Contact information

Email	stam[at]fmrib.ox.ac.uk
Tel	+44 (0) 1865 222782
Fax	+44 (0) 1865 222717

Stamatios Sotiropoulos

Honorary Research Fellow, FMRIB Analysis

Associate Professor, Sir Peter Mansfield Imaging Centre, University of Nottingham

My connections

Diffusion Modelling and Analysis Research Group
Computational Neuroscience Research Group
Connectivity Modelling Research Group
Multimodal Analysis Research Group

Research Summary

I develop tools for the analysis of diffusion MRI data of the brain. I am interested in biophysical modelling of brain connections at different scales, both for resolving tissue microstructural patterns and long-range brain connectivity. I develop computational methods for inferring this information in-vivo and I am particularly interested in data fusion approaches for combining strengths and bridging the gaps across structural modalities (for instance, diffusion MRI at different k/q-space acquisitions, histology, microscopy). I am also heavily involved with the design and implementation of new technologies that aim to improve the spatial and angular resolution of diffusion MRI acquisitions.

More specifically, I am currently involved in the following projects:

The recently funded EPSRC project on "Anatomy-Driven Brain Connectivity Mapping".
The Human Connectome Project (funded by the US NIH)
The developing Human Connectome Project (funded by ERC)
The UK Biobank Imaging Study

PubMed Search

Google Scholar Profile

ORCID ID

Nottingham webpage

Building connectomes using diffusion MRI: why, how and but.
Sotiropoulos SN. and Zalesky A., (2017), NMR Biomed
Image quality transfer and applications in diffusion MRI.
Alexander DC. et al, (2017), Neuroimage, 152, 283 - 298
On the mechanical behaviour of PEEK and HA cranial implants under impact loading.
Garcia-Gonzalez D. et al, (2017), J Mech Behav Biomed Mater, 69, 342 - 354
Image Processing and Quality Control for the first 10,000 Brain Imaging Datasets from UK Biobank
Alfaro Almagro F. et al, (2017), biorxiv
Studying neuroanatomy using MRI.
Lerch JP. et al, (2017), Nat Neurosci, 20, 314 - 326
Multimodal population brain imaging in the UK Biobank prospective epidemiological study.
Miller KL. et al, (2016), Nat Neurosci, 19, 1523 - 1536
Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images.
Andersson JLR. et al, (2016), Neuroimage, 141, 556 - 572
The Human Connectome Project's neuroimaging approach.
Glasser MF. et al, (2016), Nat Neurosci, 19, 1175 - 1187
Fusion in diffusion MRI for improved fibre orientation estimation: An application to the 3T and 7T data of the Human Connectome Project.
Sotiropoulos SN. et al, (2016), Neuroimage, 134, 396 - 409
Using Diffusion Tractography to Predict Cortical Connection Strength and Distance: A Quantitative Comparison with Tracers in the Monkey.
Donahue CJ. et al, (2016), J Neurosci, 36, 6758 - 6770
An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging.
Andersson JLR. and Sotiropoulos SN., (2016), Neuroimage, 125, 1063 - 1078
A probabilistic atlas of the cerebellar white matter.
van Baarsen KM. et al, (2016), Neuroimage, 124, 724 - 732
Structural Organization of the Corpus Callosum Predicts Attentional Shifts after Continuous Theta Burst Stimulation.
Chechlacz M. et al, (2015), J Neurosci, 35, 15353 - 15368
Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes.
Andersson JL. and Sotiropoulos SN., (2015), Neuroimage, 122, 166 - 176
High resolution whole brain diffusion imaging at 7T for the Human Connectome Project.
Vu AT. et al, (2015), Neuroimage, 122, 318 - 331
Measuring macroscopic brain connections in vivo.
Jbabdi S. et al, (2015), Nat Neurosci, 18, 1546 - 1555
Sparse Bayesian Inference of White Matter Fiber Orientations from Compressed Multi-resolution Diffusion MRI.
Pisharady PK. et al, (2015), Med Image Comput Comput Assist Interv, 9349, 117 - 124
Heritability of fractional anisotropy in human white matter: a comparison of Human Connectome Project and ENIGMA-DTI data.
Kochunov P. et al, (2015), Neuroimage, 111, 300 - 311
Normalisation of brain connectivity through compensatory behaviour, despite congenital hand absence
Hahamy A. et al, (2015), eLife, 2015
Normalisation of brain connectivity through compensatory behaviour, despite congenital hand absence.
Hahamy A. et al, (2015), Elife, 4
Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization.
Canales-Rodríguez EJ. et al, (2015), PLoS One, 10
Correction: Accelerating Fibre Orientation Estimation from Diffusion Weighted Magnetic Resonance Imaging Using GPUs.
Hernández M. et al, (2015), PLoS One, 10
Study protocol: The Whitehall II imaging sub-study.
Filippini N. et al, (2014), BMC Psychiatry, 14
Effects of image reconstruction on fiber orientation mapping from multichannel diffusion MRI: Reducing the noise floor using SENSE
Sotiropoulos SN. et al, (2013), Magnetic Resonance in Medicine, 70, 1682 - 1689
MR Diffusion Tractography
Behrens TEJ. et al, (2013), 429 - 451
Mapping Connections in Humans and Non-Human Primates. Aspirations and Challenges for Diffusion Imaging.
Van Essen DC. et al, (2013), 337 - 358
The CONNECT project: Combining macro- and micro-structure.
Assaf Y. et al, (2013), Neuroimage, 80, 273 - 282
Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project
Uǧurbil K. et al, (2013), NeuroImage, 80, 80 - 104
Advances in diffusion MRI acquisition and processing in the Human Connectome Project
Sotiropoulos SN. et al, (2013), NeuroImage, 80, 125 - 143
The minimal preprocessing pipelines for the Human Connectome Project
Glasser MF. et al, (2013), NeuroImage, 80, 105 - 124
RubiX: combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI.
Sotiropoulos SN. et al, (2013), IEEE Trans Med Imaging, 32, 969 - 982
The topographic connectome.
Jbabdi S. et al, (2013), Curr Opin Neurobiol, 23, 207 - 215
Accelerating fibre orientation estimation from diffusion weighted magnetic resonance imaging using GPUs.
Hernández M. et al, (2013), PLoS One, 8
Model-based analysis of multishell diffusion MR data for tractography: How to get over fitting problems
Jbabdi S. et al, (2012), Magnetic Resonance in Medicine, 68, 1846 - 1855
Model-based analysis of multishell diffusion MR data for tractography: how to get over fitting problems.
Jbabdi S. et al, (2012), Magn Reson Med, 68, 1846 - 1855
Modelling fibre fanning in diffusion-weighted MRI
Sotiropoulos SN. et al, (2012), Proceedings - International Symposium on Biomedical Imaging, 14 - 17
Accelerating fibre orientation estimation from diffusion weighted magnetic resonance imaging using GPUs
Hernández M. et al, (2012), Proceedings - 20th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2012, 622 - 626
Ball and rackets: Inferring fiber fanning from diffusion-weighted MRI.
Sotiropoulos SN. et al, (2012), Neuroimage, 60, 1412 - 1425
Fuzzy anatomical connectedness of the brain using single and multiple fibre orientations estimated from diffusion MRI.
Sotiropoulos SN. et al, (2010), Comput Med Imaging Graph, 34, 504 - 513
Exact and analytic bayesian inference for orientation distribution functions
Sotiropoulos SN. et al, (2010), 2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings, 1189 - 1192
Brain tractography using Q-ball imaging and graph theory: Improved connectivities through fibre crossings via a model-based approach.
Sotiropoulos SN. et al, (2010), Neuroimage, 49, 2444 - 2456
Robust graph-based tracking through crossing fibre configurations
Sotiropoulos SN. et al, (2009), Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, 1394 - 1397
In-vivo brain anatomical connectivity using diffusion magnetic resonance imaging and fuzzy connectedness
Sotiropoulos SN. et al, (2008), 8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008
A regularized two-tensor model fit to low angular resolution diffusion images using basis directions.
Sotiropoulos SN. et al, (2008), J Magn Reson Imaging, 28, 199 - 209
Assessing the direct effects of deep brain stimulation using embedded axon models.
Sotiropoulos SN. and Steinmetz PN., (2007), J Neural Eng, 4, 107 - 119
Low-cost real-time 2-D motion detection based on reconfigurable computing
Papadimitriou K. et al, (2006), IEEE Transactions on Instrumentation and Measurement, 55, 2234 - 2243
A 2-D motion detection model for low-cost embedded reconfigurable I/O devices.
Dollas A. et al, (2005), IEEE Trans Biomed Eng, 52, 1443 - 1449

