Biology subjects

Wager, T. D.

Publications and source records attributed to Wager, T. D..

5 recordsLinked to original sources

bioRxiv2018

Cognitive self-regulation influences pain-related physiology

Cognitive self-regulation can shape pain experience, but its effects on autonomic responses to painful events are unclear. In this study, participants (N = 41) deployed a cognitive strategy based on reappraisal and imagination to regulate pain up or down on different trials while skin conductance responses (SCR) and electrocardiogram (ECG) activity were recorded. Using a machine learning approach, we first developed stimulus-locked SCR and ECG physiological markers predictive of pain ratings. The physiological markers demonstrated high sensitivity and moderate specificity in predicting pain across two datasets, including an independent test dataset (N = 84). When we tested the markers on the cognitive self-regulation data, we found that cognitive self-regulation had significant impacts on both pain ratings and pain-related physiology in accordance with regulatory goals. These findings suggest that self-regulation can impact autonomic nervous system responses to painful stimuli and provide pain-related autonomic profiles for future studies.

neuroscience↗

bioRxiv2018

Multiple brain networks mediating stimulus-pain relationships in humans

The brain transforms nociceptive input into a complex pain experience comprised of sensory, affective, motivational, and cognitive components. However, it is still unclear how pain arises from nociceptive input, and which brain networks coordinate to generate pain experiences. We introduce a new high-dimensional mediation analysis technique to estimate distributed, network-level patterns mediating the relationship between stimulus intensity and pain. In a large-scale analysis of functional magnetic resonance imaging data (N=284), we identify both traditional mediators in somatosensory brain regions and additional mediators located in prefrontal, midbrain, striatal, and default-mode regions unrelated to nociception in standard analyses. The whole brain mediators are specific for pain vs. aversive sounds and are organized in five functional networks. Brain mediators explain 32% more within-subject variance of single-trial pain ratings than previous brain-based models. Our results provide a new, broader view of the networks underlying pain experience, as well as distinct targets for interventions.

neuroscience↗

bioRxiv2018

Effect Size and Power in fMRI Group Analysis

Multi-subject functional magnetic resonance imaging (fMRI) analysis is often concerned with determining whether there exists a significant population-wide activation in a comparison between two or more conditions. Typically this is assessed by testing the average value of a contrast of parameter estimates (COPE) against zero in a general linear model (GLM) analysis. In this work we investigate several aspects of this type of analysis. First, we study the effects of sample size on the sensitivity and reliability of the group analysis, allowing us to evaluate the ability of small sampled studies to effectively capture population-level effects of interest. Second, we assess the difference in sensitivity and reliability when using volumetric or surface based data. Third, we investigate potential biases in estimating effect sizes as a function of sample size. To perform this analysis we utilize the task-based fMRI data from the 500-subject release from the Human Connectome Project (HCP). We treat the complete collection of subjects (N = 491) as our population of interest, and perform a single-subject analysis on each subject in the population. We investigate the ability to recover population level effects using a subset of the population and standard analytical techniques. Our study shows that sample sizes of 40 are generally able to detect regions with high effect sizes (Cohens d > 0.8), while sample sizes closer to 80 are required to reliably recover regions with medium effect sizes (0.5 < d < 0.8). We find little difference in results when using volumetric or surface based data with respect to standard mass-univariate group analysis. Finally, we conclude that special care is needed when estimating effect sizes, particularly for small sample sizes.

neuroscience↗

bioRxiv2017

From affect to control: Functional specialization of the insula in motivation and regulation

The insula plays a key role in a wide range of brain processes, from viscerosensation and pain to motivation, emotion, and cognitive control. While human neuroimaging studies in all these domains report activations in the insula, little systematic attention is paid to anatomical subdivisions that may provide the basis for functional sub-regions. We conducted a meta-analysis of insular tasks across studies in four domains: emotion, pain, attention switching, and working memory. Using a priori subdivision of the insula based on anatomical studies, we provide evidence that different sub-regions are preferentially activated in different tasks. We suggest that the ventral anterior insula is most important for core affect, a term that describes broadly-tuned motivational states (e.g., excitement) with associated subjective feelings. The dorsal anterior insula, by contrast, may be critical for developing and updating motivational states with specific associated actions (i.e., goals). This region is activated by cognitive control tasks, pain, and some tasks that elicit affective processing. The posterior insula, including SII and portions of parietal operculum, is distinctly activated by pain, providing a double dissociation between pain and tasks that elicit emotions.

neuroscience↗

bioRxiv2016

Large-scale meta-analysis suggests low regional modularity in lateral frontal cortex.

Extensive fMRI study of human lateral frontal cortex (LFC) has yet to yield a consensus mapping between discrete anatomy and psychological states, partly due to the difficulty of inferring mental states in individual studies. Here, we used a data-driven approach to generate a comprehensive functional-anatomical mapping of LFC from 11,406 neuroimaging studies. We identified putatively separable LFC regions on the basis of whole-brain co-activation, revealing 14 clusters organized into three whole-brain networks. Next, we used multivariate classification to identify the psychological states that best predicted activity in each sub-region, resulting in preferential psychological profiles. We observed large functional differences between networks, suggesting brain networks support distinct modes of processing. Within each network, however, we observed low functional specificity, suggesting discrete psychological states are not modularly organized. Our results are consistent with the view that individual LFC regions work as part of highly parallel, distributed networks to give rise to flexible, adaptive behavior.

neuroscience↗