Aplicación de realidad aumentada para dispositivos móviles destinada a espacios culturales. Ejemplo de desarrollo: Museu Episcopal de Vic

Curs 2010-2011Proyecto de aplicación para dispositivos móviles que utiliza la Realidad Aumentada (RA) para contextualizar obras artísticas. Es un producto pensado para museos y galerías pero también para espacios exteriores que posean elementos de valor cultural. Sin embargo, y para servir de ejemplo se ha aplicado en seis piezas destacadas del Museo Episcopal de Vic. Aunque en Europa y EE.UU. los museos ya están innovando en este terreno, en España, que es donde se enmarca el proyecto, existe una carencia de elementos de mediación cultural como estos; sobre todo en los museos de arte donde las obras acostumbran a presentarse despojadas de cualquier herramienta que proporcione información complementaria. Existe, por tanto, una descontextualización del arte que crea barreras para el entendimiento del público general. De esta situación de carencia parte el objetivo principal del producto, el de aprovechar las posibilidades de la RA para mejorar la experiencia y el diálogo entre el visitante y la obra, aportando conocimiento y fomentando el aprendizaje.Project of an application for mobile devices which use Augmented Reality (AR) to contextualize works of art. It is a product designed for museums and art galleries as well as for outdoor spaces which have elements of cultural value. To serve as an example it has been applied on six relevant pieces from the “Museo Episcopal de Vic”. Unlike Europe and USA, where their museums are making innovations in this field, Spanish museums, where the project is enshrined, have a lack of cultural mediation elements like those. This fact is particularly found in the art museums whose works are usually presented stripped of any complementary informative tool. There is, therefore, an art decontextualisation that creates barriers for the general public understanding. It is precisely from this lacking situation, that the principal objective of the product starts: taking advantage of the possibilities of the RA, so that to improve both experience and dialogue between visitors and art works, contributing, in this way, to knowledge and promoting learning.Director/a: Santos M. Mateos Rusill

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of big data (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA\u27s activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

Long-term comparative effectiveness of deep brain stimulation in severe obsessive-compulsive disorder

Background: Twenty years after the first use of Deep Brain Stimulation (DBS) in obsessive-compulsive disorder (OCD), our knowledge of the long-term effects of this therapeutic option remains very limited. Objective: Our study aims to assess the long-term effectiveness and tolerability of DBS in OCD patients and to look for possible predictors of long-term response to this treatment. Methods: We studied the course of 25 patients with severe refractory OCD treated with DBS over an average follow-up period of 6.4 years (+/- 3.2) and compared them with a control group of 25 patients with severe OCD who refused DBS and maintained their usual treatment. DBS was implanted at the ventral anterior limb of the internal capsule and nucleus accumbens (vALIC-Nacc) in the first six patients and later at the bed nucleus of stria terminalis (BNST) in the rest of patients. Main outcome was change in Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) score between the two groups assessed using mixed models. Secondary effectiveness outcomes included Hamilton Depression Rating Scale (HDRS) and Global Assessment of Functioning (GAF) scores. Results: Obsessive symptoms fell by 42.5% (Y-BOCS score) in patients treated with DBS and by 4.8% in the control group. Fifty-six per cent of DBS-treated patients could be considered responders at the end of follow-up and 28% partial responders. Two patients among those who rejected DBS were partial re-sponders (8%), but none of the non-DBS group achieved criteria for complete response. HDRS and GAF scores improved significantly in 39.2% and 43.6% among DBS-treated patients, while did not significantly change in those who rejected DBS (improvement limited to 6.2% in HDRS and 4.2% in GAF scores). No statistically significant predictors of response were found. Mixed models presented very large compar-ative effect sizes for DBS (4.29 for Y-BOCS, 1.15 for HDRS and 2.54 for GAF). Few patients experienced adverse effects and most of these effects were mild and transitory. Conclusions: The long-term comparative effectiveness and safety of DBS confirm it as a valid option for the treatment of severe refractory OCD. (c) 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Looking into the genetic bases of OCD dimensions: a pilot genome-wide association study

