A Sensitive and Specific Neural Signature for Picture-Induced Negative Affect
Publication Date
June 22, 2015
Journal
PLOS Biology
Authors
Luke J. Chang, Peter J. Gianaros, Stephen B. Manuck, Anjali Krishnan, et al
Volume
13
Issue
6
Pages
e1002180
DOI
https://dx.plos.org/10.1371/journal.pbio.1002180
Publisher URL
http://journals.plos.org/plosbiology/article?id=10.1371%2Fjournal.pbio.1002180
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/26098873
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4476709
Europe PMC
http://europepmc.org/abstract/MED/26098873
Web of Science
000357339600017
Scopus
84934756388
Mendeley
http://www.mendeley.com/research/sensitive-specific-neural-signature-pictureinduced-negative-affect
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Mendeley | Further Information

{"title"=>"A sensitive and specific neural signature for picture-induced negative affect", "type"=>"journal", "authors"=>[{"first_name"=>"Luke J.", "last_name"=>"Chang", "scopus_author_id"=>"57201236717"}, {"first_name"=>"Peter J.", "last_name"=>"Gianaros", "scopus_author_id"=>"6602623596"}, {"first_name"=>"Stephen B.", "last_name"=>"Manuck", "scopus_author_id"=>"7005484132"}, {"first_name"=>"Anjali", "last_name"=>"Krishnan", "scopus_author_id"=>"48662511200"}, {"first_name"=>"Tor D.", "last_name"=>"Wager", "scopus_author_id"=>"6603627020"}], "year"=>2015, "source"=>"PLoS Biology", "identifiers"=>{"pui"=>"605059994", "issn"=>"15457885", "isbn"=>"1545-7885 (Electronic) 1544-9173 (Linking)", "doi"=>"10.1371/journal.pbio.1002180", "scopus"=>"2-s2.0-84934756388", "pmid"=>"26098873", "sgr"=>"84934756388"}, "id"=>"b994164f-ae5f-3ba8-a938-24194ffeec4c", "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.", "link"=>"http://www.mendeley.com/research/sensitive-specific-neural-signature-pictureinduced-negative-affect", "reader_count"=>192, "reader_count_by_academic_status"=>{"Unspecified"=>7, "Professor > Associate Professor"=>9, "Librarian"=>1, "Researcher"=>43, "Student > Doctoral Student"=>14, "Student > Ph. D. Student"=>51, "Student > Postgraduate"=>12, "Student > Master"=>20, "Other"=>7, "Student > Bachelor"=>13, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>12}, "reader_count_by_user_role"=>{"Unspecified"=>7, "Professor > Associate Professor"=>9, "Librarian"=>1, "Researcher"=>43, "Student > Doctoral Student"=>14, "Student > Ph. D. Student"=>51, "Student > Postgraduate"=>12, "Student > Master"=>20, "Other"=>7, "Student > Bachelor"=>13, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>1, "Professor"=>12}, "reader_count_by_subject_area"=>{"Unspecified"=>19, "Agricultural and Biological Sciences"=>17, "Business, Management and Accounting"=>2, "Computer Science"=>15, "Engineering"=>5, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>1, "Medicine and Dentistry"=>15, "Neuroscience"=>31, "Design"=>1, "Physics and Astronomy"=>1, "Psychology"=>76, "Social Sciences"=>7, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>15}, "Social Sciences"=>{"Social Sciences"=>7}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>76}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>19}, "Design"=>{"Design"=>1}, "Engineering"=>{"Engineering"=>5}, "Neuroscience"=>{"Neuroscience"=>31}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>17}, "Computer Science"=>{"Computer Science"=>15}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>2}, "Linguistics"=>{"Linguistics"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}}, "reader_count_by_country"=>{"Canada"=>2, "Belgium"=>1, "Hungary"=>1, "United States"=>4, "Japan"=>3, "China"=>1, "United Kingdom"=>2, "France"=>2, "Germany"=>4}, "group_count"=>3}

CrossRef

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2129396"], "description"=>"<p>Panel A depicts the sequence of events for a given trial. Participants view an initial fixation cross and then are instructed to look at the picture (compared to reappraise). Participants then see a photo and are asked to rate how negative they feel on a likert scale of 1–5. Panel B illustrates the temporal data reduction for each rating level using voxel-wise univariate analysis and an assumed hemodynamic response function. Panel C: these voxels are then treated as features and trained to predict ratings using LASSO-PCR with leave-one-subject-out cross validation. Subject’s data for each rating is concatenated across participants. Panel D: this multivoxel weight map pattern can be tested on new data using matrix multiplication to produce a scalar affective rating prediction. Panel E: we calculated two different types of classification accuracy: (a) the ability to discriminate between high (rating = 5) and low (rating = 1) affective ratings and (b) the ability to discriminate between high affective and high pain data.