I am a research fellow at the University of Surrey in Guildford. My research interests include brain dynamics in short and long term, as well as developing approaches and tools to facilitate adoption of best practices.
I received my undergraduate degree in medicine (MD) and my PhD in clinical neuroscience from the University of Pécs, Hungary, where I implemented various neuroimaging techniques in a clinical environment. My first post-doc project at the Biomedizinische NMR Forschungs GmbH in Max-Planck Institute for Biophysical Chemistry in Göttingen focused on real-time fMRI-based neurofeedback with special emphasis on the implementation and the optimization of the experimental setup and the analysis. I also implemented real-time fMRI-based neurofeedback while I was working at the MRC Cognition and Brain Sciences Unit in Cambridge and then at the Royal Holloway University of London.
Affiliations and memberships
In the media
My main project is on optimising neuromodulation approaches, such as tACS based on real-time neural signals, and I also collaborate in other real-time fMRI and neurofeedback projects.
Methods development and Open Science
I work on how to improve the temporal resolution and reliability of fMRI-based neurofeedback, and co-develop OpenNFT, a Python/Matlab framework for real-time fMRI neurofeedback and automatic analysis, a Matlab framework to process multimodal neuroimaging analysis pipelines. I am also a strong supporter of transparency and replicability of neuroimaging approaches, and I am involved in international initiatives such as the Brain Imaging Data Structure and Neuroimaging Data Model providing a standard of organising neuroimaging data.
- Auer, T., & Frahm, J. (2011). Confounding factors in neurofeedback training based on fMRI of motor imagery. Neuroscience Letters, 500(500), e32. doi:10.1016/j.neulet.2011.05.160
- Dewiputri, W. I., & Auer, T. (2013). Functional magnetic resonance imaging (FMRI) neurofeedback: implementations and applications. Malays J Med Sci, 20(5), 5-15.
- Gevensleben, H., Albrecht, B., Lutcke, H., Auer, T., Dewiputri, W. I., Schweizer, R., . . . Rothenberger, A. (2014). Neurofeedback of slow cortical potentials: neural mechanisms and feasibility of a placebo-controlled design in healthy adults. Front Hum Neurosci, 8, 990. doi:10.3389/fnhum.2014.00990
- Auer, T., Schweizer, R., & Frahm, J. (2015). Training Efficiency and Transfer Success in an Extended Real-Time Functional MRI Neurofeedback Training of the Somatomotor Cortex of Healthy Subjects. Front Hum Neurosci, 9, 547. doi:10.3389/fnhum.2015.00547
- Auer, T., Dewiputri, W. I., Frahm, J., & Schweizer, R. (2018). Higher-order Brain Areas Associated with Real-time Functional MRI Neurofeedback Training of the Somato-motor Cortex. Neuroscience, 378, 22-33. doi:10.1016/j.neuroscience.2016.04.034
- Bazanova, O. M., Auer, T., & Sapina, E. A. (2018). On the Efficiency of Individualized Theta/Beta Ratio Neurofeedback Combined with Forehead EMG Training in ADHD Children. Front Hum Neurosci, 12, 3. doi:10.3389/fnhum.2018.00003
- Reiner, M., Gruzelier, J., Bamidis, P. D., & Auer, T. (2018). The Science of Neurofeedback: Learnability and Effects. Neuroscience, 378, 1-10. doi:10.1016/j.neuroscience.2018.04.024
- Ros, T., Enriquez-Gepper, S., Zotev, V., …, Thibault, R. (2019). Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist). PsyArXiv, doi: 10.31234/osf.io/nyx84
- Haugg, A., Sladky, R., Skouras, S., …, Scharnowski, F. (2020). Can we predict real-time fMRI neurofeedback learning success from pre-training brain activity? bioRxiv, doi: 10.1101/2020.01.15.906388