Animal models of obsessive-compulsive disorder: utility and limitations

Obsessive-compulsive disorder (OCD) is a disabling and common neuropsychiatric condition of poorly known etiology. Many attempts have been made in the last few years to develop animal models of OCD with the aim of clarifying the genetic, neurochemical, and neuroanatomical basis of the disorder, as well as of developing novel pharmacological and neurosurgical treatments that may help to improve the prognosis of the illness. The latter goal is particularly important given that around 40% of patients with OCD do not respond to currently available therapies. This article summarizes strengths and limitations of the leading animal models of OCD including genetic, pharmacologically induced, behavioral manipulation-based, and neurodevelopmental models according to their face, construct, and predictive validity. On the basis of this evaluation, we discuss that currently labeled 'animal models of OCD' should be regarded not as models of OCD but, rather, as animal models of different psychopathological processes, such as compulsivity, stereotypy, or perseverance, that are present not only in OCD but also in other psychiatric or neurological disorders. Animal models might constitute a challenging approach to study the neural and genetic mechanism of these phenomena from a trans-diagnostic perspective. Animal models are also of particular interest as tools for developing new therapeutic options for OCD, with the greatest convergence focusing on the glutamatergic system, the role of ovarian and related hormones, and the exploration of new potential targets for deep brain stimulation. Finally, future research on neurocognitive deficits associated with OCD through the use of analogous animal tasks could also provide a genuine opportunity to disentangle the complex etiology of the disorder

Distinct etiological influences on obsessive-compulsive symptoms dimensions: A multivariate twin study

Background Obsessive-compulsive disorder (OCD) is characterized by five major dimensions, including contamination/washing, harm/checking, symmetry/ordering, hoarding, and forbidden thoughts. How these dimensions may relate etiologically to the symptoms of other obsessive-compulsive related disorders (OCRDs) and anxiety disorders (ADs) is not well known. The aim of this study was to examine the genetic and environmental overlap between each major obsessive-compulsive dimension with the symptoms of other OCRDs and ADs. MethodsTwo thousand four hundred ninety-five twins of both sexes, aged between 18 and 45 years, were recruited from the Australian Twin Registry. Measures used scores on four dimensions (obsessing (forbidden thoughts), washing, checking, and ordering) of the Obsessive-Compulsive Inventory-Revised, Dysmorphic Concerns Questionnaire, Hoarding Rating Scale, Anxiety Sensitivity Index, Social Phobia Inventory, and Stress subscale of the Depression, Anxiety, and Stress Scale. Multivariate twin modeling methods using continuous and categorized variables were performed, also controlling for age and gender. ResultsOur findings suggested that forbidden thoughts and washing demonstrated the strongest genetic overlap with other AD symptoms, while ordering was genetically related to OCRD symptoms. Common genetic influences on checking symptoms were best estimated when modeling OCRDs together with AD symptoms. Common environmental factors of ordering and checking were shared with AD symptoms. ConclusionsImportant shared genetic and environmental risk factors exist between OCD, OCRDs, and ADs, but which vary alongside the expression of its major dimension

Removing and reimplanting deep brain stimulation therapy devices in resistant OCD (when the patient does not respond): case report

Background: Deep brain stimulation (DBS) is emerging as a promising tool in the treatment of refractory obsessive-compulsive disorder (OCD) but the search for the best target still continues. This issue is especially relevant when particularly resistant profiles are observed in some patients, which have been ascribed to individual responses to DBS according to differential patterns of connectivity. As patients have been implanted, new dilemmas have emerged, such as what to do when the patient does not respond to surgery. Case presentation: Here we describe a 22-year-old male with extremely severe OCD who did not respond to treatment with DBS in the nucleus accumbens, but who did respond after explanting and reimplanting leads targeting the ventral capsule-ventral striatum region. Information regarding the position of the electrodes for both surgeries is provided and possible brain structures affected during stimulation are reviewed. To our knowledge this case is the first in the literature reporting the removal and reimplantation of DBS leads for therapeutical benefits in a patient affected by a mental disorder. Conclusion: The capability for explantation and reimplantation of leads should be considered as part of the DBS therapy reversibility profile in resistant mental disorders, as it allows application in cases of non-response to the first surgery

Brain structural alterations in obsessive-compulsive disorder patients with autogenous and reactive obsessions

