The Gut-Brain Axis and the Microbiome in Anxiety Disorders, Post-Traumatic Stress Disorder and Obsessive-Compulsive Disorder.

A large body of research supports the role of stress in several psychiatric disorders in which anxiety is a prominent symptom. Other research has indicated that the gut microbiome-immune system- brain axis is involved in a large number of disorders and that this axis is affected by various stressors. The focus of the current review is on the following stress-related disorders: generalized anxiety disorder, panic disorder, social anxiety disorder, post-traumatic stress disorder and obsessivecompulsive disorder. Descriptions of systems interacting in the gut-brain axis, microbiome-derived molecules and of pro- and prebiotics are given. Preclinical and clinical studies on the relationship of the gut microbiome to the psychiatric disorders mentioned above are reviewed. Many studies support the role of the gut microbiome in the production of symptoms in these disorders and suggest the potential for pro- and prebiotics for their treatment, but there are also contradictory findings and concerns about the limitations of some of the research that has been done. Matters to be considered in future research include longer-term studies with factors such as sex of the subjects, drug use, comorbidity, ethnicity/ race, environmental effects, diet, and exercise taken into account; appropriate compositions of pro- and prebiotics; the translatability of studies on animal models to clinical situations; and the effects on the gut microbiome of drugs currently used to treat these disorders. Despite these challenges, this is a very active area of research that holds promise for more effective, precision treatment of these stressrelated disorders in the future.

Preclinical evidence for the use of the atypical antipsychotic, brexpiprazole, for opioid use disorder.

Opioid addiction is characterized by adaptations in the mesolimbic dopamine system that occur during chronic opioid use. Alterations in dopaminergic transmission contribute to pathological drug-seeking behavior and other symptoms associated with opioid withdrawal following drug discontinuation, making drug abstinence challenging and contributing to high rates of relapse among those suffering from substance use disorder. Recently, the use of dopamine partial agonists has been proposed as a potential strategy to restore dopaminergic signalling during drug withdrawal, while avoiding the adverse side effects associated with stronger modulators of dopaminergic transmission. We investigated the effects of the atypical antipsychotic brexpiprazole, which is a partial agonist at dopamine D2 and D3 receptors, in a mouse model of opioid dependence. The development of opioid dependence in mice is characterized by locomotor sensitization, analgesic tolerance, opioid-induced hyperalgesia, and drug-seeking behavior. We set up four paradigms to model the effects of brexpiprazole on each of these adaptations that occur during chronic opioid use in male and female C57BL/6J mice. Concomitant treatment of brexpiprazole during chronic morphine administration attenuated the development of locomotor sensitization. Brexpiprazole treatment abolished morphine place preference and blocked reinstatement of this behavior following extinction. Brexpiprazole treatment did not alter morphine analgesia, nor did it impact the development of morphine tolerance. However, brexpiprazole treatment did prevent the expression of opioid-induced hyperalgesia in a tail-withdrawal assay, while failing to improve somatic withdrawal symptoms. Altogether, these results provide preclinical evidence for the efficacy of brexpiprazole as a modulator of dopamine-dependent behaviors during opioid use and withdrawal.

The effects of ketamine and classic hallucinogens on neurotrophic and inflammatory markers in unipolar treatment-resistant depression: a systematic review of clinical trials.

Although results are still preliminary, ketamine and classical hallucinogens have shown promise in recent years as novel, fast-acting antidepressants, especially for the treatment of unipolar treatment-resistant depression (TRD). Depression also seems to be related to abnormal levels of peripheral inflammatory and neurotrophic biomarkers, which may one day help to diagnose of this disorder. In this context, this systematic review of clinical trials evaluated the current evidence that relates the antidepressant effects of ketamine and classical hallucinogens on TRD with changes in inflammatory and neurotrophic biomarkers. Twelve studies were found (n = 587), 2 with oral ayahuasca (1 mL/kg) and 10 with ketamine (mostly intravenous 0.5 mg/kg) administration. Results for all biomarkers assessed were contradictory and thus inconclusive. Randomized controlled trials with bigger samples and higher statistical power are warranted to clarify if peripheral biomarkers can confidently be used to indicate and measure ketamine's and classical hallucinogens' antidepressant effect. The PROSPERO ID for this study is CRD42021249089.

