Author: Hashmi JA1, Kong J, Spaeth R, Khan S, Kaptchuk TJ, Gollub RL.
Affiliation: 1Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts 02129, Harvard Medical School, Boston, Massachusetts 02115, Department of Neurology and A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts 02129, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, and Program in Placebo Studies, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts 02215.
Conference/Journal: J Neurosci.
Date published: 2014 Mar 12
Other:
Volume ID: 34 , Issue ID: 11 , Pages: 3924-36 , Special Notes: doi: 10.1523/JNEUROSCI.3155-13.2014 , Word Count: 260
Placebo analgesia is an indicator of how efficiently the brain translates psychological signals conveyed by a treatment procedure into pain relief. It has been demonstrated that functional connectivity between distributed brain regions predicts placebo analgesia in chronic back pain patients. Greater network efficiency in baseline brain networks may allow better information transfer and facilitate adaptive physiological responses to psychological aspects of treatment. Here, we theorized that topological network alignments in resting state scans predict psychologically conditioned analgesic responses to acupuncture treatment in chronic knee osteoarthritis pain patients (n = 45). Analgesia was induced by building positive expectations toward acupuncture treatment with verbal suggestion and heat pain conditioning on a test site of the arm. This procedure induced significantly more analgesia after sham or real acupuncture on the test site than in a control site. The psychologically conditioned analgesia was invariant to sham versus real treatment. Efficiency of information transfer within local networks calculated with graph-theoretic measures (local efficiency and clustering coefficients) significantly predicted conditioned analgesia. Clustering coefficients in regions associated with memory, motivation, and pain modulation were closely involved in predicting analgesia. Moreover, women showed higher clustering coefficients and marginally greater pain reduction than men. Overall, analgesic response to placebo cues can be predicted from a priori resting state data by observing local network topology. Such low-cost synchronizations may represent preparatory resources that facilitate subsequent performance of brain circuits in responding to adaptive environmental cues. This suggests a potential utility of network measures in predicting placebo response for clinical use.
KEYWORDS:
brain network, chronic pain, placebo, predictive analysis, resting state, synchronization
PMID: 24623770