| Paper: | PS-1B.17 |
| Session: | Poster Session 1B |
| Location: | Symphony/Overture |
| Session Time: | Thursday, September 6, 18:45 - 20:45 |
| Presentation Time: | Thursday, September 6, 18:45 - 20:45 |
| Presentation: |
Poster
|
| Publication: |
2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania |
| Paper Title: |
Predicting Affective Cognitions in the Resting Adult Brain |
| Manuscript: |
Click here to view manuscript |
| DOI: |
https://doi.org/10.32470/CCN.2018.1010-0 |
| Authors: |
Keith Bush, Anthony Privratsky, Clinton Kilts, University of Arkansas for Medical Sciences, United States |
| Abstract: |
Patterns of estimated neural activity derived from resting state functional magnetic resonance imaging (rs-fMRI) have been shown to predict a wide range of cognitive and behavioral outcomes in both normative and clinical populations. Yet, without links to established cognitive processes, the functional brain states associated with the resting brain will remain unexplained, and potentially confounded, markers of individual differences. In this work we demonstrate the application of multivoxel pattern classifiers (MVPCs) to predict the valence and arousal properties of spontaneous affect processing in the task-non-engaged resting state. rs-fMRI data were acquired from subjects that were held out from a subject set that underwent image-based affect induction concurrent with fMRI to train the MVPCs. We also validated these affective predictions against a well-established, independent measure of autonomic arousal, skin conductance response. These findings suggest a new neuroimaging methodology for resting state analysis in which models, trained on cognition-specific task-based fMRI acquired from well-matched cohorts, capably predict hidden cognitive processes operating within the resting brain. |
