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.
Journal of Neuroscience
June 19, 2020
Lilian Weber, Andreea O. Diaconescu, Christoph Mathys et al.
82 citations
The auditory mismatch negativity (MMN) is reduced in schizophrenia and can also be reduced by NMDA receptor (NMDAR) antagonists, suggesting impaired predictive coding. This study tested the theory that perceptual inference depends on NMDAR-dependent hierarchical precision-weighted prediction errors (PEs). Using a hierarchical Bayesian model on single-trial EEG data from healthy volunteers given the NMDAR antagonist S-ketamine in a placebo-controlled, double-blind, within-subject design, the analysis showed that low-level PEs (about stimulus transitions) appear early (102-207 ms), while high-level PEs (about transition probability) appear later (152-199 and 215-277 ms). Ketamine significantly diminished high-level PE responses, indicating NMDAR antagonism disrupts inference on abstract statistical regularities and impairs hierarchical Bayesian inference about the world's statistical structure.
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.
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.
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.
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.
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.
Psychopharmacology
November 5, 2025
Milad Soltanzadeh, Wang Zheng, Shona G. Allohverdi et al.
1 citation
Ketamine and psilocybin, two drugs with therapeutic potential for depression, produce distinct effects on brain electrical activity. Ketamine disrupts the balance between excitation and inhibition in neural circuits, as shown by changes in the aperiodic components of EEG spectra, and reduces beta band activity. Psilocybin also reduces alpha power in similar brain regions but does not affect beta activity or aperiodic components in the same way. These differences reflect their different mechanisms: ketamine blocks NMDA receptors while psilocybin targets serotonin receptors. Ketamine's unique EEG signature supports its role as a model for prodromal psychosis.
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.
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.