Synapses, predictions, and prediction errors: A neocortical computational study of MDD using the temporal memory algorithm of HTM.

Introduction: Synapses and spines play a significant role in major depressive disorder (MDD) pathophysiology, recently highlighted by the rapid antidepressant effect of ketamine and psilocybin. According to the Bayesian brain and interoception perspectives, MDD is formalized as being stuck in affective states constantly predicting negative energy balance. To understand how spines and synapses relate to the predictive function of the neocortex and thus to symptoms, we used the temporal memory (TM), an unsupervised machine-learning algorithm. TM models a single neocortical layer, learns in real-time, and extracts and predicts temporal sequences. TM exhibits neocortical biological features such as sparse firing and continuous online learning using local Hebbian-learning rules. Methods: We trained a TM model on random sequences of upper-case alphabetical letters, representing sequences of affective states. To model depression, we progressively destroyed synapses in the TM model and examined how that affected the predictive capacity of the network. We found that the number of predictions decreased non-linearly. Results: Destroying 50% of the synapses slightly reduced the number of predictions, followed by a marked drop with further destruction. However, reducing the synapses by 25% distinctly dropped the confidence in the predictions. Therefore, even though the network was making accurate predictions, the network was no longer confident about these predictions. Discussion: These findings explain how interoceptive cortices could be stuck in limited affective states with high prediction error. Connecting ketamine and psilocybin's proposed mechanism of action to depression pathophysiology, the growth of new synapses would allow representing more futuristic predictions with higher confidence. To our knowledge, this is the first study to use the TM model to connect changes happening at synaptic levels to the Bayesian formulation of psychiatric symptomatology. Linking neurobiological abnormalities to symptoms will allow us to understand the mechanisms of treatments and possibly, develop new ones.

Dose-related effects of ketamine for antidepressant-resistant symptoms of posttraumatic stress disorder in veterans and active duty military: a double-blind, randomized, placebo-controlled multi-center clinical trial.

This study tested the efficacy of repeated intravenous ketamine doses to reduce symptoms of posttraumatic stress disorder (PTSD). Veterans and service members with PTSD (n = 158) who failed previous antidepressant treatment were randomized to 8 infusions administered twice weekly of intravenous placebo (n = 54), low dose (0.2 mg/kg; n = 53) or standard dose (0.5 mg/kg; n = 51) ketamine. Participants were assessed at baseline, during treatment, and for 4 weeks after their last infusion. Primary analyses used mixed effects models. The primary outcome measure was the self-report PTSD Checklist for DSM-5 (PCL-5), and secondary outcome measures were the Clinician-Administered PTSD Scale for DSM-5 (CAPS-5) and the Montgomery Åsberg Depression Rating Scale (MADRS). There were no significant group-by-time interactions for PTSD symptoms measured by the PCL-5 or CAPS-5. The standard ketamine dose ameliorated depression measured by the MADRS significantly more than placebo. Ketamine produced dose-related dissociative and psychotomimetic effects, which returned to baseline within 2 h and were less pronounced with repeated administration. There was no evidence of differential treatment discontinuation by ketamine dose, consistent with good tolerability. This clinical trial failed to find a significant dose-related effect of ketamine on PTSD symptoms. Secondary analyses suggested that the standard dose exerted rapid antidepressant effects. Further studies are needed to determine the role of ketamine in PTSD treatment. ClinicalTrials.gov identifier: NCT02655692.

Understanding cingulotomy's therapeutic effect in OCD through computer models.

Cingulotomy is therapeutic in OCD, but what are the possible mechanisms? Computer models that formalize cortical OCD abnormalities and anterior cingulate cortex (ACC) function can help answer this. At the neural dynamics level, cortical dynamics in OCD have been modeled using attractor networks, where activity patterns resistant to change denote the inability to switch to new patterns, which can reflect inflexible thinking patterns or behaviors. From that perspective, cingulotomy might reduce the influence of difficult-to-escape ACC attractor dynamics on other cortical areas. At the functional level, computer formulations based on model-free reinforcement learning (RL) have been used to describe the multitude of phenomena ACC is involved in, such as tracking the timing of expected outcomes and estimating the cost of exerting cognitive control and effort. Different elements of model-free RL models of ACC could be affected by the inflexible cortical dynamics, making it challenging to update their values. An agent can also use a world model, a representation of how the states of the world change, to plan its actions, through model-based RL. OCD has been hypothesized to be driven by reduced certainty of how the brain's world model describes changes. Cingulotomy might improve such uncertainties about the world and one's actions, making it possible to trust the outcomes of these actions more and thus reduce the urge to collect more sensory information in the form of compulsions. Connecting the neural dynamics models with the functional formulations can provide new ways of understanding the role of ACC in OCD, with potential therapeutic insights.

In silico hippocampal modeling for multi-target pharmacotherapy in schizophrenia.

