Investigating Spatio-Temporal Dynamics in Emotion-Cognition Interactions: A Multivoxel Pattern Analysis Approach

Poster Session A - Saturday, April 13, 2024, 2:30 – 4:30 pm EDT, Sheraton Hall ABC

Reyhaneh Bakhtiari¹ (reyhaneh@gmail.com), Brea Chouinard², Andrea T Shafer³, Matthew Moore^4,5, Florin Dolcos^6,7,8,9, Anthony Singhal^1,9; ¹Department of Psychology, University of Alberta, Edmonton, AB, Canada, ²Faculty of Kinesiology, Sport, and Recreation, University of Alberta, Edmonton, AB, Canada, ³Specialty Care, Neurology and Oncology Baltimore, MD, USA, ⁴War Related Illness and Injury Study Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA, ⁵Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA, ⁶Beckman Institute for Advanced Science & Technology, University of Illinois at Urbana-Champaign, USA, ⁷Neuroscience Program, University of Illinois at Urbana-Champaign, USA, ⁸Department of Psychology, University of Illinois at Urbana-Champaign, USA, ⁹Neuroscience & Mental Health Institute, University of Alberta, Edmonton, AB, Canada

This study explored spatio-temporal associations between emotion and cognition neural systems – specifically, the ventral affective processing system (VAS) and the dorsal executive system (DES), which overlap with the ventral attention network (VAN) and dorsal attention network (DAN), respectively. While prior research has underscored that emotion-cognition interactions are facilitated through communication among these systems/networks, the link between their temporal and spatial aspects is not well understood. Here, we involved a modification to the multivoxel pattern analysis (MVPA) approach for analysing simultaneously collected EEG and fMRI data acquired from twenty-two young adults, who participated in an emotional oddball task. A linear support vector classifier was employed to identify spheres with a 2-voxel radius, whose patterns of activity accurately predicted the presented stimuli (targets, sad distractors, and fearful distractors). Several regions, including those within VAS (e.g., ventrolateral prefrontal cortex) and DES (e.g., dorsolateral prefrontal cortex) were identified. Then, different event-related potential (ERP) components (including P100 and P300) were computed for each trial and incorporated into the classifier, and the performance of each of these classifiers was subsequently compared with that of the original classifier. As predicted, in general, the addition of information led to improved classifier performance, which was particularly significant over dorsal regions when augmented with P300 measures. Overall, these findings underscore the convergence of brain activity measures across diverse spatio-temporal resolutions. This approach supports the feasibility of employing MVPA techniques to analysing multi-modal brain imaging, to understand neural mechanisms underlying emotion-cognition interactions among large-scale functional networks at various spatio-temporal scales.

Topic Area: EMOTION & SOCIAL: Emotion-cognition interactions