Original Report| Volume 16, ISSUE 5, P472-477, May 2015

Comparison of Machine Classification Algorithms for Fibromyalgia: Neuroimages Versus Self-Report

Published:February 20, 2015DOI:


      • Machine learning models were used on data from fibromyalgia patients and controls.
      • We compared neuroimaging and self-report data as methods of group classification.
      • Self-report outperformed neuroimaging data for determining group membership.


      Recent studies have posited that machine learning (ML) techniques accurately classify individuals with and without pain solely based on neuroimaging data. These studies claim that self-report is unreliable, making “objective” neuroimaging classification methods imperative. However, the relative performance of ML on neuroimaging and self-report data have not been compared. This study used commonly reported ML algorithms to measure differences between “objective” neuroimaging data and “subjective” self-report (ie, mood and pain intensity) in their ability to discriminate between individuals with and without chronic pain. Structural magnetic resonance imaging data from 26 individuals (14 individuals with fibromyalgia and 12 healthy controls) were processed to derive volumes from 56 brain regions per person. Self-report data included visual analog scale ratings for pain intensity and mood (ie, anger, anxiety, depression, frustration, and fear). Separate models representing brain volumes, mood ratings, and pain intensity ratings were estimated across several ML algorithms. Classification accuracy of brain volumes ranged from 53 to 76%, whereas mood and pain intensity ratings ranged from 79 to 96% and 83 to 96%, respectively. Overall, models derived from self-report data outperformed neuroimaging models by an average of 22%. Although neuroimaging clearly provides useful insights for understanding neural mechanisms underlying pain processing, self-report is reliable and accurate and continues to be clinically vital.


      The present study compares neuroimaging, self-reported mood, and self-reported pain intensity data in their ability to classify individuals with and without fibromyalgia using ML algorithms. Overall, models derived from self-reported mood and pain intensity data outperformed structural neuroimaging models.

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        • Aha D.W.
        • Kibler D.
        • Albert M.K.
        Instance-based learning algorithms.
        Mach Learn. 1991; 6: 37-66
        • Alciati A.
        • Sgiarovello P.
        • Atzeni F.
        • Sarzi-Puttini P.
        Psychiatric problems in fibromyalgia: Clinical and neurobiological links between mood disorders and fibromyalgia.
        Reumatismo. 2012; 64: 268-274
        • Apkarian A.V.
        • Hashmi J.A.
        • Baliki M.N.
        Pain and the brain: Specificity and plasticity of the brain in clinical chronic pain.
        Pain. 2011; 152: S49-S64
        • Arora R.
        • Suman S.
        Comparative analysis of classification algorithms on different datasets using WEKA.
        Int J Comput App. 2012; 54: 21-25
        • Brown J.E.
        • Chatterjee N.
        • Younger J.
        • Mackey S.
        Towards a physiology-based measure of pain: Patterns of human brain activity distinguish painful from non-painful thermal stimulation.
        PLoS One. 2011; 6: e24124
        • Callan D.
        • Mills L.
        • Nott C.
        • England R.
        • England S.
        A tool for classifying individuals with chronic back pain: Using multivariate pattern analysis with functional magnetic resonance imaging data.
        PLoS One. 2014; 9: e98007
        • Fischl B.
        • Salat D.H.
        • Busa E.
        • Albert M.
        • Dieterich M.
        • Haselgrove C.
        • van der Kouwe Am
        • Killiany R.
        • Kennedy D.
        • Klaveness S.
        • Montillo A.
        • Makris N.
        • Rosen B.
        • Dale A.M.
        Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain.
        Neuron. 2002; 33: 341-355
        • Hall M.
        • Frank E.
        • Holmes G.
        • Pfahringer B.
        • Reutemann P.
        • Witten I.H.
        The WEKA data mining software: An update.
        ACM SIGKDD Explorations Newsletter. 2009; 11: 10-18
        • Jovicich J.
        • Czanner S.
        • Han X.
        • Salat D.
        • van der Kouwe A.
        • Quinn B.
        • Pacheco J.
        • Albert M.
        • Killiany R.
        • Blacker D.
        • Maguire P.
        • Rosas D.
        • Makris N.
        • Gollub R.
        • Dale A.
        • Dickerson B.C.
        • Fischl B.
        MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths.
        Neuroimage. 2009; 46: 177-192
        • Keerthi S.S.
        • Shevade S.K.
        • Bhattacharyya C.
        • Murthy K.R.K.
        Improvements to Platt's SMO algorithm for SVM classifier design.
        Neural Comput. 2001; 13: 637-649
        • Langley P.
        • Iba W.
        • Thompson K.
        An analysis of Bayesian classifiers.
        in: Proceedings of the National Conference on Artificial Intelligence. AAAI Press, San Jose, CA1992: 223-228
        • Lecessie S.
        • Vanhouwelingen J.C.
        Ridge estimators in logistic-regression.
        Appl Stat. 1992; 41: 191-201
        • Letzen J.E.
        • Sevel L.S.
        • Gay C.W.
        • O’Shea A.M.
        • Craggs J.G.
        • Price D.D.
        • Robinson M.E.
        Test-retest reliability of pain-related brain activity in healthy controls undergoing experimental thermal pain.
        J Pain. 2014; 15: 1008-1014
        • National Institutes of Health
        Biomarkers for chronic pain using functional brain connectivity.
        (Available at:) (Accessed 2014)
        • Orrù G.
        • Pettersson-Yeo W.
        • Marquand A.F.
        • Sartori G.
        • Mechelli A.
        Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review.
        Neurosci Biobehav Rev. 2012; 36: 1140-1152
        • Pereira F.
        • Mitchell T.
        • Botvinick M.
        Machine learning classifiers and fMRI: A tutorial overview.
        Neuroimage. 2009; 45: S199-S209
        • Platt J.C.
        Sequential minimal optimization: A fast algorithm for training support vector machines.
        1998 (MSR-TR-98–14)
        • Quinlan J.R.
        C4. 5: Programs for Machine Learning.
        Morgan Kaufmann Publishers, Inc, San Mateo, CA1993
        • Robinson M.E.
        • Staud R.
        • Price D.D.
        Pain measurement and brain activity: Will neuroimages replace pain ratings?.
        J Pain. 2013; 14: 323-327
      1. Singhi SK, Liu H: Feature subset selection bias for classification learning, in Proceedings of the 23rd International Conference on Machine Learning. NY, ACM, 2006, pp 849-856

