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Using magnetic resonance imaging to visualize the brain in chronic pain

Bosma, Rachael L.a; Hemington, Kasey S.a,b; Davis, Karen D.a,b,c,*

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doi: 10.1097/j.pain.0000000000000941

The development of neuroimaging has revolutionized our understanding of the brain’s instrumental role in pain perception. Magnetic resonance imaging–based methods have provided noninvasive access to visualize the brain in awake, feeling humans. Common techniques include functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging to assess white matter (diffusion tensor imaging and tractography) and grey matter (volumetric, cortical thickness analyses) (Fig. 1). Each of these techniques capitalizes on biological properties of the tissues to generate contrast in the image. In fMRI, detection of brain function is derived from neurovascular coupling mechanisms in which neural activity drives changes in local blood flow that can be detected because of the difference in magnetic properties between oxygenated (oxy Hb) and deoxygenated hemoglobin (deoxy Hb).1 In diffusion imaging, restricted (white matter) vs unrestricted (eg, cerebral spinal fluid) diffusion of water has different magnetic properties that provide contrast in the image and provide details about the tissue architecture.8 These techniques do not provide a direct measure of neural functioning or anatomical structure unlike neurophysiological recordings or tracer methodologies.1,8

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.4


K. D. Davis is supported by grants from the Canadian Institutes of Health Research (CIHR) and the Mayday fund. K. S. Hemington and R. L. Bosma are both recipients of CIHR doctoral and postdoctoral awards, respectively.


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Chronic pain; neuroimaging; Structural MRI; Cortical thickness analysis; diffusion weighted imaging; fMRI; DTI; Functional connectivity; Voxel-based morphometry; Cortical thickness analyses

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