Neuroimaging-based biomarkers for pain: state of the field and current directions

Abstract

Chronic pain is an endemic problem involving both peripheral and brain pathophysiology. Although biomarkers have revolutionized many areas of medicine, biomarkers for pain have remained controversial and relatively underdeveloped. With the realization that biomarkers can reveal pain-causing mechanisms of disease in brain circuits and in the periphery, this situation is poised to change. In particular, brain pathophysiology may be diagnosable with human brain imaging, particularly when imaging is combined with machine learning techniques designed to identify predictive measures embedded in complex data sets. In this review, we explicate the need for brain-based biomarkers for pain, some of their potential uses, and some of the most popular machine learning approaches that have been brought to bear. Then, we evaluate the current state of pain biomarkers developed with several commonly used methods, including structural magnetic resonance imaging, functional magnetic resonance imaging and electroencephalography. The field is in the early stages of biomarker development, but these complementary methodologies have already produced some encouraging predictive models that must be tested more extensively across laboratories and clinical populations.

Keywords: Biomarkers; EEG; MRI; MVPA; Machine learning; Neuroimaging; Pain.

Figure 1.

Timeline of machine learning articles for pain: a timeline showing the number of published articles per neuroimaging technique or combinations of techniques for pain studies investigating biomarkers (47 in total). Studies include the use of EEG, task fMRI (denoted fMRI), rs-fMRI, sMRI, or a combination of techniques (denoted combined) and use a cross-validation method for their predictive model. EEG, electroencephalography; fMRI, functional magnetic resonance imaging; rs-fMRI, resting-state functional magnetic resonance imaging; sMRI, structural magnetic resonance imaging.