Biology subjects

Duff, E.

Publications and source records attributed to Duff, E..

3 recordsLinked to original sources

bioRxiv2018

Exploring the prediction of emotional valence and pharmacologic effect across fMRI studies of antidepressants.

BackgroundClinically approved antidepressants modulate the brains emotional valence circuits, suggesting that the response of these circuits could serve as a biomarker for screening candidate antidepressant drugs. However, it is necessary that these modulations can be reliably detected. Here, we apply a cross-validated predictive model to classify emotional valence and pharmacologic effect across eleven task-based fMRI datasets (n=306) exploring the effect of antidepressant administration on emotional face processing.\n\nMethodsWe created subject-level contrast of parameter estimates of the emotional faces task and used the Shen whole-brain parcellation scheme to define 268 subject-level features that trained a cross-validated gradient-boosting machine protocol to classify emotional valence (fearful vs happy face visual conditions) and pharmacologic effect (drug vs placebo administration) within and across studies.\n\nResultsWe found patterns of brain activity that classify emotional valence with a statistically significant level of accuracy (70% across-all-subjects; range from 50-87% across-study). Our classifier failed to consistently discriminate drug from placebo. Subject population (healthy or unhealthy), treatment group (drug or placebo), and drug administration protocol (dose and duration) affected this accuracy with similar populations better predicting one another.\n\nConclusionsWe found limited evidence that antidepressants modulated brain response in a consistent manner, however found a consistent signature for emotional valence. Variable functional patterns across studies suggest that predictive modeling can inform biomarker development in mental health and in pharmacotherapy development. Our results suggest that case-controlled designs and more standardized protocols are required for functional imaging to provide robust biomarkers for drug development.

neuroscience↗

bioRxiv2017

Geospatial statistics of high field functional MRI reveal topographical clustering for binocular stereo depth in early visual cortex

Significance statementFunctional topography is present throughout the cerebral cortex, often in the form of columns or clusters of neurons with similar functional properties within identified cortical regions. Most of the evidence for these structures comes from work with non-human species. Using high-field strength magnetic resonance imaging in living human cortex, the search for this structure is frustrated by the presence of a background of local spatial correlations in the signal across the cortical surface. This structured background, which we term neural dust, is a form of noise that imposes a fundamental limit on the detection of clustering and topography. We apply a novel analysis approach that quantifies the background of spatial correlations, so that we achieve reliable identification of high-signal clusters of activation in the human cortex at a scale previously only visible with invasive optical imaging in animals. We searched specifically in visual cortex for correlates of binocular stereoscopic depth. We show both that signals for stereoscopic depth are clustered into specific zones within the cortex and that these signals occur within spatially extended cortical domains, which have similar preferences for the stereoscopic depth. The revealed structures are reliably identified in all subjects tested and in repeated testing of individual subjects. Our methods provide an objective approach for defining the size and locations of clusters of activation within functional images of neural activity.\n\nAbstractA characteristic principle of the organization of cerebral neocortex is the presence of clusters or columns of neurons with similar functional properties. Animals with forward-facing eyes exploit slight differences between the images in the two eyes to determine binocular depth using stereopsis. Evidence for an organized structure in human visual cortex for the representation of stereoscopic depth has proved elusive. Using 7-tesla functional MRI, with gradient-echo echo-planar imaging at 0.75 mm isotropic resolution and a novel analytical approach based on geospatial mapping methods, we find that clustered responses for disparity-defined depth can be clearly segregated from a background of spatially correlated signals in all subjects tested. High-signal clusters are associated with cortical domains as large as 12-15mm across the cortical surface, in which nearby points in the cortical map tend to respond to the same stereoscopic depth. These domains are found predominantly within visual cortical area V3A.

neuroscience↗

bioRxiv2017

Disambiguating brain functional connectivity

Functional connectivity (FC) analyses of correlations of neural activity are used extensively in neuroimaging and electrophysiology to gain insights into neural interactions. However, correlation fails to distinguish sources as different as changes in neural signal amplitudes or noise levels. This ambiguity substantially diminishes the value of FC for inferring system properties and clinical states. Network modelling approaches may avoid ambiguities, but require specific assumptions. We present an enhancement to FC analysis with improved specificity of inferences, minimal assumptions and no reduction in flexibility. The Additive Signal Change (ASC) approach characterises FC changes into certain prevalent classes involving additions of signal. With FMRI data, the approach reveals a rich diversity of signal changes underlying measured changes in FC, bringing into question standard interpretations. The ASC method can also be used to disambiguate other measures of dependency, such as regression and coherence, providing a flexible tool for the analysis of neural data.

neuroscience↗