Corrigendum: The Role of Dopamine in Schizophrenia from a Neurobiological and Evolutionary Perspective: Old Fashioned, but Still in Vogue

Pharmacological modulation of dopamine D1 and D2 receptors reveals distinct neural networks related to probabilistic learning in non-human primates

The neurotransmitter dopamine (DA) has a multifaceted role in healthy and disordered brains through its action on multiple subtypes of dopaminergic receptors. How modulation of these receptors controls behavior by altering connectivity across intrinsic brain-wide networks remains elusive. Here we performed parallel behavioral and resting-state functional MRI experiments after administration of two different DA receptor antagonists in macaque monkeys. Systemic administration of SCH-23390 (D1 antagonist) disrupted probabilistic learning when subjects had to learn new stimulus-reward associations and diminished functional connectivity (FC) in cortico-cortical and fronto-striatal connections. By contrast, haloperidol (D2 antagonist) improved learning and broadly enhanced FC in cortical connections. Further comparison between the effect of SCH-23390/haloperidol on behavioral and resting-state FC revealed specific cortical and subcortical networks associated with the cognitive and motivational effects of DA, respectively. Thus, we reveal the distinct brain-wide networks that are associated with the dopaminergic control of learning and motivation via DA receptors.

Understanding translational research in schizophrenia: A novel insight into animal models

Schizophrenia affects millions of people worldwide and is a major challenge for the scientific community. Like most psychotic diseases, it is also considered a complicated mental disorder caused by an imbalance in neurotransmitters. Due to the complexity of neuropathology, it is always a complicated disorder. The lack of proper understanding of the pathophysiology makes the disorder unmanageable in clinical settings. However, due to recent advances in animal models, we hope we can have better therapeutic approaches with more success in clinical settings. Dopamine, glutamate, GABA, and serotonin are the neurotransmitters involved in the pathophysiology of schizophrenia. Various animal models have been put forward based on these neurotransmitters, including pharmacological, neurodevelopmental, and genetic models. Polymorphism of genes such as dysbindin, DICS1, and NRG1 has also been reported in schizophrenia. Hypothesis based on dopamine, glutamate, and serotonin are considered successful models of schizophrenia on which drug therapies have been designed to date. New targets like the orexin system, muscarinic and nicotinic receptors, and cannabinoid receptors have been approached to alleviate the negative and cognitive symptoms. The non-pharmacological models like the post-weaning social isolation model (maternal deprivation), the isolation rearing model etc. have been also developed to mimic the symptoms of schizophrenia and to create and test new approaches of drug therapy which is a breakthrough at present in psychiatric disorders. Different behavioral tests have been evaluated in these specific models. This review will highlight the currently available animal models and behavioral tests in psychic disorders concerning schizophrenia.

Treatment resistant psychosis in children and adolescents and clozapine: Nuances

With proliferation in research on high-risk psychosis spectrum diseases, it is crucial to distinguish a prodrome or psychosis-like episode in children and adolescents from true psychosis. The limited role of psychopharmacology in such circumstances is well-documented, underlining the difficulties in diagnosing treatment resistance. To add to the confusion is emerging data on the head-to-head comparison trials for treatment-resistant and treatment-refractory schizophrenia. Clozapine, the gold-standard drug for resistant schizophrenia and other psychotic psychopathology, lacks FDA or manufacturer guidelines for use in the pediatric population. Possibly due to developmental pharmacokinetic (PK) considerations, clozapine-related side effects are more commonly seen in children than adults. Despite evidence of an increased risk for seizures and hematological problems in children, clozapine is widely used off-label. Clozapine reduces the severity of resistant childhood schizophrenia, aggression, suicidality, and severe non-psychotic illness. There is inconsistent prescribing, administration, and monitoring of clozapine, and limited database evidence-backed guidelines. Despite the overwhelming efficacy, problems remain regarding unambiguous indications of use and risk-benefits assessments. This article reviews the nuances in the diagnosis of treatment resistance psychosis in childhood and adolescents and its management, in particular highlighting the evidence base for clozapine in this population group.

