Automated Speech Analysis in Bipolar Disorder: The CALIBER Study Protocol and Preliminary Results.

Background: Bipolar disorder (BD) involves significant mood and energy shifts reflected in speech patterns. Detecting these patterns is crucial for diagnosis and monitoring, currently assessed subjectively. Advances in natural language processing offer opportunities to objectively analyze them. Aims: To (i) correlate speech features with manic-depressive symptom severity in BD, (ii) develop predictive models for diagnostic and treatment outcomes, and (iii) determine the most relevant speech features and tasks for these analyses. Methods: This naturalistic, observational study involved longitudinal audio recordings of BD patients at euthymia, during acute manic/depressive phases, and after-response. Patients participated in clinical evaluations, cognitive tasks, standard text readings, and storytelling. After automatic diarization and transcription, speech features, including acoustics, content, formal aspects, and emotionality, will be extracted. Statistical analyses will (i) correlate speech features with clinical scales, (ii) use lasso logistic regression to develop predictive models, and (iii) identify relevant speech features. Results: Audio recordings from 76 patients (24 manic, 21 depressed, 31 euthymic) were collected. The mean age was 46.0 ± 14.4 years, with 63.2% female. The mean YMRS score for manic patients was 22.9 ± 7.1, reducing to 5.3 ± 5.3 post-response. Depressed patients had a mean HDRS-17 score of 17.1 ± 4.4, decreasing to 3.3 ± 2.8 post-response. Euthymic patients had mean YMRS and HDRS-17 scores of 0.97 ± 1.4 and 3.9 ± 2.9, respectively. Following data pre-processing, including noise reduction and feature extraction, comprehensive statistical analyses will be conducted to explore correlations and develop predictive models. Conclusions: Automated speech analysis in BD could provide objective markers for psychopathological alterations, improving diagnosis, monitoring, and response prediction. This technology could identify subtle alterations, signaling early signs of relapse. Establishing standardized protocols is crucial for creating a global speech cohort, fostering collaboration, and advancing BD understanding.

Central effects of acute intranasal insulin on neuroimaging, cognitive, and behavioural outcomes: A systematic review.

The distribution of insulin receptors throughout the brain implicates insulin in physiological functions and disease states, including cognition, appetite, mood, and metabolic disorders. Intranasally administered insulin offers a non-invasive approach for isolating and investigating brain insulin action. This systematic review synthesized the effects of acute intranasal insulin on neuroimaging, cognitive, and behavioural outcomes reported in 48 studies in adults. Age, sex, body mass index, and insulin resistance were found to moderate brain insulin action. Neuroimaging studies showed insulin affects brain activity, cerebral blood flow, and functional connectivity in regions like the hypothalamus, amygdala, and insula. Insulin also modified cognitive function, eating behaviour, and the stress response. Nonetheless, inconsistencies in study designs, dosages, and outcome measures necessitate standardized methodologies to better understand central insulin action. Taken together, insulin's ability to modify stress and fear, appetite and eating behaviour, and cognitive function in both healthy and diseased individuals highlight its potential in the therapeutic and mechanistic exploration of highly prevalent psychiatric, metabolic, and cognitive conditions like mood disorders, obesity, and Alzheimer's disease.

Post-traumatic stress and joint hypermobility in children and adolescents of Nepal after exposure to an earthquake.

A substantial body of literature has traditionally addressed the connection between the exposure to catastrophic events and the development of Post-Traumatic Stress disorder (PTSD), especially in the vulnerable stratum of children and adolescents. However, little is known about their biological predisposing factors, and further research is needed, especially in the context of the recent earthquakes in Turkey and Syria. The data of this study was collected 4 months after the 2015 earthquakes in Nepal, with the objective of providing new evidence to the field and documenting the role of a new potential predisposing factor: the Joint Hypermobility Syndrome (JHS). 941 subjects from three different regions of the country, aged 8-18 years, were assessed in a school-based cross-sectional investigation. PTSD, as the main response variable, was assessed using the Child PTSD Symptom Scale (CPSS) questionnaire and analysed considering three sub-dimensions: the severity of symptoms, the severity of impairment, and both taken together. JHS was assessed using the Screening Questionnaire to detect Hypermobility (SQ-CH) questionnaire. The severity of symptoms was strongly predicted by the distance to the epicentre. Females showed more severe symptomatology, but a lower perturbation in the daily functioning. Younger children reported a greater functional impairment. JHS group showed more severe PTSD than non-JHS group. We observed variability in the severity of PTSD according to previously known risk factors such as the distance to the epicentre, sex, and age. We also found an association between PTSD and JHS, which is discussed in reference to the neuroconnective endophenotype. It might be useful to consider the role of each variable when planning a mass intervention after a disaster.

Glucagon-like peptide 1 agonist and effects on reward behaviour: A systematic review.

