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Combining different pharmaceuticals may be beneficial when treating disorders with complex neurobiology, including alcohol use disorder (AUD). The gut-brain peptides amylin and GLP-1 may be of potential interest as they individually reduce alcohol intake in rodents. While the combination of amylin receptor (AMYR) and glucagon-like peptide-1 receptor (GLP-1R) agonists have been found to decrease feeding and body weight in obese male rats synergistically, their combined impact on alcohol intake is unknown.
Methods:
Therefore, the effect of the combination of an AMYR (salmon calcitonin (sCT)) and a GLP-1R (dulaglutide) agonist on alcohol intake in rats of both sexes was explored in two separate alcohol-drinking experiments. The first alcohol-drinking experiment evaluated the potential of adding sCT to an ongoing dulaglutide treatment, whereas the second alcohol-drinking experiment examined the effect when adding sCT and dulaglutide simultaneously.
Results:
When adding sCT to an ongoing dulaglutide treatment, a reduction in alcohol intake was observed in both male and female rats. However, when combining sCT and dulaglutide simultaneously, an initial reduction in alcohol intake was observed in rats of both sexes, whereas tolerance towards treatment was observed. In both alcohol-drinking experiments, this treatment combination consistently decreased food consumption and body weight in males and females. While the treatment combination did not affect inflammatory mediators, the gene expression of AMYR or GLP-1R, it changed fat tissue morphology.
Conclusions:
Further investigation needs to be done on the combination of AMYR and GLP-1R agonists to assess their combined effects on alcohol intake.
Antipsychotics effective for schizophrenia approved prior to 2024 shared the common mechanism of postsynaptic dopamine D2 receptor antagonism or partial agonism. Positive psychosis symptoms correlate with excessive presynaptic dopamine turnover and release, yet this postsynaptic mechanism improved positive symptoms only in some patients, and with concomitant risk for off-target motor and endocrine adverse effects; moreover, these agents showed no benefit for negative symptoms and cognitive dysfunction. The sole exception was data supporting cariprazine’s superiority to risperidone for negative symptoms. The muscarinic M1/M4 agonist xanomeline was approved in September 2024 and represents the first of a new antipsychotic class. This novel mechanism improves positive symptoms by reducing presynaptic dopamine release. Xanomeline also lacks any D2 receptor affinity and is not associated with motor or endocrine side effects. Of importance, xanomeline treated patients with higher baseline levels of cognitive dysfunction in clinical trials data saw cognitive improvement, a finding likely related to stimulation of muscarinic M1 receptors. Treatment resistance is seen in one-third of schizophrenia patients. These individuals do not have dopamine dysfunction underlying their positive symptoms, and therefore show limited response to antipsychotics that target dopamine neurotransmission. Clozapine remains the only medication with proven efficacy for resistant schizophrenia, and with unique benefits for persistent impulsive aggression and suicidality. New molecules are being studied to address the array of positive, negative and cognitive symptoms of schizophrenia; however, until their approval, clinicians must be familiar with currently available agents and be adept at prescribing clozapine.
Schizophrenia spectrum disorders are brain diseases that are developmental dementias (dementia praecox). Their pathology begins in utero with psychosis most commonly becoming evident in adolescence and early adulthood. It is estimated they afflict the U.S. population at a prevalence rate of approximately 0.8%. Genetic studies indicate that these brain diseases are about 80% determined by genes and about 20% determined by environmental risk factors. Inheritance is polygenic with some 270 gene loci having been identified as contributing to the risk for schizophrenia. Interestingly, many of the identified gene loci and gene polymorphisms are involved in brain formation and maturation. The identified genetic and epigenetic risks give rise to a brain in which neuroblasts migrate abnormally, assume abnormal locations and orientations, and are vulnerable to excessive neuronal and synaptic loss, resulting in overt psychotic illness. The illness trajectory of schizophrenia then is one of loss of brain mass related to the number of active psychotic exacerbations and the duration of untreated illness. In this context, molecules such as dopamine, glutamate, and serotonin play critical roles with respect to positive, negative, and cognitive domains of illness. Acutely, antipsychotics ameliorate active psychotic illness, especially positive signs and symptoms. The long-term effects of antipsychotic medications have been debated; however, the bulk of imaging data suggest that antipsychotics slow but do not reverse the illness trajectory of schizophrenia. Long-acting injectable antipsychotics (LAI) appear superior in this regard. Clozapine remains the “gold standard” in managing treatment-resistant schizophrenia.
