Tibor Auer

neuroinformatician, neurohacker, neurodata scientist, neuroscience opener

Bio

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 neeuroimaging 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.

Research Interest

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.

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.