Voluntary Enhancement of Neural Signatures of Affiliative Emotion Using fMRI Neurofeedback
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{"title"=>"Voluntary enhancement of neural signatures of affiliative emotion using fMRI neurofeedback", "type"=>"journal", "authors"=>[{"first_name"=>"Jorge", "last_name"=>"Moll", "scopus_author_id"=>"7102497419"}, {"first_name"=>"Julie H.", "last_name"=>"Weingartner", "scopus_author_id"=>"56178116600"}, {"first_name"=>"Patricia", "last_name"=>"Bado", "scopus_author_id"=>"37071996200"}, {"first_name"=>"Rodrigo", "last_name"=>"Basilio", "scopus_author_id"=>"55020780100"}, {"first_name"=>"João R.", "last_name"=>"Sato", "scopus_author_id"=>"56668321500"}, {"first_name"=>"Bruno R.", "last_name"=>"Melo", "scopus_author_id"=>"54964218000"}, {"first_name"=>"Ivanei E.", "last_name"=>"Bramati", "scopus_author_id"=>"6508119287"}, {"first_name"=>"Ricardo", "last_name"=>"De Oliveira-Souza", "scopus_author_id"=>"6701468414"}, {"first_name"=>"Roland", "last_name"=>"Zahn", "scopus_author_id"=>"7102439467"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84901339426", "pmid"=>"24847819", "pui"=>"373161008", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "scopus"=>"2-s2.0-84901339426", "doi"=>"10.1371/journal.pone.0097343", "issn"=>"19326203"}, "id"=>"068a8b1f-2964-322e-9854-fd4222a9ebb4", "abstract"=>"In Ridley Scott's film \"Blade Runner\", empathy-detection devices are employed to measure affiliative emotions. Despite recent neurocomputational advances, it is unknown whether brain signatures of affiliative emotions, such as tenderness/affection, can be decoded and voluntarily modulated. Here, we employed multivariate voxel pattern analysis and real-time fMRI to address this question. We found that participants were able to use visual feedback based on decoded fMRI patterns as a neurofeedback signal to increase brain activation characteristic of tenderness/affection relative to pride, an equally complex control emotion. Such improvement was not observed in a control group performing the same fMRI task without neurofeedback. Furthermore, the neurofeedback-driven enhancement of tenderness/affection-related distributed patterns was associated with local fMRI responses in the septohypothalamic area and frontopolar cortex, regions previously implicated in affiliative emotion. This demonstrates that humans can voluntarily enhance brain signatures of tenderness/affection, unlocking new possibilities for promoting prosocial emotions and countering antisocial behavior.", "link"=>"http://www.mendeley.com/research/voluntary-enhancement-neural-signatures-affiliative-emotion-using-fmri-neurofeedback", "reader_count"=>105, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>8, "Researcher"=>28, "Student > Doctoral Student"=>9, "Student > Ph. D. Student"=>20, "Student > Postgraduate"=>8, "Student > Master"=>14, "Other"=>2, "Student > Bachelor"=>10, "Lecturer > Senior Lecturer"=>2, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>8, "Researcher"=>28, "Student > Doctoral Student"=>9, "Student > Ph. D. Student"=>20, "Student > Postgraduate"=>8, "Student > Master"=>14, "Other"=>2, "Student > Bachelor"=>10, "Lecturer > Senior Lecturer"=>2, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>7, "Agricultural and Biological Sciences"=>11, "Philosophy"=>2, "Business, Management and Accounting"=>1, "Computer Science"=>4, "Economics, Econometrics and Finance"=>1, "Engineering"=>5, "Materials Science"=>1, "Medicine and Dentistry"=>14, "Neuroscience"=>16, "Physics and Astronomy"=>3, "Psychology"=>37, "Social Sciences"=>3}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>14}, "Social Sciences"=>{"Social Sciences"=>3}, "Physics and Astronomy"=>{"Physics and Astronomy"=>3}, "Psychology"=>{"Psychology"=>37}, "Unspecified"=>{"Unspecified"=>7}, "Engineering"=>{"Engineering"=>5}, "Neuroscience"=>{"Neuroscience"=>16}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>11}, "Computer Science"=>{"Computer Science"=>4}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Philosophy"=>{"Philosophy"=>2}}, "reader_count_by_country"=>{"Canada"=>1, "Netherlands"=>3, "United States"=>3, "Japan"=>1, "China"=>1, "Brazil"=>2, "United Kingdom"=>1, "Mexico"=>1, "France"=>1, "Portugal"=>1, "Germany"=>1}, "group_count"=>12}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1506157"], "description"=>"<p>Distributed voxel patterns that best distinguished between tenderness/affection vs. pride within the neurofeedback group, measured across all fMRI sessions used for Support Vector Machine (SVM) classification. For display purposes, SVM weight maps were restricted to the 2% most discriminant voxels (highest absolute values of discriminant hyperplane coefficients). The color range shows voxels present in at least 40% (blue) or above 66% (red-yellow) of the subjects.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "Behavioral neuroscience", "Cognitive neuroscience", "psychology", "behavior", "emotions", "neurology", "mathematics", "Applied mathematics", "algorithms", "voxels", "arising", "multivariate"], "article_id"=>1033325, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Jorge Moll", "Julie H. Weingartner", "Patricia Bado", "Rodrigo Basilio", "João R. Sato", "Bruno R. Melo", "Ivanei E. Bramati", "Ricardo de Oliveira-Souza", "Roland Zahn"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0097343.g006", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Discriminant_voxels_arising_from_multivariate_pattern_analysis_/1033325", "title"=>"Discriminant voxels arising from multivariate pattern analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-21 03:27:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/1506156"], "description"=>"<p>Subject 12 spontaneously reported having changed the emotional elicitation strategy during the last run, possibly leading to the observed inverse classification pattern.