Neuroimaging-based biomarkers for two components of pain

Summary

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

Top Publications

  1. pmc Characterization and reduction of cardiac- and respiratory-induced noise as a function of the sampling rate (TR) in fMRI
    Dietmar Cordes
    Department of Physics, Ryerson University, Toronto, ON M5B2K3, Canada Department of Psychology and Neuroscience, University of Colorado Boulder, CO 80309, USA Electronic address
    Neuroimage 89:314-30. 2014
  2. pmc An fMRI-based neurologic signature of physical pain
    Tor D Wager
    Department of Psychology and Neuroscience, University of Colorado, Boulder, 80305, USA
    N Engl J Med 368:1388-97. 2013
  3. pmc Opposing effects of expectancy and somatic focus on pain
    Natalie E Johnston
    Department of Neuroscience, Weill Cornell Medical College, New York, New York, USA
    PLoS ONE 7:e38854. 2012
  4. pmc Common representation of pain and negative emotion in the midbrain periaqueductal gray
    Jason T Buhle
    Social Cognitive Affective Neuroscience Unit, Department of Psychology, Columbia University, 406 Schermerhorn Hall, 1190 Amsterdam Avenue, NY 10027, USA
    Soc Cogn Affect Neurosci 8:609-16. 2013
  5. pmc Ventromedial prefrontal-subcortical systems and the generation of affective meaning
    Mathieu Roy
    Department of Psychology, Columbia University, New York, NY 10027, USA
    Trends Cogn Sci 16:147-56. 2012
  6. ncbi Distraction and placebo: two separate routes to pain control
    Jason T Buhle
    Columbia University, New York, NY, USA
    Psychol Sci 23:246-53. 2012
  7. pmc The placebo effect: advances from different methodological approaches
    Karin Meissner
    Institute of Medical Psychology, Ludwig Maximilians University of Munich, D 80336 Munich, Germany
    J Neurosci 31:16117-24. 2011
  8. pmc Estimating and testing variance components in a multi-level GLM
    Martin A Lindquist
    Department of Statistics, Columbia University, New York, NY 10027, USA
    Neuroimage 59:490-501. 2012
  9. pmc Large-scale automated synthesis of human functional neuroimaging data
    Tal Yarkoni
    Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, Colorado, USA
    Nat Methods 8:665-70. 2011
  10. pmc Social rejection shares somatosensory representations with physical pain
    Ethan Kross
    Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA
    Proc Natl Acad Sci U S A 108:6270-5. 2011

Scientific Experts

  • Iris Asllani
  • Tal Yarkoni
  • TOR DESSART WAGER
  • Ethan Kross
  • Jason T Buhle
  • Dietmar Cordes
  • Lauren Y Atlas
  • Natalie E Johnston
  • Martin A Lindquist
  • Mathieu Roy
  • Karin Meissner
  • Rajesh R Nandy
  • Scott Schafer
  • Kevin N Ochsner
  • Peter Mende-Siedlecki
  • Jochen Weber
  • Hedy Kober
  • Kateri McRae
  • Brent L Hughes
  • Jonathan J Friedman
  • Bradford L Stevens
  • Daphna Shohamy
  • Julie Spicer
  • Magne Arve Flaten
  • Ulrike Bingel
  • Luana Colloca
  • Alison Watson

Detail Information

Publications12

  1. pmc Characterization and reduction of cardiac- and respiratory-induced noise as a function of the sampling rate (TR) in fMRI
    Dietmar Cordes
    Department of Physics, Ryerson University, Toronto, ON M5B2K3, Canada Department of Psychology and Neuroscience, University of Colorado Boulder, CO 80309, USA Electronic address
    Neuroimage 89:314-30. 2014
    ..At these values, the high-frequency aliased cardiac rate can be eliminated by digital filtering without affecting the BOLD-related signal. ..
  2. pmc An fMRI-based neurologic signature of physical pain
    Tor D Wager
    Department of Psychology and Neuroscience, University of Colorado, Boulder, 80305, USA
    N Engl J Med 368:1388-97. 2013
    ..Functional magnetic resonance imaging (fMRI) holds promise for identifying objective measures of pain, but brain measures that are sensitive and specific to physical pain have not yet been identified...
  3. pmc Opposing effects of expectancy and somatic focus on pain
    Natalie E Johnston
    Department of Neuroscience, Weill Cornell Medical College, New York, New York, USA
    PLoS ONE 7:e38854. 2012
    ..Overall, the results show that attention to the body cannot explain pain-enhancing expectancy effects, and that focusing on sensory/discriminative aspects of pain might be a useful pain-regulation strategy when severe pain is expected...
  4. pmc Common representation of pain and negative emotion in the midbrain periaqueductal gray
    Jason T Buhle
    Social Cognitive Affective Neuroscience Unit, Department of Psychology, Columbia University, 406 Schermerhorn Hall, 1190 Amsterdam Avenue, NY 10027, USA
    Soc Cogn Affect Neurosci 8:609-16. 2013
    ..In sum, these data sets comprised 198 additional participants. We found increased activity in PAG in all eight studies. Taken together, these findings suggest PAG is a key component of human affective responses...
  5. pmc Ventromedial prefrontal-subcortical systems and the generation of affective meaning
    Mathieu Roy
    Department of Psychology, Columbia University, New York, NY 10027, USA
    Trends Cogn Sci 16:147-56. 2012
    ....
  6. ncbi Distraction and placebo: two separate routes to pain control
    Jason T Buhle
    Columbia University, New York, NY, USA
    Psychol Sci 23:246-53. 2012
    ..Together, these data suggest that placebo analgesia does not depend on active redirection of attention and that expectancy and distraction can be combined to maximize pain relief...
  7. pmc The placebo effect: advances from different methodological approaches
    Karin Meissner
    Institute of Medical Psychology, Ludwig Maximilians University of Munich, D 80336 Munich, Germany
    J Neurosci 31:16117-24. 2011
    ..The integration of these different methodological approaches is a key objective, motivating our scientific pursuits toward a placebo research that can inform and guide important future scientific knowledge...
  8. pmc Estimating and testing variance components in a multi-level GLM
    Martin A Lindquist
    Department of Statistics, Columbia University, New York, NY 10027, USA
    Neuroimage 59:490-501. 2012
    ..In sum, tests of variance provides a way of selecting regions that show significant inter-individual variability for subsequent analyses that attempt to explain those individual differences...
  9. pmc Large-scale automated synthesis of human functional neuroimaging data
    Tal Yarkoni
    Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, Colorado, USA
    Nat Methods 8:665-70. 2011
    ..Collectively, our results have validated a powerful and generative framework for synthesizing human neuroimaging data on an unprecedented scale...
  10. pmc Social rejection shares somatosensory representations with physical pain
    Ethan Kross
    Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA
    Proc Natl Acad Sci U S A 108:6270-5. 2011
    ..These results give new meaning to the idea that rejection "hurts." They demonstrate that rejection and physical pain are similar not only in that they are both distressing--they share a common somatosensory representation as well...
  11. pmc Predicting individual differences in placebo analgesia: contributions of brain activity during anticipation and pain experience
    Tor D Wager
    Department of Psychology, University of Colorado, Boulder, Colorado 80309, USA
    J Neurosci 31:439-52. 2011
    ..This approach provides a framework that will allow prediction accuracy to increase as new studies provide more precise information for future predictive models...