</p>", "links"=>[], "tags"=>["emotion", "mesoscale patterns", "brain representations", "intensity", "aversive images", "e.g", "salience", "accuracy", "affective processes", "Specific Neural Signature", "subcortical systems"], "article_id"=>1457056, "categories"=>["Uncategorised"], "users"=>["Luke J. Chang", "Peter J. Gianaros", "Stephen B. Manuck", "Anjali Krishnan", "Tor D. Wager"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002180.g001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experimental_paradigm_and_analysis_overview_/1457056", "title"=>"Experimental paradigm and analysis overview.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-22 03:36:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/2129442"], "description"=>"<p>All balanced accuracies reported in this table result from single interval classification on the test sample (<i>n</i> = 47; see <a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002180#pbio.1002180.s013\" target=\"_blank\">S2 Table</a> for forced-choice test). Analyses involving Level 5 and/or Level 1 comparisons exclude participants that did not rate any stimuli with that label. Accuracy values reflect the ability to discriminate the conditions compared, but are signed so that values >50% indicate the proportion of participants for which high intensity was classified as greater than low intensity for high vs. low analyses, or emotion was greater than pain for Emotion vs. Pain analyses. Values < 50% indicate the proportion of participants for which low intensity was classified as greater than high intensity or pain was classified as greater than emotion. For example, the 10.7% emotion classification of the NPS in the Emotion vs. Pain analysis should be interpreted as a 89.3% hit rate in discriminating pain from emotion. Correlations reflect Pearson correlations between participant’s pattern responses to levels of affective intensity and self-reported ratings averaged across participants.</p><p><sup>+</sup>Indicates that accuracy is significantly different from chance (50%) using a two-tailed binomial test.</p><p>*Indicates accuracy is significantly different from PINES performance using a two-sample, two-tailed z-test for proportions (only tested on Emotion 5 versus 1 and Emotion versus Pain columns).</p><p>Single-cluster and “virtual lesion” analysis.</p>", "links"=>[], "tags"=>["emotion", "mesoscale patterns", "brain representations", "intensity", "aversive images", "e.g", "salience", "accuracy", "affective processes", "Specific Neural Signature", "subcortical systems"], "article_id"=>1457086, "categories"=>["Uncategorised"], "users"=>["Luke J. Chang", "Peter J. Gianaros", "Stephen B. Manuck", "Anjali Krishnan", "Tor D. Wager"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002180.t002", "stats"=>{"downloads"=>4, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Single_cluster_and_8220_virtual_lesion_8221_analysis_/1457086", "title"=>"Single-cluster and “virtual lesion” analysis.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-06-22 03:36:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/2129438"], "description"=>"<p>Panel A illustrates the spatial distribution of the three anatomical ROIs used in all analyses (amygdala = yellow, insula = red, ACC = cyan). Panel B depicts the average activation within each ROI across participants for each level of emotion and pain in the emotion hold out (<i>n</i> = 61) and pain test datasets (<i>n</i> = 28). Error bars reflect ±1 standard error. Panel C illustrates the spatial topography of the PINES and NPS patterns within each of these anatomical ROIs. While these plots show one region, correlations reported in the text reflect bilateral patterns.</p>", "links"=>[], "tags"=>["emotion", "mesoscale patterns", "brain representations", "intensity", "aversive images", "e.g", "salience", "accuracy", "affective processes", "Specific Neural Signature", "subcortical systems"], "article_id"=>1457082, "categories"=>["Uncategorised"], "users"=>["Luke J. Chang", "Peter J. Gianaros", "Stephen B. Manuck", "Anjali Krishnan", "Tor D. Wager"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002180.g005", "stats"=>{"downloads"=>4, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Region_of_interest_analysis_/1457082", "title"=>"Region of interest analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-22 03:36:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/2129427"], "description"=>"<p>This figure illustrates differences in the spatial topography in the thresholded PINES and NPS patterns and their predictions in independent emotion (<i>n</i> = 61) and pain (<i>n</i> = 28) test data. Panel A depicts the PINES thresholded at <i>p</i> < 0.001 uncorrected (see <a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002180#pbio.1002180.g002\" target=\"_blank\">Fig 2</a>). Panel B depicts the average standardized PINES and NPS pattern responses at each level of emotion calculated using a spatial correlation. Error bars reflect ±1 standard error. Panel C depicts the NPS thresholded at false discovery rate (FDR) q < 0.05 whole-brain corrected. Panel D depicts the average standardized PINES and NPS pattern responses at each pain level calculated using a spatial correlation. Error bars reflect ±1 standard error.