Royal Academy of Engineering Research Fellow
I am a new principal investigator in FMRIB Analysis group, and my research is funded by the Royal Academy of Engineering. My research aims to design new machine learning tools that can use non-invasive functional brain imaging such as fMRI, and make predictions about personalised traits (e.g. age or IQ) and disease (e.g. Dementia).
Recently, we developed stochastic PROFUMO (sPROFUMO), that simultaneously estimates brain networks for big populations (e.g. UK Biobank with expected 100,000 people) and every individual person. Links to paper, code repository, and FSL course lectures on s/PROFUMO.
Before joining FMRIB, I did my PhD at Cambridge University, funded by Cambridge International Scholarship Scheme. My project was aimed at developing brain connectivity methods for magnetoencephalography, and their application to understand brain networks underlying semantic memory.
I am pleased to consider applications from prospective PhD (DPhil) and MSc students. Our research is highly collaborative and students will be co-supervised by two or more advisors. Please email me your CV and research interests to discuss possible projects.
Within capacity, I am happy to undertake 'application mentoring' for those who belong to underrepresented groups in STEM. Specifically, if you are applying to a PhD programme or postdoc positions (not supervised by me), I would be happy to provide feedback on your application materials and do mock interviews.
Hierarchical modelling of functional brain networks in population and individuals from big fMRI data.
Farahibozorg S-R. et al, (2021), Neuroimage
Distinct Roles for the Anterior Temporal Lobe and Angular Gyrus in the Spatiotemporal Cortical Semantic Network
Farahibozorg S-R. et al, (2022), Cerebral Cortex
Adaptive cortical parcellations for source reconstructed EEG/MEG connectomes.
Farahibozorg S-R. et al, (2018), Neuroimage, 169, 23 - 45
Age- and sex-related variations in the brain white matter fractal dimension throughout adulthood: an MRI study.
Farahibozorg S. et al, (2015), Clin Neuroradiol, 25, 19 - 32
Modelling subject variability in the spatial and temporal characteristics of functional modes.
Harrison SJ. et al, (2020), Neuroimage, 222
Detecting large-scale networks in the human brain using high-density electroencephalography.
Liu Q. et al, (2017), Hum Brain Mapp, 38, 4631 - 4643