Subanesthetic ketamine alters EEG signal complexity: Implications for treatment stratification in depression

. 2025 Oct 01 ; 386 () : 119477. [epub] 20250524

Jazyk angličtina Země Nizozemsko Médium print-electronic

Typ dokumentu klinické zkoušky kontrolované, časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid40419149
Odkazy

PubMed 40419149
DOI 10.1016/j.jad.2025.119477
PII: S0165-0327(25)00919-X
Knihovny.cz E-zdroje

Major depressive disorder, particularly its treatment-resistant form (TRD), poses significant treatment challenges. Ketamine, an N-methyl-d-aspartate receptor antagonist, has shown promise in rapidly alleviating depressive symptoms by influencing neuroplasticity and glutamatergic modulation, which are thought to influence brain activity complexity. In this placebo-controlled study, we examined the effects of subanesthetic doses of intravenous ketamine on EEG signal complexity in 24 MDD patients, 21 of whom had TRD. Treatment response was defined by a ≥ 33 % reduction in Montgomery-Åsberg Depression Rating Scale (MADRS) after ketamine administration. Patients underwent eyes-closed resting state EEG recording pre-, start-, end- and 24 h post-infusion, analyzed for temporospatial and spatiotemporal Lempel-Ziv complexity (LZCT and LZCS). Results indicated that ketamine significantly increased whole-brain LZCT during infusion compared to placebo (sodium chloride 0.9 %) (16.90 % vs. -4.84 %, 95 % CI 4.29 to 39.18, p = 0.017). Elevated LZCT at end-pre was associated with less short-term symptom improvement the following day. Conversely, lower pretreatment occipital LZCT (0.33 vs. 0.46, 95 % CI 0.007 to 0.26, p = 0.040) predicted a favorable response to ketamine, supported by a logistic regression model with an ROC area of 0.75. No significant changes were observed in LZCS, suggesting limited utility as a biomarker. In conclusion, occipital LZCT could serve as an effective predictive biomarker for ketamine's therapeutic effects in MDD, with implications for patients with TRD. This underscores the potential of EEG complexity measures in stratifying treatment and enhancing our understanding of the neural impacts of ketamine in depressive disorders.

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