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.

Possibility of Assessing Pain with Biomarkers in Psychiatric Disorders

“…Some studies have demonstrated the ability to predict pain from fMRI changes to distinguish painful heat from nonpainful heat. These fNMR studies have also found that there is a difference between physical and social pain and that all fMRI changes are suppressed after opioid administration [30,31].…”

“…Other studies have developed fMRI patterns that can predict the intensity of a patient's pain using machine learning analysis. Pattern activity has been demonstrated in areas of the brain that are associated with damaging stimuli pain and include all structures associated with pain stimulus processing, such as the thalamus, anterior and posterior insula, somatosensory cortex, and anterior cingulate cortex [29,30].…”

Transcriptional and cellular signatures of cortical morphometric similarity remodelling in chronic pain

“…offered insights into key central mechanisms that might contribute to chronic pain, such as the sensitization of an array of nervous system pathways, imbalances in the facilitatory and inhibitory descending modulation pathways from the brain that regulate the transmission of noxious information in the spinal cord, neuroinflammation and glial dysfunction, among others [10][11][12][13][14] . These findings have fuelled a number of neuroimaging studies in humans seeking to unravel how chronic pain affects brain structure, functioning and neurochemistry, and what form of brain pathophysiology in these patients might be amenable to be targeted by different pharmacological and non-pharmacological treatments 15,16 .…”

Pain and breathlessness: Salient, somatosensory and similar, but not the same

“…Pattern weights limited to individual regions can also be used to obtain local pattern responses (Woo et al 2014). This approach is part of a major trend in neuroimaging research using pattern information to assess pain (Rosa & Seymour 2014;Mano et al 2018;van der Miesen et al 2019;Ung et al 2012;Marquand et al 2010) and other cognitive and affective processes. Multivariate brain models integrate brain information into a single optimized prediction, and test predictions on new, independent individuals, providing unbiased estimates of effect size (Reddan et al 2017) and capturing information across multiple spatial scales (Miyawaki et al 2008;Hackmack et al 2012;Haynes 2015;Lindquist et al 2017).…”