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Papers of the Week

Papers: 2 Mar 2019 - 8 Mar 2019


Human Studies

2019 Jul




Editor's Pick

Plasticity in the dynamic pain connectome associated with ketamine-induced neuropathic pain relief.


Rogachov A, Bhatia A, Cheng JC, Bosma RL, Kim JA, Osborne NR, Hemington KS, Venkat Raghavan L, Davis KD
Pain. 2019 Jul; 160(7):1670-1679.
PMID: 30839433.


Therapeutic interventions for neuropathic pain (NP), such as the NMDA-antagonist ketamine, can vary widely in effectiveness; Here, we conducted a longitudinal functional MRI study to test the hypothesis that the pain relieving effect of ketamine is due to reversal of abnormalities in regional low frequency brain oscillations (LFOs) and abnormal cross-network functional connectivity (FC) of the dynamic pain connectome. We found that: 1) ketamine decreased regional LFOs in the posterior cingulate cortex (PCC) of the default mode network (DMN), 2) a machine learning algorithm demonstrated that treatment-induced brain changes could be used to make generalizable inferences about pain relief, 3) treatment responders exhibited a significant decrease in cross-network static FC between the PCC and regions of the sensorimotor (SM) and salience networks following treatment, 4) the degree of reduced cross-network FC was correlated with the amount of pain relief, and 5) ketamine treatment did not produce significant differences in static or dynamic FC within the ascending nociceptive- or descending antinociceptive pathway. These findings support the proposition that regional LFOs contribute to cross-network connectivity that underlie the effectiveness of ketamine to produce significant relief of neuropathic pain. Together with our recent findings that pre-treatment dynamic FC of the descending antinociceptive pathway can predict ketamine treatment outcomes, these new findings indicate that pain relief from ketamine arises from a combination of a strong and flexible pre-treatment FC of the descending antinocieptive pathway together with plasticity (reduction) of cross-network connectivity of the DMN with SM and salience networks.