In this competitive renewal application, we propose to extend our research on the underlying mechanisms of endogenous pain modulation via placebo analgesia. Specifically, we aim to investigate the temporal characteristics of the neural networks associated with processing different aspects of pain related information. Previous funding allowed us to develop and refine the ecologically valid techniques and brain imaging protocols routinely used to study pain in asymptomatic and clinical populations. With these tools, we have shown that the brain regions within a general cognitive/affective network have distinct interaction profiles in painful and placebo analgesic conditions. In this proposal, we will refine these techniques to improve the identification of brain regions (i.e., neural networks) associated with processing specific facets of pain related information, and how that information is processed over time (i.e., modeling the temporal characteristics of information processing within and between conditions, and across different neural networks). The three specific aims of this proposal will allow us to reach these goals.
In Aim 1, we will differentiate the neural networks primarily associated with afferent pain processing from cognitive-affective pain modulation using behavioral, psychophysical, and fMRI data.
For Aim 2, we will detail the brain regions that comprise the neural networks involved with specific facets of pain and pain modulation (e.g., anxiety and expectation). Once the neural networks have been identified, in Aim 3, we will describe the temporal characteristics and interactions of these networks as they process information within and between conditions. The results from Aim 3 will help clarify and account for the changes in neural networks that accompany changes in behavioral pain ratings.
These aims require the combination of psychological, psychophysical, structural and functional MRI data and advanced statistical analyses, all of which we have extensive experience and expertise with, and thus are confident in the success of this project.
Nearly 1 in 5 Americans suffers from a pain condition. This research addresses the need to understand the structure and function of the neural networks that process and modulate pain. A more robust understanding of endogenous pain modulation will ultimately lead to enhanced treatments, patient satisfaction, and outcomes of pain conditions.
|Letzen, Janelle E; Robinson, Michael E (2017) Negative mood influences default mode network functional connectivity in patients with chronic low back pain: implications for functional neuroimaging biomarkers. Pain 158:48-57|
|Letzen, Janelle E; Boissoneault, Jeff; Sevel, Landrew S et al. (2016) Test-retest reliability of pain-related functional brain connectivity compared with pain self-report. Pain 157:546-51|
|Kisaalita, Nkaku R; Hurley, Robert W; Staud, Roland et al. (2016) Placebo Use in Pain Management: A Mechanism-Based Educational Intervention Enhances Placebo Treatment Acceptability. J Pain 17:257-69|
|Robinson, Michael; Boissoneault, Jeff; Sevel, Landrew et al. (2016) The Effect of Base Rate on the Predictive Value of Brain Biomarkers. J Pain 17:637-41|
|Sevel, Landrew S; Letzen, Janelle E; Staud, Roland et al. (2016) Interhemispheric Dorsolateral Prefrontal Cortex Connectivity is Associated with Individual Differences in Pain Sensitivity in Healthy Controls. Brain Connect 6:357-64|
|Letzen, Janelle E; Boissoneault, Jeff; Sevel, Landrew S et al. (2016) What reliability can and cannot tell us about pain report and pain neuroimaging. Pain 157:1575-6|
|Wager, Tor D; Woo, Choong-Wan (2016) Reply. Pain 157:1576-7|
|Sevel, Landrew S; Craggs, Jason G; Price, Donald D et al. (2015) Placebo analgesia enhances descending pain-related effective connectivity: a dynamic causal modeling study of endogenous pain modulation. J Pain 16:760-8|
|Sevel, Landrew S; O'Shea, Andrew M; Letzen, Janelle E et al. (2015) Effective connectivity predicts future placebo analgesic response: A dynamic causal modeling study of pain processing in healthy controls. Neuroimage 110:87-94|
|Robinson, Michael E; O'Shea, Andrew M; Craggs, Jason G et al. (2015) Comparison of machine classification algorithms for fibromyalgia: neuroimages versus self-report. J Pain 16:472-7|
Showing the most recent 10 out of 34 publications