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "Behavioral neuroscience", "Cognitive neuroscience", "psychology", "behavior", "emotions", "neurology", "mathematics", "Applied mathematics", "algorithms", "trials", "classified", "Neurofeedback", "classification"], "article_id"=>1033324, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Jorge Moll", "Julie H. Weingartner", "Patricia Bado", "Rodrigo Basilio", "João R. Sato", "Bruno R. Melo", "Ivanei E. Bramati", "Ricardo de Oliveira-Souza", "Roland Zahn"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0097343.g005", "stats"=>{"downloads"=>2, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Percentage_of_trials_correctly_classified_as_characteristic_of_tenderness_affection_for_each_participant_of_the_neurofeedback_group_at_the_last_classification_run_/1033324", "title"=>"Percentage of trials correctly classified as characteristic of tenderness/affection for each participant of the neurofeedback group at the last classification run.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-21 03:27:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/1506153"], "description"=>"<p>(a) Difference in percentage of trials classified as characteristic of tenderness/affection between last and first fMRI sessions used for SVM classification. Results are shown for the neurofeedback (NFB) and the non-neurofeedback control group (CTR). (b) Percentage of trials classified as characteristic of tenderness at the last fMRI session (chance level = 50%).</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "Behavioral neuroscience", "Cognitive neuroscience", "psychology", "behavior", "emotions", "neurology", "mathematics", "Applied mathematics", "algorithms", "vector", "classification"], "article_id"=>1033321, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Jorge Moll", "Julie H. Weingartner", "Patricia Bado", "Rodrigo Basilio", "João R. Sato", "Bruno R. Melo", "Ivanei E. Bramati", "Ricardo de Oliveira-Souza", "Roland Zahn"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0097343.g004", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Support_Vector_Machine_SVM_classification_results_/1033321", "title"=>"Support Vector Machine (SVM) classification results.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-21 03:27:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/1506151"], "description"=>"<p>The mask includes sectors of the frontal, temporal, parietal and subcortical areas previously implicated in social emotions. The mask excludes brain regions involved in sensorimotor or visuo-spatial processing from SVM training and decoding.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "Behavioral neuroscience", "Cognitive neuroscience", "psychology", "behavior", "emotions", "neurology", "mathematics", "Applied mathematics", "algorithms", "employed", "vector"], "article_id"=>1033319, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Jorge Moll", "Julie H. Weingartner", "Patricia Bado", "Rodrigo Basilio", "João R. Sato", "Bruno R. Melo", "Ivanei E. Bramati", "Ricardo de Oliveira-Souza", "Roland Zahn"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0097343.g002", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Feature_mask_employed_for_Support_Vector_Machine_SVM_input_data_/1033319", "title"=>"Feature mask employed for Support Vector Machine (SVM) input data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-21 03:27:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/1506152"], "description"=>"<p>Septohypothalamic (a) and frontopolar cortex (FPC) anatomical masks (b) used for Small Volume Correction (SVC) in the univariate analyses of fMRI responses. These <i>a priori</i> masks contained 63 and 255 voxels (3×3×3 mm voxel size), respectively.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "Behavioral neuroscience", "Cognitive neuroscience", "psychology", "behavior", "emotions", "neurology", "mathematics", "Applied mathematics", "algorithms", "anatomical"], "article_id"=>1033320, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Jorge Moll", "Julie H. Weingartner", "Patricia Bado", "Rodrigo Basilio", "João R. Sato", "Bruno R. Melo", "Ivanei E. Bramati", "Ricardo de Oliveira-Souza", "Roland Zahn"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0097343.g003", "stats"=>{"downloads"=>3, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_priori_anatomical_masks_/1033320", "title"=>"<i>A priori</i> anatomical masks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-21 03:27:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/1506159", "https://ndownloader.figshare.com/files/1506160", "https://ndownloader.figshare.com/files/1506161"], "description"=>"<div><p>In Ridley Scott’s film “Blade Runner”, empathy-detection devices are employed to measure affiliative emotions. Despite recent neurocomputational advances, it is