predictors and a signature of chronic pain · 27-05-2015 · as back pain patients transition to...
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Predictors and a signature of Chronic Pain
A. Vania Apkarian, PhD
Northwestern University Chicago, IL
May 27, 2015 PRF, NIH, DC
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Cartesian view, 1644
End Organ Afferents
Spinal Sensitization
Cortical plasticity
Acute to chronic pain
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The CRITICAL question • End organ MRI predicts only 1% of chronic back pain
• Brain imaging studies are all cross-sectional The critical question is: Only a small proportion of subjects with a similar injury develop chronic pain. Why? How?
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Transition from acute to chronic back pain
A longitudinal and cross-sectional, observational study. Recruit subjects with acute back pain of 4-12 weeks, with no history of back pain in prior one year, and with back pain >5/10 at entry. For one to three years monitor brain parameters. As back pain patients transition to either recovery or to chronic pain, What is the role of the brain?
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SBP Persisting (SBPp) (no change in pain)
Recovering (SBPr) (>20% decrease in pain)
Predictors
Consequences
Baseline Acute Sub-acute Chronic
Visit 5 3 years
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Clinical pain parameters with transition to chronic pain
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Baseline Acute Sub-acute Chronic
SBPr SBPp
Func
tiona
l co
nnec
tivity
Unbiased
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Activation of Corticostriatal Circuitry Relieves Chronic Neuropathic Pain Lee et al., J Neuroscience, April 1, 2015
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White matter differences at baseline distinguish SBPp from SBPr
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Baseline Acute Sub-acute Chronic
SBPr SBPp
Func
tiona
l co
nnec
tivity
W
hite
mat
ter
diffu
sivi
ty
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Tracking pain chronification in the full cohort of SBP and for a subgroup for up to 3 years
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Limbic-brain white matter network clusters to 3 communities: mPFC-Nac-Amy
white matter and functional connectivity distinguishes SBP groups
mPFC-Nac-Amy WM network
mPFC-Nac-Amy Func Network
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Hippocampus size is larger in subjects where back pain recovers (SBPr) and constant over 3 years
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Amygdala size is larger in subjects where back pain recovers (SBPr) and constant over 3 years
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Amygdala and hippocampus shape differences are seen between SBPp and SBPr, persistently
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Persisting (SBPp)
Recovering (SBPr) Predictors
Acute Sub-acute Chronic
FC WM Size
mPFC-Nac-Amy
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Full model for PREDICTING development of chronic pain at 1 year based on brain values observed within weeks after start of back pain
Model prediction is ~90% correct
Mu-opiate gene contribution is ~ 5% of variance
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Full model for PREDICTING development of chronic pain at 1 year based on brain values observed within weeks after start of back pain
Model prediction is ~85% correct
Depression contribution is ~ 3% of variance
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PREDICTING intensity of back pain at 1 year based on parameters observed within weeks after start of back pain
Model prediction is now only ~40% correct
Prediction is poor and dominated by pain at entry into study
Brain contribution is ~ 10% of variance
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Can we identify a signature of chronic pain common across conditions and species?
Analysis of resting state fMRI regarding network graph properties: Chronic pain is characterized by a global disruption of information sharing.
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Resting state brain networks: undirected functional connectivity
7kx7k Correlation
matrix
Fixed density network
Number of links for a given node
Generate link maps for patients & controls Contrast them as a function of number of links
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Basic elements of a graph
Bullmore, E.T. & Sporns, O. 2012
Degree = 4
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Deriving Kd: a global network measure of rank order disruption
Mean control = 150 OFF-SITE subjects from NITRC connectome1000
Degree = 4 Degree map
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Kd is about -0.3 in chronic pain patients and related to pain intensity (at link density of 10%)
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Kd emerges in time during transition to chronic pain
Acute Sub-acute Chronic 0 6 months 1 year
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Kd emerges in time in rats after a neuropathic injury
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Four distinct stages for chronic pain
1. Predisposition 3. Transition 4. Maintenance …
2. Injury
mPFC-NAc
3 = 1 + 2 + mesolimbic learning
WM vulnerability
Limbic Structures + Genes
Cortical reorganization Emotional pain state Rank order disruption
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Summary
• Brain characteristics determine propensity for chronic pain. Therefore, it is a NEUOLGICAL vulnerability.
• Chronic pain state globally disrupts information flow/sharing in the brain
• in proportion to the intensity of the pain, • commonly across types of chronic pain, • and even in anesthetized neuropathic rats.
•Chronic pain is a brain “network disease” state of decreased segregation and increased randomization.
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Acknowledgements
Marwan Baliki
Funding: NIH NINDS, NIDDK, NIDA, NIDCR
P. Geha J. Paice N. Harden T. Schnitzer D. Gitelman T. Parrish R. Levy M. Centeno H. Berra C. Lavarella
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