Significance of an Electrochemical Sensor and Nanocomposites: Toward the Electrocatalytic Detection of Neurotransmitters and Their Importance within the Physiological System

A prominent neurotransmitter (NT), dopamine (DA), is a chemical messenger that transmits signals between one neuron to the next to pass on a signal to and from the central nervous system (CNS). The imbalanced concentration of DA may cause numerous neurological sicknesses and syndromes, for example, Parkinson's disease (PD) and schizophrenia. There are many types of NTs in the brain, including epinephrine, norepinephrine (NE), serotonin, and glutamate. Electrochemical sensors have offered a creative direction to biomedical analysis and testing. Researches are in progress to improve the performance of sensors and develop new protocols for sensor design. This review article focuses on the area of sensor growth to discover the applicability of polymers and metallic particles and composite materials as tools in electrochemical sensor surface incorporation. Electrochemical sensors have attracted the attention of researchers as they possess high sensitivity, quick reaction rate, good controllability, and instantaneous detection. Efficient complex materials provide considerable benefits for biological detection as they have exclusive chemical and physical properties. Due to distinctive electrocatalytic characteristics, metallic nanoparticles add fascinating traits to materials that depend on the material's morphology and size. Herein, we have collected much information on NTs and their importance within the physiological system. Furthermore, the electrochemical sensors and corresponding techniques (such as voltammetric, amperometry, impedance, and chronoamperometry) and the different types of electrodes' roles in the analysis of NTs are discussed. Furthermore, other methods for detecting NTs include optical and microdialysis methods. Finally, we show the advantages and disadvantages of different techniques and conclude remarks with future perspectives.

Infra-Low Frequency Neurofeedback rapidly ameliorates schizophrenia symptoms: A case report of the first session

A 38-year-old army officer started therapy in 2020 with a four-year history of auditory hallucinations and delusions of reference, persecution and grandeur, symptoms that were resistant to traditional antipsychotic medications. He follows an integrative psychotherapy program that aims to reduce his anxiety, continues his antipsychotic medications, and has Infra-Low Frequency Neurofeedback. After his initial assessment he had a 40 min session of Infra-Low Frequency Neurofeedback before any other kind of intervention. Before and immediately after the session he completed the SCL-90 scale and the Visual Analog Scale covering 20 aspects of his psychological and physical state as well as his schizophrenic symptoms. This first Neurofeedback session had dramatic effects on his psychotic symptoms, levels of anxiety and psychosomatic condition, before his first psychotherapy session and/or any changes in his antipsychotic medication. The above results have great importance due to the severity and chronicity of schizophrenia. Informed consent was obtained from the participant for the publication of this case report (including all data and images).

The prediction-error hypothesis of schizophrenia: new data point to circuit-specific changes in dopamine activity

Schizophrenia is a severe psychiatric disorder affecting 21 million people worldwide. People with schizophrenia suffer from symptoms including psychosis and delusions, apathy, anhedonia, and cognitive deficits. Strikingly, schizophrenia is characterised by a learning paradox involving difficulties learning from rewarding events, whilst simultaneously 'overlearning' about irrelevant or neutral information. While dysfunction in dopaminergic signalling has long been linked to the pathophysiology of schizophrenia, a cohesive framework that accounts for this learning paradox remains elusive. Recently, there has been an explosion of new research investigating how dopamine contributes to reinforcement learning, which illustrates that midbrain dopamine contributes in complex ways to reinforcement learning, not previously envisioned. This new data brings new possibilities for how dopamine signalling contributes to the symptomatology of schizophrenia. Building on recent work, we present a new neural framework for how we might envision specific dopamine circuits contributing to this learning paradox in schizophrenia in the context of models of reinforcement learning. Further, we discuss avenues of preclinical research with the use of cutting-edge neuroscience techniques where aspects of this model may be tested. Ultimately, it is hoped that this review will spur to action more research utilising specific reinforcement learning paradigms in preclinical models of schizophrenia, to reconcile seemingly disparate symptomatology and develop more efficient therapeutics.