Effects of Transcranial Direct Current Stimulation on Cognitive Deficits in Depression: A Systematic Review.

Background: Major depressive disorder is the leading cause of mental health-related burden globally and up to one-third of major depressive disorder patients never achieve remission. Transcranial Direct Current Stimulation is a non-invasive intervention used to treat individuals diagnosed with major depressive disorder and bipolar disorder. Since the last transcranial direct current stimulation review specifically focusing on cognitive symptoms in major depressive disorder, twice as many papers have been published. Methods: A systematic review was conducted with 5 electronic databases from database inception until March 21, 2022. Randomized controlled trials with at least 1 arm evaluating transcranial direct current stimulation in adults (diagnosed with major depressive disorder or bipolar disorder using the Diagnostic and Statistical Manual of Mental Disorders or International Classification of Diseases criteria) aged 18 or older were included. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were adopted. Results: : A total of 972 participants were included across 14 studies (60.5% female; mean age of 47.0 years [SD = 16.8]). Nine studies focused on participants with major depressive disorder and all studies used the Diagnostic and Statistical Manual of Mental Disorders to diagnose the participants. Seven out of the 14 studies showed significant improvements in at least 1 cognitive outcome measure in the active transcranial direct current stimulation group compared to the sham group. Several cognitive measures were used across studies, and 12 of the 14 studies reported mild-to-moderate side effects from treatment. Conclusion: : Current transcranial direct current stimulation literature has shown limited evidence for the treatment of cognitive impairments in major depressive disorder and bipolar disorder. Future research that applies machine learning algorithms may enable us to distinguish responders from non-responders, increasing clinical benefits of transcranial direct current stimulation.

Molecular Pathways of the Therapeutic Effects of Ayahuasca, a Botanical Psychedelic and Potential Rapid-Acting Antidepressant.

Ayahuasca is a psychoactive brew traditionally used in indigenous and religious rituals and ceremonies in South America for its therapeutic, psychedelic, and entheogenic effects. It is usually prepared by lengthy boiling of the leaves of the bush Psychotria viridis and the mashed stalks of the vine Banisteriopsis caapi in water. The former contains the classical psychedelic N,N-dimethyltryptamine (DMT), which is thought to be the main psychoactive alkaloid present in the brew. The latter serves as a source for ß-carbolines, known for their monoamine oxidase-inhibiting (MAOI) properties. Recent preliminary research has provided encouraging results investigating ayahuasca's therapeutic potential, especially regarding its antidepressant effects. On a molecular level, pre-clinical and clinical evidence points to a complex pharmacological profile conveyed by the brew, including modulation of serotoninergic, glutamatergic, dopaminergic, and endocannabinoid systems. Its substances also interact with the vesicular monoamine transporter (VMAT), trace amine-associated receptor 1 (TAAR1), and sigma-1 receptors. Furthermore, ayahuasca's components also seem to modulate levels of inflammatory and neurotrophic factors beneficially. On a biological level, this translates into neuroprotective and neuroplastic effects. Here we review the current knowledge regarding these molecular interactions and how they relate to the possible antidepressant effects ayahuasca seems to produce.

Superior temporal gyrus functional connectivity predicts transcranial direct current stimulation response in Schizophrenia: A machine learning study.

