Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

FMRI is capable of measuring changes in local blood oxygenation, and this enables it to detect changes in neuronal activity. To begin with fMRI experiments analysed changes related to task-induced activity within subjects, but more recently the intrinsic spontaneous fluctuations seen in resting-state fMRI have become of great interest for investigating connectivity. Our research in FMRI Modelling and Analysis covers both task-based and resting-state fMRI.

Much of our current work in this area is focussed on resting-state network modelling, looking at new ways to identify complex (e.g., overlapping and dynamic) functionally-meaningful networks. This includes voxel-wise methods (MELODIC, dual-regression), network models (NETMATS) and investigations into non-stationary network modelling (Temporal Functional Modes). Much of this work is shared with work in the Connectivity Modelling area.

We work closely with our collaborators at the OHBA Analysis Group and with the Statistical Imaging Neuroscience group at the Donders, Nijmegen.

 

 

 

 

 

 

 

 

Selected publications

Large-scale Probabilistic Functional Modes from resting state fMRI

Journal article

Harrison SJ. et al, (2015), NeuroImage, 109, 217 - 231

Functional connectomics from resting-state fMRI

Other

Smith SM. et al, (2013), Trends in Cognitive Sciences, 17, 666 - 682

Resting-state fMRI in the Human Connectome Project

Journal article

Smith SM. et al, (2013), NeuroImage, 80, 144 - 168

Function in the human connectome: Task-fMRI and individual differences in behavior

Journal article

Barch DM. et al, (2013), NeuroImage, 80, 169 - 189

Task-driven ICA feature generation for accurate and interpretable prediction using fMRI

Journal article

Duff EP. et al, (2012), NeuroImage, 60, 189 - 203

Correspondence of the brain's functional architecture during activation and rest

Journal article

Smith SM. et al, (2009), Proceedings of the National Academy of Sciences, 106, 13040 - 13045

Variability in fMRI: A re‐examination of inter‐session differences

Journal article

Smith SM. et al, (2005), Human Brain Mapping, 24, 248 - 257

Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging

Journal article

Beckmann CF. and Smith SM., (2004), IEEE Transactions on Medical Imaging, 23, 137 - 152

Fully Bayesian Spatio-Temporal Modeling of FMRI Data

Journal article

Woolrich MW. et al, (2004), IEEE Transactions on Medical Imaging, 23, 213 - 231

Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data

Journal article

Woolrich MW. et al, (2001), NeuroImage, 14, 1370 - 1386