Publication

Distributed Neural Processing Predictors of Multi-dimensional Properties of Affect

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Last modified
  • 03/03/2025
Type of Material
Authors
    Keith A. Bush, University of Arkansas for Medical SciencesCory S. Inman, Emory UniversityStephan Hamann, Emory UniversityClinton D. Kilts, University of Arkansas for Medical SciencesG. Andrew James, University of Arkansas for Medical Sciences
Language
  • English
Date
  • 2017-09-14
Publisher
  • Frontiers Media
Publication Version
Copyright Statement
  • © 2017 Bush, Inman, Hamann, Kilts and James.
License
Final Published Version (URL)
Title of Journal or Parent Work
ISSN
  • 1662-5161
Volume
  • 11
Grant/Funding Information
  • Data collection for this work was supported by the KL2 Scholars Program (KL2TR000063; to GJ) and the UAMS Translational Research Institute (TRI; UL1TR000039) through the NIH National Center for Research Resources and the National Center for Advancing Translational Sciences.
Supplemental Material (URL)
Abstract
  • Recent evidence suggests that emotions have a distributed neural representation, which has significant implications for our understanding of the mechanisms underlying emotion regulation and dysregulation as well as the potential targets available for neuromodulation-based emotion therapeutics. This work adds to this evidence by testing the distribution of neural representations underlying the affective dimensions of valence and arousal using representational models that vary in both the degree and the nature of their distribution. We used multi-voxel pattern classification (MVPC) to identify whole-brain patterns of functional magnetic resonance imaging (fMRI)-derived neural activations that reliably predicted dimensional properties of affect (valence and arousal) for visual stimuli viewed by a normative sample (n = 32) of demographically diverse, healthy adults. Inter-subject leave-one-out cross-validation showed whole-brain MVPC significantly predicted (p < 0.001) binarized normative ratings of valence (positive vs. negative, 59% accuracy) and arousal (high vs. low, 56% accuracy). We also conducted group-level univariate general linear modeling (GLM) analyses to identify brain regions whose response significantly differed for the contrasts of positive versus negative valence or high versus low arousal. Multivoxel pattern classifiers using voxels drawn from all identified regions of interest (all-ROIs) exhibited mixed performance; arousal was predicted significantly better than chance but worse than the whole-brain classifier, whereas valence was not predicted significantly better than chance. Multivoxel classifiers derived using individual ROIs generally performed no better than chance. Although performance of the all-ROI classifier improved with larger ROIs (generated by relaxing the clustering threshold), performance was still poorer than the whole-brain classifier. These findings support a highly distributed model of neural processing for the affective dimensions of valence and arousal. Finally, joint error analyses of the MVPC hyperplanes encoding valence and arousal identified regions within the dimensional affect space where multivoxel classifiers exhibited the greatest difficulty encoding brain states – specifically, stimuli of moderate arousal and high or low valence. In conclusion, we highlight new directions for characterizing affective processing for mechanistic and therapeutic applications in affective neuroscience.
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Keywords
Research Categories
  • Psychology, General

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