Publications #

Note: (shared) first and last authorships are highlighted

Preprints #

• Bögemann, S., Krause, F., van Kraaij, A., Marciniak, M., van Leeuwen, J., Weermeijer, J. D. M., … Hermans, E. (2024, September 9). Triggering just-in-time adaptive interventions based on real-time detection of daily-life stress. https://doi.org/10.31234/osf.io/b5r7j

• Pamplona, G. S. P., Zweerings, J., Lor, C. S., deErney, L., Roecher, E., Taebi, A., Hellrung, L., Amano, K., Scheinost, D., Krause, F., Rosenberg, M. D., Ionta, S., Brem, S., Hermans, E., Mathiak, K. & Scharnowski, F. (2024). Neural Mechanisms of Feedback Processing and Behavioral Adaptation during Neurofeedback Training. bioRxiv 2024.08.19.608543. https://doi.org/10.1101/2024.08.19.608543

• Pereira, M., Terpstra, N., Rutiku, R., Sandberg, K.*, Dresler, M.* & Krause, F.* (2024). Neural correlates of nightmares revisited: findings from large-scale fMRI cohorts. bioRxiv 2024.07.02.601684. https://doi.org/10.1101/2024.07.02.601684

• Petri-Romão, P., Puhlmann, L., Zerban, M., Ahrens, K. F., Arias-Vásquez, A., Basten, U., … Kalisch, R. (2024, March 27). Comparative investigation of appraisal style measures in their predictive potential for stress resilience and implications for predictive modeling of resilience. https://doi.org/10.31234/osf.io/58ft9

• Krause, F., van Leeuwen, J., Bögemann, S., Tutunji, R., Roelofs, K., van Kraaij, A., Stiphout, R. & Hermans, E. (2023, July 31). Predicting resilience from psychological and physiological daily-life measures. https://doi.org/10.31219/osf.io/f28uv

• Tutunji, R., Krentz, M., Kogias, N., de Voogd, L., Krause, F., Vassena, E. & Hermans, E.J. (2023). Changes in large-scale neural networks under stress are linked to affective reactivity to stress in real life. bioRxiv 2023.03.28.534537. https://doi.org/10.1101/2023.03.28.534537

• Krentz, M., Tutunji, R., Kogias, N., Mahadevan, H.M., Reppmann, Z.C., Krause, F. & Hermans, E. (2023). A Comparison of fMRI Data-Derived and Physiological Data-Derived Methods for Physiological Noise Correction. bioRxiv 2023.02.22.529506. https://doi.org/10.1101/2023.02.22.529506

• Krause, F., Linden, D.E. & Hermans, E.J. (2022, October 2). Getting stress-related disorders under control: The unique opportunity for neurofeedback-based therapy. https://doi.org/10.31219/osf.io/vxnbk

• Krause, F., & Lindemann, O. (2021, May 17). On implementing timing-accurate computer-based experiments. Retrieved from https://doi.org/10.31234/osf.io/k5vd9

Journal articles #

• Krause, F., Linden, D.E.J. & Hermans, E.J. (2024). Getting stress-related disorders under control: the untapped potential of neurofeedback. Trends in Neurosciences. https://doi.org/10.1016/j.tins.2024.08.007
  

• Krause, F. & Kogias, N. (2024). Scan Session Tool: (f)MRI scan session documentation and archiving. Journal of Open Source Software, 9(100), 6126, https://doi.org/10.21105/joss.06126
  

• Tutunji, R., Kogias, N., Kapteijns, B., Krentz, M., Krause, F., Vassena, E., Hermans, E.J. (2023). Detecting Prolonged Stress in Real Life Using Wearable Biosensors and Ecological Momentary Assessments: Naturalistic Experimental Study. Journal of Medical Internet Research, 25:e39995. https://doi.org/10.2196/39995
  

• Kogias, N., Geurts, D.E.M., Krause, F., Speckens A.E.M. & Hermans, E.J. (2023). Study protocol for a randomised controlled trial investigating the effects of Mindfulness Based Stress Reduction on stress regulation and associated neurocognitive mechanisms in stressed university students: The MindRest study. BMC Psychology 11, 194. https://doi.org/10.1186/s40359-023-01220-4
  

• Sixtus, E.*, Krause, F.*, Lindemann, O. & Fischer, M. (2023). A sensorimotor perspective on numerical cognition. Trends in Cognitive Sciences, 27(4), 367–378. https://doi.org/10.1016/j.tics.2023.01.002
  

• Krause, F., Kogias, N., Krentz, M., Lührs, M., Goebel, R. & Hermans, E.J. (2021). Self-regulation of stress-related large-scale brain network balance using real-time fMRI neurofeedback. NeuroImage, 243, 118257. https://doi.org/10.1016/j.neuroimage.2021.118527
  