Alzheimer’s dementia (AD) is a progressive, neurodegenerative disease often accompanied by neuropsychiatric symptoms that profoundly impact both patients and caregivers. Agitation is among the most prevalent and distressing of these symptoms and often requires treatment. Appropriate therapeutic interventions depend on understanding the biological basis of agitation and how it may be affected by treatment. This narrative review discusses a proposed pathophysiology of agitation in Alzheimer’s dementia based on convergent evidence across research approaches. Available data indicate that agitation in Alzheimer’s dementia is associated with an imbalance of activity between key prefrontal and subcortical brain regions. The monoamine neurotransmitter systems serve as key modulators of activity within these brain regions and circuits and are rendered abnormal in AD. Patients with AD who exhibited agitation symptoms during life have alterations in neurotransmitter nuclei and related systems when the brain is examined at autopsy. The authors present a model of agitation in Alzheimer’s dementia in which noradrenergic hyperactivity along with serotonergic deficits and dysregulated striatal dopamine release contribute to agitated and aggressive behaviors.
Modification of mRNA by methylation is involved in post-transcriptional regulation of gene expression by affecting the splicing, transport, stability and translation of mRNA. Methylation of adenosine at N6 (m6A) is one of the most common and important cellular modification occurring in the mRNA of eukaryotes. Evidence that m6A mRNA methylation is involved in regulation of stress response and that its dysregulation may contribute to the pathogenesis of neuropsychiatric disorders is accumulating. We have examined the acute and subchronic (up to 18 days once per day intraperitoneally) effect of the first METTL3/METTL14 activator compound CHMA1004 (methyl-piperazine-2-carboxylate) at two doses (1 and 5 mg/kg) in male and female rats. CHMA1004 had a locomotor activating and anxiolytic-like profile in open field and elevated zero-maze tests. In female rats sucrose consumption and swimming in Porsolt’s test were increased. Nevertheless, CHMA1004 did not exhibit strong psychostimulant-like properties: CHMA1004 had no effect on 50-kHz ultrasonic vocalizations except that it reduced the baseline difference between male and female animals, and acute drug treatment had no effect on extracellular dopamine levels in striatum. Subchronic CHMA1004 altered ex vivo catecholamine levels in several brain regions. RNA sequencing of female rat striata after subchronic CHMA1004 treatment revealed changes in the expression of a number of genes linked to dopamine neuron viability, neurodegeneration, depression, anxiety and stress response. Conclusively, the first-in-class METTL3/METTL14 activator compound CHMA1004 increased locomotor activity and elicited anxiolytic-like effects after systemic administration, demonstrating that pharmacological activation of RNA m6A methylation has potential for neuropsychiatric drug development.
We all have to eat, and what we eat has been established by numerous cultural forces. When we begin to view food as fuel for our brain, we may have to confront our dietary eating patterns in order to enhance brain health and mental strength. The consumption of hyperpalatable foods, often ultra-processed with excess sugar and fat, can lead to self-medication with food and to compromised brain health. The motivation and reward system in our brain that facilitates our habits includes the overconsumption of unhealthy food. This chapter covers the critical neurodestructive conditions that are impacted by our diet (dementia, Alzheimer’s disease, inflammation, oxidation, elevated blood sugar, malfunctioning gut microbiome); argues that ultra-processed foods and comfort foods with high concentrations of sugar and fat are bad for the brain, highly addictive, and targets for self-medication; and concludes with foods to avoid and foods to consume to optimize brain health and mental strength.
Substance use among lawyers is a common way to self-medicate stress, anxiety, and depression and to fuel overwork. To facilitate an understanding of how substances of abuse work in the brain, it is helpful to grasp the basics of neurotransmission. Information travels through the brain via chains of neurons. This information is an electrical impulse while in the brain cell, but to travel across the gap between neurons, the information uses chemicals called neurotransmitters. The site of action for self-medicating substances is at that gap, which is called a synapse. Different substances cause various changes in the brain by influencing the synapses of those lawyers who use them. These drugs are divided by substances that stimulate and can fuel overwork (caffeine, nicotine, amphetamine, cocaine) and sedatives that can calm stress and anxiety (alcohol, cannabis, opioids). Some lawyers use prescribed antidepressant medications. All of them impact the brain at the gap between brain cells, the synapse, where communication involves neurotransmitters and their receptors.
