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A Sensitive and Specific Neural Signature for Picture-Induced Negative Affect.

Chang LJ, Gianaros PJ, Manuck SB, Krishnan A, Wager TD - PLoS Biol. (2015)

Bottom Line: The signature was comprised of mesoscale patterns spanning multiple cortical and subcortical systems, with no single system necessary or sufficient for predicting experience.Furthermore, it was not reducible to activity in traditional "emotion-related" regions (e.g., amygdala, insula) or resting-state networks (e.g., "salience," "default mode").Overall, this work identifies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology & Neuroscience, University of Colorado, Boulder, Colorado, United States of America.

ABSTRACT
Neuroimaging has identified many correlates of emotion but has not yet yielded brain representations predictive of the intensity of emotional experiences in individuals. We used machine learning to identify a sensitive and specific signature of emotional responses to aversive images. This signature predicted the intensity of negative emotion in individual participants in cross validation (n =121) and test (n = 61) samples (high-low emotion = 93.5% accuracy). It was unresponsive to physical pain (emotion-pain = 92% discriminative accuracy), demonstrating that it is not a representation of generalized arousal or salience. The signature was comprised of mesoscale patterns spanning multiple cortical and subcortical systems, with no single system necessary or sufficient for predicting experience. Furthermore, it was not reducible to activity in traditional "emotion-related" regions (e.g., amygdala, insula) or resting-state networks (e.g., "salience," "default mode"). Overall, this work identifies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes.

No MeSH data available.


PINES clustering based on shared patterns of connectivity.This figure depicts the results of the hierarchical clustering analysis of the functional connectivity of the largest regions from the p < 0.001 thresholded PINES pattern. Clusters were defined by performing hierarchical agglomerative clustering with ward linkage on the trial-by-trial local pattern responses for each region using Euclidean distance. Data were ranked and normalized within each participant and then aggregated by concatenating all 61 subjects’ trial x region data matrices. Panel A depicts the dendrogram separated by each functional network. Panel B depicts the spatial distribution of the networks. Colors correspond to the dendrogram labels.
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pbio.1002180.g006: PINES clustering based on shared patterns of connectivity.This figure depicts the results of the hierarchical clustering analysis of the functional connectivity of the largest regions from the p < 0.001 thresholded PINES pattern. Clusters were defined by performing hierarchical agglomerative clustering with ward linkage on the trial-by-trial local pattern responses for each region using Euclidean distance. Data were ranked and normalized within each participant and then aggregated by concatenating all 61 subjects’ trial x region data matrices. Panel A depicts the dendrogram separated by each functional network. Panel B depicts the spatial distribution of the networks. Colors correspond to the dendrogram labels.

Mentions: For this analysis, we calculated pattern responses within each of the largest regions in the PINES (p < .001, k = 10 voxels; see S1 Methods) for every individual trial within each participant and used a robust clustering algorithm to group the PINES regions into separate networks based on similar patterns of trial-by-trial covariation (see Methods). The best solution contained nine separate clusters, which provides a descriptive characterization of the subnetworks that comprise the PINES (Fig 6, S3 Table) that is broadly consistent with constructionist accounts of emotion [12] and previous meta-analyses of emotion-related networks [17]. These subnetworks included (a) two networks encompassing different parts of the visual cortex (e.g., lateral occipital cortex [LOC] and occipital pole) consistent with the visual modality of the stimuli, (b) a left amygdala-right aINS-right putamen network, which has been implicated in multiple forms of arousal and salience, (c) a network that includes bilateral posterior parahippocampi and the precuneus, which are broadly involved in memory and other forms of contextual processing, and (d) a network that includes parts of the dmPFC and PCC that are likely involved in social cognition but are distinct from more executive processes [64,65]. An additional network that includes the right somatosensory cortex and contralateral cerebellum may be involved in preparing for the rating action but may also play a more fundamental role in the emotion generation process [66].