Obsessive-compulsive disorder (OCD) is a clinically heterogeneous condition. Although structural brain alterations have been consistently reported in OCD, their interaction with particular clinical subtypes deserves further examination. Among other approaches, a two-group classification in patients with autogenous and reactive obsessions has been proposed. The purpose of the present study was to assess, by means of a voxel-based morphometry analysis, the putative brain structural correlates of this classification scheme in OCD patients. Ninety-five OCD patients and 95 healthy controls were recruited. Patients were divided into autogenous (n = 30) and reactive (n = 65) sub-groups. A structural magnetic resonance image was acquired for each participant and pre-processed with SPM8 software to obtain a volume-modulated gray matter map. Whole-brain and voxel-wise comparisons between the study groups were then performed. In comparison to the autogenous group, reactive patients showed larger gray matter volumes in the right Rolandic operculum. When compared to healthy controls, reactive patients showed larger volumes in the putamen (bilaterally), while autogenous patients showed a smaller left anterior temporal lobe. Also in comparison to healthy controls, the right middle temporal gyrus was smaller in both patient subgroups. Our results suggest that autogenous and reactive obsessions depend on partially dissimilar neural substrates. Our findings provide some neurobiological support for this classification scheme and contribute to unraveling the neurobiological basis of clinical heterogeneity in OCD

Basolateral amygdala-ventromedial prefrontal cortex connectivity predicts cognitive behavioural therapy outcome in adults with obsessive-compulsive disorder

Background: cognitive behavioural therapy (CBT), including exposure and ritual prevention, is a first-line treatment for obsessive-compulsive disorder (OCD), but few reliable predictors of CBT outcome have been identified. Based on research in animal models, we hypothesized that individual differences in basolateral amygdala-ventromedial prefrontal cortex (BLA-vmPFC) communication would predict CBT outcome in patients with OCD. Methods: we investigated whether BLA-vmPFC resting-state functional connectivity (rs-fc) predicts CBT outcome in patients with OCD. We assessed BLA-vmPFC rs-fc in patients with OCD on a stable dose of a selective serotonin reuptake inhibitor who then received CBT and in healthy control participants. Results: we included 73 patients with OCD and 84 healthy controls in our study. Decreased BLA-vmPFC rs-fc predicted a better CBT outcome in patients with OCD and was also detected in those with OCD compared with healthy participants. Additional analyses revealed that decreased BLA-vmPFC rs-fc uniquely characterized the patients with OCD who responded to CBT. Limitations: we used a sample of convenience, and all patients were receiving pharmacological treatment for OCD. Conclusion: in this large sample of patients with OCD, BLA-vmPFC functional connectivity predicted CBT outcome. These results suggest that future research should investigate the potential of BLA-vmPFC pathways to inform treatment selection for CBT across patients with OCD and anxiety disorders

Importance of immunometabolic markers for the classification of patients with major depressive disorder using machine learning

Background: Although there is scientific evidence of the presence of immunometabolic alterations in major depression, not all patients present them. Recent studies point to the association between an inflammatory phenotype and certain clinical symptoms in patients with depression. The objective of our study was to classify major depression disorder patients using supervised learning algo-rithms or machine learning, based on immunometabolic and oxidative stress biomarkers and lifestyle habits.Methods: Taking into account a series of inflammatory and oxidative stress biomarkers (C-reactive protein (CRP), tumor necrosis factor (TNF), 4-hydroxynonenal (HNE) and glutathione), metabolic risk markers (blood pressure, waist circumference and glucose, triglyceride and cholesterol levels) and lifestyle habits of the participants (physical activity, smoking and alcohol consumption), a study was carried out using machine learning in a sample of 171 participants, 91 patients with depression (71.42% women, mean age = 50.64) and 80 healthy subjects (67.50% women, mean age = 49.12).The algorithm used was the support vector machine, performing cross validation, by which the subdivision of the sample in training (70%) and test (30%) was carried out in order to estimate the precision of the model. The prediction of belonging to the patient group (MDD patients versus control subjects), melancholic type (melancholic versus non-melancholic patients) or resistant depression group (treatment-resistant versus non -treatment-resistant) was based on the importance of each of the immunometabolic and lifestyle variables.Results: With the application of the algorithm, controls versus patients, such as patients with melancholic symptoms versus non-melancholic symptoms, and resistant versus non-resistant symptoms in the test phase were optimally classified.The variables that showed greater importance, according to the results of the area under the ROC curve, for the discrimination between healthy subjects and patients with depression were current alcohol consumption (AUC = 0.62), TNF-alpha levels (AUC = 0.61), glutathione redox status (AUC = 0.60) and the performance of both moderate (AUC = 0.59) and vigorous physical exercise (AUC = 0.58). On the other hand, the most important variables for classifying melancholic patients in relation to lifestyle habits were past (AUC = 0.65) and current (AUC = 0.60) tobacco habit, as well as walking routinely (AUC = 0.59) and in relation to immunometabolic markers were the levels of CRP (AUC = 0.62) and glucose (AUC = 0.58).In the analysis of the importance of the variables for the classification of treatment-resistant patients versus non-resistant patients, the systolic blood pressure (SBP) variable was shown to be the most relevant (AUC = 0.67). Other immunometabolic variables were also among the most important such as TNF-alpha (AUC = 0.65) and waist circumference (AUC = 0.64). In this case, sex (AUC = 0.59) was also relevant along with alcohol (AUC = 0.58) and tobacco (AUC = 0.56) consumption.Conclusions: The results obtained in our study show that it is possible to predict the diagnosis of depression and its clinical typology from immunometabolic markers and lifestyle habits, using machine learning techniques. The use of this type of methodology could facilitate the identification of patients at risk of presenting depression and could be very useful for managing clinical heterogeneity