Treatment of schizophrenia has had limited success in treating core cognitive symptoms. The evidence of multi-gene involvement suggests that multi-target therapy may be needed. Meanwhile, the complexity of schizophrenia pathophysiology and psychopathology, coupled with the species-specificity of much of the symptomatology, places limits on analysis via animal models, in vitro assays, and patient assessment. Multiscale computer modeling complements these traditional modes of study. Using a hippocampal CA3 computer model with 1200 neurons, we examined the effects of alterations in NMDAR, HCN (Ih current), and GABAAR on information flow (measured with normalized transfer entropy), and in gamma activity in local field potential (LFP). We found that altering NMDARs, GABAAR, Ih, individually or in combination, modified information flow in an inverted-U shape manner, with information flow reduced at low and high levels of these parameters. Theta-gamma phase-amplitude coupling also had an inverted-U shape relationship with NMDAR augmentation. The strong information flow was associated with an intermediate level of synchrony, seen as an intermediate level of gamma activity in the LFP, and an intermediate level of pyramidal cell excitability. Our results are consistent with the idea that overly low or high gamma power is associated with pathological information flow and information processing. These data suggest the need for careful titration of schizophrenia pharmacotherapy to avoid extremes that alter information flow in different ways. These results also identify gamma power as a potential biomarker for monitoring pathology and multi-target pharmacotherapy.

Efficacy and safety of a fatty acid amide hydrolase inhibitor (PF-04457845) in the treatment of cannabis withdrawal and dependence in men: a double-blind, placebo-controlled, parallel group, phase 2a single-site randomised controlled trial.

BACKGROUND: Cannabis is one of the most widely used drugs worldwide. Cannabis use disorder is characterised by recurrent use of cannabis that causes significant clinical and functional impairment. There are no approved pharmacological treatments for cannabis use disorder. One approach is to potentiate endocannabinoid signalling by inhibiting fatty acid amide hydrolase (FAAH), the enzyme that degrades the endocannabinoid anandamide. We aimed to test the efficacy and safety of the FAAH-inhibitor PF-04457845 in reduction of cannabis withdrawal and cannabis use in men who were daily cannabis users. METHODS: We did a double-blind, placebo-controlled, parallel group phase 2a trial at one site in men aged 18-55 years with cannabis dependence according to DSM-IV criteria (equivalent to cannabis use disorder in DSM-5). After baseline assessments, participants were randomly assigned (2:1) to receive PF-04457845 (4 mg per day) or placebo using a fixed block size of six participants, stratified by severity of cannabis use and desire to quit. Participants were admitted to hospital for 5 days (maximum 8 days) to achieve abstinence and precipitate cannabis withdrawal, after which they were discharged to continue the remaining 3 weeks of treatment as outpatients. The primary endpoints were treatment-related differences in cannabis withdrawal symptoms during hospital admission, and week 4 (end of treatment) self-reported cannabis use and urine THC-COOH concentrations in the intention-to-treat population. The study is registered at ClinicalTrials.gov, number NCT01618656. FINDINGS: Between Sept 12, 2012, and Jan 18, 2016, 46 men were randomly assigned to PF-04457845 and 24 to placebo. Adherence to study medication was 88%, as confirmed by video-calling and pill count, and corroborated by corresponding drug and anandamide concentrations in blood. Relative to placebo, treatment with PF-04457845 was associated with reduced symptoms of cannabis withdrawal (first day of treatment mean symptom score 11·00 [95% CI 7·78-15·57] vs 6·04 [4·43-8·24]; difference 4·96 [0·71-9·21]; padj=0·048; second day of treatment 11·74 [8·28-16·66] vs 6·02 [4·28-8·47]; difference 5·73 [1·13-10·32]; padj=0·035) and related mood symptoms during the inpatient phase. Additionally, treatment with PF-04457845 was associated with lower self-reported cannabis use at 4 weeks (mean 1·27 joints per day [95% CI 0·82-1·97] vs 0·40 [0·25-0·62]; difference 0·88 [0·29-1·46]; p=0·0003) and lower urinary THC-COOH concentrations (mean 657·92 ng/mL [95% CI 381·60-1134·30] vs 265·55 [175·60-401·57]; difference 392·37 [17·55-767·18)]; p=0·009). Eight (17%) patients in the PF-04457845 group and four (17%) in the placebo group discontinued during the treatment period. During the 4-week treatment phase, 20 (43%) of 46 participants in the PF-04457845 group and 11 (46%) of 24 participants in the placebo group had an adverse event. There were no serious adverse events. INTERPRETATION: PF-04457845, a novel FAAH inhibitor, reduced cannabis withdrawal symptoms and cannabis use in men, and might represent an effective and safe approach for the treatment of cannabis use disorder. FUNDING: United States National Institute of Drug Abuse (NIDA).

Cannabinoid-glutamate interactions and neural oscillations: implications for psychosis.