Computational Mechanism for the Effect of Psychosis Community Treatment: A Conceptual Review From Neurobiology to Social Interaction

The computational underpinnings of positive psychotic symptoms have recently received significant attention. Candidate mechanisms include some combination of maladaptive priors and reduced updating of these priors during perception. A potential benefit of models with such mechanisms is their ability to link multiple levels of explanation, from the neurobiological to the social, allowing us to provide an information processing-based account of how specific alterations in self-self and self-environment interactions result in the experience of positive symptoms. This is key to improving how we understand the experience of psychosis. Moreover, it points us toward more comprehensive avenues for therapeutic research by providing a putative mechanism that could allow for the generation of new treatments from first principles. In order to demonstrate this, our conceptual paper will discuss the application of the insights from previous computational models to an important and complex set of evidence-based clinical interventions with strong social elements, such as coordinated specialty care clinics (CSC) in early psychosis and assertive community treatment (ACT). These interventions may include but also go beyond psychopharmacology, providing, we argue, structure and predictability for patients experiencing psychosis. We develop the argument that this structure and predictability directly counteract the relatively low precision afforded to sensory information in psychosis, while also providing the patient more access to external cognitive resources in the form of providers and the structure of the programs themselves. We discuss how computational models explain the resulting reduction in symptoms, as well as the predictions these models make about potential responses of patients to modifications or to different variations of these interventions. We also link, via the framework of computational models, the patient's experiences and response to interventions to putative neurobiology.

Potentially repurposable drugs for schizophrenia identified from its interactome

We previously presented the protein-protein interaction network of schizophrenia associated genes, and from it, the drug-protein interactome which showed the drugs that target any of the proteins in the interactome. Here, we studied these drugs further to identify whether any of them may potentially be repurposable for schizophrenia. In schizophrenia, gene expression has been described as a measurable aspect of the disease reflecting the action of risk genes. We studied each of the drugs from the interactome using the BaseSpace Correlation Engine, and shortlisted those that had a negative correlation with differential gene expression of schizophrenia. This analysis resulted in 12 drugs whose differential gene expression (drug versus normal) had an anti-correlation with differential expression for schizophrenia (disorder versus normal). Some of these drugs were already being tested for their clinical activity in schizophrenia and other neuropsychiatric disorders. Several proteins in the protein interactome of the targets of several of these drugs were associated with various neuropsychiatric disorders. The network of genes with opposite drug-induced versus schizophrenia-associated expression profiles were significantly enriched in pathways relevant to schizophrenia etiology and GWAS genes associated with traits or diseases that had a pathophysiological overlap with schizophrenia. Drugs that targeted the same genes as the shortlisted drugs, have also demonstrated clinical activity in schizophrenia and other related disorders. This integrated computational analysis will help translate insights from the schizophrenia drug-protein interactome to clinical research - an important step, especially in the field of psychiatric drug development which faces a high failure rate.

Closed-Loop Implantable Therapeutic Neuromodulation Systems Based on Neurochemical Monitoring

Closed-loop or intelligent neuromodulation allows adjustable, personalized neuromodulation which usually incorporates the recording of a biomarker, followed by implementation of an algorithm which decides the timing (when?) and strength (how much?) of stimulation. Closed-loop neuromodulation has been shown to have greater benefits compared to open-loop neuromodulation, particularly for therapeutic applications such as pharmacoresistant epilepsy, movement disorders and potentially for psychological disorders such as depression or drug addiction. However, an important aspect of the technique is selection of an appropriate, preferably neural biomarker. Neurochemical sensing can provide high resolution biomarker monitoring for various neurological disorders as well as offer deeper insight into neurological mechanisms. The chemicals of interest being measured, could be ions such as potassium (K⁺), sodium (Na⁺), calcium (Ca²⁺), chloride (Cl⁻), hydrogen (H⁺) or neurotransmitters such as dopamine, serotonin and glutamate. This review focusses on the different building blocks necessary for a neurochemical, closed-loop neuromodulation system including biomarkers, sensors and data processing algorithms. Furthermore, it also highlights the merits and drawbacks of using this biomarker modality.