Dr Tibor Auer
About
Biography
My research interests include assessing and improving reproducibility in neuroimaging, as well as developing approaches and tools to characterise the robustness of research findings and facilitate the 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 a broad spectrum of in vivo MR techniques in a clinical environment. Then, I joined the methods-oriented Biomedizinische NMR Forschungs GmbH in Max-Planck Institute for Biophysical Chemistry in Göttingen, studied the assumptions and mechanisms underlying neurofeedback training (NFT), a non-invasive intervention, and optimised the experimental setup and protocol. At the MRC Cognition and Brain Sciences Unit in Cambridge and the Royal Holloway University of London in Egham, I led the development of Automatic Analysis, a reproducible and scalable neuroimaging analysis pipeline, and contributed to international harmonisation initiatives, including the Brain Imaging Data Structure and the Neuroimaging Data Model. Combining my commitment to clinically applicable research and my keen interest in powerful and reliable methods, I joined the University of Surrey, School of Psychology to investigate neurocognitive changes during healthy ageing and assess and optimise electric and neurofeedback-based neuromodulation.
Areas of specialism
Affiliations and memberships
News
In the media
ResearchResearch interests
Reproducibility and methodological variation
I am aware of the huge gap between the volume of neuroimaging findings and their translation into mental health research and practice, a gap that can be partially attributed to the lack of reproducibility and confidence in the findings. My primary research interest is to assess and mitigate the effect of methodological variation in neuroimaging to improve its translation potential.
Methods development and Open Science
I am enthusiastic about understanding upcoming methodological improvements in data acquisition, processing, and analysis, to combine and develop new approaches. I lead the international group developing Automatic Analysis, a Matlab framework to process multimodal neuroimaging analysis pipelines and co-develop OpenNFT, a Python/Matlab framework for real-time fMRI neurofeedback. I am also a strong supporter of the harmonisation of neuroimaging approaches, and I am involved in international initiatives such as the Brain Imaging Data Structure and Neuroimaging Data Model providing a standard for organising neuroimaging data and analysis.
Neuromodulation
I am interested in optimising the experimental setup and understanding the assumptions and mechanisms underlying non-invasive neuromodulation, including fMRI-based neurofeedback training (NFT) and transcranial alternating current stimulation (tACS). I combine cutting-edge neuroimaging methods, including EEG, fMRI and machine learning, to understand neural signals and identify them as candidate neuromarkers for personalising neuromodulation.
Research interests
Reproducibility and methodological variation
I am aware of the huge gap between the volume of neuroimaging findings and their translation into mental health research and practice, a gap that can be partially attributed to the lack of reproducibility and confidence in the findings. My primary research interest is to assess and mitigate the effect of methodological variation in neuroimaging to improve its translation potential.
Methods development and Open Science
I am enthusiastic about understanding upcoming methodological improvements in data acquisition, processing, and analysis, to combine and develop new approaches. I lead the international group developing Automatic Analysis, a Matlab framework to process multimodal neuroimaging analysis pipelines and co-develop OpenNFT, a Python/Matlab framework for real-time fMRI neurofeedback. I am also a strong supporter of the harmonisation of neuroimaging approaches, and I am involved in international initiatives such as the Brain Imaging Data Structure and Neuroimaging Data Model providing a standard for organising neuroimaging data and analysis.
Neuromodulation
I am interested in optimising the experimental setup and understanding the assumptions and mechanisms underlying non-invasive neuromodulation, including fMRI-based neurofeedback training (NFT) and transcranial alternating current stimulation (tACS). I combine cutting-edge neuroimaging methods, including EEG, fMRI and machine learning, to understand neural signals and identify them as candidate neuromarkers for personalising neuromodulation.
Publications
Highlights
Clinical and Cognitive Neuroscience
- Auer, T., et al. (2008). Identifying seizure-onset zone and visualizing seizure spread by fMRI: a case report. Epileptic Disord.One of my most challenging PhD projects was the analysis of an fMRI capturing an epileptic seizure. I combined neurological considerations and adaptive data exploration and analytics and implemented a “travelling wave” approach to identify seizure onset and visualise seizure spread intuitively.
- Auer, T., et al. (2009). Does obstetric brachial plexus injury influence speech dominance? Annals of Neurology.I investigated the causality between handedness and language lateralisation confronting the dominant theory that handedness is secondary. Acquiring fMRI of a vulnerable sample, such as children with obstetric brachial plexus injury, and using a correlation technique, I demonstrated that hand usage could influence language lateralisation.
- 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 NeurosciMy first paper on neurofeedback training (NFT) identified and defined training efficiency and transfer success as two specific performance metrics. Moreover, I found training efficiency highly correlated to and predictive of transfer success; therefore, I could provide some practical guidance for NFTs.
- Auer, T., et al. (2018). Higher-order Brain Areas Associated with Real-time Functional MRI Neurofeedback Training of the Somato-motor Cortex. NeuroscienceInvestigating the functional connectivity during NFT, I emphasised the role of cognitive control and skill learning rather than conditioning as the main mechanisms underlying fMRI-based NFT. I also identified the anterior midcingulate cortex (aMCC) as the “mediator” of cognitive control in NFT.
- Auer, T., Lorenz, R., Violante, I. (2022). Auer, Tibor; Lorenz, Romy; Violante, Ines (2022): Decoding frequency-specific modulation of fMRI network connectivity: a tACS study. Figshare.I employed uni- and multivariate analyses and demonstrated that tACS has differentiable effects on both the magnitude of the fMRI activity and the effective connectivity. I also proved that effective connectivity in the dorsal attention and control networks is the most robust in decoding the stimulation frequency.
Neuroinformatics
- Cusack R., Vicente-Grabovetsky A., Mitchell D. J., Wild C. J., Auer T., et al. (2015) Automatic analysis (aa): Efficient neuroimaging workflows and parallel processing using Matlab and XML. Frontiers in NeuroinformaticsI took over the coordination of aa’s development in 2014. I improved its versatility and robustness by implementing parallel processing and integrating foundational tools for functional connectivity, multivariate analysis, diffusion tensor and kurtosis imaging, and M/EEG. I have redesigned the results reporting with quality assurance and implemented provenance capturing.
- Taylor, J. R., Williams, N., Cusack, R., Auer, T., et al. (2017). The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample. NeuroimageI employed and developed Automatic Analysis (aa) as the central processing pipeline for the Cambridge Centre for Ageing and Neuroscience (CamCAN) project, which demonstrated aa’s efficiency. As an outcome, I provided data, both raw and preprocessed according to the best practices, thus triggering and supporting more than a hundred studies.
- Gorgolewski, K. J., Auer, T., et al. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific DataI faced several informatics issues and challenges while working on the CamCAN project. Therefore, I joined the International Neuroinformatics Coordinating Facility (INCF) Neuroimaging Data Sharing Task Force. In collaboration with the Center for Reproducible Neuroscience, I worked on the BIDS standard, which became the gold standard for neuroimaging data management.
- Maumet, C., Auer, T., et al. (2016). Sharing brain mapping statistical results with the neuroimaging data model. Scientific DataI joined the initiative to harmonise the description of the results and the provenance of neuroimaging analyses. I contributed to the data model by defining several terms and their semantic relationships. The major foundation tools, including SPM, FSL, and AFNI, have adopted the data model.
- Karakuzu, A., Appelhoff, S., Auer, T., et al. (2022). qMRI-BIDS: An extension to the brain imaging data structure for quantitative magnetic resonance imaging data. Scientific DataI contributed to the extension of BIDS to provide clear guidance on how to store and share novel quantitative imaging data and tools.
- 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