The Role of Humanization and Robustness of Large Language Models in Conversational Artificial Intelligence for Individuals With Depression: A Critical Analysis.

Unlabelled: Large language model (LLM)-powered services are gaining popularity in various applications due to their exceptional performance in many tasks, such as sentiment analysis and answering questions. Recently, research has been exploring their potential use in digital health contexts, particularly in the mental health domain. However, implementing LLM-enhanced conversational artificial intelligence (CAI) presents significant ethical, technical, and clinical challenges. In this viewpoint paper, we discuss 2 challenges that affect the use of LLM-enhanced CAI for individuals with mental health issues, focusing on the use case of patients with depression: the tendency to humanize LLM-enhanced CAI and their lack of contextualized robustness. Our approach is interdisciplinary, relying on considerations from philosophy, psychology, and computer science. We argue that the humanization of LLM-enhanced CAI hinges on the reflection of what it means to simulate "human-like" features with LLMs and what role these systems should play in interactions with humans. Further, ensuring the contextualization of the robustness of LLMs requires considering the specificities of language production in individuals with depression, as well as its evolution over time. Finally, we provide a series of recommendations to foster the responsible design and deployment of LLM-enhanced CAI for the therapeutic support of individuals with depression.

Considering the Role of Human Empathy in AI-Driven Therapy.

Recent breakthroughs in artificial intelligence (AI) language models have elevated the vision of using conversational AI support for mental health, with a growing body of literature indicating varying degrees of efficacy. In this paper, we ask when, in therapy, it will be easier to replace humans and, conversely, in what instances, human connection will still be more valued. We suggest that empathy lies at the heart of the answer to this question. First, we define different aspects of empathy and outline the potential empathic capabilities of humans versus AI. Next, we consider what determines when these aspects are needed most in therapy, both from the perspective of therapeutic methodology and from the perspective of patient objectives. Ultimately, our goal is to prompt further investigation and dialogue, urging both practitioners and scholars engaged in AI-mediated therapy to keep these questions and considerations in mind when investigating AI implementation in mental health.

Usability Comparison Among Healthy Participants of an Anthropomorphic Digital Human and a Text-Based Chatbot as a Responder to Questions on Mental Health: Randomized Controlled Trial.

BACKGROUND: The use of chatbots in mental health support has increased exponentially in recent years, with studies showing that they may be effective in treating mental health problems. More recently, the use of visual avatars called digital humans has been introduced. Digital humans have the capability to use facial expressions as another dimension in human-computer interactions. It is important to study the difference in emotional response and usability preferences between text-based chatbots and digital humans for interacting with mental health services. OBJECTIVE: This study aims to explore to what extent a digital human interface and a text-only chatbot interface differed in usability when tested by healthy participants, using BETSY (Behavior, Emotion, Therapy System, and You) which uses 2 distinct interfaces: a digital human with anthropomorphic features and a text-only user interface. We also set out to explore how chatbot-generated conversations on mental health (specific to each interface) affected self-reported feelings and biometrics. METHODS: We explored to what extent a digital human with anthropomorphic features differed from a traditional text-only chatbot regarding perception of usability through the System Usability Scale, emotional reactions through electroencephalography, and feelings of closeness. Healthy participants (n=45) were randomized to 2 groups that used a digital human with anthropomorphic features (n=25) or a text-only chatbot with no such features (n=20). The groups were compared by linear regression analysis and t tests. RESULTS: No differences were observed between the text-only and digital human groups regarding demographic features. The mean System Usability Scale score was 75.34 (SD 10.01; range 57-90) for the text-only chatbot versus 64.80 (SD 14.14; range 40-90) for the digital human interface. Both groups scored their respective chatbot interfaces as average or above average in usability. Women were more likely to report feeling annoyed by BETSY. CONCLUSIONS: The text-only chatbot was perceived as significantly more user-friendly than the digital human, although there were no significant differences in electroencephalography measurements. Male participants exhibited lower levels of annoyance with both interfaces, contrary to previously reported findings.

Generation of a Deep Mouse Brain Spectral Library for Transmembrane Proteome Profiling in Mental Disease Models.