Importance of immunometabolic markers for the classification of patients with major depressive disorder using machine learning

Background: Although there is scientific evidence of the presence of immunometabolic alterations in major depression, not all patients present them. Recent studies point to the association between an inflammatory phenotype and certain clinical symptoms in patients with depression. The objective of our study was to classify major depression disorder patients using supervised learning algorithms or machine learning, based on immunometabolic and oxidative stress biomarkers and lifestyle habits. Methods: Taking into account a series of inflammatory and oxidative stress biomarkers (C-reactive protein (CRP), tumor necrosis factor (TNF), 4-hydroxynonenal (HNE) and glutathione), metabolic risk markers (blood pressure, waist circumference and glucose, triglyceride and cholesterol levels) and lifestyle habits of the participants (physical activity, smoking and alcohol consumption), a study was carried out using machine learning in a sample of 171 participants, 91 patients with depression (71.42% women, mean age = 50.64) and 80 healthy subjects (67.50% women, mean age = 49.12). The algorithm used was the support vector machine, performing cross validation, by which the subdivision of the sample in training (70%) and test (30%) was carried out in order to estimate the precision of the model. The prediction of belonging to the patient group (MDD patients versus control subjects), melancholic type (melancholic versus non-melancholic patients) or resistant depression group (treatment-resistant versus non-treatment-resistant) was based on the importance of each of the immunometabolic and lifestyle variables. Results: With the application of the algorithm, controls versus patients, such as patients with melancholic symptoms versus non-melancholic symptoms, and resistant versus non-resistant symptoms in the test phase were optimally classified. The variables that showed greater importance, according to the results of the area under the ROC curve, for the discrimination between healthy subjects and patients with depression were current alcohol consumption (AUC = 0.62), TNF-α levels (AUC = 0.61), glutathione redox status (AUC = 0.60) and the performance of both moderate (AUC = 0.59) and vigorous physical exercise (AUC = 0.58). On the other hand, the most important variables for classifying melancholic patients in relation to lifestyle habits were past (AUC = 0.65) and current (AUC = 0.60) tobacco habit, as well as walking routinely (AUC = 0.59) and in relation to immunometabolic markers were the levels of CRP (AUC = 0.62) and glucose (AUC = 0.58). In the analysis of the importance of the variables for the classification of treatment-resistant patients versus non-resistant patients, the systolic blood pressure (SBP) variable was shown to be the most relevant (AUC = 0.67). Other immunometabolic variables were also among the most important such as TNF-α (AUC = 0.65) and waist circumference (AUC = 0.64). In this case, sex (AUC = 0.59) was also relevant along with alcohol (AUC = 0.58) and tobacco (AUC = 0.56) consumption. Conclusions: The results obtained in our study show that it is possible to predict the diagnosis of depression and its clinical typology from immunometabolic markers and lifestyle habits, using machine learning techniques. The use of this type of methodology could facilitate the identification of patients at risk of presenting depression and could be very useful for managing clinical heterogeneity.This study was supported in part by grants from the Carlos III Health Institute through the Ministry of Science, Innovation and Universities (PI15/00662, PI15/0039, PI15/00204, PI19/01040), co-funded by the European Regional Development Fund (ERDF) “A way to build Europe”, CIBERSAM, and the Catalan Agency for the Management of University and Research Grants (AGAUR 2017 SGR 1247). We also thank CERCA Programme/Generalitat de Catalunya for institutional support. Work partially supported by Biobank HUB-ICO-IDIBELL, integrated in the Spanish Biobank Network and funded by Instituto de Salud Carlos III (PT17/0015/0024) and by Xarxa Bancs de Tumors de Catalunya sponsored by Pla Director d’Oncologia de Catalunya (XBTC). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. YSC work is supported by the FPI predoctoral grant (FPI 2016/17) from Universidad Autonoma de Madrid. VS received an Intensification of the Research Activity Grant from the Instituto de Salud Carlos III (INT21/00055) during 202