• Mehler, D.M.A., Williams, A.N., Whittaker, J.R., Krause, F., Lührs, M., Kunas, S., Wise, R.G., Shetty, H.G.M., Turner, D.L. & Linden, D.E.J. (2020). Graded fMRI Neurofeedback Training of Motor Imagery in Middle Cerebral Artery Stroke Patients: A Preregistered Proof-of-Concept Study. Frontiers in Human Neuroscience, 14:226. https://doi.org/10.3389/fnhum.2020.00226
  

• Krause, F., Benjamins, C., Eck, J., Lührs, M., van Hoof, R. & Goebel, R. (2019). Active head motion reduction in Magnetic Resonance Imaging using tactile feedback. Human Brain Mapping, 40(14), 4026-4037. https://doi.org/10.1002/hbm.24683
  

• Lührs, M., Riemenschneider, B., Eck, J., Benitez, A., Poser, B.A., Heinecke, A., Krause, F., Esposito, F., Sorger, B., Hennig, J. & Goebel, R. (2019). The potential of MR-Encephalography for BCI/Neurofeedback applications with high temporal resolution. NeuroImage, 194, 228-243. https://doi.org/10.1016/j.neuroimage.2019.03.046
  

• Krause, F., Meyer, M., Bekkering, H., Hunnius, S. & Lindemann, O. (2019). Interaction between perceptual and motor magnitudes in early childhood. Cognitive Development, 49, 11-19. https://doi.org/10.1016/j.cogdev.2018.11.001
  

• Mehler, D., Williams, A., Krause, F., Lührs, M., Shetty, H.,Turner, D., Linden, D. & Whittaker, J. (2019). The BOLD response in primary motor cortex and supplementary motor area during kinesthetic motor imagery based graded fMRI neurofeedback. NeuroImage, 184, 36–44. https://doi.org/10.1016/j.neuroimage.2018.09.007
  

• Krause, F., Benjamins, C., Lührs, M., Eck, J., Noirhomme, Q., Rosenke, M., Brunheim, S., Sorger, B. & Goebel, R. (2017). Real-time fMRI-based self- regulation of brain activation acrosss different visual feedback presentations. Brain-Computer Interfaces, 4(1–2), 87–101. https://doi.org/10.1080/2326263X.2017.1307096
  

• Krause, F., Bekkering, H., Pratt, J. & Lindemann, O. (2017). Interaction between numbers and size during visual search. Psychological Research, 81(3), 664-677. https://doi.org/10.1007/s00426-016-0771-4
  

• Krause, F., Lindemann, O., Toni, I. & Bekkering, H. (2014). Different brains process numbers differently: Structural bases of individual differences in spatial and non-spatial number representations. Journal of Cognitive Neuroscience 26(4), 768-776. https://doi.org/10.1162/jocn_a_00518
  

• Krause, F. & Lindemann, O. (2014). Expyriment: A Python library for cognitive and neuroscientific experiments. Behavior Research Methods, 46(2), 416-428. https://doi.org/10.3758/s13428-013-0390-6
  

• Krause, F., Bekkering, H. & Lindemann, O. (2013). A feeling for numbers: shared metric for symbolic and tactile numerosities. Frontiers in Psychology 4:7. https://doi.org/10.3389/fpsyg.2013.00007
  

• Lindemann, O. & Krause, F. (2012). Zählen mit den Fingern: Verkörperung oder Veranschaulichung? Lernen und Lernstörungen, 1(1), 60-62. https://doi.org/10.1024/2235-0977/a000009
  

Books #

• Krause, F. (2014). Numbers and magnitude in the brain: A sensorimotor grounding of numerical cognition (Doctoral Dissertation). Retrieved from http://hdl.handle.net/2066/130434
  

Abstracts #

• Mehler, D., Williams, A., Krause, F., Luehrs, M., Shetty, H.,Turner, D., Linden, D. & Whittaker, J. (2017). Supplementary motor area, but not primary motor cortex - graded realtime fMRI neurofeedback training and its translation to stroke patients. Journal of Cerebral Blood Flow and Metabolism, 37, 378-379. https://doi.org/10.1177/0271678X17695986

• Krause, F., Benjamins, C., Lührs, M., Eck, J., Noirhomme, Q., Rosenke, M., Brunheim, S., Sorger, B. & Goebel, R. (2016). Real-time self-regulation across multiple visual neurofeedback presentations. Proceedings of the 6th International Brain-Computer Interface Meeting, 132. https://doi.org/10.3217/978-3-85125-467-9-132

Other #

• Krause, F. (2017). MagniShoot – An educational game developed in 24 hours. Donders Wonders - On Brains and Science. blog.donders.ru.nl/?p=7327&lang=en&

• Krause, F. (2017). Maak getalgrootte voelbar. Jeugd in School en Wereld.

• Krause, F. & Lindemann, O. (2014). Erratum to: Expyriment: A Python library for cognitive and neuroscientific experiments. Behavior Research Methods, 46(2), 429. doi: 10.3758/s13428-013-0436-9

*: Authors contributed equally