Transmembrane (TM) proteins constitute over 30% of the mammalian proteome and play essential roles in mediating cell-cell communication, synaptic transmission, and plasticity in the central nervous system. Many of these proteins, especially the G protein-coupled receptors (GPCRs), are validated or candidate drug targets for therapeutic development for mental diseases, yet their expression profiles are underrepresented in most global proteomic studies. Herein, we establish a brain TM protein-enriched spectral library based on 136 data-dependent acquisition runs acquired from various brain regions of both naïve mice and mental disease models. This spectral library comprises 3043 TM proteins including 171 GPCRs, 231 ion channels, and 598 transporters. Leveraging this library, we analyzed the data-independent acquisition data from different brain regions of two mouse models exhibiting depression- or anxiety-like behaviors. By integrating multiple informatics workflows and library sources, our study significantly expanded the mental stress-perturbed TM proteome landscape, from which a new GPCR regulator of depression was verified by in vivo pharmacological testing. In summary, we provide a high-quality mouse brain TM protein spectral library to largely increase the TM proteome coverage in specific brain regions, which would catalyze the discovery of new potential drug targets for the treatment of mental disorders.

Assessing the Alignment of Large Language Models With Human Values for Mental Health Integration: Cross-Sectional Study Using Schwartz's Theory of Basic Values.

BACKGROUND: Large language models (LLMs) hold potential for mental health applications. However, their opaque alignment processes may embed biases that shape problematic perspectives. Evaluating the values embedded within LLMs that guide their decision-making have ethical importance. Schwartz's theory of basic values (STBV) provides a framework for quantifying cultural value orientations and has shown utility for examining values in mental health contexts, including cultural, diagnostic, and therapist-client dynamics. OBJECTIVE: This study aimed to (1) evaluate whether the STBV can measure value-like constructs within leading LLMs and (2) determine whether LLMs exhibit distinct value-like patterns from humans and each other. METHODS: In total, 4 LLMs (Bard, Claude 2, Generative Pretrained Transformer [GPT]-3.5, GPT-4) were anthropomorphized and instructed to complete the Portrait Values Questionnaire-Revised (PVQ-RR) to assess value-like constructs. Their responses over 10 trials were analyzed for reliability and validity. To benchmark the LLMs' value profiles, their results were compared to published data from a diverse sample of 53,472 individuals across 49 nations who had completed the PVQ-RR. This allowed us to assess whether the LLMs diverged from established human value patterns across cultural groups. Value profiles were also compared between models via statistical tests. RESULTS: The PVQ-RR showed good reliability and validity for quantifying value-like infrastructure within the LLMs. However, substantial divergence emerged between the LLMs' value profiles and population data. The models lacked consensus and exhibited distinct motivational biases, reflecting opaque alignment processes. For example, all models prioritized universalism and self-direction, while de-emphasizing achievement, power, and security relative to humans. Successful discriminant analysis differentiated the 4 LLMs' distinct value profiles. Further examination found the biased value profiles strongly predicted the LLMs' responses when presented with mental health dilemmas requiring choosing between opposing values. This provided further validation for the models embedding distinct motivational value-like constructs that shape their decision-making. CONCLUSIONS: This study leveraged the STBV to map the motivational value-like infrastructure underpinning leading LLMs. Although the study demonstrated the STBV can effectively characterize value-like infrastructure within LLMs, substantial divergence from human values raises ethical concerns about aligning these models with mental health applications. The biases toward certain cultural value sets pose risks if integrated without proper safeguards. For example, prioritizing universalism could promote unconditional acceptance even when clinically unwise. Furthermore, the differences between the LLMs underscore the need to standardize alignment processes to capture true cultural diversity. Thus, any responsible integration of LLMs into mental health care must account for their embedded biases and motivation mismatches to ensure equitable delivery across diverse populations. Achieving this will require transparency and refinement of alignment techniques to instill comprehensive human values.

[Get used to digital therapeutics (DTx) : they will take the scene in health care !] / La médecine du futur. Habituez-vous aux moyens de traitements digitaux : ils arrivent !

Digital therapeutics are making headways in medical treatment at large. They are used alone or in combination with standard medical treatment in various fields, aiming at essentially behavioral changes. As we are facing a growing imbalance between demand and resources, these digital treatments offer a powerful way for patient empowerment.