TFG 2014/2015

Amb aquesta publicació, EINA, Centre universitari de Disseny i Art adscrit a la Universitat Autònoma de Barcelona, dóna a conèixer el recull dels Treballs de Fi de Grau presentats durant el curs 2014-2015. Voldríem que un recull com aquest donés una idea més precisa de la tasca que es realitza a EINA per tal de formar nous dissenyadors amb capacitat de respondre professionalment i intel·lectualment a les necessitats i exigències de la nostra societat. El treball formatiu s’orienta a oferir resultats que responguin tant a paràmetres de rigor acadèmic i capacitat d’anàlisi del context com a l’experimentació i la creació de nous llenguatges, tot fomentant el potencial innovador del disseny.Con esta publicación, EINA, Centro universitario de diseño y arte adscrito a la Universidad Autónoma de Barcelona, da a conocer la recopilación de los Trabajos de Fin de Grado presentados durante el curso 2014-2015. Querríamos que una recopilación como ésta diera una idea más precisa del trabajo que se realiza en EINA para formar nuevos diseñadores con capacidad de responder profesional e intelectualmente a las necesidades y exigencias de nuestra sociedad. El trabajo formativo se orienta a ofrecer resultados que respondan tanto a parámetros de rigor académico y capacidad de análisis, como a la experimentación y la creación de nuevos lenguajes, al tiempo que se fomenta el potencial innovador del diseño.With this publication, EINA, University School of Design and Art, affiliated to the Autonomous University of Barcelona, brings to the public eye the Final Degree Projects presented during the 2014-2015 academic year. Our hope is that this volume might offer a more precise idea of the task performed by EINA in training new designers, able to speak both professionally and intellectually to the needs and demands of our society. The educational task is oriented towards results that might respond to the parameters of academic rigour and the capacity for contextual analysis, as well as to considerations of experimentation and the creation of new languages, all the while reinforcing design’s innovative potential

Genetic and Environmental Risk factors associated with Obsessive-Compulsive Disorder and its symptom dimensions: A twin study

