fMRI brown bag: April 19, 2023
Please join us for an fMRI brown bag given by Ke Bo (CANLab - Dartmouth College), who will be presenting Deconstructing the brain bases of emotion regulation: A systems identification approach using Bayes factors and Feilong Ma (Haxby Lab - Dartmouth College), who will be presenting A cortical surface template for human neuroscience.
Time: 4-5 p.m.
Location: Moore Hall, room 202
Deconstructing the brain bases of emotion regulation: A systems-identification approach using bayes factors
Abstract: Cognitive reappraisal is fundamental to cognitive therapies and everyday emotion regulation. Analyses using Bayes factors and an axiomatic systems-identification approach identified four reappraisal-related components encompassing distributed neural activity patterns across two independent fMRI studies (n=182 and n=176): (1) An anterior prefrontal system selectively involved in cognitive reappraisal; (2) A fronto-parietal-insular system engaged by both reappraisal and emotion generation, demonstrating a general role in appraisal; (3) A largely subcortical system activated during negative emotion generation but unaffected by reappraisal, including amygdala, hypothalamus, and periaqueductal gray; and (4) a posterior cortical system of negative emotion-related regions down-regulated by reappraisal. These systems predicted individual differences in reappraisal success and were differentially related to neurotransmitter binding maps, suggesting a novel role for cannabinoid and serotonin systems in reappraisal. These findings challenge ‘limbic’-centric models of reappraisal and provide new systems-level targets for assessing and enhancing emotion regulation.
A cortical surface template for human neuroscience
Abstract: Neuroimaging data analysis relies on normalization to standard anatomical templates to resolve macroanatomical differences across brains. Existing human cortical surface templates sample locations unevenly because of distortions introduced by inflation of the folded cortex into a standard shape. Here we present the onavg template, which affords uniform sampling of the cortex. We created the onavg template based on openly-available high-quality structural scans of 1,031 brains—25 times more than existing cortical templates. We optimized the vertex locations based on cortical anatomy, achieving an even distribution. We observed consistently higher multivariate pattern classification accuracies and representational geometry inter-subject correlations based on onavg than on other templates, and onavg only needs 3⁄4 as much data to achieve the same performance compared to other templates. The optimized sampling also reduces CPU time across algorithms by 1.3%–22.4% due to less variation in the number of vertices in each searchlight.