Les traitements numériques font irruption dans l'arsenal thérapeutique. Ils sont utilisés seuls ou en association avec des approches médicamenteuses classiques. Ils visent souvent des changements comportementaux dont on souligne de plus en plus l'importance dans différents domaines de la santé. Vu l'augmentation significative des besoins en soins de santé, dans un contexte tendu de limitations de ressources, ils offrent un moyen d'autonomisation de la prise en charge par les patients.

Capacity of Generative AI to Interpret Human Emotions From Visual and Textual Data: Pilot Evaluation Study.

BACKGROUND: Mentalization, which is integral to human cognitive processes, pertains to the interpretation of one's own and others' mental states, including emotions, beliefs, and intentions. With the advent of artificial intelligence (AI) and the prominence of large language models in mental health applications, questions persist about their aptitude in emotional comprehension. The prior iteration of the large language model from OpenAI, ChatGPT-3.5, demonstrated an advanced capacity to interpret emotions from textual data, surpassing human benchmarks. Given the introduction of ChatGPT-4, with its enhanced visual processing capabilities, and considering Google Bard's existing visual functionalities, a rigorous assessment of their proficiency in visual mentalizing is warranted. OBJECTIVE: The aim of the research was to critically evaluate the capabilities of ChatGPT-4 and Google Bard with regard to their competence in discerning visual mentalizing indicators as contrasted with their textual-based mentalizing abilities. METHODS: The Reading the Mind in the Eyes Test developed by Baron-Cohen and colleagues was used to assess the models' proficiency in interpreting visual emotional indicators. Simultaneously, the Levels of Emotional Awareness Scale was used to evaluate the large language models' aptitude in textual mentalizing. Collating data from both tests provided a holistic view of the mentalizing capabilities of ChatGPT-4 and Bard. RESULTS: ChatGPT-4, displaying a pronounced ability in emotion recognition, secured scores of 26 and 27 in 2 distinct evaluations, significantly deviating from a random response paradigm (P<.001). These scores align with established benchmarks from the broader human demographic. Notably, ChatGPT-4 exhibited consistent responses, with no discernible biases pertaining to the sex of the model or the nature of the emotion. In contrast, Google Bard's performance aligned with random response patterns, securing scores of 10 and 12 and rendering further detailed analysis redundant. In the domain of textual analysis, both ChatGPT and Bard surpassed established benchmarks from the general population, with their performances being remarkably congruent. CONCLUSIONS: ChatGPT-4 proved its efficacy in the domain of visual mentalizing, aligning closely with human performance standards. Although both models displayed commendable acumen in textual emotion interpretation, Bard's capabilities in visual emotion interpretation necessitate further scrutiny and potential refinement. This study stresses the criticality of ethical AI development for emotional recognition, highlighting the need for inclusive data, collaboration with patients and mental health experts, and stringent governmental oversight to ensure transparency and protect patient privacy.

Recent advances in anxiety disorders: Focus on animal models and pathological mechanisms.

Anxiety disorders have become one of the most severe psychiatric disorders, and the incidence is increasing every year. They impose an extraordinary personal and socioeconomic burden. Anxiety disorders are influenced by multiple complex and interacting genetic, psychological, social, and environmental factors, which contribute to disruption or imbalance in homeostasis and eventually cause pathologic anxiety. The selection of a suitable animal model is important for the exploration of disease etiology and pathophysiology, and the development of new drugs. Therefore, a more comprehensive understanding of the advantages and limitations of existing animal models of anxiety disorders is helpful to further study the underlying pathological mechanisms of the disease. This review summarizes animal models and the pathogenesis of anxiety disorders, and discusses the current research status to provide insights for further study of anxiety disorders.

Kombucha: Challenges for Health and Mental Health.

BACKGROUND: Increasing research into probiotics is showing potential benefits for health in general and mental health in particular. Kombucha is a recent beverage and can be considered a probiotic drink, but little is known about its effects on physical and mental health. This product is experiencing growth in the market; however, there are no scientific results to support its potential for physical and mental health. AIM: This review article aims to draw attention to this issue and to highlight the lack of studies in this area. KEY FINDINGS AND CONCLUSIONS: The lack of legislation for the correct marketing of this product may also constrain clinical studies. However, clinical studies are of utmost importance for an in-depth understanding of the effects of this product on the human body. More research is needed, not only to better understand the impact of Kombucha on the human body, but also to ensure the application of regulatory guidelines for its production and marketing and enable its safe and effective consumption.

