Towards a Physiology-Based Measure of Pain: Patterns of Human Brain Activity Distinguish Painful from Non-Painful Thermal Stimulation
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{"title"=>"Towards a physiology-based measure of pain: Patterns of human brain activity distinguish painful from non-painful thermal stimulation", "type"=>"journal", "authors"=>[{"first_name"=>"Justin E.", "last_name"=>"Brown", "scopus_author_id"=>"55757784754"}, {"first_name"=>"Neil", "last_name"=>"Chatterjee", "scopus_author_id"=>"36875904700"}, {"first_name"=>"Jarred", "last_name"=>"Younger", "scopus_author_id"=>"7004534885"}, {"first_name"=>"Sean", "last_name"=>"Mackey", "scopus_author_id"=>"7006089174"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"362552353", "sgr"=>"80052759329", "issn"=>"19326203", "pmid"=>"21931652", "scopus"=>"2-s2.0-80052759329", "doi"=>"10.1371/journal.pone.0024124", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)"}, "id"=>"6883a556-6e96-366e-a39f-543c3be35ce8", "abstract"=>"Pain often exists in the absence of observable injury; therefore, the gold standard for pain assessment has long been self-report. Because the inability to verbally communicate can prevent effective pain management, research efforts have focused on the development of a tool that accurately assesses pain without depending on self-report. Those previous efforts have not proven successful at substituting self-report with a clinically valid, physiology-based measure of pain. Recent neuroimaging data suggest that functional magnetic resonance imaging (fMRI) and support vector machine (SVM) learning can be jointly used to accurately assess cognitive states. Therefore, we hypothesized that an SVM trained on fMRI data can assess pain in the absence of self-report. In fMRI experiments, 24 individuals were presented painful and nonpainful thermal stimuli. Using eight individuals, we trained a linear SVM to distinguish these stimuli using whole-brain patterns of activity. We assessed the performance of this trained SVM model by testing it on 16 individuals whose data were not used for training. The whole-brain SVM was 81% accurate at distinguishing painful from non-painful stimuli (p<0.0000001). Using distance from the SVM hyperplane as a confidence measure, accuracy was further increased to 84%, albeit at the expense of excluding 15% of the stimuli that were the most difficult to classify. Overall performance of the SVM was primarily affected by activity in pain-processing regions of the brain including the primary somatosensory cortex, secondary somatosensory cortex, insular cortex, primary motor cortex, and cingulate cortex. Region of interest (ROI) analyses revealed that whole-brain patterns of activity led to more accurate classification than localized activity from individual brain regions. Our findings demonstrate that fMRI with SVM learning can assess pain without requiring any communication from the person being tested. We outline tasks that should be completed to advance this approach toward use in clinical settings.", "link"=>"http://www.mendeley.com/research/towards-physiologybased-measure-pain-patterns-human-brain-activity-distinguish-painful-nonpainful-th", "reader_count"=>147, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>12, "Student > Doctoral Student"=>5, "Researcher"=>20, "Student > Ph. D. Student"=>33, "Student > Postgraduate"=>12, "Student > Master"=>23, "Other"=>8, "Student > Bachelor"=>17, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>2, "Professor"=>10}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>12, "Student > Doctoral Student"=>5, "Researcher"=>20, "Student > Ph. D. Student"=>33, "Student > Postgraduate"=>12, "Student > Master"=>23, "Other"=>8, "Student > Bachelor"=>17, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>2, "Professor"=>10}, "reader_count_by_subject_area"=>{"Unspecified"=>10, "Agricultural and Biological Sciences"=>21, "Arts and Humanities"=>1, "Chemistry"=>1, "Computer Science"=>10, "Decision Sciences"=>1, "Engineering"=>11, "Nursing and Health Professions"=>4, "Mathematics"=>1, "Medicine and Dentistry"=>45, "Neuroscience"=>14, "Pharmacology, Toxicology and Pharmaceutical Science"=>2, "Sports and Recreations"=>1, "Physics and Astronomy"=>1, "Psychology"=>22, "Social Sciences"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>45}, "Social Sciences"=>{"Social Sciences"=>2}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Sports and Recreations"=>{"Sports and Recreations"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>22}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>10}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>2}, "Arts and Humanities"=>{"Arts and Humanities"=>1}, "Engineering"=>{"Engineering"=>11}, "Chemistry"=>{"Chemistry"=>1}, "Neuroscience"=>{"Neuroscience"=>14}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>21}, "Computer Science"=>{"Computer Science"=>10}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>4}}, "reader_count_by_country"=>{"Canada"=>2, "Colombia"=>1, "United States"=>4, "Japan"=>2, "China"=>2, "Denmark"=>1, "Mexico"=>1, "United Kingdom"=>4, "Italy"=>2, "Portugal"=>1, "Germany"=>2, "Spain"=>1}, "group_count"=>6}

