Genomes and Genes
Neuroimaging-based biomarkers for two components of pain
Principal Investigator: Edward E Smith
Abstract: DESCRIPTION (provided by applicant): This application addresses broad Challenge Area (03) Biomarker Discovery and Validation, and Specific Challenge topic 03-DA-101, Biomarkers for Pain. The challenge Pain is a central health problem that affects quality of life and productivity for a large segment of the population. Lost work time due to pain costs America an estimated $65 billion annually. The challenge in this request for applications is to define robust and meaningful biomarkers that can serve as objective, quantitative measures of pain-related processes. Currently, pain assessment is based almost exclusively on patients'self- reports, which are inherently limited by the complex relationship between biological nociceptive (pain-related) processes and patients'verbal or written descriptions of pain. Objective biomarkers would be useful for both understanding pain and for predicting pain when self-reports are unavailable or unreliable. They would accelerate the pace of research on pain and enhance patient care in several ways. First, biomarkers could serve as intermediate outcome measures in clinical trials and treatments, making clinical trials less costly and treatments better matched to patients'individual needs. Second, biomarkers could help develop new interventions for pain that directly target specific brain systems, i.e., with non-invasive brain stimulation, which would open up new possibilities for pain management. Third, research on biomarkers could improve the quality of decision-making about pain in legal contexts. Our approach We outline a proposal for using functional magnetic resonance imaging (fMRI), in conjunction with other methods, to develop and validate biomarkers for two components of reported pain experience in an acute, experimentally induced pain setting. A central part of this endeavor is the use of machine-learning algorithms to develop optimal fMRI-based predictors of pain. Machine-learning based biomarkers are essentially patterns of activity across brain regions with maximal predictive accuracy and discriminative validity for separating physical and non-physical (e.g., social or emotional) pain. These patterns can serve as the basis for tracking multiple components of pain processing without relying on self-report. Rather than simply trying to develop alternative measures that predict self-reports, however, we place a particular emphasis on identifying separable component brain processes that make different contributions to pain reports. By identifying biomarkers for component processes underlying pain, we aim to develop objective, brain-based measures of "intermediate phenotypes" that could be independently targeted for treatment, and so are useful in their own right beyond their ability to provide surrogate measures for pain reports. PUBLIC HEALTH RELEVANCE: The ability to identify the brain components that contribute to pain in a particular individual would allow care providers to select treatments appropriate for the individual. We have specific reason to believe that at least two separable components can be identified. New results from our laboratory suggest that at least two distinct brain networks make separable contributions to reported pain. One network, the "pain-processing network" (PPN), responds to painful peripheral stimulation (e.g., heat on the skin), and includes classic pain-processing regions. A second network, the "emotional appraisal network" (EAN), does not respond to stimulation of the body, but is involved in the cognitive generation of emotion. In our recent fMRI work on pain, both networks appear to make independent contributions to predicting how much pain a person will report in response to a given stimulus. Our goal is to develop biomarkers based on these two putative components. We plan to achieve these broad goals using converging evidence from three complementary approaches, each of which will be pursued in parallel and addressed in one Specific Aim. In Aim 1, we will develop fMRI- based pain biomarkers using machine learning techniques. We will use a set of fMRI data from several acute thermal pain experiments collected in our laboratory over the past 3 years (combined N = 157). These datasets have highly homogenous scanning and experimental protocols and were designed with the goal of fMRI-based pain prediction in mind. Their existence will allow us to accelerate the pace of computational biomarker development. In Aim 2, we will test whether the fMRI-based biomarkers we develop are causally related to pain, using combined fMRI and transcranial magnetic stimulation (TMS). Biomarkers that are causally related to pain are most likely to be useful as targets for drug or other treatments. We will stimulate regions within the PPN and EAN during fMRI scanning, and test whether effects on activity and connectivity in each network predict changes in pain experience. In Aim 3, we will extend our biomarker validation to chronic pain. We will test whether biomarkers developed in Aims 1 and 2 predict patterns of hypersensitivity to sensory stimuli in patients with focal lesions in the PPN and EAN. Together, these approaches provide three complementary ways of developing and validating fMRI-based biomarkers for pain. ) PUBLIC HEALTH RELEVANCE: This application addresses broad Challenge Area (03) Biomarker Discovery and Validation, and Specific Challenge topic 03-DA-101, Biomarkers for Pain. Pain is a central health problem that affects quality of life and productivity for a large segment of the population, but research and clinical care are hampered by the lack of objective, quantitative measures of biological processes that contribute to pain. Pain-processing biomarkers would be useful for both understanding the generation of pain within the brain (and individual differences therein) and for predicting pain when self-reports are unavailable or unreliable. They would accelerate the pace of research on pain and enhance patient care by a) serving as intermediate outcome measures in clinical trials and treatments, making clinical trials less costly and treatments better matched to patients'individual needs;and b) providing a foundation for new treatments that target the brain systems involved directly;and c) providing guidelines for decision-making about pain in legal contexts.
Funding Period: ----------------2009 - ---------------2011-
more information: NIH RePORT
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