Research

• Probabilistic tractography

Figure shows log probability of connection to the Splenium of the Corpus Callosum (seeded 5mm above the current slice). Probability distributions for the principle diffusion direction are calculated at each voxel, and these probabilities are integrated over all possible connecting paths. See Behrens et al. MRM 2003, or Behrens et al. 2007 for an extension to multiple fibre orientations per voxel. This work has been implemented as the FMRIB Diffusion Toolbox (FDT) part of FSL (Fmrib's Sofware Library)

Collaborators: Mark Woolrich, Mark Jenkinson, Heidi Johansen-Berg, Rita Nunes, Stuart Clare, Paul Matthews, Mike Brady, Steve Smith (Oxford)

• Connectivity-based segmentation

Using probabilistic diffusion tractography (Behrens et al. MRM 2003) we were able to segment the human thalamus on the basis of its connectivity to the cortex (Behrens et al. Nat Neurosci 2003).
We propose that the resulting subdivisions correspond to groups of nuclei and have used this approach to produce a probabilistic atlas of the human thalamus based on its cortical connectivity (Johansen-Berg et al. Cereb Cortex 2004)

Collaborators: Mark Woolrich, Emma Sillery, Katie Sheehan, Steve Smith, Paul Matthews (Oxford)
Alan Thomspon, Olga Cicarelli, Claudia Wheeler-Kingshott, Phil Boulby, Gareth Barker (UCL, London)

We tested the validity of connectivity-based segmentation of the thalamus by comparing the location of functional activations during executive (red spheres) and motor (blue spheres) tasks. Activation during motor tasks co-localised with thaalmic volumes projecting to sensory (green), motor (blue) and premotor (brown) cortices (ii). Activation during exective and memory tasks co-localised with the thalamic volume projecting to prefrontal cortex (dark grey). See Johansen-Berg et al. Cereb Cortex 2004

Collaborators: Tim Behrens, Emma Sillery, Steve Smith, Paul Matthews (Oxford)
Alan Thomspon, Olga Cicarelli (UCL, London)

• Parcellation by changes in connectivity profiles

Using probabilistic diffusion tractography (Behrens et al, 2002a) we generate connectivity profiles for points along the cortex. By detecting where these profiles change we are able to define borders between functional-anatomical regions. In the example above, a change in connectivity defines the border between SMA and pre-SMA(C). The connectivity-defined border corresponds closely to the funcitonally-defined border from fMRI(A). See Johansen-Berg et al. PNAS 2004

Collaborators: Des Higham (Strathclyde)
Matt Robson, Ivana Drobnjak, Steve Smith, Mike Brady, Matthew Rushworth, Paul Matthews (Oxford)

• Global probabilistic tractography

We readdressed the diffusion tractography problem in a global and probabilistic manner. Instead of tracking through local orientations, we parametrise the connexions between brain regions at a global level, and then infer on global and local parameters simultaneously in a Bayesian framework. This approach offers a number of important benefits. The global nature of the tractography reduces sensitivity to local noise and modelling errors. By constraining tractography to ensure a connexion is found, and then inferring on the exact location of the connexion, we increase the robustness of connectivity-based parcellations, allowing parcellations of connexions that were previously invisible to tractography. The Bayesian framework allows a direct comparison of the evidence for connecting and non-connecting models, to test whether the connexion is supported by the data. Crucially, by explicit parametrisation of the connexion between brain regions, we infer on a parameter that is shared with models of functional connectivity. This model is a first step toward the joint inference on functional and anatomical connectivity. See Jbabdi et al. NeuroImage 2007

Collaborators: Tim Behrens, Mark Woolrich, Jesper Andersson (Oxford)