Machine Learning Identifies Large-Scale Reward-Related Activity Modulated by Dopaminergic Enhancement in Major Depression

Brain-based graph-theoretical predictive modeling to map the trajectory of anhedonia, impulsivity, and hypomania from the human functional connectome

Clinical assessments often fail to discriminate between unipolar and bipolar depression and identify individuals who will develop future (hypo)manic episodes. To address this challenge, we developed a brain-based graph-theoretical predictive model (GPM) to prospectively map symptoms of anhedonia, impulsivity, and (hypo)mania. Individuals seeking treatment for mood disorders (n = 80) underwent an fMRI scan, including (i) resting-state and (ii) a reinforcement-learning (RL) task. Symptoms were assessed at baseline as well as at 3- and 6-month follow-ups. A whole-brain functional connectome was computed for each fMRI task, and the GPM was applied for symptom prediction using cross-validation. Prediction performance was evaluated by comparing the GPM to a corresponding null model. In addition, the GPM was compared to the connectome-based predictive modeling (CPM). Cross-sectionally, the GPM predicted anhedonia from the global efficiency (a graph theory metric that quantifies information transfer across the connectome) during the RL task, and impulsivity from the centrality (a metric that captures the importance of a region) of the left anterior cingulate cortex during resting-state. At 6-month follow-up, the GPM predicted (hypo)manic symptoms from the local efficiency of the left nucleus accumbens during the RL task and anhedonia from the centrality of the left caudate during resting-state. Notably, the GPM outperformed the CPM, and GPM derived from individuals with unipolar disorders predicted anhedonia and impulsivity symptoms for individuals with bipolar disorders. Importantly, the generalizability of cross-sectional models was demonstrated in an external validation sample. Taken together, across DSM mood diagnoses, efficiency and centrality of the reward circuit predicted symptoms of anhedonia, impulsivity, and (hypo)mania, cross-sectionally and prospectively. The GPM is an innovative modeling approach that may ultimately inform clinical prediction at the individual level.

Effects of a single dose of amisulpride on functional brain changes during reward- and motivation-related processing using task-based fMRI in healthy subjects and patients with major depressive disorder - study protocol for a randomized clinical trial

BACKGROUND

Anhedonia and other deficits in reward- and motivation-related processing in psychiatric patients, including patients with major depressive disorder (MDD), represent a high unmet medical need. Neurobiologically, these deficits in MDD patients are mainly associated with low dopamine function in a frontostriatal network. In this study, alterations in brain activation changes during reward processing and at rest in MDD patients compared with healthy subjects are explored and the effects of a single low dose of the dopamine D2 receptor antagonist amisulpride are investigated.

METHODS

This is a randomized, controlled, double-blind, single-dose, single-center parallel-group clinical trial to assess the effects of a single dose of amisulpride (100 mg) on blood-oxygenation-level-dependent (BOLD) responses during reward- and motivation-related processing in healthy subjects (n = 60) and MDD patients (n = 60). Using functional magnetic resonance imaging (fMRI), BOLD responses are assessed during the monetary incentive delay (MID) task (primary outcome). Exploratory outcomes include BOLD responses and behavioral measures during the MID task, instrumental learning task, effort-based decision-making task, social incentive delay task, and probabilistic reward task as well as changes in resting state functional connectivity and cerebral blood flow.

DISCUSSION

This study broadly covers all aspects of reward- and motivation-related processing as categorized by the National Institute of Mental Health Research Domain Criteria and is thereby an important step towards precision psychiatry. Results regarding the immediate effects of a dopaminergic drug on deficits in reward- and motivation-related processing not only have the potential to significantly broaden our understanding of underlying neurobiological processes but might eventually also pave the way for new treatment options.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05347199. April 12, 2022.

A narrative review of non-racemic amisulpride (SEP-4199) for treatment of depressive symptoms in bipolar disorder and LB-102 for treatment of schizophrenia

INTRODUCTION

The challenges posed by treatment-resistant schizophrenia and depressive symptoms have led to ongoing difficulties despite the availability of antipsychotics and antidepressants. This review addresses the potential of amisulpride analogs, particularly SEP-4199, in addressing these challenges through enhanced efficacy and reduced side effects.