Preclinical and clinical data suggest that the cannabinoid and glutamatergic systems are implicated in the pathophysiology of schizophrenia (SZ), the prototypical psychotic disorder. This has led to distinct "cannabis" and "ketamine" models of SZ, respectively. However, these two models need not be mutually exclusive. Indeed, in several brain regions implicated in the putative neural circuitry of SZ (e.g., hippocampus, frontal cortex, cerebellum), cannabinoid receptor type 1 (CB1Rs) and glutamate N-methyl-D-aspartate receptors (NMDARs) have direct and indirect interactions. CB1R agonists and NMDAR antagonists act upon gamma-aminobutyric acid (GABA) interneurons to reduce GABAergic neurotransmission. This would be predicted to result in the unsynchronized activity of pyramidal neurons, disrupting neural network oscillations involved in information processing, thus leading to psychotomimetic effects. Hence, the overarching aim of the current review is to synthesize the known literature on cannabinoids and glutamate in the context of neural oscillations in SZ. First, discussion of SZ and the basic mechanisms of neural oscillations are discussed, including a summary of the role of theta (4-7 Hz) and gamma (30-80 Hz) oscillations in neurocognition. Next, a brief review of the role of the cannabinoid and glutamatergic systems in SZ is outlined, followed by discussion of the known synaptic interactions between these two systems. Finally, the potential role of CB1Rs and NMDARs, both independently and in combination, on neural oscillations in relation to psychotic symptoms is considered. It is hoped that this review will yield a series of testable hypotheses that may be used to further elucidate the pathophysiology of SZ.

Multiscale modeling in the clinic: diseases of the brain and nervous system.

Computational neuroscience is a field that traces its origins to the efforts of Hodgkin and Huxley, who pioneered quantitative analysis of electrical activity in the nervous system. While also continuing as an independent field, computational neuroscience has combined with computational systems biology, and neural multiscale modeling arose as one offshoot. This consolidation has added electrical, graphical, dynamical system, learning theory, artificial intelligence and neural network viewpoints with the microscale of cellular biology (neuronal and glial), mesoscales of vascular, immunological and neuronal networks, on up to macroscales of cognition and behavior. The complexity of linkages that produces pathophysiology in neurological, neurosurgical and psychiatric disease will require multiscale modeling to provide understanding that exceeds what is possible with statistical analysis or highly simplified models: how to bring together pharmacotherapeutics with neurostimulation, how to personalize therapies, how to combine novel therapies with neurorehabilitation, how to interlace periodic diagnostic updates with frequent reevaluation of therapy, how to understand a physical disease that manifests as a disease of the mind. Multiscale modeling will also help to extend the usefulness of animal models of human diseases in neuroscience, where the disconnects between clinical and animal phenomenology are particularly pronounced. Here we cover areas of particular interest for clinical application of these new modeling neurotechnologies, including epilepsy, traumatic brain injury, ischemic disease, neurorehabilitation, drug addiction, schizophrenia and neurostimulation.

Neuronal calcium wave propagation varies with changes in endoplasmic reticulum parameters: a computer model.

Calcium (Ca²âº) waves provide a complement to neuronal electrical signaling, forming a key part of a neuron's second messenger system. We developed a reaction-diffusion model of an apical dendrite with diffusible inositol triphosphate (IP3), diffusible Ca²âº, IP3 receptors (IP3Rs), endoplasmic reticulum (ER) Ca²âº leak, and ER pump (SERCA) on ER. Ca²âº is released from ER stores via IP3Rs upon binding of IP3 and Ca²âº. This results in Ca²âº-induced-Ca²âº-release (CICR) and increases Ca²âº spread. At least two modes of Ca²âº wave spread have been suggested: a continuous mode based on presumed relative homogeneity of ER within the cell and a pseudo-saltatory model where Ca²âº regeneration occurs at discrete points with diffusion between them. We compared the effects of three patterns of hypothesized IP3R distribution: (1) continuous homogeneous ER, (2) hotspots with increased IP3R density (IP3R hotspots), and (3) areas of increased ER density (ER stacks). All three modes produced Ca²âº waves with velocities similar to those measured in vitro (approximately 50-90 µm /sec). Continuous ER showed high sensitivity to IP3R density increases, with time to onset reduced and speed increased. Increases in SERCA density resulted in opposite effects. The measures were sensitive to changes in density and spacing of IP3R hotspots and stacks. Increasing the apparent diffusion coefficient of Ca²âº substantially increased wave speed. An extended electrochemical model, including voltage-gated calcium channels and AMPA synapses, demonstrated that membrane priming via AMPA stimulation enhances subsequent Ca²âº wave amplitude and duration. Our modeling suggests that pharmacological targeting of IP3Rs and SERCA could allow modulation of Ca²âº wave propagation in diseases where Ca²âº dysregulation has been implicated.