The multidimensional nature of obsessive-compulsive disorder (OCD) has been consistently reported. Clinical and biological characteristics have been associated with OCD dimensions in different ways. Studies suggest the existence of specific genetic bases for the different OCD dimensions. In this study, we analyze the genomic markers, genes, gene ontology and biological pathways associated with the presence of aggressive/checking, symmetry/order, contamination/cleaning, hoarding, and sexual/religious symptoms, as assessed via the Dimensional Yale-Brown Obsessive Compulsive Scale (DY-BOCS) in 399 probands. Logistic regression analyses were performed at the single-nucleotide polymorphism (SNP) level. Gene-based and enrichment analyses were carried out for common (SNPs) and rare variants. No SNP was associated with any dimension at a genome-wide level (p < 5 × 10−8). Gene-based analyses showed one gene to be associated with hoarding (SETD3, p = 1.89 × 10−08); a gene highly expressed in the brain and which plays a role in apoptotic processes and transcriptomic changes, and another gene associated with aggressive symptoms (CPE; p = 4.42 × 10−6), which is involved in neurotrophic functions and the synthesis of peptide hormones and neurotransmitters. Different pathways or biological processes were represented by genes associated with aggressive (zinc ion response and lipid metabolism), order (lipid metabolism), sexual/religious (G protein-mediated processes) and hoarding (metabolic processes and anion transport) symptoms after FDR correction; while no pathway was associated with contamination. Specific genomic bases were found for each dimension assessed, especially in the enrichment analyses. Further research with larger samples and different techniques, such as next-generation sequencing, are needed to better understand the differential genetics of OCD dimensions

Sleep disturbances in obsessive-compulsive disorder: influence of depression symptoms and trait anxiety

Background: Sleep disturbances have been reported in obsessive-compulsive disorder (OCD) patients, with heterogeneous results. The aim of our study was to assess sleep function in OCD and to investigate the relationship between sleep and the severity of obsessive-compulsive (OC) symptoms, depressive symptoms and trait anxiety. Methods: Sleep quality was measured in 61 OCD patients and 100 healthy controls (HCs) using the Pittsburgh Sleep Quality Index (PSQI). Multiple linear regression was conducted to explore the association between sleep and psychopathological measures; a mediation analysis was also performed. Results: OCD patients showed poor sleep quality and more sleep disturbances compared to HCs. The severity of depression, trait anxiety and OC symptomatology were correlated with poor sleep quality. Multiple linear regression analyses controlling for potential confounders revealed that the severity of depression and trait anxiety were independently related to poor sleep quality in OCD. A mediation analysis showed that both the severity of trait anxiety and depression mediate the relationship between the severity of OC symptoms and poor sleep quality among patients with OCD. Conclusions: Our findings support the existence of sleep disturbances in OCD. Trait anxiety and depression play a key role in sleep quality among OCD patients

First manic/hypomanic episode in obsessive-compulsive disorder patients treated with antidepressants: a systematic review

High doses of antidepressants, particularly clomipramine and selective serotonin reuptake inhibitors (SSRIs), are the well-established treatment for obsessive-compulsive disorder (OCD), but manic/hypomanic episodes are potential adverse events associated with this treatment. A systematic literature review was performed on manic/ hypomanic episodes in non-bipolar OCD patients. Clinical, sociodemographic and antidepressant characteristics during the manic/hypomanic switch were extracted using descriptive statistics. Data were obtained from 20 case reports and case series. Switching episodes mostly appeared in the first 12 weeks after antidepressant initiation and took place more frequently during SSRI use (mostly fluoxetine) in 64.3% of cases. Clomipramine and SSRI use differed non-significantly between the switching episodes that appeared during the first 12 weeks of antidepressant treatment and the episodes that appeared beyond 12 weeks. Switching episodes emerging before 12 weeks were associated with a lower defined daily dose of antidepressants than episodes emerging after 12 weeks. These findings suggest that there are two independent characteristics involved in manic/hypomanic switch in OCD: a) they appeared most frequently with SSRI use (fluoxetine) regardless of the time of it use, and b) episodes appeared in the first 12 weeks after SSRI or clomipramine initiation had a lower dose of antidepressant than episodes appeared after 12 weeks

Viability study of machine learning-based prediction of COVID-19 pandemic impact in obsessive-compulsive disorder patients