AREAS COVERED

This review focuses on the pharmacological profile of amisulpride analogs, exemplified by LB-102 and its derivative SEP-4199. PubMed gathered articles (up to 10 March 2023) on 'amisulpride,' 'schizophrenia,' 'bipolar disorder,' and 'major depressive disorder;' ClinicalTrials.gov tracked SEP-4199 and LB-102 trials. LB-102, a newly identified N-methylated analog of amisulpride, exhibits enhanced lipophilicity at lower doses, as demonstrated in a phase 1 study, indicating significant promise for therapeutic applications. The discovery of SEP-4199, a non-racemic analog composed of R- and S-enantiomers in an 85:15 ratio, is discussed, emphasizing its potential to enhance antidepressant effects while minimizing extrapyramidal side effects via selective D2 receptor binding. Recent phase 2 trials have demonstrated SEP-4199's efficacy in treating depressive symptoms in bipolar disorder I, capitalizing on D2-mediated anti-anhedonic and D3-mediated reward effects.

EXPERT OPINION

The development of SEP-4199 presents a potential breakthrough for managing depressive symptoms in bipolar disorder I. Further exploration of D2 and D3 receptor-mediated effects could lead to improved treatment strategies.

Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review

BACKGROUND

Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges.

OBJECTIVE

This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality.

METHODS

A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided.

RESULTS

A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126).

CONCLUSIONS

These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.

Neurofunctional mapping of reward anticipation and outcome for major depressive disorder: a voxel-based meta-analysis

Aberrations in how people form expectations about rewards and how they respond to receiving rewards are thought to underlie major depressive disorder (MDD). However, the underlying mechanism linking the appetitive reward system, specifically anticipation and outcome, is still not fully understood. To examine the neural correlates of monetary anticipation and outcome in currently depressed subjects with MDD, we performed two separate voxel-wise meta-analyses of functional neuroimaging studies using the monetary incentive delay task. During reward anticipation, the depressed patients exhibited an increased response in the bilateral middle cingulate cortex (MCC) extending to the anterior cingulate cortex, the medial prefrontal cortex, the left inferior frontal gyrus (IFG), and the postcentral gyrus, but a reduced response in the mesolimbic circuit, including the left striatum, insula, amygdala, right cerebellum, striatum, and IFG, compared to controls. During the outcome stage, MDD showed higher activity in the left inferior temporal gyrus, and lower activity in the mesocortical pathway, including the bilateral MCC, left caudate nucleus, precentral gyrus, thalamus, cerebellum, right striatum, insula, IFG, middle frontal gyrus, and temporal pole. Our findings suggest that cMDD may be characterised by state-dependent hyper-responsivity in cortical regions during the anticipation phase, and hypo-responsivity of the mesocortico-limbic circuit across the two phases of the reward response. Our study showed dissociable neural circuit responses to monetary stimuli during reward anticipation and outcome, which help to understand the dysfunction in different aspects of reward processing, particularly motivational v. hedonic deficits in depression.

Personalized Diagnosis and Treatment for Neuroimaging in Depressive Disorders

Depressive disorders are highly heterogeneous in nature. Previous studies have not been useful for the clinical diagnosis and prediction of outcomes of major depressive disorder (MDD) at the individual level, although they provide many meaningful insights. To make inferences beyond group-level analyses, machine learning (ML) techniques can be used for the diagnosis of subtypes of MDD and the prediction of treatment responses. We searched PubMed for relevant studies published until December 2021 that included depressive disorders and applied ML algorithms in neuroimaging fields for depressive disorders. We divided these studies into two sections, namely diagnosis and treatment outcomes, for the application of prediction using ML. Structural and functional magnetic resonance imaging studies using ML algorithms were included. Thirty studies were summarized for the prediction of an MDD diagnosis. In addition, 19 studies on the prediction of treatment outcomes for MDD were reviewed. We summarized and discussed the results of previous studies. For future research results to be useful in clinical practice, ML enabling individual inferences is important. At the same time, there are important challenges to be addressed in the future.