unknown whether brain signatures of affiliative emotions, such as tenderness/affection, can be decoded and voluntarily modulated. Here, we employed multivariate voxel pattern analysis and real-time fMRI to address this question. We found that participants were able to use visual feedback based on decoded fMRI patterns as a neurofeedback signal to increase brain activation characteristic of tenderness/affection relative to pride, an equally complex control emotion. Such improvement was not observed in a control group performing the same fMRI task without neurofeedback. Furthermore, the neurofeedback-driven enhancement of tenderness/affection-related distributed patterns was associated with local fMRI responses in the septohypothalamic area and frontopolar cortex, regions previously implicated in affiliative emotion. This demonstrates that humans can voluntarily enhance brain signatures of tenderness/affection, unlocking new possibilities for promoting prosocial emotions and countering antisocial behavior.</p></div>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "Behavioral neuroscience", "Cognitive neuroscience", "psychology", "behavior", "emotions", "neurology", "mathematics", "Applied mathematics", "algorithms", "enhancement", "neural", "signatures", "affiliative", "fmri"], "article_id"=>1033327, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Jorge Moll", "Julie H. Weingartner", "Patricia Bado", "Rodrigo Basilio", "João R. Sato", "Bruno R. Melo", "Ivanei E. Bramati", "Ricardo de Oliveira-Souza", "Roland Zahn"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0097343.s001", "https://dx.doi.org/10.1371/journal.pone.0097343.s002", "https://dx.doi.org/10.1371/journal.pone.0097343.s003"], "stats"=>{"downloads"=>31, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Voluntary_Enhancement_of_Neural_Signatures_of_Affiliative_Emotion_Using_fMRI_Neurofeedback_/1033327", "title"=>"Voluntary Enhancement of Neural Signatures of Affiliative Emotion Using fMRI Neurofeedback", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-05-21 03:27:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/1506158"], "description"=>"<p>Comparison of tenderness/affection <i>vs.</i> pride, using the change in frequency of attaining tenderness/affection-characteristic patterns observed in the last <i>vs.</i> first classification sessions as a covariate of interest. Significant effects (FWE-corrected) were observed in <i>a priori-</i>defined septohypothalamic (Septohyp) and frontopolar (FPC) regions of interest. Septohyp, MNI coordinates −6, 2, −11; cluster size (k) = 33 voxels; Z = 3.03, <i>p = </i>.046. FPC, MNI coordinates 3, 56, 22; k = 95; Z = 3.80; <i>p = </i>.016. All values are FWE-corrected using Small Volume Correction (SVC). There were no regions surviving FWE correction at the whole brain level.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "Behavioral neuroscience", "Cognitive neuroscience", "psychology", "behavior", "emotions", "neurology", "mathematics", "Applied mathematics", "algorithms", "Neurofeedback"], "article_id"=>1033326, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Jorge Moll", "Julie H. Weingartner", "Patricia Bado", "Rodrigo Basilio", "João R. Sato", "Bruno R. Melo", "Ivanei E. Bramati", "Ricardo de Oliveira-Souza", "Roland Zahn"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0097343.g007", "stats"=>{"downloads"=>2, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Regions_of_increased_BOLD_activity_during_neurofeedback_training_/1033326", "title"=>"Regions of increased BOLD activity during neurofeedback training.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-21 03:27:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/1506149"], "description"=>"<p>The position of each diamond and triangle (representing brain volumes) relative to the decision boundary (multivariate separation hyperplane, dashed line) reflects the SVM classification of single brain volumes as a distributed activation pattern associated either with tenderness/affection or pride. The feedback stimuli comprised rings with different degrees of distortion (20 levels). The most distorted shape was associated with incorrect classification and the progressively smoother rings were associated with increasing distance of the correctly classified example from the SVM decision boundary.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "neuroscience", "neuroimaging", "Functional magnetic resonance imaging", "Behavioral neuroscience", "Cognitive neuroscience", "psychology", "behavior", "emotions", "neurology", "mathematics", "Applied mathematics", "algorithms", "depiction", "vector", "algorithm", "classification"], "article_id"=>1033317, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Jorge Moll", "Julie H. Weingartner", "Patricia Bado", "Rodrigo Basilio", "João R. Sato", "Bruno R. Melo", "Ivanei E. Bramati", "Ricardo de Oliveira-Souza", "Roland Zahn"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0097343.g001", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Graphic_depiction_of_Support_Vector_Machine_SVM_algorithm_used_for_classification_and_neurofeedback_/1033317", "title"=>"Graphic depiction of Support Vector Machine (SVM) algorithm used for classification and neurofeedback.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-21 03:27:39"}

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