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/735959"], "description"=>"<p>Using the activity from six regions of interest (ROIs), SVMs were used to distinguish the painful stimuli from the nonpainful stimuli. For each ROI, this table displays the SVM's average accuracy, and positive and negative predictive value (PV) when tested on participants in the testing group (N = 8). Error is reported as 1 standard deviation. An asterisk indicates performance measures that are significantly greater than chance (<i>p<0.</i>05). Anterior cingulate cortex (ACC). Primary somatosensory cortex (S1). Secondary somatosensory cortex (S2). Prefrontal cortex (PFC).</p>", "links"=>[], "tags"=>["roi", "svm"], "article_id"=>406313, "categories"=>["Physiology", "Neuroscience", "Genetics"], "users"=>["Justin E. Brown", "Neil Chatterjee", "Jarred Younger", "Sean Mackey"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0024124.t002", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_the_ROI_SVM_Classifiers_/406313", "title"=>"Performance of the ROI SVM Classifiers.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 14:48:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/735772"], "description"=>"<p>The classifier's performance was assessed at increasing distance thresholds. As the distance threshold increased, an increasing number of stimuli were excluded on the grounds that stimuli nearest the separating hyperplane were most likely misclassified. In this figure, performance is plotted as a function of the percentage of stimuli that have been excluded from classification. Dotted lines display the performance computed at each distance threshold. Solid lines display a third degree polynomial fit to those data.</p>", "links"=>[], "tags"=>["whole-brain", "svm", "classifier"], "article_id"=>406122, "categories"=>["Physiology", "Neuroscience", "Genetics"], "users"=>["Justin E. Brown", "Neil Chatterjee", "Jarred Younger", "Sean Mackey"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0024124.g001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_the_Whole_Brain_SVM_Classifier_is_Increased_by_a_Distance_Threshold_/406122", "title"=>"Performance of the Whole-Brain SVM Classifier is Increased by a Distance Threshold.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 14:47:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/735850"], "description"=>"<p>A permutation test was run to determine which brain regions significantly affected the whole-brain SVM classification. This figure illustrates brain regions that fall within the 90<sup>th</sup> percentile of the null distribution that was determined by permutation. Regions in the 99<sup>th</sup> percentile (<i>p<0.01)</i> are noted in the <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024124#s3\" target=\"_blank\">results</a> section. Shades of red indicate regions where greater BOLD signal influenced the SVM to classify a stimulus as painful. Shades of blue indicate regions where greater BOLD signal influenced the SVM to classify a stimulus as non-painful.</p>", "links"=>[], "tags"=>["regions", "influenced", "whole-brain", "svm"], "article_id"=>406204, "categories"=>["Physiology", "Neuroscience", "Genetics"], "users"=>["Justin E. Brown", "Neil Chatterjee", "Jarred Younger", "Sean Mackey"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0024124.g002", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Brain_Regions_that_Most_Influenced_the_Whole_Brain_SVM_Classifier_/406204", "title"=>"Brain Regions that Most Influenced the Whole-Brain SVM Classifier.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 14:48:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/735986"], "description"=>"<p>For each participant in the testing group, the SVM was used to distinguish the painful stimuli from the nonpainful stimuli. For each participant in the testing group, and for their group average, this table displays the SVM's overall accuracy, and positive and negative predictive value (PV). Error is reported as 1 standard deviation. An asterisk indicates performance measures that are significantly greater than chance (<i>p<0.</i>05).</p>", "links"=>[], "tags"=>["whole-brain", "svm"], "article_id"=>406348, "categories"=>["Physiology", "Neuroscience", "Genetics"], "users"=>["Justin E. Brown", "Neil Chatterjee", "Jarred Younger", "Sean Mackey"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0024124.t001", "stats"=>{"downloads"=>0, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_the_Whole_Brain_SVM_Classifier_/406348", "title"=>"Performance of the Whole-Brain SVM Classifier.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 14:48:58"}

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Relative Metric

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