I use tools from network science, machine learning and deep learning to extract quantitative information from magnetic resonance imaging scans of human brains. I am most interested in the methods used to pre-process and analyse imaging data, as these are often relevant across a wide range of application scenarios, in health and disease. Recently, I have been most interested in deep learning for pre-processing and enhancement of portable ultra-low-field neuroimaging, null models for statistical inference in network neuroscience, and the practical relevance of quantitative neuroimaging in clinical settings.

I also co-lead two third-year modules on the BSc Neuroscience and Psychology, on Machine Learning in Neuroscience and Computational Neuroscience. 

Please see my  Research Staff Profile for more detail

Key publications:

• Váša F & Mišić B., 2022. Null models in network neuroscience. Nature Reviews Neuroscience.
• Váša F et al., 2021. Rapid processing and quantitative evaluation of structural brain scans for adaptive multimodal imaging. Human Brain Mapping. 
• Váša F et al., 2020. Conservative and disruptive modes of adolescent change in brain functional connectivity. Proc. Nat. Acad. Sci. USA.
• Váša F et al., 2018. Adolescent tuning of association cortex in human structural brain networks. Cerebral Cortex.
  
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Key collaborators:

• Prof Robert Leech, King's College London
• Prof. Edward T Bullmore, University of Cambridge
• Dr. Bratislav Mišić, Montréal Neurological Institute
• Dr. Jakob Seidlitz, University of Pennsylvania