</p>", "links"=>[], "tags"=>["emotion", "mesoscale patterns", "brain representations", "intensity", "aversive images", "e.g", "salience", "accuracy", "affective processes", "Specific Neural Signature", "subcortical systems"], "article_id"=>1457073, "categories"=>["Uncategorised"], "users"=>["Luke J. Chang", "Peter J. Gianaros", "Stephen B. Manuck", "Anjali Krishnan", "Tor D. Wager"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002180.g004", "stats"=>{"downloads"=>2, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Affective_and_pain_responses_to_PINES_and_NPS_/1457073", "title"=>"Affective and pain responses to PINES and NPS.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-22 03:36:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/2129441"], "description"=>"<p>All balanced accuracies reported in this table result from single-interval classification on the test dataset <i>(n</i> = 47; see <a href=\"http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002180#pbio.1002180.s012\" target=\"_blank\">S1 Table</a> for forced-choice test). Analyses involving Level 5 and/or Level 1 comparisons exclude participants that did not rate any stimuli with that label. Accuracy values reflect the ability to discriminate the conditions compared, but are signed, so that values >50% indicate the proportion of participants for which high intensity was classified as greater than low intensity, for high vs. low analyses, or emotion was greater than pain, for Emotion vs. Pain analyses. Values < 50% indicate the proportion of participants for which low intensity was classified as greater than high intensity or pain was classified as greater than emotion. For example, the 10.7% emotion classification of the NPS in the Emotion vs. Pain analysis should be interpreted as a 89.3% hit rate in discriminating pain from emotion. Correlations reflect Pearson correlations between participant’s pattern responses to levels of affective intensity and self-reported ratings averaged across participants.</p><p><sup>☨</sup>Please note that this column does not reflect accuracy but rather percent classified as emotion.</p><p><sup>+</sup>Indicates that accuracy is significantly different from chance (50%), using a two-tailed dependent binomial test.</p><p>*Indicates accuracy significantly different from PINES performance using a two-sample two-tailed z-test for proportions (only tested on Emotion 5 versus 1 and Emotion versus Pain columns).</p><p>Patten sensitivity and specificity.</p>", "links"=>[], "tags"=>["emotion", "mesoscale patterns", "brain representations", "intensity", "aversive images", "e.g", "salience", "accuracy", "affective processes", "Specific Neural Signature", "subcortical systems"], "article_id"=>1457085, "categories"=>["Uncategorised"], "users"=>["Luke J. Chang", "Peter J. Gianaros", "Stephen B. Manuck", "Anjali Krishnan", "Tor D. Wager"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002180.t001", "stats"=>{"downloads"=>4, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Patten_sensitivity_and_specificity_/1457085", "title"=>"Patten sensitivity and specificity.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-06-22 03:36:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/2129421"], "description"=>"<p>This figure depicts results from our within-participant analysis, in which the PINES was retrained separately for each participant to predict ratings to individual photos. Panel A shows the voxels in the weight map that are consistently different from zero across participants using a one sample <i>t</i> test thresholded at <i>p</i> < 0.001 uncorrected. Panel B shows a histogram of standardized emotion predictions (correlation) for each participant. The dotted red line reflects the average cross validated PINES correlation for predicting each photo’s rating. Panel C depicts how well each participant’s ratings were predicted by the PINES (y-axis) versus an idiographically trained, cross-validated map using their individual brain data (x-axis). Each point on the graph reflects one participant. The dotted red line reflects the identity line. Any data point above the identity line indicates that the participant was better fit by the PINES than their own weight map.</p>", "links"=>[], "tags"=>["emotion", "mesoscale patterns", "brain representations", "intensity", "aversive images", "e.g", "salience", "accuracy", "affective processes", "Specific Neural Signature", "subcortical systems"], "article_id"=>1457067, "categories"=>["Uncategorised"], "users"=>["Luke J. Chang", "Peter J. Gianaros", "Stephen B. Manuck", "Anjali Krishnan", "Tor D. Wager"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002180.g003", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Within_participant_emotion_prediction_/1457067", "title"=>"Within participant emotion prediction.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-22 03:36:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/2129440"], "description"=>"<p>This figure depicts the results of the hierarchical clustering analysis of the functional connectivity of the largest regions from the <i>p</i> < 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.