogn. Res. Princ. Implic. 2, 38 (2017).
    • 12.
      Zhang, R. et al. Sleep deprivation effects on brain state dynamics are associated with dopamine D2 receptor availability via network control theory. Biol. Psychiatry https://doi.org/10.1016/j.biopsych.2024.08.001
    • 13.
      Zhang, R.-L., Yu, S., Gao, R., Wang, L. & He, X. How does technology-based embodied learning affect learning effectiveness? Based on a systematic literature review and meta-analytic approach. Interact. Learn. Environ. 1–24. https://doi.org/10.1080/10494820.2025.2479176
    • 14.

      Understanding the development of interest and self-efficacy in active-learning undergraduate physics courses

      Dou, R; Brewe, E; Potvin, G; Zwolak, J; Hazari, Z. Int. J. Sci. Educ Vol. 40, .
    • 15.

      Brain activity links performance in science reasoning with conceptual approach

      Bartley, J; Riedel, M; Salo, T; Boeving, E; Bottenhorn, K; Bravo, E; Odean, R; Nazareth, A; Laird, R; Sutherland, M. NPJ Sci. Learn Vol. 4, .
    • 16.

      Characterizing active learning environments in physics using network analysis and classroom observations

      Commeford, K; Brewe, E; Traxler, A. Phys. Rev. Phys. Educ. Res Vol. 17, .
    • 17.

      Impact of an active learning physics workshop on secondary school students self-efficacy and ability

      Durk, J; Davies, A; Hughes, R; Jardine-Wright, L. Phys. Rev. Phys. Educ. Res Vol. 16, .
    • 18.

      Functional MRI brain state occupancy in the presence of cerebral small vessel disease—a pre-registered replication analysis of the Hamburg City Health Study

      Ingwersen T. Imaging Neurosci Vol. 2, (2024): 1-17.
    • 19.

      Initial learning in the brain: from rules to action

      Fregni S; Wolfensteller U; Ruge H. Imaging Neurosci Vol. 2, (2024): 1-34.
    • 20.

      Hebbian priming of human motor learning

      Bjørndal JR; Beck MM; Jespersen L; Christiansen L; Lundbye-Jensen J. Nat. Commun Vol. 15, (2024).