INTRODUCTION: The roles of metabolic signals, including Glucagon-like peptide 1 (GLP-1), have been implicated in multiple domains outside metabolic regulation. There is a growing interest in repurposing Glucagon-like peptide 1 receptor agonists (GLP-1RAs) as therapeutics for motivation and reward-related behavioural disturbances. Herein, we aim to systematically review the extant evidence on the potential effects of GLP-1RAs on the reward system. METHODS: The study followed PRISMA guidelines using databases such as OVID, PubMed, Scopus, and Google Scholar. The search focused on "Reward Behavior" and "Glucagon Like Peptide 1 Receptor Agonists" and was restricted to human studies. Quality assessment achieved by the NIH's Quality Assessment of Controlled Intervention Studies RESULTS: GLP-1RAs consistently reduced energy intake and influenced reward-related behaviour. These agents have been associated with decreased neurocortical activation in response to higher rewards and food cues, particularly high-calorie foods, and lowered caloric intake and hunger levels. DISCUSSION: GLP-1RAs show promise in addressing reward dysfunction linked to food stimuli, obesity, and T2DM. They normalize insulin resistance, and might also modulate dopaminergic signalling and reduce anhedonia. Their effects on glycemic variability and cravings suggest potential applications in addiction disorders.

Lithium therapy and weight change in people with bipolar disorder: A systematic review and meta-analysis.

Lithium remains the gold standard maintenance treatment for Bipolar Disorder (BD). However, weight gain is a side effect of increasing relevance due to its metabolic implications. We conducted a systematic review and meta-analysis aimed at summarizing evidence on the use of lithium and weight change in BD. We followed the PRISMA methodology, searching Pubmed, Scopus and Web of Science. From 1003 screened references, 20 studies were included in the systematic review and 9 included in the meta-analysis. In line with the studies included in the systematic review, the meta-analysis revealed that weight gain with lithium was not significant, noting a weight increase of 0.462 Kg (p = 0158). A shorter duration of treatment was significantly associated with more weight gain. Compared to placebo, there were no significant differences in weight gain. Weight gain was significantly lower with lithium than with active comparators. This work reveals a low impact of lithium on weight change, especially compared to some of the most widely used active comparators. Our results could impact clinical decisions.

Biased accuracy in multisite machine-learning studies due to incomplete removal of the effects of the site.

Brain MRI researchers conducting multisite studies, such as within the ENIGMA Consortium, are very aware of the importance of controlling the effects of the site (EoS) in the statistical analysis. Conversely, authors of the novel machine-learning MRI studies may remove the EoS when training the machine-learning models but not control them when estimating the models' accuracy, potentially leading to severely biased estimates. We show examples from a toy simulation study and real MRI data in which we remove the EoS from both the "training set" and the "test set" during the training and application of the model. However, the accuracy is still inflated (or occasionally shrunk) unless we further control the EoS during the estimation of the accuracy. We also provide several methods for controlling the EoS during the estimation of the accuracy, and a simple R package ("multisite.accuracy") that smoothly does this task for several accuracy estimates (e.g., sensitivity/specificity, area under the curve, correlation, hazard ratio, etc.).

Psychiatric Clinical Profiles and Pharmacological Interactions in COVID-19 Inpatients Referred to a Consultation Liaison Psychiatry Unit: a Cross-Sectional Study.

The Coronavirus Disease 2019 (COVID-19) can affect mental health in different ways. There is little research about psychiatric complications in hospitalized patients with COVID-19. The aim of the study was to describe the psychiatric clinical profile and pharmacological interactions in COVID-19 inpatients referred to a Consultation-Liaison Psychiatry (CLP) unit. This is a cross-sectional study, carried out at a tertiary hospital in Spain, in inpatients admitted because of COVID-19 and referred to our CLP Unit from March 17,2020 to April 28,2020. Clinical data were extracted from electronic medical records. The patients were divided in three groups depending on psychiatric diagnosis: delirium, severe mental illness (SMI) and non-severe mental illness (NSMI). Of 71 patients included (median [ICR] age 64 [54-73] years; 70.4% male), 35.2% had a delirium, 18.3% had a SMI, and 46.5% had a NSMI. Compared to patients with delirium and NSMI, patients with SMI were younger, more likely to be institutionalized and were administered less anti-COVID19 drugs. Mortality was higher among patients with delirium (21.7%) than those with SMI (0%) or NSMI (9.45%). The rate of side effects due to interactions between anti-COVID19 and psychiatric drugs was low, mainly drowsiness (4.3%) and borderline QTc prolongation (1.5%). Patients affected by SMI were more often undertreated for COVID-19. However, the rate of interactions was very low, and avoidable with a proper evaluation and drug-dose adjustment. Half of the patients with SMI were institutionalized, suggesting that living conditions in residential facilities could make them more vulnerable to infection.