Timothy E Behrens | PubMed Health

PubMed2006

Determining anatomical connectivities between cortical and brainstem pain processing regions in humans: a diffusion tensor imaging study in healthy controls.

Neuroimaging methods have so far identified various structures in the brain involved in the processing of pain and its control. However, our understanding of their anatomical connectivities is relatively weak. Diffusion tensor imaging (DTI), a magnetic resonance imaging-based method, allows in vivo mapping of the anatomical connections in the human brain and was used to investigate the white matter connections originating from the periaquaductal grey (PAG) and nucleus cuneiformis (NCF). We performed DTI on 8 healthy right-handed male volunteers. Group analysis showed that tract paths could be defined and their likelihood quantified for connections between the PAG and separately for the NCF, to the prefrontal cortex, amygdala, thalamus, hypothalamus and rostroventral medial medulla bilaterally. The connections identified confirm the existence of an anatomical circuitry for the functionally characterised top-down influences on pain processing via brainstem structures in humans.

Adult↗

PubMed2006

Variational Bayes inference of spatial mixture models for segmentation.

Mixture models are commonly used in the statistical segmentation of images. For example, they can be used for the segmentation of structural medical images into different matter types, or of statistical parametric maps into activating and nonactivating brain regions in functional imaging. Spatial mixture models have been developed to augment histogram information with spatial regularization using Markov random fields (MRFs). In previous work, an approximate model was developed to allow adaptive determination of the parameter controlling the strength of spatial regularization. Inference was performed using Markov Chain Monte Carlo (MCMC) sampling. However, this approach is prohibitively slow for large datasets. In this work, a more efficient inference approach is presented. This combines a variational Bayes approximation with a second-order Taylor expansion of the components of the posterior distribution, which would otherwise be intractable to Variational Bayes. This provides inference on fully adaptive spatial mixture models an order of magnitude faster than MCMC. We examine the behavior of this approach when applied to artificial data with different spatial characteristics, and to functional magnetic resonance imaging statistical parametric maps.

Algorithms↗