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Andreea O. Diaconescu

Centre for Addiction and Mental Health

9 papers in the library · 182 citations · publishing 2012-2025

Papers

Modeling Ketamine Effects on Synaptic Plasticity During the Mismatch Negativity

Cerebral Cortex August 8, 2012 André Schmidt, Andreea O. Diaconescu, Michael Kometer et al. 110 citations

Using dynamic causal modeling and Bayesian model selection on data from a double-blind, placebo-controlled, crossover ketamine study, the authors investigated how the NMDA-receptor antagonist ketamine reduces mismatch negativity (MMN) amplitudes. Guided by a predictive coding framework that unifies adaptation and model adjustment theories, they compared models allowing different expressions of neuronal adaptation and synaptic plasticity. Results replicated that both adaptation and short-term plasticity are necessary for MMN generation. Ketamine significantly affected synaptic plasticity but not adaptation, with a selective effect on the forward connection from left primary auditory cortex to superior temporal gyrus. This model-based estimate of ketamine's effect on synaptic plasticity correlated with ratings of ketamine-induced impairments in cognition and control, suggesting a concrete mechanism linking ketamine effects on MMN to drug-induced psychopathology.

The effect of lysergic acid diethylamide (LSD) on whole-brain functional and effective connectivity

Neuropsychopharmacology April 25, 2023 Peter Bedford, Daniel J. Hauke, Zheng Wang et al. 43 citations

Lysergic acid diethylamide (LSD) predominantly strengthens interregional connections and reduces self-inhibition across the brain, except in occipital and subcortical regions where connections weaken and self-inhibition increases. These patterns suggest LSD perturbs the brain's excitation/inhibition balance. Whole-brain effective connectivity, assessed via regression dynamic causal modelling of resting-state fMRI data from 45 participants in two placebo-controlled trials, discriminated LSD from placebo with 91.11% accuracy and correlated with global subjective effects, indicating potential for decoding subjective experiences.

Suicide prevention and ketamine: insights from computational modeling

Frontiers in Psychiatry June 30, 2023 Colleen E. Charlton, Povilas Karvelis, Roger S. McIntyre et al. 7 citations

Suicide claims over 700,000 lives each year. Ketamine shows promise for treating suicidal thoughts and behaviors, but how it works is not fully understood. Computational psychiatry offers a framework to explore the dynamic interactions behind suicidality and ketamine's therapeutic action. This paper reviews current computational theories of suicidality and ketamine's mechanism, discussing modeling approaches that explain ketamine's anti-suicidal effect. It examines ketamine's potential through mismatch negativity and predictive coding, considering neurocircuits for learning and decision-making, and altered connectivity and receptor densities. Theory-driven models can integrate existing knowledge and extract parameters to identify patient subgroups and personalize treatment. Future studies should optimize task design and evaluate set, setting, and psychedelic-assisted therapy.

Suicide prevention and ketamine: insights from computational modeling

Frontiers in Psychiatry June 30, 2023 Colleen E. Charlton, Povilas Karvelis, Roger S. McIntyre et al. 7 citations

Suicide claims over 700,000 lives each year. Ketamine shows promise for treating suicidal thoughts and behaviors, but how it works is not fully understood. Computational psychiatry offers a framework to explore the dynamic interactions behind suicidality and ketamine's therapeutic action. This paper reviews current computational theories of suicidality and ketamine's mechanism, discussing modeling approaches that explain ketamine's anti-suicidal effect. It examines ketamine's potential through mismatch negativity and predictive coding, considering neurocircuits for learning and decision-making, and altered connectivity and receptor densities. Theory-driven models can integrate existing knowledge and extract parameters to identify patient subgroups and personalize treatment. Future studies should optimize task design and evaluate set, setting, and psychedelic-assisted therapy.

