Highlights
- •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.
Abstract
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.
Perspective
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.
Key words
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Article info
Publication history
Published online: February 20, 2015
Accepted:
February 4,
2015
Received in revised form:
January 31,
2015
Received:
October 21,
2014
Footnotes
This research was supported by grants 5R01AT001424 and 5R01NS038767 from the National Institutes of Health. The authors have no conflicts of interest to declare.
Identification
Copyright
© 2015 American Pain Society. Published by Elsevier Inc. All rights reserved.
