Biological psychiatry
April 1, 2016
Mohamed Sherif, Rajiv Radhakrishnan, Deepak Cyril D'Souza et al.
127 citations
Controlled laboratory studies in healthy humans show that cannabinoid agonists—both plant-derived and synthetic—produce positive, negative, and cognitive symptoms resembling schizophrenia. These effects are time-locked to drug administration, dose-related, and transient. The magnitude of effects is similar to ketamine but qualitatively distinct from other psychotomimetic drugs. In individuals with schizophrenia, cannabinoid agonists transiently worsen symptoms despite antipsychotic treatment, and no beneficial effects have been found, challenging the self-medication hypothesis. Genetic polymorphisms in dopamine-related genes (COMT, DAT1, AKT1) may moderate these effects. Cannabinoid-induced dopamine release does not fully account for the psychotomimetic effects; interactions among endocannabinoid, GABA, and glutamate systems affecting neural oscillations offer a plausible mechanism.
Frontiers in Psychiatry
February 23, 2023
Rammohan Shukla, Mohamed Sherif, Mostafa Z. Khalil et al.
1 citation
Destroying synapses in a machine-learning model of the neocortex reduces the confidence of its predictions before reducing their number. The model, based on the temporal memory algorithm, was trained on random letter sequences representing affective states. Removing 50% of synapses only slightly lowered the number of predictions, but a 25% reduction distinctly dropped prediction confidence. This suggests that in major depressive disorder, synaptic loss in interoceptive cortices could trap the brain in limited affective states with high prediction error. The growth of new synapses, as proposed for ketamine and psilocybin, would allow more confident and futuristic predictions.
bioRxiv (Cold Spring Harbor Laboratory)
July 3, 2022
Mohamed Sherif, Mostafa Z. Khalil, Rammohan Shukla et al.
1 citation
preprint
Synaptic atrophy in major depressive disorder may impair the brain's ability to confidently predict future affective states, even when predictions remain accurate. Using a temporal memory algorithm that mimics a single neocortical layer with Hebbian learning, researchers simulated depression by progressively destroying synapses. Destroying 50% of synapses slightly reduced the number of predictions, but a 25% reduction distinctly lowered prediction confidence. This suggests that in depression, interoceptive cortices become stuck in limited affective states with high prediction error. Treatments like ketamine and psilocybin may help by growing new synapses, enabling more confident and futuristic predictions.