Distributed Neural Processing Predictors of Multi-dimensional Properties of Affect
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2017-09-01
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| Series: | Frontiers in Human Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/article/10.3389/fnhum.2017.00459/full |
| _version_ | 1819047505044701184 |
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| author | Keith A. Bush Cory S. Inman Stephan Hamann Clinton D. Kilts G. Andrew James |
| author_facet | Keith A. Bush Cory S. Inman Stephan Hamann Clinton D. Kilts G. Andrew James |
| author_sort | Keith A. Bush |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T11:01:25Z |
| format | Article |
| id | doaj.art-c80650e27f1948feb973c24da303c148 |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-12-21T11:01:25Z |
| publishDate | 2017-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-c80650e27f1948feb973c24da303c1482022-12-21T19:06:20ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612017-09-011110.3389/fnhum.2017.00459265521Distributed Neural Processing Predictors of Multi-dimensional Properties of AffectKeith A. Bush0Cory S. Inman1Stephan Hamann2Clinton D. Kilts3G. Andrew James4Brain Imaging Research Center, University of Arkansas for Medical Sciences, Little RockAR, United StatesDepartment of Psychology, Emory University, AtlantaGA, United StatesDepartment of Psychology, Emory University, AtlantaGA, United StatesBrain Imaging Research Center, University of Arkansas for Medical Sciences, Little RockAR, United StatesBrain Imaging Research Center, University of Arkansas for Medical Sciences, Little RockAR, United StatesRecent 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.http://journal.frontiersin.org/article/10.3389/fnhum.2017.00459/fullaffectIAPSMVPASVMfMRIneural representation |
| spellingShingle | Keith A. Bush Cory S. Inman Stephan Hamann Clinton D. Kilts G. Andrew James Distributed Neural Processing Predictors of Multi-dimensional Properties of Affect Frontiers in Human Neuroscience affect IAPS MVPA SVM fMRI neural representation |
| title | Distributed Neural Processing Predictors of Multi-dimensional Properties of Affect |
| title_full | Distributed Neural Processing Predictors of Multi-dimensional Properties of Affect |
| title_fullStr | Distributed Neural Processing Predictors of Multi-dimensional Properties of Affect |
| title_full_unstemmed | Distributed Neural Processing Predictors of Multi-dimensional Properties of Affect |
| title_short | Distributed Neural Processing Predictors of Multi-dimensional Properties of Affect |
| title_sort | distributed neural processing predictors of multi dimensional properties of affect |
| topic | affect IAPS MVPA SVM fMRI neural representation |
| url | http://journal.frontiersin.org/article/10.3389/fnhum.2017.00459/full |
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