The brain has an automated system designed to keep humans alive by promoting the search for, and remembering the location of, food. It is the motivation and reward system. The main neurotransmitter that drives our motivation and reward system is dopamine, which is the transmitter of repeat behavior. Our habits are formed by this system, and modern society offers numerous substances and activities to indulge in what can become habitual. Beneficial habits include exercise and eating lots of vegetables. Unhealthy habits include drinking too much alcohol, eating too much comfort food, and spending too much time on social media. Our habits often take hold because we use them to soothe our stress, anxiety, and depression. Habits are hard to break because they are established in our brains in networks of our brain cells.
The catechol-o-methyltransferase (COMT) inhibitor tolcapone constitutes a potentially useful probe of frontal cortical dopaminergic function. The aim of this systematic review was to examine what is known of effects of tolcapone on human cognition in randomized controlled studies.
Methods
The study protocol was preregistered on the Open Science Framework. A systematic review was conducted using PubMed to identify relevant randomized controlled trials examining the effects of tolcapone on human cognition. Identified articles were then screened against inclusion and exclusion criteria.
Results
Of the 22 full-text papers identified, 13 randomized control trials were found to fit the pre-specified criteria. The most consistent finding was that tolcapone modulated working memory; however, the direction of effect appeared to be contingent on the COMT polymorphism (more consistent evidence of improvement in Val–Val participants). There were insufficient nature and number of studies for meta-analysis.
Conclusion
The cognitive improvements identified upon tolcapone administration, in some studies, are likely to be due to the level of dopamine in the prefrontal cortex being shifted closer to its optimum, per an inverted U model of prefrontal function. However, the results should be interpreted cautiously due to the small numbers of studies. Given the centrality of cortical dopamine to understanding human cognition, studies using tolcapone in larger samples and across a broader set of cognitive domains would be valuable. It would also be useful to explore the effects of different dosing regimens (different doses; and single versus repeated administration).
Interactions between the endocannabinoid system (ECS) and neurotransmitter systems might mediate the risk of developing a schizophrenia spectrum disorder (SSD). Consequently, we investigated in patients with SSD and healthy controls (HC) the relations between (1) plasma concentrations of two prototypical endocannabinoids (N-arachidonoylethanolamine [anandamide] and 2-arachidonoylglycerol [2-AG]) and (2) striatal dopamine synthesis capacity (DSC), and glutamate and y-aminobutyric acid (GABA) levels in the anterior cingulate cortex (ACC). As anandamide and 2-AG might reduce the activity of these neurotransmitters, we hypothesized negative correlations between their plasma levels and the abovementioned neurotransmitters in both groups.
Methods
Blood samples were obtained from 18 patients and 16 HC to measure anandamide and 2-AG plasma concentrations. For all subjects, we acquired proton magnetic resonance spectroscopy scans to assess Glx (i.e. glutamate plus glutamine) and GABA + (i.e. GABA plus macromolecules) concentrations in the ACC. Ten patients and 14 HC also underwent [18F]F-DOPA positron emission tomography for assessment of striatal DSC. Multiple linear regression analyses were used to investigate the relations between the outcome measures.
Results
A negative association between 2-AG plasma concentration and ACC Glx concentration was found in patients (p = 0.008). We found no evidence of other significant relationships between 2-AG or anandamide plasma concentrations and dopaminergic, glutamatergic, or GABAergic measures in either group.
Conclusions
Our preliminary results suggest an association between peripheral 2-AG and ACC Glx levels in patients.
An altered behavioral response to positive reinforcement has been proposed to be a core deficit in attention deficit hyperactivity disorder (ADHD). The spontaneously hypertensive rat (SHR), a congenic animal strain, displays a similarly altered response to reinforcement. The presence of this genetically determined phenotype in a rodent model allows experimental investigation of underlying neural mechanisms. Behaviorally, the SHR displays increased preference for immediate reinforcement, increased sensitivity to individual instances of reinforcement relative to integrated reinforcement history, and a steeper delay of reinforcement gradient compared to other rat strains. The SHR also shows less development of incentive to approach sensory stimuli, or cues, that predict reward after repeated cue-reward pairing. We consider the underlying neural mechanisms for these characteristics. It is well known that midbrain dopamine neurons are initially activated by unexpected reward and gradually transfer their responses to reward-predicting cues. This finding has inspired the dopamine transfer deficit (DTD) hypothesis, which predicts certain behavioral effects that would arise from a deficient transfer of dopamine responses from actual rewards to reward-predicting cues. We argue that the DTD predicts the altered responses to reinforcement seen in the SHR and individuals with ADHD. These altered responses to reinforcement in turn predict core symptoms of ADHD. We also suggest that variations in the degree of dopamine transfer may underlie variations in personality dimensions related to altered reinforcement sensitivity. In doing so, we highlight the value of rodent models to the study of human personality.