Edible Mushrooms as a Potential Component of Dietary Interventions for Major Depressive Disorder

Dietary interventions for people suffering from major depressive disorder (MDD) are an ongoing field of research. In this article, we present a comprehensive background for understanding the possibility of using edible medicinal mushrooms as an adjunctive treatment for MDD. We start with a brief history of MDD, its diagnosis, epidemiology and treatment, and the effects of diet on depression symptoms, followed by a review of neurobiological, behavioral, and clinical studies of medicinal mushrooms. We specifically highlight the results of preclinical and clinical studies on dietary supplementation with three selected mushroom species: Lion’s mane (Hericium erinaceus), Caterpillar mushroom (Cordyceps militaris), and Lingzhi/Reishi (Ganoderma lucidum). Preliminary small-sample clinical studies suggest that Lion’s mane can influence well-being of humans. In the case of Reishi, the results of clinical studies are equivocal, while in the case of Caterpillar Mushroom, such studies are underway. Edible mushrooms contain 5-hydroxy-L-tryptophan (5-HTP), which is a direct precursor of serotonin—a neurotransmitter targeted in pharmacotherapy of MDD. Therefore, in light of the well-recognized role of stress as a pathogenic factor of MDD, we also describe the neurobiological mechanisms of the interaction between stress and serotonergic neurotransmission; and summarize the current state of knowledge on dietary supplementation with 5-HTP in MDD.

Influences of dopaminergic system dysfunction on late-life depression

Deficits in cognition, reward processing, and motor function are clinical features relevant to both aging and depression. Individuals with late-life depression often show impairment across these domains, all of which are moderated by the functioning of dopaminergic circuits. As dopaminergic function declines with normal aging and increased inflammatory burden, the role of dopamine may be particularly salient for late-life depression. We review the literature examining the role of dopamine in the pathogenesis of depression, as well as how dopamine function changes with aging and is influenced by inflammation. Applying a Research Domain Criteria (RDoC) Initiative perspective, we then review work examining how dopaminergic signaling affects these domains, specifically focusing on Cognitive, Positive Valence, and Sensorimotor Systems. We propose a unified model incorporating the effects of aging and low-grade inflammation on dopaminergic functioning, with a resulting negative effect on cognition, reward processing, and motor function. Interplay between these systems may influence development of a depressive phenotype, with an initial deficit in one domain reinforcing decline in others. This model extends RDoC concepts into late-life depression while also providing opportunities for novel and personalized interventions.

The ups and downs of relating nondrug reward activation to substance use risk in adolescents

Purpose of review

A wealth of epidemiological and cohort research, together with a healthy dose of anecdote, has characterized late-adolescence and emerging adulthood as a time of increased substance use and other risky behaviors. This review will address whether differences between adolescents or between adolescents and other age groups in dopaminergic mesolimbic recruitment by (non-drug) rewards inferred from functional magnetic resonance imaging (fMRI) could partially explain morbidity and mortality from risky-behavior-related causes in adolescents.

Recent findings

Recent findings do not suggest a definitive directionality with regard to whether increased vs decreased mesolimbic responsiveness to nondrug rewards correlates with real-world risk-taking. Inconsistent relationships between reward-activation and real-world risky behavior in these reports reflect in part methodological differences as well as conceptual differences between populations in terms of whether tepid mesolimbic recruitment by rewards is a marker of psychiatric health.

Summary

There are several potential reasons why the directionality of relationships between reward-elicited brain activation and substance use risk (specifically) might differ. These factors include differences between adolescents in histories/exposure of substance use, motivation for substance use, the component of the instrumental behavior being studied, and the cognitive demands of the incentive tasks. Systematic manipulation of these discrepant study factors might offer a way forward to clarify how motivational neurocircuit function relates to addiction risk in adolescents.

Artificial intelligence for brain diseases: A systematic review

Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications. A systematic and careful literature search in major databases such as Pubmed, Scopus, and Web of Science was carried out using "artificial intelligence" and "brain" as main keywords. Further references were integrated by cross-referencing from key articles. 155 studies out of 2696 were identified, which actually made use of AI algorithms for different purposes (diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment). Artificial neural networks have risen to prominent positions among the most widely used analytical tools. Classic machine learning approaches such as support vector machine and random forest are still widely used. Task-specific algorithms are designed for solving specific problems. Brain images are one of the most used data types. AI has the possibility to improve clinicians' decision-making ability in neuroscience applications. However, major issues still need to be addressed for a better practical use of AI in the brain. To this aim, it is important to both gather comprehensive data and build explainable AI algorithms.