</p>", "links"=>[], "tags"=>["emotion", "mesoscale patterns", "brain representations", "intensity", "aversive images", "e.g", "salience", "accuracy", "affective processes", "Specific Neural Signature", "subcortical systems"], "article_id"=>1457084, "categories"=>["Uncategorised"], "users"=>["Luke J. Chang", "Peter J. Gianaros", "Stephen B. Manuck", "Anjali Krishnan", "Tor D. Wager"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002180.g006", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_PINES_clustering_based_on_shared_patterns_of_connectivity_/1457084", "title"=>"PINES clustering based on shared patterns of connectivity.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-22 03:36:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/2129473", "https://ndownloader.figshare.com/files/2129474", "https://ndownloader.figshare.com/files/2129475", "https://ndownloader.figshare.com/files/2129476", "https://ndownloader.figshare.com/files/2129477", "https://ndownloader.figshare.com/files/2129478", "https://ndownloader.figshare.com/files/2129479", "https://ndownloader.figshare.com/files/2129480", "https://ndownloader.figshare.com/files/2129481", "https://ndownloader.figshare.com/files/2129482", "https://ndownloader.figshare.com/files/2129483", "https://ndownloader.figshare.com/files/2129484", "https://ndownloader.figshare.com/files/2129485", "https://ndownloader.figshare.com/files/2129486"], "description"=>"<div><p>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 (<i>n</i> =121) and test (<i>n</i> = 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.</p></div>", "links"=>[], "tags"=>["emotion", "mesoscale patterns", "brain representations", "intensity", "aversive images", "e.g", "salience", "accuracy", "affective processes", "Specific Neural Signature", "subcortical systems"], "article_id"=>1457112, "categories"=>["Uncategorised"], "users"=>["Luke J. Chang", "Peter J. Gianaros", "Stephen B. Manuck", "Anjali Krishnan", "Tor D. Wager"], "doi"=>["https://dx.doi.org/10.1371/journal.pbio.1002180.s001", "https://dx.doi.org/10.1371/journal.pbio.1002180.s002", "https://dx.doi.org/10.1371/journal.pbio.1002180.s003", "https://dx.doi.org/10.1371/journal.pbio.1002180.s004", "https://dx.doi.org/10.1371/journal.pbio.1002180.s005", "https://dx.doi.org/10.1371/journal.pbio.1002180.s006", "https://dx.doi.org/10.1371/journal.pbio.1002180.s007", "https://dx.doi.org/10.1371/journal.pbio.1002180.s008", "https://dx.doi.org/10.1371/journal.pbio.1002180.s009", "https://dx.doi.org/10.1371/journal.pbio.1002180.s010", "https://dx.doi.org/10.1371/journal.pbio.1002180.s011", "https://dx.doi.org/10.1371/journal.pbio.1002180.s012", "https://dx.doi.org/10.1371/journal.pbio.1002180.s013", "https://dx.doi.org/10.1371/journal.pbio.1002180.s014"], "stats"=>{"downloads"=>30, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_Sensitive_and_Specific_Neural_Signature_for_Picture_Induced_Negative_Affect_/1457112", "title"=>"A Sensitive and Specific Neural Signature for Picture-Induced Negative Affect", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-06-22 03:36:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/2129415"], "description"=>"<p>Panel A depicts the PINES pattern thresholded using a 5,000 sample bootstrap procedure at <i>p</i> < 0.001 uncorrected. Blowout sections show the spatial topography of the pattern in the left amygdala, right insula, and posterior cingulate cortex. Panel B shows the predicted affective rating compared to the actual ratings for the cross validated participants (<i>n</i> = 121) and the separate holdout test data set (<i>n</i> = 61). Accuracies reflect forced-choice comparisons between high and low and high, medium, and low ratings. Panel C depicts an average peristimulus plot of the PINES response to the holdout test dataset (<i>n</i> = 61). This reflects the average PINES response at every repetition time (TR) in the timeseries separated by the rating. Panel D illustrates an item analysis which shows the average PINES response to each photo by the average ratings to the photos in the separate test dataset (<i>n</i> = 61). Error bars reflect ±1 standard error.</p>", "links"=>[], "tags"=>["emotion", "mesoscale patterns", "brain representations", "intensity", "aversive images", "e.g", "salience", "accuracy", "affective processes", "Specific Neural Signature", "subcortical systems"], "article_id"=>1457061, "categories"=>["Uncategorised"], "users"=>["Luke J. Chang", "Peter J. Gianaros", "Stephen B. Manuck", "Anjali Krishnan", "Tor D. Wager"], "doi"=>"https://dx.doi.org/10.1371/journal.pbio.1002180.g002", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_PINES_/1457061", "title"=>"PINES.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-06-22 03:36:06"}

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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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