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  • Mechanisms underlying cortical activity during value-guided choice.

    8 June 2018

    When choosing between two options, correlates of their value are represented in neural activity throughout the brain. Whether these representations reflect activity that is fundamental to the computational process of value comparison, as opposed to other computations covarying with value, is unknown. We investigated activity in a biophysically plausible network model that transforms inputs relating to value into categorical choices. A set of characteristic time-varying signals emerged that reflect value comparison. We tested these model predictions using magnetoencephalography data recorded from human subjects performing value-guided decisions. Parietal and prefrontal signals matched closely with model predictions. These results provide a mechanistic explanation of neural signals recorded during value-guided choice and a means of distinguishing computational roles of different cortical regions whose activity covaries with value.

  • Bayesian analysis of neuroimaging data in FSL.

    18 June 2018

    Typically in neuroimaging we are looking to extract some pertinent information from imperfect, noisy images of the brain. This might be the inference of percent changes in blood flow in perfusion FMRI data, segmentation of subcortical structures from structural MRI, or inference of the probability of an anatomical connection between an area of cortex and a subthalamic nucleus using diffusion MRI. In this article we will describe how Bayesian techniques have made a significant impact in tackling problems such as these, particularly in regards to the analysis tools in the FMRIB Software Library (FSL). We shall see how Bayes provides a framework within which we can attempt to infer on models of neuroimaging data, while allowing us to incorporate our prior belief about the brain and the neuroimaging equipment in the form of biophysically informed or regularising priors. It allows us to extract probabilistic information from the data, and to probabilistically combine information from multiple modalities. Bayes can also be used to not only compare and select between models of different complexity, but also to infer on data using committees of models. Finally, we mention some analysis scenarios where Bayesian methods are impractical, and briefly discuss some practical approaches that we have taken in these cases.

  • Distinct right frontal lobe activation in language processing following left hemisphere injury.

    19 February 2018

    Right hemisphere activation during functional imaging studies of language has frequently been reported following left hemisphere injury. Few studies have anatomically characterized the specific right hemisphere structures engaged. We used functional MRI (fMRI) with verbal fluency tasks in 12 right-handed patients with left temporal lobe epilepsy (LTLE) and 12 right-handed healthy controls to localize language-related activity in the right inferior frontal gyrus (RIFG). During the phonemic task, LTLE patients activated a significantly more posterior region of the right anterior insula/frontal operculum than healthy controls (P = 0.02). Activation of the left inferior frontal gyrus (LIFG) did not differ significantly between the two groups. This suggests that, following left hemisphere injury, language-related processing in the right hemisphere differs from that with a functionally normal left hemisphere. The localization of activation in the left and right inferior frontal gyri was determined with respect to the anatomical sub-regions pars opercularis (Pop), pars triangularis (Ptr) and pars orbitalis (Por). In the LIFG, both healthy controls (8 out of 12) and LTLE patients (9 out of 12) engaged primarily Pop during phonemic fluency. Activations in the RIFG, however, were located mostly in the anterior insula/frontal operculum in both healthy controls (8 out of 12) and LTLE patients (8 out of 12), albeit in distinct regions. Mapping the locations of peak voxels in relation to previously obtained cytoarchitectonic maps of Broca's area confirmed lack of homology between activation regions in the left and right IFG. Verbal fluency-related activation in the RIFG was not anatomically homologous to LIFG activation in either patients or controls. To test more directly whether RIFG activation shifts in a potentially adaptive manner after left hemisphere injury, fMRI studies were performed in a patient prior to and following anatomical left hemispherectomy for the treatment of Rasmussen's encephalitis. An increase in activation magnitude and posterior shift in location were found in the RIFG after hemispherectomy for both phonemic and semantic tasks. Together, these results suggest that left temporal lobe injury is associated with potentially adaptive changes in right inferior frontal lobe functions in processing related to expressive language.

  • Human connectomics.

    31 May 2018

    Recent advances in non-invasive neuroimaging have enabled the measurement of connections between distant regions in the living human brain, thus opening up a new field of research: Human connectomics. Different imaging modalities allow the mapping of structural connections (axonal fibre tracts) as well as functional connections (correlations in time series), and individual variations in these connections may be related to individual variations in behaviour and cognition. Connectivity analysis has already led to a number of new insights about brain organization. For example, segregated brain regions may be identified by their unique patterns of connectivity, structural and functional connectivity may be compared to elucidate how dynamic interactions arise from the anatomical substrate, and the architecture of large-scale networks connecting sets of brain regions may be analysed in detail. The combined analysis of structural and functional networks has begun to reveal components or modules with distinct patterns of connections that become engaged in different cognitive tasks. Collectively, advances in human connectomics open up the possibility of studying how brain connections mediate regional brain function and thence behaviour.

  • Addressing a systematic vibration artifact in diffusion-weighted MRI.

    26 April 2018

    We have identified and studied a pronounced artifact in diffusion-weighted MRI on a clinical system. The artifact results from vibrations of the patient table due to low-frequency mechanical resonances of the system which are stimulated by the low-frequency gradient switching associated with the diffusion-weighting. The artifact manifests as localized signal-loss in images acquired with partial Fourier coverage when there is a strong component of the diffusion-gradient vector in the left-right direction. This signal loss is caused by local phase ramps in the image domain which shift the apparent k-space center for a particular voxel outside the covered region. The local signal loss masquerades as signal attenuation due to diffusion, severely disrupting the quantitative measures associated with diffusion-tensor imaging (DTI). We suggest a way to improve the interpretation of affected DTI data by including a co-regressor which accounts for the empirical response of regions affected by the artifact. We also demonstrate that the artifact may be avoided by acquiring full k-space data, and that subsequent increases in TE can be avoided by employing parallel acceleration.

  • What is the most interesting part of the brain?

    26 April 2018

    Creative ideas and rigorous analysis are the hallmarks of much impactful science. However, there is an oft-aired suspicion in the neuroscience community that some scientists start with an advantage, simply because of the brain region or behaviour they study. We tested this unstated hypothesis by regressing the journal impact factor against both the pattern of brain activity and the experimental keywords across thousands of brain imaging studies. We found the results to be illuminating.

  • Mixture models with adaptive spatial regularization for segmentation with an application to FMRI data.

    31 May 2018

    Mixture models are often used in the statistical segmentation of medical images. For example, they can be used for the segmentation of structural images into different matter types or of functional statistical parametric maps (SPMs) into activations and nonactivations. Nonspatial mixture models segment using models of just the histogram of intensity values. Spatial mixture models have also been developed which augment this histogram information with spatial regularization using Markov random fields. However, these techniques have control parameters, such as the strength of spatial regularization, which need to be tuned heuristically to particular datasets. We present a novel spatial mixture model within a fully Bayesian framework with the ability to perform fully adaptive spatial regularization using Markov random fields. This means that the amount of spatial regularization does not have to be tuned heuristically but is adaptively determined from the data. We examine the behavior of this model when applied to artificial data with different spatial characteristics, and to functional magnetic resonance imaging SPMs.