Transcranial direct current stimulation (tDCS) is a promising adjuvant treatment for persistent auditory verbal hallucinations (AVH) in Schizophrenia (SZ). Nonetheless, there is considerable inter-patient variability in the treatment response of AVH to tDCS in SZ. Machine-learned models have the potential to predict clinical response to tDCS in SZ. This study aims to examine the feasibility of identifying SZ patients with persistent AVH (SZ-AVH) who will respond to tDCS based on resting-state functional connectivity (rs-FC). Thirty-four SZ-AVH patients underwent resting-state functional MRI at baseline followed by add-on, twice-daily, 20-min sessions with tDCS (conventional/high-definition) for 5 days. A machine learning model was developed to identify tDCS treatment responders based on the rs-FC pattern, using the left superior temporal gyrus (LSTG) as the seed region. Functional connectivity between LSTG and brain regions involved in auditory and sensorimotor processing emerged as the important predictors of the tDCS treatment response. L1-regularized logistic regression model had an overall accuracy of 72.5% in classifying responders vs. non-responders. This model outperformed the state-of-the-art convolutional neural networks (CNN) model-both without (59.41%) and with pre-training (68.82%). It also outperformed the L1-logistic regression model trained with baseline demographic features and clinical scores of SZ patients. This study reports the first evidence that rs-fMRI-derived brain connectivity pattern can predict the clinical response of persistent AVH to add-on tDCS in SZ patients with 72.5% accuracy.

Health resource utilization and cost before versus after initiation of second-generation long-acting injectable antipsychotics among adults with schizophrenia in Alberta, Canada: a retrospective, observational single-arm study.

BACKGROUND: Long-acting injectable (LAI) antipsychotics, along with community treatment orders (CTOs), are used to improve treatment effectiveness through adherence among individuals with schizophrenia. Understanding real-world medication adherence, and healthcare resource utilization (HRU) and costs in individuals with schizophrenia overall and by CTO status before and after second generation antipsychotic (SGA)-LAI initiation may guide strategies to optimize treatment among those with schizophrenia. METHODS: This retrospective observational single-arm study utilized administrative health data from Alberta, Canada. Adults (≥ 18 years) with schizophrenia who initiated a SGA-LAI (no use in the previous 2-years) between April 1, 2014 and March 31, 2016, and had ≥ 1 additional dispensation of a SGA-LAI were included; index date was the date of SGA-LAI initiation. Medication possession ratio (MPR) was determined, and paired t-tests were used to examine mean differences in all-cause and mental health-related HRU and costs (Canadian dollars), comprised of hospitalizations, physician visits, emergency department visits, and total visits, over the 2-year post-index and 2-year pre-index periods. Analyses were stratified by presence or absence of an active CTO during the pre-index and/or post-index periods. RESULTS: Among 1,211 adults with schizophrenia who initiated SGA-LAIs, 64% were males with a mean age of 38 (standard deviation [SD] 14) years. The mean overall antipsychotic MPR was 0.39 (95% confidence interval [CI] 0.36, 0.41) greater during the 2-year post-index period (0.84 [SD 0.26]) compared with the 2-year pre-index period (0.45 [SD 0.40]). All-cause and mental health-related HRU and costs were lower post-index versus pre-index (p < 0.001) for hospitalizations, physician visits, emergency department visits, and total visits; mean total all-cause HRU costs were $33,788 (95% CI -$38,993, -$28,583) lower post- versus pre-index ($40,343 [SD $68,887] versus $74,131 [SD $75,941]), and total mental health-related HRU costs were $34,198 (95%CI -$39,098, -$29,297) lower post- versus pre-index ($34,205 [SD $63,428] versus $68,403 [SD $72,088]) per-patient. Forty-three percent had ≥ 1 active CTO during the study period; HRU and costs varied according to CTO status. CONCLUSIONS: SGA-LAIs are associated with greater medication adherence, and lower HRU and costs however the latter vary according to CTO status.

Ayahuasca, a potentially rapid acting antidepressant: focus on safety and tolerability.

