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.
Hierarchical modelling of functional brain networks in population and individuals from big fMRI data
Farahibozorg S-R. et al, (2021), NeuroImage, 243, 118513 - 118513
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), Clinical Neuroradiology, 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), Human Brain Mapping, 38, 4631 - 4643