Laboratory paradigms are widely used to study fear learning in posttraumatic stress disorder (PTSD). Recent basic science models demonstrate that, during fear learning, patterns of activity in large neuronal ensembles for the conditioned stimuli (CS) begin to reinstate neural activity patterns for the unconditioned stimuli (US), suggesting a direct way of quantifying fear memory strength for the CS. Here, we translate this concept to human neuroimaging and test the impact of post-learning dopaminergic neurotransmission on fear memory strength during fear acquisition, extinction, and recall among women with PTSD in a re-analysis of previously reported data.
Methods
Participants (N = 79) completed a context-dependent fear acquisition and extinction task on day 1 and extinction recall tests 24 h later. We decoded activity patterns in large-scale functional networks for the US, then applied this decoder to activity patterns toward the CS on day 1 and day 2.
Results
US decoder output for the CS+ increased during acquisition and decreased during extinction in networks traditionally implicated in human fear learning. The strength of US neural reactivation also predicted individuals skin conductance responses. Participants randomized to receive L-DOPA (n = 43) following extinction on day 1 demonstrated less US neural reactivation on day 2 relative to the placebo group (n = 28).
Conclusion
These results support neural reactivation as a measure of memory strength between competing memories of threat and safety and further demonstrate the role of dopaminergic neurotransmission in the consolidation of fear extinction memories.
Reinforcement learning (RL) is a computational framework for an active agent to learn behaviors on the basis of a scalar reward feedback. The theory of reinforcement learning was developed in the artificial intelligence community with intuitions from psychology and animal learning theory and mathematical basis in control theory. It has been successfully applied to tasks like game playing and robot control. Reinforcement learning gives a theoretical account of behavioral learning in humans and animals and underlying brain mechanisms, such as dopamine signaling and the basal ganglia circuit. Reinforcement learning serves as the “common language” for engineers, biologists, and cognitive scientists to exchange their problems and findings in goal-directed behaviors. This chapter introduces the basic theoretical framework of reinforcement learning and reviews its impacts in artificial intelligence, neuroscience, and cognitive science.
This chapter first reviews advanced methods in reinforcement learning (RL), namely, hierarchical RL, distributional RL, meta-RL, RL as inference, inverse RL, and multi-agent RL. Computational and cognitive models based on reinforcement learning are then presented, including detailed models of the basal ganglia, variety of dopamine neuron responses, roles of serotonin and other neuromodulators, intrinsic reward and motivation, neuroeconomics, and computational psychiatry.
Although learning was a key focus during the early years of mathematical psychology, the cognitive revolution of the 1960s caused the field to languish for several decades. Two breakthroughs in neuroscience resurrected the field. The first was the discovery of long-term potentiation and long-term depression, which served as promising models of learning at the cellular level. The second was the discovery that humans have multiple learning and memory systems that each require a qualitatively different kind of model. Currently, the field is well represented at all of Marr’s three levels of analysis. Descriptive and process models of human learning are dominated by two different, but converging, approaches – one rooted in Bayesian statistics and one based on popular machine-learning algorithms. Implementational models are in the form of neural networks that mimic known neuroanatomy and account for learning via biologically plausible models of synaptic plasticity. Models of all these types are reviewed, and advantages and disadvantages of the different approaches are considered.
Introduction to desire and how it relates to sex, including similarities and differences against other desires. Biology of desire and reward pathways in the brain. Reward transmitters such as dopamine and chemical messengers such as oxytocin.
Prior evidence indicates that negative symptom severity and cognitive deficits, in people with schizophrenia (PSZ), relate to measures of reward-seeking and loss-avoidance behavior (implicating the ventral striatum/VS), as well as uncertainty-driven exploration (reliant on rostrolateral prefrontal cortex/rlPFC). While neural correlates of reward-seeking and loss-avoidance have been examined in PSZ, neural correlates of uncertainty-driven exploration have not. Understanding neural correlates of uncertainty-driven exploration is an important next step that could reveal insights to how this mechanism of cognitive and negative symptoms manifest at a neural level.
