Using magnetic resonance imaging to visualize the brain in chronic pain
Neuroimaging can evaluate the engagement of brain regions during pain,5,9 the impact of context (eg, attentional state),2 and how these regions interact and organize into static and dynamic networks.9,10 fMRI can be used to study responses to stimulus-evoked acute pain, while analysis of activity during task-free “resting state” can identify aberrant intrinsic functioning and spontaneous activity in chronic pain patients. For example, studies have shown that brain connectivity in regions of the salience and default mode brain networks is abnormal in chronic back pain patients,3,7 with partial restoration after treatment.3
Peripheral nerve and central nervous system white matter can be assessed using diffusion-weighted imaging and diffusion tensor imaging. Tractography can be used to delineate pathways and quantify abnormalities in chronic pain patients and with group-based analyses such as tract-based spatial statistics with diffusion metrics (fractional anisotropy, axial, radial, and mean diffusivity) indicative of white matter integrity, demyelination, neuroinflammation, and edema.6 Diffusion abnormalities correspond to pain outcomes and pain severity.5
Grey matter assessment, such as cortical thickness analysis or voxel-based morphometry, can elucidate the relationship between pain severity and treatment outcomes and grey matter abnormalities (eg, due to changes in neuronal size or number, synaptogenesis, dendritic branching, axon sprouting, synaptic pruning, neuronal cell death, alterations in vasculature, and the size/number of glial cells).5 For example, gray matter volumes of the amygdala and hippocampal brain in sub-acute back pain patients predict risk for chronic pain.11
Some imaging findings are commonly seen across diverse chronic pain conditions and others do not generalize as they are related to specific disease states. Recent advances in neuroimaging are applying machine learning to predict treatment outcomes in chronic pain patients to inform prognostics for personalized pain management. Paralleling this work, it is imperative to consider the ethical and legal implications of using brain imaging for diagnostics.