INTRODUCTION: Ayahuasca is a psychedelic brew originally used by Amazonian indigenous groups and in religious rituals. Pre-clinical and observational studies have demonstrated its possible potential as an antidepressant, and open- and placebo-controlled clinical trials corroborated these results. For it to become an approved treatment for depression, its safety and tolerability need to be assessed and documented. AREAS COVERED: We have gathered data regarding the occurrence of adverse events (AEs) in all reported randomized, placebo-controlled trials with healthy and clinical populations involving ayahuasca administration (n = 108 ayahuasca administrations). We systematically categorized these results, recorded their prevalence, and discussed the possible mechanisms related to their emergence. EXPERT OPINION: There were no reports of serious AEs, indicating a relative safety of ayahuasca administration in controlled settings. Most common AEs included nausea, vomiting, headaches, and transient increases in cardiovascular measurements. Ayahuasca research is still in its infancy, especially concerning the absence of large and robust clinical trials to verify its antidepressant effects. Dose standardization, legal prohibition of the possession of its alkaloids and how traditional communities will be compensated if ayahuasca becomes an approved medicine are the biggest obstacles to overcome for its future use in the therapeutic context.

Using Machine Learning to Predict Remission in Patients With Major Depressive Disorder Treated With Desvenlafaxine.

BACKGROUND: Major depressive disorder (MDD) is a common and burdensome condition that has low rates of treatment success for each individual treatment. This means that many patients require several medication switches to achieve remission; selecting an effective antidepressant is typically a sequential trial-and-error process. Machine learning techniques may be able to learn models that can predict whether a specific patient will respond to a given treatment, before it is administered. This study uses baseline clinical data to create a machine-learned model that accurately predicts remission status for a patient after desvenlafaxine (DVS) treatment. METHODS: We applied machine learning algorithms to data from 3,399 MDD patients (90% of the 3,776 subjects in 11 phase-III/IV clinical trials, each described using 92 features), to produce a model that uses 26 of these features to predict symptom remission, defined as an 8-week Hamilton Depression Rating Scale score of 7 or below. We evaluated that learned model on the remaining held-out 10% of the data (n = 377). RESULTS: Our resulting classifier, a trained linear support vector machine, had a holdout set accuracy of 69.0%, significantly greater than the probability of classifying a patient correctly by chance. We demonstrate that this learning process is stable by repeatedly sampling part of the training dataset and running the learner on this sample, then evaluating the learned model on the held-out instances of the training set; these runs had an average accuracy of 67.0% ± 1.8%. CONCLUSIONS: Our model, based on 26 clinical features, proved sufficient to predict DVS remission significantly better than chance. This may allow more accurate use of DVS without waiting 8 weeks to determine treatment outcome, and may serve as a first step toward changing psychiatric care by incorporating clinical assistive technologies using machine-learned models.

Prediction of Obsessive-Compulsive Disorder: Importance of Neurobiology-Aided Feature Design and Cross-Diagnosis Transfer Learning.

BACKGROUND: Machine learning applications using neuroimaging provide a multidimensional, data-driven approach that captures the level of complexity necessary for objectively aiding diagnosis and prognosis in psychiatry. However, models learned from small training samples often have limited generalizability, which continues to be a problem with automated diagnosis of mental illnesses such as obsessive-compulsive disorder (OCD). Earlier studies have shown that features incorporating prior neurobiological knowledge of brain function and combining brain parcellations from various sources can potentially improve the overall prediction. However, it is unknown whether such knowledge-driven methods can provide a performance that is comparable to state-of-the-art approaches based on neural networks. METHODS: In this study, we apply a transparent and explainable multiparcellation ensemble learning framework EMPaSchiz (Ensemble algorithm with Multiple Parcellations for Schizophrenia prediction) to the task of predicting OCD, based on a resting-state functional magnetic resonance imaging dataset of 350 subjects. Furthermore, we apply transfer learning using the features found effective for schizophrenia to OCD to leverage the commonality in brain alterations across these psychiatric diagnoses. RESULTS: We show that our knowledge-based approach leads to a prediction performance of 80.3% accuracy for OCD diagnosis that is better than domain-agnostic and automated feature design using neural networks. Furthermore, we show that a selection of reduced feature sets can be transferred from schizophrenia to the OCD prediction model without significant loss in prediction performance. CONCLUSIONS: This study presents a machine learning framework for OCD prediction with neurobiology-aided feature design using resting-state functional magnetic resonance imaging that is generalizable and reasonably interpretable.

