title: Diagnostic classification based on functional connectivity in chronic pain: model optimization in fibromyalgia and rheumatoid arthritis.
abstract: OBJECTIVE
  The combination of functional magnetic resonance imaging (fMRI) of the brain with multivariate pattern analysis (MVPA) has been proposed as a possible diagnostic tool. Goal of this investigation was to identify potential functional connectivity (FC) differences in the salience network (SN) and default mode network (DMN) between fibromyalgia syndrome (FMS), rheumatoid arthritis (RA), and controls (HC) and to evaluate the diagnostic applicability of derived pattern classification approaches.
  
  
  METHODS
  The resting period during an fMRI examination was retrospectively analyzed in women with FMS (n = 17), RA (n = 16), and HC (n = 17). FC was calculated for SN and DMN subregions. Classification accuracies of discriminative MVPA models were evaluated with cross-validation: (1) inferential test of a single method, (2) explorative model optimization.
  
  
  RESULTS
  No inferentially tested model was able to classify subjects with statistically significant accuracy. However, the diagnostic ability for the differential diagnostic problem exhibited a trend to significance (accuracy: 69.7%, P = .086). Optimized models in the explorative analysis reached accuracies up to 73.5% (FMS vs. HC), 78.8% (RA vs. HC), and 78.8% (FMS vs. RA) whereas other models performed at or below chance level. Comparable support vector machine approaches performed above average for all three problems.
  
  
  CONCLUSIONS
  Observed accuracies are not sufficient to reliably differentiate between FMS and RA for diagnostic purposes. However, some indirect evidence in support of the feasibility of this approach is provided. This exploratory analysis constitutes a fundamental model optimization effort to be based on in further investigations.
authors: Department of Clinical Radiology, University Hospital Münster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149 Münster, North Rhine-Westphalia, Germany. Electronic address: benedikt.sundermann@ukmuenster.de.
authors: Benedikt
authors: B
authors: Sundermann
authors: Department of Psychosomatics and Psychotherapy, University Hospital Münster, Münster, North Rhine-Westphalia, Germany.
authors: Markus
authors: M
authors: Burgmer
authors: Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, University Hospital Münster, Münster, North Rhine-Westphalia, Germany.
authors: Esther
authors: E
authors: Pogatzki-Zahn
authors: Akademie für Manuelle Medizin, Münster, North Rhine-Westphalia, Germany.
authors: Markus
authors: M
authors: Gaubitz
authors: Department of Pediatrics, Clemens-Hospital Münster, Münster, North Rhine-Westphalia, Germany.
authors: Christoph
authors: C
authors: Stüber
authors: Department of Otorhinolaryngology, St. Anna-Klinik, Wuppertal, North Rhine-Westphalia, Germany.
authors: Erik
authors: E
authors: Wessolleck
authors: Department of Psychosomatics and Psychotherapy, University Hospital Münster, Münster, North Rhine-Westphalia, Germany.
authors: Gereon
authors: G
authors: Heuft
authors: Department of Clinical Radiology, University Hospital Münster, Albert-Schweitzer-Campus 1, Gebäude A1, 48149 Münster, North Rhine-Westphalia, Germany.
authors: Bettina
authors: B
authors: Pfleiderer
mesh_terms: D001172:Arthritis, Rheumatoid / Q000150:complications / Q000503:physiopathology*; D001921:Brain / Q000503:physiopathology*; D059350:Chronic Pain / Q000209:etiology / Q000503:physiopathology*; D003198:Computer Simulation; D063132:Connectome / Q000379:methods*; D005260:Female; D005356:Fibromyalgia / Q000150:complications / Q000503:physiopathology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D008959:Models, Neurological; D009415:Nerve Net / Q000503:physiopathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: MVPA; chronic pain; classification; fMRI; functional connectivity