Current development of biosensing technologies towards diagnosis of mental diseases.

The biosensor is an instrument that converts the concentration of biomarkers into electrical signals for detection. Biosensing technology is non-invasive, lightweight, automated, and biocompatible in nature. These features have significantly advanced medical diagnosis, particularly in the diagnosis of mental disorder in recent years. The traditional method of diagnosing mental disorders is time-intensive, expensive, and subject to individual interpretation. It involves a combination of the clinical experience by the psychiatrist and the physical symptoms and self-reported scales provided by the patient. Biosensors on the other hand can objectively and continually detect disease states by monitoring abnormal data in biomarkers. Hence, this paper reviews the application of biosensors in the detection of mental diseases, and the diagnostic methods are divided into five sub-themes of biosensors based on vision, EEG signal, EOG signal, and multi-signal. A prospective application in clinical diagnosis is also discussed.

Mendelian randomization analysis does not reveal a causal influence of mental diseases on osteoporosis.

Introduction: Osteoporosis (OP) is primarily diagnosed through bone mineral density (BMD) measurements, and it often leads to fracture. Observational studies suggest that several mental diseases (MDs) may be linked to OP, but the causal direction of these associations remain unclear. This study aims to explore the potential causal association between five MDs (Schizophrenia, Depression, Alzheimer's disease, Parkinson's disease, and Epilepsy) and the risk of OP. Methods: First, single-nucleotide polymorphisms (SNPs) were filtered from summary-level genome-wide association studies using quality control measures. Subsequently, we employed two-sample Mendelian randomization (MR) analysis to indirectly analyze the causal effect of MDs on the risk of OP through bone mineral density (in total body, femoral neck, lumbar spine, forearm, and heel) and fractures (in leg, arm, heel, spine, and osteoporotic fractures). Lastly, the causal effect of the MDs on the risk of OP was evaluated directly through OP. MR analysis was performed using several methods, including inverse variance weighting (IVW)-random effects, IVW-fixed effects, maximum likelihood, weighted median, MR-Egger regression, and penalized weighted median. Results: The results did not show any evidence of a causal relationship between MDs and the risk of OP (with almost all P values > 0.05). The robustness of the above results was proved to be good. Discussion: In conclusion, this study did not find evidence supporting the claim that MDs have a definitive impact on the risk of OP, which contradicts many existing observational reports. Further studies are needed to determine the potential mechanisms of the associations observed in observational studies.

Brain Network Topology in Deficit and Non-Deficit Schizophrenia: Application of Graph Theory to Local and Global Indices.

Patients with deficit schizophrenia (SZD) suffer from primary and enduring negative symptoms. Limited pieces of evidence and neuroimaging studies indicate they differ from patients with non-deficit schizophrenia (SZND) in neurobiological aspects, but the results are far from conclusive. We applied for the first time, graph theory analyses to discriminate local and global indices of brain network topology in SZD and SZND patients compared with healthy controls (HC). High-resolution T1-weighted images were acquired for 21 SZD patients, 21 SZND patients, and 21 HC to measure cortical thickness from 68 brain regions. Graph-based metrics (i.e., centrality, segregation, and integration) were computed and compared among groups, at both global and regional networks. When compared to HC, at the regional level, SZND were characterized by temporoparietal segregation and integration differences, while SZD showed widespread alterations in all network measures. SZD also showed less segregated network topology at the global level in comparison to HC. SZD and SZND differed in terms of centrality and integration measures in nodes belonging to the left temporoparietal cortex and to the limbic system. SZD is characterized by topological features in the network architecture of brain regions involved in negative symptomatology. Such results help to better define the neurobiology of SZD (SZD: Deficit Schizophrenia; SZND: Non-Deficit Schizophrenia; SZ: Schizophrenia; HC: healthy controls; CC: clustering coefficient; L: characteristic path length; E: efficiency; D: degree; CCnode: CC of a node; CCglob: the global CC of the network; Eloc: efficiency of the information transfer flow either within segregated subgraphs or neighborhoods nodes; Eglob: efficiency of the information transfer flow among the global network; FDA: Functional Data Analysis; and Dmin: estimated minimum densities).

Paraburkholderia sabiae administration alters zebrafish anxiety-like behavior via gut microbial taurine metabolism.