The Relationship Between the Gut Microbiome-Immune System-Brain Axis and Major Depressive Disorder.

Major depressive disorder (MDD) is a prominent cause of disability worldwide. Current antidepressant drugs produce full remission in only about one-third of MDD patients and there are no biomarkers to guide physicians in selecting the best treatment for individuals. There is an urgency to learn more about the etiology of MDD and to identify new targets that will lead to improved therapy and hopefully aid in predicting and preventing MDD. There has been extensive interest in the roles of the immune system and the gut microbiome in MDD and in how these systems interact. Gut microbes can contribute to the nature of immune responses, and a chronic inflammatory state may lead to increased responsiveness to stress and to development of MDD. The gut microbiome-immune system-brain axis is bidirectional, is sensitive to stress and is important in development of stress-related disorders such as MDD. Communication between the gut and brain involves the enteric nervous system (ENS), the autonomic nervous system (ANS), neuroendocrine signaling systems and the immune system, and all of these can interact with the gut microbiota. Preclinical studies and preliminary clinical investigations have reported improved mood with administration of probiotics and prebiotics, but large, carefully controlled clinical trials are now necessary to evaluate their effectiveness in treating MDD. The roles that several gut microbe-derived molecules such as neurotransmitters, short chain fatty acids and tryptophan play in MDD are reviewed briefly. Challenges and potential future directions associated with studying this important axis as it relates to MDD are discussed.

Differential power of placebo across major psychiatric disorders: a preliminary meta-analysis and machine learning study.

The placebo effect across psychiatric disorders is still not well understood. In the present study, we conducted meta-analyses including meta-regression, and machine learning analyses to investigate whether the power of placebo effect depends on the types of psychiatric disorders. We included 108 clinical trials (32,035 participants) investigating pharmacological intervention effects on major depressive disorder (MDD), bipolar disorder (BD) and schizophrenia (SCZ). We developed measures based on clinical rating scales and Clinical Global Impression scores to compare placebo effects across these disorders. We performed meta-analysis including meta-regression using sample-size weighted bootstrapping techniques, and machine learning analysis to identify the disorder type included in a trial based on the placebo response. Consistently through multiple measures and analyses, we found differential placebo effects across the three disorders, and found lower placebo effect in SCZ compared to mood disorders. The differential placebo effects could also distinguish the condition involved in each trial between SCZ and mood disorders with machine learning. Our study indicates differential placebo effect across MDD, BD, and SCZ, which is important for future neurobiological studies of placebo effects across psychiatric disorders and may lead to potential therapeutic applications of placebo on disorders more responsive to placebo compared to other conditions.

Clozapine: Why Is It So Uniquely Effective in the Treatment of a Range of Neuropsychiatric Disorders?

Clozapine is superior to other antipsychotics as a therapy for treatment-resistant schizophrenia and schizoaffective disorder with increased risk of suicidal behavior. This drug has also been used in the off-label treatment of bipolar disorder, major depressive disorder (MDD), and Parkinson's disease (PD). Although usually reserved for severe and treatment-refractory cases, it is interesting that electroconvulsive therapy (ECT) has also been used in the treatment of these psychiatric disorders, suggesting some common or related mechanisms. A literature review on the applications of clozapine and electroconvulsive therapy (ECT) to the disorders mentioned above was undertaken, and this narrative review was prepared. Although both treatments have multiple actions, evidence to date suggests that the ability to elicit epileptiform activity and alter EEG activity, to increase neuroplasticity and elevate brain levels of neurotrophic factors, to affect imbalances in the relationship between glutamate and γ-aminobutyric acid (GABA), and to reduce inflammation through effects on neuron-glia interactions are common underlying mechanisms of these two treatments. This evidence may explain why clozapine is effective in a range of neuropsychiatric disorders. Future increased investigations into epigenetic and connectomic changes produced by clozapine and ECT should provide valuable information about these two treatments and the disorders they are used to treat.