A Sensitive and Specific Neural Signature for Picture-Induced Negative Affect.

Chang LJ, Gianaros PJ, Manuck SB, Krishnan A, Wager TD - PLoS Biol. (2015)

PINES clustering based on shared patterns of connectivity.This figure depicts the results of the hierarchical clustering analysis of the functional connectivity of the largest regions from the p < 0.001 thresholded PINES pattern. Clusters were defined by performing hierarchical agglomerative clustering with ward linkage on the trial-by-trial local pattern responses for each region using Euclidean distance. Data were ranked and normalized within each participant and then aggregated by concatenating all 61 subjects’ trial x region data matrices. Panel A depicts the dendrogram separated by each functional network. Panel B depicts the spatial distribution of the networks. Colors correspond to the dendrogram labels.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4476709&req=5

pbio.1002180.g006: PINES clustering based on shared patterns of connectivity.This figure depicts the results of the hierarchical clustering analysis of the functional connectivity of the largest regions from the p < 0.001 thresholded PINES pattern. Clusters were defined by performing hierarchical agglomerative clustering with ward linkage on the trial-by-trial local pattern responses for each region using Euclidean distance. Data were ranked and normalized within each participant and then aggregated by concatenating all 61 subjects’ trial x region data matrices. Panel A depicts the dendrogram separated by each functional network. Panel B depicts the spatial distribution of the networks. Colors correspond to the dendrogram labels.
Mentions: For this analysis, we calculated pattern responses within each of the largest regions in the PINES (p < .001, k = 10 voxels; see S1 Methods) for every individual trial within each participant and used a robust clustering algorithm to group the PINES regions into separate networks based on similar patterns of trial-by-trial covariation (see Methods). The best solution contained nine separate clusters, which provides a descriptive characterization of the subnetworks that comprise the PINES (Fig 6, S3 Table) that is broadly consistent with constructionist accounts of emotion [12] and previous meta-analyses of emotion-related networks [17]. These subnetworks included (a) two networks encompassing different parts of the visual cortex (e.g., lateral occipital cortex [LOC] and occipital pole) consistent with the visual modality of the stimuli, (b) a left amygdala-right aINS-right putamen network, which has been implicated in multiple forms of arousal and salience, (c) a network that includes bilateral posterior parahippocampi and the precuneus, which are broadly involved in memory and other forms of contextual processing, and (d) a network that includes parts of the dmPFC and PCC that are likely involved in social cognition but are distinct from more executive processes [64,65]. An additional network that includes the right somatosensory cortex and contralateral cerebellum may be involved in preparing for the rating action but may also play a more fundamental role in the emotion generation process [66].

Bottom Line: The signature was comprised of mesoscale patterns spanning multiple cortical and subcortical systems, with no single system necessary or sufficient for predicting experience.Furthermore, it was not reducible to activity in traditional "emotion-related" regions (e.g., amygdala, insula) or resting-state networks (e.g., "salience," "default mode").Overall, this work identifies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology & Neuroscience, University of Colorado, Boulder, Colorado, United States of America.

ABSTRACT
Neuroimaging has identified many correlates of emotion but has not yet yielded brain representations predictive of the intensity of emotional experiences in individuals. We used machine learning to identify a sensitive and specific signature of emotional responses to aversive images. This signature predicted the intensity of negative emotion in individual participants in cross validation (n =121) and test (n = 61) samples (high-low emotion = 93.5% accuracy). It was unresponsive to physical pain (emotion-pain = 92% discriminative accuracy), demonstrating that it is not a representation of generalized arousal or salience. The signature was comprised of mesoscale patterns spanning multiple cortical and subcortical systems, with no single system necessary or sufficient for predicting experience. Furthermore, it was not reducible to activity in traditional "emotion-related" regions (e.g., amygdala, insula) or resting-state networks (e.g., "salience," "default mode"). Overall, this work identifies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes.

No MeSH data available.