Interventions to the gut microbiome manipulate the gut-brain axis and could be useful in the treatment of anxiety and depression. In this study, we demonstrated that administration of the bacterium Paraburkholderia sabiae reduces anxiety-like behavior in adult zebrafish. P. sabiae administration increased the diversity of the zebrafish gut microbiome. Linear discriminant analysis Effect Size (LEfSe) analysis revealed that the populations of Actinomycetales including Nocardiaceae, Nocardia, Gordoniaceae, Gordonia, Nakamurellaceae, and Aeromonadaceae were reduced, whereas those of Rhizobiales including Xanthobacteraceae, Bradyrhizobiaceae, Rhodospirillaceae, and Pirellulaceae were increased in the gut microbiome. Functional analysis using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) predicted that P. sabiae administration altered taurine metabolism in the zebrafish gut, and we demonstrated that P. sabiae administration increased the taurine concentration in the brain. Since taurine functions as an antidepressant neurotransmitter in vertebrates, our results suggest that P. sabiae could improve anxiety-like behavior in zebrafish via the gut-brain axis.

Estimations of the number people with mental diseases associated with toxoplasmosis and identification of risk factors by continent.

Strong evidence exists based on metanalysis of the relationship between toxoplasmosis and many psychiatric diseases: schizophrenia, bipolar disorder, and suicidal behavior. Herein, we estimate the number of cases based on the attributable fraction due to toxoplasmosis on these diseases. The population attributable fraction of mental disease associated with toxoplasmosis was 20,4% for schizophrenia; 27,3% for bipolar disorder; and 0,29% for suicidal behavior (self-harm). The lower and upper estimated number of people with mental disease associated with toxoplasmosis was 4'816.491 and 5'564.407 for schizophrenia; 6'348.946 and 7'510.118,82 for bipolar disorder; and 24.310 and 28.151 for self-harm; for a global total lower estimated number of 11'189.748 and global total upper estimated number of 13'102.678 people with mental disease associated with toxoplasmosis for the year 2019. According to the prediction through the Bayesian model of risk factors for toxoplasmosis associated with mental disease, these varied in importance geographically; thus, in Africa, the most important risk factor was water contamination and in the European region, the cooking conditions of meats. Toxoplasmosis and mental health should be a research priority given the enormous potential impact of reducing this parasite in the general population.

Why are we still living in the past? Sri Lanka needs urgent and timely reforms of its archaic mental health laws.

Mental health legislation protects the rights of people with mental illnesses. However, despite major social, political and cultural changes, Sri Lankan mental health services still operate on laws enacted mostly during the British rule more than a century ago, in the pre-psychotropics era, and focusing more on the detention of people with mental illnesses than on their treatment. It is high time all stakeholders made efforts for the much-awaited new Mental Health Act to pass through parliament urgently to meet the needs and protect the rights of patients, their caregivers and service providers.

Young Adults and Sick Leave Length of Mental Illnesses.

Objectives: The objective was to explore whether a sick leave length related to mental morbidity differs across different occupational categories. Methods: In the analysis, registry of sick leaves was analyzed. Provided analysis is focused on the length of sick leaves related to mental diseases caused by substance use or other factors. Dependent variable is the sick leave length, and the independent variables are the categories of disease and occupation. Kruskal-Wallis test, Shapiro-Wilk test, and Brown-Forsythe (B-F) are used. Results: There are differences in mental sick leave lengths caused by substance use or other factors. In the case of mental illnesses attributable to drugs, differences in the sick leave duration among different working groups were not found. Considering mental disorders caused by other factors, there are differences in the sick leave duration among different working groups. Conclusions: There is no evidence of longer sick leave in people diagnosed with mental disorder related to substance use. Differences in occupational categories do not relate to sick leave length.

Infections and mental diseases: from tuberculosis to COVID-19. / Infekcje a choroby psychiczne: od gruzlicy do COVID-19.

