Pain Neuroimaging in Humans: A Primer for Beginners and Non-Imagers

Abstract

Human pain neuroimaging has exploded in the past 2 decades. During this time, the broader neuroimaging community has continued to investigate and refine methods. Another key to progress is exchange with clinicians and pain scientists working with other model systems and approaches. These collaborative efforts require that non-imagers be able to evaluate and assess the evidence provided in these reports. Likewise, new trainees must design rigorous and reliable pain imaging experiments. In this article we provide a guideline for designing, reading, evaluating, analyzing, and reporting results of a pain neuroimaging experiment, with a focus on functional and structural magnetic resonance imaging. We focus in particular on considerations that are unique to neuroimaging studies of pain in humans, including study design and analysis, inferences that can be drawn from these studies, and the strengths and limitations of the approach.

Perspective: This article provides an overview of the concepts and considerations of structural and functional magnetic resonance neuroimaging studies. The primer is written for those who are not familiar with brain imaging. We review key concepts related to recruitment and study sample, experimental design, data analysis and data interpretation.

Keywords: Pain; functional connectivity; functional magnetic resonance imaging; guidelines; magnetic resonance imaging; multivoxel pattern analysis; structural magnetic resonance imaging.

Figure 1.

Number of pain imaging studies since the advent of fMRI. These numbers were determined with a search on PubMed using the search terms: “(Pain or nocicept*) AND brain AND (fMRI OR MRI) NOT CT NOT PET NOT EEG NOT review.” The search was restricted to 5-year time windows. The left panel shows the number of publications per 5-year window. The panel on the right shows the cumulative number of studies over the years. The dashed line and the grey point have been extrapolated on the basis of the current number of studies published from 2015 to January 2017.

Figure 2.

Stimulus convolution. Here, a hypothetical pattern of stimuli is convolved with the HRF. In the upper panel, an eventrelated design is convolved to a double-gamma function to model the HRF. In the lower panel, the convolution of a block design study is shown. A prolonged stimulus would, in theory, elicit repeated HRFs. These are summated to produce a regressor to produce an idealized model of brain activity correlated to the stimulus. Note that the peak of the HRF is delayed with respect to the onset of the stimulus, and the offshoot is delayed with respect to the offset of the stimulus.

Figure 3.

fMRI preprocessing pipeline. This flow chart represents 1 common fMRI preprocessing pipeline. The inclusion/ exclusion of each step, as well as the order of steps, may vary on the basis of a study’s unique goals and analysis plan. Optional steps are shaded in gray, the step specific to rs-fMRI (but optional in task-based studies) has a dashed outline. Abbreviation: MR, magnetic resonance. *Steps requiring quality check. Adapted from: Poldrack et al.