| Literature DB >> 32763021 |
Rachael L Sumner1, Rebecca McMillan2, Meg J Spriggs3, Doug Campbell4, Gemma Malpas4, Elizabeth Maxwell4, Carolyn Deng4, John Hay4, Rhys Ponton2, Frederick Sundram5, Suresh D Muthukumaraswamy2.
Abstract
Major depressive disorder negatively impacts the sensitivity and adaptability of the brain's predictive coding framework. The current electroencephalography study into the antidepressant properties of ketamine investigated the downstream effects of ketamine on predictive coding and short-term plasticity in thirty patients with depression using the auditory roving mismatch negativity (rMMN). The rMMN paradigm was run 3-4 h after a single 0.44 mg/kg intravenous dose of ketamine or active placebo (remifentanil infused to a target plasma concentration of 1.7 ng/mL) in order to measure the neural effects of ketamine in the period when an improvement in depressive symptoms emerges. Depression symptomatology was measured using the Montgomery-Asberg Depression Rating Scale (MADRS); 70% of patients demonstrated at least a 50% reduction their MADRS global score. Ketamine significantly increased the MMN and P3a event related potentials, directly contrasting literature demonstrating ketamine's acute attenuation of the MMN. This effect was only reliable when all repetitions of the post-deviant tone were used. Dynamic causal modelling showed greater modulation of forward connectivity in response to a deviant tone between right primary auditory cortex and right inferior temporal cortex, which significantly correlated with antidepressant response to ketamine at 24 h. This is consistent with the hypothesis that ketamine increases sensitivity to unexpected sensory input and restores deficits in sensitivity to prediction error that are hypothesised to underlie depression. However, the lack of repetition suppression evident in the MMN evoked data compared to studies of healthy adults suggests that, at least within the short term, ketamine does not improve deficits in adaptive internal model calibration.Entities:
Keywords: Depression; Dynamic causal modelling; EEG; MMN; Predictive coding
Year: 2020 PMID: 32763021 DOI: 10.1016/j.euroneuro.2020.07.009
Source DB: PubMed Journal: Eur Neuropsychopharmacol ISSN: 0924-977X Impact factor: 4.600