[spa] El trastorno obsesivo-compulsivo (TOC) es una enfermedad mental con una clara etiología multifactorial que engloba tanto componentes biológicos como psicosociales no del todo esclarecidos. Los estudios con gemelos son considerados una de las principales herramientas en la genética de conducta para identificar marcadores genéticos relevantes que subyacen a patologías complejas. En la presente tesis se pretende, mediante modelos de ecuaciones estructurales con gemelos, esclarecer los componentes biológicos del TOC al examinar los factores genéticos específicos y compartidos que existen entre las dimensiones de síntomas obsesivos (pensamientos prohibidos, comprobación, orden/simetría y contaminación/limpieza), síntomas de ansiedad (pánico, ansiedad generalizada y fobia social) y síntomas de otros trastornos del espectro obsesivo (trastorno dismórfico corporal y acumulación), en una muestra no-clínica de 2495 gemelos de entre 18 y 45 años. En segundo lugar estudiar el patrón de relaciones causales entre los síntomas TOC, del espectro ansioso y obsesivo, utilizando una nueva metodología de análisis con gemelos. Los resultados de esta tesis muestran por primera vez un patrón de heredabilidad diferente entre hombres y mujeres en los síntomas del espectro obsesivo (en concreto en el trastorno dismórfico corporal y de acumulación). Por otro lado, se demuestra una clara implicación del componente ansioso en la etiología del TOC, compartiendo tanto factores genéticos (especialmente en la dimensión de contaminación y pensamientos prohibidos) como ambientales (principalmente en orden/simetría y comprobación). Los síntomas del trastorno dismórfico corporal (TDC) también comparten genética con el TOC, especialmente con la dimensión obsesiva de comprobación. Los resultados demuestran que la genética asociada al TOC no se explica mejor con los trastornos del espectro obsesivo, sino que la ansiedad es un constructo esencial en su etiología. En la misma línea, la presencia de síntomas TOC aumenta la probabilidad de padecer síntomas de pánico y ansiedad generalizada; mientras que la presencia de síntomas de fobia social incrementa significativamente la probabilidad de desarrollar síntomas TOC. La presencia de síntomas obsesivos aumenta la posibilidad de que el mismo sujeto pueda presentar síntomas de acumulación a lo largo de la vida. Estos hallazgos ayudarán a futuros estudios a esclarecer qué factores genéticos, ambientales y/o epigenéticos específicos están asociados al TOC o son compartidos con otros trastornos relacionados. [eng] There is an incomplete understanding of the heritability and the specific genetic bases of Obsessive-Compulsive Disorder (OCD). It has a complex multifactorial etiology comprising both biological as well as psychosocial components not clearly elucidated. Moreover, OCD is clinically heterogeneous and it is unknown whether this complex phenotypic reflects distinct or partially distinct etiology mechanisms. Until recently OCD was designated as an anxiety disorder (AD) due to the similarities in phenomenology, comorbidity and aggregation in families suffering from various ADs. In this context, it was implicitly understood that OCD was sharing common etiological factors with other ADs. However, the idea that disorders not classified in the anxiety group could also be associated with OCD has generated an intense and controversial debate about whether OCD is indeed and AD or, in fact, more closely related to other disorders, with the anxiety merely as a consequence of OCD symptoms. Twin studies are considered a key tool in behavioral genetics used to dissect the nature (genetic) versus the nurture (environmental) contributions to individual phenotypes. Also, twin studies are one of the best ways of identifying genetic markers relevant to understand the etiological factors that underlie complex psychopathologies. Unfortunately, there is a lack of empirical twin studies that have actually directly compared the OCD, obsessive-compulsive and related disorders (OCRDs) and ADs together. OBJECTIVES: To clarify biological components of OCD etiology by examining specific and shared genetic factors between Obsessive-Compulsive (OC) symptom dimensions (forbidden thoughts, checking, symmetry/ordering and washing) anxiety disorders (social phobia (SP), generalized anxiety disorder (GAD) and panic disorder (PD)) and OC spectrum disorders (body-dysmorphic disorder (BDD), hoarding disorder (HD) and hypochondriasis (HYP)). As a second aim this thesis would like to study the patter of causation relationships between OCD, the ADs and the OCRDs, using a new statistical approach to infer causation in twin data (ICE FALCON). A non-clinical sample of 2,495 male and female twins of 18 to 45 years old was used to address these objectives. The results of the present thesis show for the first time that the patterns of heritability of hypochondriasis, BDD and HD symptoms present genetic sex differences. Moreover, it shows that anxiety makes an essential contribution to the complex etiology of OCD. In the causation inference analysis it was demonstrated that OCD symptoms increase an individual’s probability to develop GAD, PD and HD symptoms, but not revers; and that social phobia increases an individual probability to develop OCD symptoms. Regarding OCD dimensions this thesis found that forbidden thoughts and washing show the highest association with the AD symptoms while symmetry/ordering shows the highest genetic specificity. On the other hand, checking dimension is genetically associated with other OCRDs (specifically BDD) and AD symptoms. Finally, only checking and symmetry/ordering dimensions share environmental factors with the ADs. In summary, this thesis answers questions of high clinical and diagnostic relevance for OCD, one of the most prevalent mental disorders (2-3%) and one of the largest generators of chronic suffering and cost in the current health programs. All the results from this thesis are related to the biological basis underlying the disorder and are aimed to elucidate more stable endophenotypes of analysis. This could have a large impact on both, improving patients’ quality of life and in the reduction of costs associated with the handling of a complex and heterogeneous disorder