Gary B. Rollman,
Emeritus Professor of Psychology,
University of Western Ontario
(In addition to links below, see weekly archives in the right column)
Wednesday, September 14, 2011
NIH Common Fund Strategic Planning | Biomarkers for chronic pain using functional brain connectivity
Major obstacle/challenge to overcome: Chronic pain is a debilitating condition affecting at least 116 million American adults, resulting in significantly reduced quality of life and an estimated annual cost of $560 – 635 billion 1. Unfortunately, its assessment is based solely on subjective self-report, using limited scales or measures, which are unsuitable for elucidating the different types and causes of pain (i.e., pain endophenotypes) and for rigorously evaluating the impact of targeted interventions. Self-report measures also hamper progress in the monitoring required to precisely dose a medication and then evaluate its comparative effectiveness among different individuals.Also, importantly, the field of pain management has been long challenged by the twin fears of undertreating pain in those who are suffering vs. triggering or facilitating a drug problem.Because of all these obstacles, there is a pressing need for a standardized, brief and simple measurement that can translate, or at least reproducibly correlate, subjective pain experience into objective and quantitative readings for both clinical and research purposes.
Emerging scientific opportunity ripe for Common Fund investment:In functional neuroimaging, there has been a recent explosion of findings on functional connectivity (FC) between brain regions, especially in the resting-state (RSFC), which is defined as the signal coherence between discrete brain regions in the absence of a cognitive task. RSFC has uncovered discrete functional networks, where the strength or activity coherence can be quantified. Based on recent reports of differences in intrinsic brain network connectivity between patients with chronic pain and controls, it has been suggested that RSFC could be a suitable platform to develop objective biomarkers of pain.Moreover, recent expansion of neuroimage data-sharing, especially of RSFC data in the 1000 Functional Connectomes Project, has demonstrated that data from different sources can be pooled to define subtypes of populations stratified by age, gender, medical conditions, and other variables, to enhance statistical power for discovery. If this level of between-labs consistency turns out to also apply to pain related measurements, RSFC could revolutionize the field of pain research and management.
Common Fund investment that could accelerate scientific progress in this field:Chronic pain is a clinical condition characteristic of a wide range of physical syndromes that collectively span the programmatic purview of many different ICs.A request for applications (RFA) on this topic to fund five or six research project grants would enable multi-disciplinary teams (comprised of pain clinicians, functional neuroimagers, and computational/network neuroscientists) to 1) develop techniques for image time-series analysis to identify brain RSFC signatures of different types of chronic pain, and 2) test the value of said signatures in a clinical context.For example, R21/R33 phased-innovation awards would enable initial collaborations to assess basic cross-sectional differences between controls and patients with different syndromes of chronic pain, and to develop and optimize new analytical tools for better identification of sensitive and specific RSFC biomarkers of pain.The common fund program concept would also enable comparative effectiveness research, data harmonization across funded projects, foster a consortium on pain RSFC biomarkers, and inform prospective evidence-based, personalized care of pain.
Potential impact of Common Fund investment: Advances in image acquisition and data analytic approaches could yield a level of objectivity, sensitivity, and specificity that would be unprecedented for chronic pain. In theory, a single resting-state functional MRI scan could serve as a diagnostic procedure akin to a head MRI for brain cancer or other neurological diagnoses. Data derived from that scan may not only provide an objective and reliable marker, but also help identify optimum therapeutic approaches, lowering the costs and loss of productivity associated with ineffective pain treatments. Validation of pain biomarkers is critical in the development of pain medications and for the adequate use of prescription analgesics matched to the needs of individuals. When the proposed program achieves its objectives, the collaborative effort among funded projects will complete the characterization and validation phase of functional brain connectivity as biomarkers for chronic pain, helping to bring evidence-based, personalized management of pain closer to reality.