2018 Conference on Cognitive Computational Neuroscience

5-8 September 2018    Philadelphia, Pennsylvania

Technical Program

Paper Detail

Paper: PS-2B.38
Session: Poster Session 2B
Location: Symphony/Overture
Session Time: Friday, September 7, 19:30 - 21:30
Presentation Time:Friday, September 7, 19:30 - 21:30
Presentation: Poster
Publication: 2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania
Paper Title: Structural support for brain state transitions that contribute to working memory
Manuscript:  Click here to view manuscript
DOI: https://doi.org/10.32470/CCN.2018.1038-0
Authors: Eli Cornblath, Rastko Ciric, Graham Baum, Kosha Ruparel, Tyler Moore, Ruben Gur, Raquel Gur, David Roalf, Theodore Satterthwaite, Danielle Bassett, University of Pennsylvania, United States
Abstract: In the healthy brain, large-scale white matter architecture and local neuronal membrane properties facilitate seamless transitions between cognitive states. However, the manner in which white matter supports the brain’s recurrent spatial activity patterns, or states, remains unknown. Using a large (n = 690) community-based sample of healthy youths from the Philadelphia Neurodevelopmental Cohort, we identify common brain states by applying unsupervised clustering to functional neuroimaging data acquired during the resting state and during the performance of an n-back working memory task. Highly active regions in the cluster centroids closely mirror resting state functional networks, with higher state probabilities in visual and frontoparietal (FPN) states during task and default mode network (FPN) states during rest. State transition probabilities differ between rest and n-back and change over the course of normative neurodevelopment. Using diffusion-weighted imaging acquired from the same subjects, we show that increasing structural connectivity between highly active regions in each state positively correlates with the probability of transitioning between the respective states. Decreased FPN state probabilities and increased visual state transition probabilities positively predict working memory performance. Overall, these findings shed new light on the relationship between brain structure and brain activity, as well as the role of regional coactivation in cognition.