        • Sundermann B.
        • Burgmer M.
        • Pogatzki-Zahn E.
        • Gaubitz M.
        • Stüber C.
        • Wessolleck E.
        • Heuft G.
        • Pfleiderer B.
        Diagnostic classification based on functional connectivity in chronic pain: Model optimization in fibromyalgia and rheumatoid arthritis.
        Acad Radiol. 2014; 21: 369-377
        • Ung H.
        • Brown J.E.
        • Johnson K.A.
        • Younger J.
        • Hush J.
        • Mackey S.
        Multivariate classification of structural MRI data detects chronic low back pain.
        Cereb Cortex. 2014; 24: 1037-1044
        • Varma S.
        • Simon R.
        Bias in error estimation when using cross-validation for model selection.
        BMC Bioinformatics. 2006; 7: 91
        • Wager T.D.
        • Atlas L.Y.
        • Lindquist M.A.
        • Roy M.
        • Woo C.W.
        • Kross E.
        An fMRI-based neurologic signature of physical pain.
        N Engl J Med. 2013; 368: 1388-1397
        • Wartolowska K.
        How neuroimaging can help us to visualise and quantify pain?.
        Eur J Pain Suppl. 2011; 5: 323-327
        • Wolfe F.
        • Smythe H.A.
        • Yunus M.B.
        • Bennett R.M.
        • Bombardier C.
        • Goldenberg D.L.
        • Tugwell P.
        • Campbell S.M.
        • Abeles M.
        • Clark P.
        • Fam A.G.
        • Farber S.J.
        • Flechtner J.J.
        • Franklin C.M.
        • Gatter R.A.
        • Hamaty D.
        • Lessard J.
        • Lichtbroun A.S.
        • Masi A.T.
        • McCain G.A.
        • Reynolds W.J.
        • Romano T.J.
        • Russell I.J.
        • Sheon R.P.
        The American College of Rheumatology 1990 criteria for the classification of fibromyalgia: Report of the multicenter criteria committee.
        Arthritis Rheum. 1990; 33: 160-172