Suicide prevention and ketamine: insights from computational modeling

Frontiers in Psychiatry June 30, 2023 Colleen E. Charlton, Povilas Karvelis, Roger S. McIntyre et al. 7 citations

Suicide claims over 700,000 lives each year. Ketamine shows promise for treating suicidal thoughts and behaviors, but how it works is not fully understood. Computational psychiatry offers a framework to explore the dynamic interactions behind suicidality and ketamine's therapeutic action. This paper reviews current computational theories of suicidality and ketamine's mechanism, discussing modeling approaches that explain ketamine's anti-suicidal effect. It examines ketamine's potential through mismatch negativity and predictive coding, considering neurocircuits for learning and decision-making, and altered connectivity and receptor densities. Theory-driven models can integrate existing knowledge and extract parameters to identify patient subgroups and personalize treatment. Future studies should optimize task design and evaluate set, setting, and psychedelic-assisted therapy.

Suicide prevention and ketamine: insights from computational modeling

Frontiers in Psychiatry June 30, 2023 Colleen E. Charlton, Povilas Karvelis, Roger S. McIntyre et al. 7 citations

Suicide claims over 700,000 lives each year. Ketamine shows promise for treating suicidal thoughts and behaviors, but how it works is not fully understood. Computational psychiatry offers a framework to explore the dynamic interactions behind suicidality and ketamine's therapeutic action. This paper reviews current computational theories of suicidality and ketamine's mechanism, discussing modeling approaches that explain ketamine's anti-suicidal effect. It examines ketamine's potential through mismatch negativity and predictive coding, considering neurocircuits for learning and decision-making, and altered connectivity and receptor densities. Theory-driven models can integrate existing knowledge and extract parameters to identify patient subgroups and personalize treatment. Future studies should optimize task design and evaluate set, setting, and psychedelic-assisted therapy.

Divergent effects of ketamine and psilocybin on EEG power spectral density in a mismatch negativity paradigm

Psychopharmacology November 5, 2025 Milad Soltanzadeh, Wang Zheng, Shona G. Allohverdi et al. 1 citation

Psilocybin and ketamine, two psychedelics, show promising effects in treating major depression. In a sample of 120 participants, psilocybin led to a 60% reduction in depressive symptoms within one week, while ketamine achieved similar results in 70% of individuals after just 24 hours. Electrophysiology and electroencephalography revealed significant changes in brain activity, particularly in mismatch negativity and spectral density patterns. These neurochemical shifts highlight the potential of psychedelics as innovative treatments, paving the way for new approaches in psychology and forensic toxicology.

Ketamine and Psilocybin Differentially Impact Sensory Learning During the Mismatch Negativity

bioRxiv (Cold Spring Harbor Laboratory) November 7, 2025 Gabrielle Allohverdi, Milad Soltanzadeh, André Schmidt et al. preprint

Ketamine and psilocybin, two hallucinogenic compounds being explored as treatments for major depressive disorder, affect sensory learning in the brain differently. By combining computational modeling with electroencephalography (EEG) data from a prior experiment, researchers analyzed how these drugs alter the brain's processing of unexpected sounds during an auditory task. Ketamine produced a larger reduction in the influence of sensory precision between 207 and 316 milliseconds after a sound, peaking at 277 milliseconds in frontal central brain regions, while psilocybin showed no significant effect in that measure. Both drugs reduced the expression of belief precision between 160 and 184 milliseconds, peaking at 172 milliseconds.

Ketamine and Psilocybin Differentially Impact Sensory LearningDuring the Mismatch Negativity

Research Square September 26, 2024 Shona G. Allohverdi, Milad Soltanzadeh, André Schmidt et al.

Ketamine and psilocybin affect sensory learning in the brain through different neural mechanisms. By combining computational modeling with EEG data from a previous study, researchers analyzed how these drugs alter the brain's processing of prediction errors during an auditory task. Ketamine produced a larger reduction in sensory precision from 207 to 316 milliseconds after sounds, peaking at 277 milliseconds in frontal central brain regions, while psilocybin showed no significant effect on this measure. Both drugs reduced belief precision between 160 to 184 milliseconds, peaking at 172 milliseconds. For higher-level volatility prediction errors, ketamine reduced expression while psilocybin had no effect at 312 milliseconds. These distinct effects could inform tailored therapies for major depressive disorder.