Diagnostic Classification Based on Functional Connectivity in Chronic Pain

Sundermann, Benedikt; Burgmer, Markus; Pogatzki-Zahn, Esther; Gaubitz, M.; Stüber, Christoph; Wessolleck, E.; Heuft, Gereon; Pfleiderer, Bettina

doi:10.1016/j.acra.2013.12.003

Cited by 29 publications

(24 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous attempts have established neurophysiological differences in pain-related processing in FM patients[15; 24; 25; 56] but have not yet successfully discriminated patients from healthy participants at the individual-person level (cf. [67]). A recently published study based on brain anatomy findings shows ability to discriminate FM patients with from 53% to 76% accuracy in different datasets[61].…”

Section: Discussionmentioning

confidence: 99%

Towards a neurophysiological signature for fibromyalgia

López‐Solà

Woo

Pujol

et al. 2016

Pain

210

194

View full text Add to dashboard Cite

Fibromyalgia (FM) patients show characteristically enhanced unpleasantness to painful and non-painful sensations accompanied by altered neural responses. The diagnostic potential of such neural alterations, including their sensitivity and specificity to FM (vs. healthy controls) is unknown. We identify a brain signature that characterizes FM central pathophysiology at the neural systems level. We included 37 FM patients and 35 matched healthy controls, and analyzed fMRI responses to (i) painful pressure and (ii) non-painful multisensory (visual-auditory-tactile) stimulation. We used machine-learning techniques to identify a brain-based FM signature. When exposed to the same painful stimuli, FM patients showed greater Neurologic Pain Signature (NPS, Wager 2013) responses. In addition, a new pain-related classifier (‘FM-pain’) revealed augmented responses in sensory integration (insula/operculum) and self-referential (e.g., medial prefrontal) regions in FM, and reduced responses in the lateral frontal cortex. A ‘Multisensory’ classifier trained on non-painful sensory stimulation revealed augmented responses in insula/operculum, posterior cingulate, and medial prefrontal regions, and reduced responses in primary/secondary sensory cortices, basal ganglia and cerebellum. Combined activity in the NPS, FM-pain, and Multisensory patterns classified patients vs. controls with 92% sensitivity and 94% specificity in out-of-sample individuals. Enhanced NPS responses partly mediated mechanical hypersensitivity, and correlated with depression and disability(puncorrected<0.05); FM-pain and Multisensory responses correlated with clinical pain(puncorrected<0.05). The study provides initial characterization of individual FM-patients based on pathophysiological, symptom-related brain features. If replicated, these brain features may constitute objective neural targets for therapeutic interventions. The results establish a framework for assessing therapeutic mechanisms and predicting treatment response at the individual level.

show abstract

Section: Discussionmentioning

confidence: 99%

Towards a neurophysiological signature for fibromyalgia

López‐Solà

Woo

Pujol

et al. 2016

Pain

210

194

View full text Add to dashboard Cite

show abstract

“…Ung and colleagues 22 previously described successful classification of chronic low back pain from HC with 76% accuracy (26% greater than base rate) using a support vector machine (SVM) model trained by structural MRI features. Additionally, Sundermann and colleagues 21 reported 73.5% accuracy in classifying FM from HC using SVM classifiers with resting state functional MRI connectivity features. We were able to produce similar results in accurately classifying FM patients and HC using a J48 decision tree (75.50% accuracy; 22.17% above base rate) and SVM (72.17% accuracy; 18.84% above base rate) classifiers tested with 100 iterations of cross-validation.…”

Section: Discussionmentioning

confidence: 99%

“…Multiple SVM models using different combinations of parameters may result in wide variations of accuracy. For a practical example related to FM diagnosis, Sunderman and colleagues 21 report results of various combinations of parameters used in SVM models, with accuracy rates ranging from 0% to 73.5%. We used a nested cross validation approach, in which all tuning steps are repeated in each fold of cross validation, to avoid biased estimates of accuracy 23 .…”

Section: Discussionmentioning

confidence: 99%

“…These classification studies typically employ sophisticated multivariate statistical approaches, which are said to provide empirically derived algorithms to discriminate between individuals with and without pain. A number of these studies have even suggested that these indices reflect “objective biomarkers” of pain, or act as a surrogate for patients’ self-report 5,6,21,22,24 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

O’Shea

Craggs

Price

et al. 2015

The Journal of Pain

View full text Add to dashboard Cite

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 has not been compared. This study used commonly reported ML algorithms to measure differences between “objective” neuroimaging data and “subjective” self-report (i.e., mood and pain intensity) in their ability to discriminate between individuals with and without chronic pain. Structural MRI data from 26 individuals (14 individuals with fibromyalgia, 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 (i.e., anger, anxiety, depression, frustration, 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 53–76%, whereas mood and pain intensity ratings ranged from 79–96% and 83–96%, 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, accurate, and continues to be clinically vital.

show abstract

“…Furthermore, although other similar studies exist, the general principles illustrated here remain the same across studies. 2,17,21 For each of these studies, we performed 3 sets of calculations considering population base rate, clinic base rate, and study sample base rate. Because biomarkers are often proposed on the basis of their presumed clinical utility, PPV and NPV were also recalculated on the basis of a conservative 90% base rate for each condition in a clinic setting.…”

mentioning

confidence: 99%