Paraneoplastic and autoimmune encephalitis: Alterations of mood and emotion.

Autoimmune encephalitis often produces signs and symptoms that appear to be at the interface between neurology and psychiatry. Since psychiatric symptoms are often prominent, patients are often first seen in a psychiatric setting. Therefore it is important that psychiatrists, as well as neurologists, be able to recognize autoimmune encephalitis, a task that is often difficult. Early diagnosis of autoimmune encephalitis is crucial as this will usually result in a better outcome for the patient. This chapter provides an introduction to various autoimmune encephalitides and describes their pathophysiology and the possible associated neuropsychiatric, neuropsychological (cognitive), and neurological (sensory-motor) signs and symptoms. This chapter also reviews the possible treatments of these associated signs and symptoms.

Mononitrate Isosorbide as an Adjunctive Therapy in Schizophrenia: A Randomized Controlled Crossover Trial.

BACKGROUND: Schizophrenia is a complex disabling mental disorder, and many patients present poor response to available treatments. Accumulating evidence about the role of the glutamate/nitric oxide pathway in mediating the positive and negative symptoms of schizophrenia suggests potential benefits of drugs that modulate this system. The aim of this study was to test the efficacy of isosorbide mononitrate (ISMN) as an adjunctive therapy for symptomatic outpatients with schizophrenia. METHODS: This was a 2-month randomized, double-blind, placebo-controlled trial with 24 schizophrenia patients. Participants were treated with ISMN 50 mg for 1 month and placebo for another month in a crossover design. The Positive and Negative Syndrome Scale (PANSS), Clinical Global Impression Scale, Global Assessment of Functioning, and MATRICS Cognitive Consensual Battery were used for symptom assessment and arterial spin labeling was used to assess brain activation patterns. RESULTS: We found significant differences in the total, general, and positive subscales of the PANSS, Global Assessment of Functioning scores, and Clinical Global Impression scores during treatment with ISMN relative to placebo. No treatment effects were found comparing scores in the MATRICS Cognitive Consensual Battery and the negative subscale of the PANSS between the active and placebo conditions. A post hoc analysis of neuroimaging data showed reduced activity in the thalamus in subgroup of patients with severe psychopathology. CONCLUSIONS: Schizophrenia patients with persistent symptoms showed significant improvement after 4 weeks of treatment with ISMN 50 mg/d compared with placebo. Isosorbide mononitrate added beneficial effects to antipsychotic treatment in terms of positive symptoms and functioning.

Hallucinogenic/psychedelic 5HT2A receptor agonists as rapid antidepressant therapeutics: Evidence and mechanisms of action.

Major depressive disorder (MDD) is among the most prevalent mental health disorders worldwide, and it is associated with a reduced quality of life and enormous costs to health care systems. Available drug treatments show low-to-moderate response in most patients, with almost a third of patients being non-responders (treatment-resistant). Furthermore, most currently available medications need several weeks to achieve therapeutic effects, and the long-term use of these drugs is often associated with significant unwanted side effects and resultant reductions in treatment compliance. Therefore, more effective, safer, and faster-acting antidepressants with enduring effects are needed. Together with ketamine, psychedelics (or classic or serotoninergic hallucinogens) such as lysergic acid diethylamide (LSD), psilocybin, and ayahuasca are among the few compounds with recent human evidence of fast-acting antidepressant effects. Several studies in the 1950s to 1970s reported antidepressive and anxiolytic effects of these drugs, which are being confirmed by modern trials (LSD, one trial; psilocybin, five trials; ayahuasca, two trials). The effects of these drugs appear to be produced primarily by their agonism at serotonin (5-hydroxytryptamine, 5-HT) receptors, especially the 5-HT2A receptor. Considering the overall burden of MDD and the necessity of new therapeutic options, the promising (but currently limited) evidence of safety and efficacy of psychedelics has encouraged the scientific community to explore more fully their beneficial effects in MDD.