Abstract
Chronic pain is one of the most prevalent health problems in the world today, yet neurological markers, critical to diagnosis of chronic pain, are still largely unknown. The ability to objectively identify individuals with chronic pain using functional magnetic resonance imaging (fMRI) data is important for the advancement of diagnosis, treatment, and theoretical knowledge of brain processes associated with chronic pain. The purpose of our research is to investigate specific neurological markers that could be used to diagnose individuals experiencing chronic pain by using multivariate pattern analysis with fMRI data. We hypothesize that individuals with chronic pain have different patterns of brain activity in response to induced pain. This pattern can be used to classify the presence or absence of chronic pain. The fMRI experiment consisted of alternating 14 seconds of painful electric stimulation (applied to the lower back) with 14 seconds of rest. We analyzed contrast fMRI images in stimulation versus rest in pain-related brain regions to distinguish between the groups of participants: 1) chronic pain and 2) normal controls. We employed supervised machine learning techniques, specifically sparse logistic regression, to train a classifier based on these contrast images using a leave-one-out cross-validation procedure. We correctly classified 92.3% of the chronic pain group (N = 13) and 92.3% of the normal control group (N = 13) by recognizing multivariate patterns of activity in the somatosensory and inferior parietal cortex. This technique demonstrates that differences in the pattern of brain activity to induced pain can be used as a neurological marker to distinguish between individuals with and without chronic pain. Medical, legal and business professionals have recognized the importance of this research topic and of developing objective measures of chronic pain. This method of data analysis was very successful in correctly classifying each of the two groups.
Highlights
Chronic pain, defined as pain that persists for an extended time after the injury has completed the healing phase [4,5], is one of the most prevalent health problems in developed countries [1,2,3]
Our study extends the use of functional magnetic resonance imaging (fMRI) data and multivariate pattern analysis techniques to classify individuals with and without chronic pain
All procedures were approved by the New England Institutional Review Board (NEIRB) in accordance with the principles expressed in the Declaration of Helsinki
Summary
Chronic pain, defined as pain that persists for an extended time after the injury has completed the healing phase [4,5], is one of the most prevalent health problems in developed countries [1,2,3]. The increased medical expenses, lost income and lowered productivity make chronic pain one of the most costly health problems in the world. Interest, and expense associated with chronic pain, there is still no widely accepted objective measure of chronic pain. Diagnosis of chronic pain is based primarily on the subjective reports of the individual [8,9], or subjective reports by care providers. While a doctor may identify the presence of a fever by asking the patient if they feel hot or cold, an objective measure of temperature taken by a thermometer greatly improves the diagnosis
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