Background: Machine learning modeling can provide valuable support in different areas of mental health, because it enables to make rapid predictions and therefore support the decision making, based on valuable data. However, few studies have applied this method to predict symptoms’ worsening, based on sociodemographic, contextual, and clinical data. Thus, we applied machine learning techniques to identify predictors of symptomatologic changes in a Spanish cohort of OCD patients during the initial phase of the COVID-19 pandemic. Methods: 127 OCD patients were assessed using the Yale–Brown Obsessive-Compulsive Scale (Y-BOCS) and a structured clinical interview during the COVID-19 pandemic. Machine learning models for classification (LDA and SVM) and regression (linear regression and SVR) were constructed to predict each symptom based on patient’s sociodemographic, clinical and contextual information. Results: A Y-BOCS score prediction model was generated with 100% reliability at a score threshold of ± 6. Reliability of 100% was reached for obsessions and/or compulsions related to COVID-19. Symptoms of anxiety and depression were predicted with less reliability (correlation R of 0.58 and 0.68, respectively). The suicidal thoughts are predicted with a sensitivity of 79% and specificity of 88%. The best results are achieved by SVM and SVR. Conclusion: Our findings reveal that sociodemographic and clinical data can be used to predict changes in OCD symptomatology. Machine learning may be valuable tool for helping clinicians to rapidly identify patients at higher risk and therefore provide optimized care, especially in future pandemics. However, further validation of these models is required to ensure greater reliability of the algorithms for clinical implementation to specific objectives of interest.Sandra Carvalho receives scholarship and support from the Portuguese Foundation for Science and Technology (FCT), co-funded through COMPETE 2020 – PO Competitividade e Internacionalização/Portugal 2020/European Union, FEDER (Fundos Europeus Estruturais e de Investimento – FEEI) under the number:PTDC/PSI-ESP/29701/2017.publishe

A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data

Background and objective: As a response to the ongoing COVID-19 pandemic, several prediction models in the existing literature were rapidly developed, with the aim of providing evidence-based guidance. However, none of these COVID-19 prediction models have been found to be reliable. Models are commonly assessed to have a risk of bias, often due to insufficient reporting, use of non-representative data, and lack of large-scale external validation. In this paper, we present the Observational Health Data Sciences and Informatics (OHDSI) analytics pipeline for patient-level prediction modeling as a standardized approach for rapid yet reliable development and validation of prediction models. We demonstrate how our analytics pipeline and open-source software tools can be used to answer important prediction questions while limiting potential causes of bias (e.g., by validating phenotypes, specifying the target population, performing large-scale external validation, and publicly providing all analytical source code). Methods: We show step-by-step how to implement the analytics pipeline for the question: ‘In patients hospitalized with COVID-19, what is the risk of death 0 to 30 days after hospitalization?’. We develop models using six different machine learning methods in a USA claims database containing over 20,000 COVID-19 hospitalizations and externally validate the models using data containing over 45,000 COVID-19 hospitalizations from South Korea, Spain, and the USA. Results: Our open-source software tools enabled us to efficiently go end-to-end from problem design to reliable Model Development and evaluation. When predicting death in patients hospitalized with COVID-19, AdaBoost, random forest, gradient boosting machine, and decision tree yielded similar or lower internal and external validation discrimination performance compared to L1-regularized logistic regression, whereas the MLP neural network consistently resulted in lower discrimination. L1-regularized logistic regression models were well calibrated. Conclusion: Our results show that following the OHDSI analytics pipeline for patient-level prediction modelling can enable the rapid development towards reliable prediction models. The OHDSI software tools and pipeline are open source and available to researchers from all around the world.</p

Changes in the stool and oropharyngeal microbiome in obsessive-compulsive disorder

Although the etiology of obsessive-compulsive disorder (OCD) is largely unknown, it is accepted that OCD is a complex disorder. There is a known bi-directional interaction between the gut microbiome and brain activity. Several authors have reported associations between changes in gut microbiota and neuropsychiatric disorders, including depression or autism. Furthermore, a pediatric-onset neuropsychiatric OCD-related syndrome occurs after streptococcal infection, which might indicate that exposure to certain microbes could be involved in OCD susceptibility. However, only one study has investigated the microbiome of OCD patients to date. We performed 16S ribosomal RNA gene-based metagenomic sequencing to analyze the stool and oropharyngeal microbiome composition of 32 OCD cases and 32 age and gender matched controls. We estimated different α- and β-diversity measures and performed LEfSe and Wilcoxon tests to assess differences in bacterial distribution. OCD stool samples showed a trend towards lower bacterial α-diversity, as well as an increase of the relative abundance of Rikenellaceae, particularly of the genus Alistipes, and lower relative abundance of Prevotellaceae, and two genera within the Lachnospiraceae: Agathobacer and Coprococcus. However, we did not observe a different Bacteroidetes to Firmicutes ratio between OCD cases and controls. Analysis of the oropharyngeal microbiome composition showed a lower Fusobacteria to Actinobacteria ratio in OCD cases. In conclusion, we observed an imbalance in the gut and oropharyngeal microbiomes of OCD cases, including, in stool, an increase of bacteria from the Rikenellaceae family, associated with gut inflammation, and a decrease of bacteria from the Coprococcus genus, associated with DOPAC synthesis

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors