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. 2016 May 1;79(9):765-775.
doi: 10.1016/j.biopsych.2015.02.028. Epub 2015 Feb 27.

Large-Scale Persistent Network Reconfiguration Induced by Ketamine in Anesthetized Monkeys: Relevance to Mood Disorders

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Large-Scale Persistent Network Reconfiguration Induced by Ketamine in Anesthetized Monkeys: Relevance to Mood Disorders

Qian Lv et al. Biol Psychiatry. .

Abstract

Background: Ketamine is a highly attractive candidate for developing fast-onset antidepressant agents; however, the relevant brain circuits that underlie sustained, efficacious antidepressant effects remain largely unknown.

Methods: We used a holistic scheme combining whole-brain resting-state fMRI and graph theoretical analysis to examine the sustained effects on brain networks after administration of a single dose of ketamine and to identify the brain regions and circuits preferentially targeted by ketamine. Topological differences in functional networks of anesthetized macaque monkeys were compared between ketamine (.5 mg/kg) and saline treatment after 18 hours.

Results: We observed persistent global reconfiguration of small-world properties in response to ketamine intake, accompanied by large-scale downregulation of functional connectivity, most prominently in the orbital prefrontal cortex, the subgenual and posterior cingulate cortices, and the nucleus accumbens. Intriguingly, intrinsic connectivity with the medial prefrontal areas in the reward circuits were selectively downregulated. Global and regional regulations of the brain networks precisely opposed the maladaptive alterations in the depressed brain.

Conclusions: Our results demonstrated that local synaptic plasticity triggered by blockade of N-methyl-D-aspartic acid receptors was capable of translating into prolonged network reconfiguration in the distributed cortico-limbic-striatal circuit, providing mechanistic insight into developing specific loci or circuit-targeted, long-term therapeutics.

Keywords: Functional connectivity; Graph theory; Ketamine; Macaque monkey; Mood disorders; Neural network plasticity.

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