Methods
We acquired fMRI data from 29 PSZ and 36 controls performing the Temporal Utility Integration decision-making task. Computational analyses estimated parameters corresponding to learning rates for both positive and negative reward prediction errors (RPEs) and the degree to which participates relied on representations of relative uncertainty. Trial-wise estimates of expected value, certainty, and RPEs were generated to model fMRI data.
Results
Behaviorally, PSZ demonstrated reduced reward-seeking behavior compared to controls, and negative symptoms were positively correlated with loss-avoidance behavior. This finding of a bias toward loss avoidance learning in PSZ is consistent with previous work. Surprisingly, neither behavioral measures of exploration nor neural correlates of uncertainty in the rlPFC differed significantly between groups. However, we showed that trial-wise estimates of relative uncertainty in the rlPFC distinguished participants who engaged in exploratory behavior from those who did not. rlPFC activation was positively associated with intellectual function.
Conclusions
These results further elucidate the nature of reinforcement learning and decision-making in PSZ and healthy volunteers.
This chapter introduces machine learning in contemporary artificial intelligence. The first section looks at an expert system developed in the early days of AI research – ID3, which employs a decision-tree-based algorithm. The second section looks at advances in deep learning, which has transformed modern machine learning. We introduce a deep learning model inspired by the mammalian visual system, illustrating how it can extract hierarchical information from the raw data. The third section addresses two examples of neural networks -- autoencoders and convolutional neural networks, which can feature in layers of deep learning networks. The last section looks at a distinct type of machine learning -- reinforcement learning. We explain how deep reinforcement learning has made possible the two most spectacular milestones in artificial intelligence - AlphaGo and AlphaGo Zero.
Background Urban birth, urban living, and ethnic minority status are established risk factors for schizophrenia, but the mechanisms are unclear. Previous evidence suggests a causal role of social exposures and adverse experiences, but experimental evidence is scarce. Methods We combine multimodal neuroimaging with ecological momentary assessment, geolocation and geospatial analysis in an epidemiological longitudinal sample in Germany. Results We find that established risk factors converge on the perigenual cingulate-amygdala-ventral striatal pathway as shown by structural and functional imaging, supporting a role for the ventral-striatal system in psychosis risk. Using a combination of PET and fMRI data in migrants, we suggest a mechanistic link to psychosis by increased dopamine release and synthesis in striatum secondary to prefrontal dysregulation. Importantly, the regulatory system identified overlaps with that implicated in racial stereotyping and prejudice. Moreover, an experiment measuring information flow during an exchange between migrants and non-migrants indicates that during a trust interaction, cultural distance governs the exchange. Conclusions This work shows a convergent risk circuit related to minority position and migration that could guide primary prevention of schizophrenia through reduction of manifestation risk by contextual intervention.
Although antipsychotics were discovered over fifty years ago, it took another decade until dopamine antagonism was demonstrated as central to their clinical effectiveness. Since accumulated evidence implicates the dopamine system in the pathophysiology of schizophrenia, all licensed first-line treatments operate primarily via antagonism of the dopamine D2 receptor. However, dopamine D2 receptor blockade does not effectively treat negative, cognitive and affective symptoms and, in a significant proportion of patients, it does not improve positive symptoms either. Therefore, additional neurochemical targets were considered. The “revised dopamine hypothesis” proposes that positive symptoms emerge due to hyperactive dopamine transmission in mesolimbic areas, while hypoactive dopamine transmission via the mesocortical pathway in the prefrontal cortex is linked to negative, cognitive, and partly affective symptoms. In this context, the role of D3 receptors were recognised. However, there is also evidence for the involvement of other neurotransmitter systems, suggesting that dopamine signalling relies on a suite of receptors that are thought to either facilitate or inhibit neurotransmitter activity through several interconnected neural circuits. Furthermore, there seem to be clusters of symptoms that cross the boundaries of disorders. Symptoms having similar pathophysiology at neurotransmitter level can be treated with the same drug or class of drugs. Thus, one particular drug might be effective in more than one indication. This lecture aims to illustrate the process of a new drug development by explaining how the underlying pathophysiology on receptor level impacts clinical studies and vice versa.