COVID-19 pandemics reactivated interest in the relationship between mental diseases and infectious factors. In this narrative review, such association for tuberculosis, syphilis, toxoplasmosis, influenza, and COVID-19 was discussed. A connection between tuberculosis and melancholia was postulated for several centuries. In the 1950s, an anti-tuberculosis drug, iproniazid, was found to exert an antidepressant effect. In the 20th century, it was demonstrated that psychiatric disturbances connected with syphilis can be treated by an inoculation of malaria, which initiated immunotherapy. Increased frequency of Toxoplasma gondi infections in psychiatric illnesses was found and higher risk of the illnesses following the infection in pregnancy. A more frequent incidence of schizophrenia in persons born during the influenza pandemic in the second half of the 20th century was evidenced. Mental disturbances can result from an ancient infection of the human genome by retroviruses. Infection during pregnancy can increase their risk in later life. Pathogenic can be also an infection in adult life. COVID-19 causes significant early and late consequences for mental health. Two-year pandemics observation brought data on the therapeutic action of psychotropic drugs on SARS-CoV-2. Despite previous data on the antiviral action of lithium, a significant effect of this ion of the prevalence and course on the COVID-19 was not confirmed.

The Role of AMPARs Composition and Trafficking in Synaptic Plasticity and Diseases.

AMPA receptors are tetrameric ionic glutamate receptors, which mediate 90% fast excitatory synaptic transmission induced by excitatory glutamate in the mammalian central nervous system through the activation or inactivation of ion channels. The alternation of synaptic AMPA receptor number and subtype is thought to be one of the primary mechanisms that involve in synaptic plasticity regulation and affect the functions in learning, memory, and cognition. The increasing of surface AMPARs enhances synaptic strength during long-term potentiation, whereas the decreasing of AMPARs weakens synaptic strength during the long-term depression. It is closely related to the AMPA receptor as well as its subunits assembly, trafficking, and degradation. The dysfunction of any step in these precise regulatory processes is likely to induce the disorder of synaptic transmission and loss of neurons, or even cause neuropsychiatric diseases ultimately. Therefore, it is useful to understand how AMPARs regulate synaptic plasticity and its role in related neuropsychiatric diseases via comprehending architecture and trafficking of the receptors. Here, we reviewed the progress in structure, expression, trafficking, and relationship with synaptic plasticity of AMPA receptor, especially in anxiety, depression, neurodegenerative disorders, and cerebral ischemia.

Research progress on excitotoxicity of glutamate and its modulators / 中国药理学通报

Excitotoxicity mainly refers to the toxic effect of the excitatory neurotransmitter glutamate.Long-term activation of glutamate receptors will cause a series of neurotoxicity, eventually leading to the loss of neuronal function and cell death.Triggering excitotoxicity may involve changes in glutamate and calcium metabolism, dysfunction of glutamate transporter, N-methyl-D-aspartate receptor(NMDAR)and mitochondria, and production of ROS.Therefore, we here review the occurrence of these mechanisms and introduce the pathological mechanism of glutamate excitotoxicity in Alzheimer's disease(AD), Parkinson's disease(PD), depression and epilepsy.Finally, this review briefly describes the regulatory effects of broad-spectrum glutamic satellite modulators, marine compounds and Chinese herbal medicines on the glutamatergic system.

Progress on correlation between intestinal flora and mental diseases / 中国药理学通报

The impact of intestinal flora on human and animal health and diseases has attracted much attention both at home and abroad in recent years. The intestinal flora constitutes the intestinal microecosystem and plays an important role in physiological activities such as nutrition, metabolism, growth and development, barrier protection, and immunity. In this article, the relationship between intestinal dysbiosis and psychiatric diseases has been reviewed from two aspects:metagenomic characterization of intestinal microflora diversity in neurological diseases and validation of the relationship between intestinal flora and psychiatric diseases by fecal bacteria transplantation in germ-free mice. In addition, the microbial-gut-brain axis theory has been proposed in recent years, which links the nerve-endocrine-immune system to form a two-way signaling pathway. Intestinal flora plays an important role in regulating the central nervous system by promoting neurotransmitter release, endocrine, and immunity. The system plays an important role. Changes in intestinal flora mainly affect the host's nervous system through vagus nerve pathways, endocrine pathways, immune pathways, etc, thereby triggering or aggravating depression, autism, Alzheimer's disease, schizophrenia, Parkinson's disease, etc. This article reviews the relationships between host-related neurological abnormalities, intestinal flora imbalance and mental diseases, and discusses the research methods, research progress, and mechanism of the correlation between intestinal flora imbalance and mental diseases to research progress on microbe-gut-brain axis.