A Tool for Classifying Individuals with Chronic Back Pain: Using Multivariate Pattern Analysis with Functional Magnetic Resonance Imaging Data

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.

Neuroimaging-based biomarker discovery and validation

“…Recent advances in human neuroimaging, including functional and structural Magnetic Resonance Imaging (fMRI/sMRI) combined with machine learning techniques, are bringing us closer to the goal of developing objective, brain-based markers of the neural functions and neuropathology that underlie chronic pain [2; 7; 25; 33]. These brain measures are particularly promising as biomarkers for chronic pain.…”

Neuroscience in court: The painful truth

“…CPD compares scans taken of a person's brain after they received an electric shock to a database of images from 30 individuals with and without chronic pain. People with chronic pain respond to a stimulus differently from healthy controls, and the company has developed an algorithm that allows it to distinguish between the two with 92% accuracy 5 . CPD president and co-founder Shaun England says that he expects a scan to cost between $5,000 and $6,000.…”

Beschreibung und empirische Fundierung des verhaltenstherapeutischen Moduls der MiSpEx-Intervention

“…5 Das Programm ist sowohl präventiv als auch rehabilitativ einsetzbar. [9,10]. Aktuelle Studien zeigen, dass mit einer anhaltenden intrinsischen Hirnaktivierung im präfrontalen Kortex eine zusätzliche Synchronisierung mit dem sensomotorischen Kortex einhergeht [11].…”