Key Publications

Advances in diffusion MRI acquisition and processing in the Human Connectome Project
Sotiropoulos SN. et al, (2013), NeuroImage, 80, 125 - 143
Fusion in diffusion MRI for improved fibre orientation estimation: An application to the 3T and 7T data of the Human Connectome Project.
Sotiropoulos SN. et al, (2016), Neuroimage, 134, 396 - 409
Measuring macroscopic brain connections in vivo.
Jbabdi S. et al, (2015), Nat Neurosci, 18, 1546 - 1555
Ball and rackets: Inferring fiber fanning from diffusion-weighted MRI.
Sotiropoulos SN. et al, (2012), Neuroimage, 60, 1412 - 1425
Model-based analysis of multishell diffusion MR data for tractography: how to get over fitting problems.
Jbabdi S. et al, (2012), Magn Reson Med, 68, 1846 - 1855

Recent Publications

Building connectomes using diffusion MRI: why, how and but.
Sotiropoulos SN. and Zalesky A., (2017), NMR Biomed
Image quality transfer and applications in diffusion MRI.
Alexander DC. et al, (2017), Neuroimage, 152, 283 - 298
On the mechanical behaviour of PEEK and HA cranial implants under impact loading.
Garcia-Gonzalez D. et al, (2017), J Mech Behav Biomed Mater, 69, 342 - 354
Image Processing and Quality Control for the first 10,000 Brain Imaging Datasets from UK Biobank
Alfaro Almagro F. et al, (2017), biorxiv
Studying neuroanatomy using MRI.
Lerch JP. et al, (2017), Nat Neurosci, 20, 314 - 326

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