Autoreactive T cells bypass negative selection and respond to self-antigen stimulation during infection.

Central and peripheral tolerance prevent autoimmunity by deleting the most aggressive CD8(+) T cells but they spare cells that react weakly to tissue-restricted antigen (TRA). To reveal the functional characteristics of these spared cells, we generated a transgenic mouse expressing the TCR of a TRA-specific T cell that had escaped negative selection. Interestingly, the isolated TCR matches the affinity/avidity threshold for negatively selecting T cells, and when developing transgenic cells are exposed to their TRA in the thymus, only a fraction of them are eliminated but significant numbers enter the periphery. In contrast to high avidity cells, low avidity T cells persist in the antigen-positive periphery with no signs of anergy, unresponsiveness, or prior activation. Upon activation during an infection they cause autoimmunity and form memory cells. Unexpectedly, peptide ligands that are weaker in stimulating the transgenic T cells than the thymic threshold ligand also induce profound activation in the periphery. Thus, the peripheral T cell activation threshold during an infection is below that of negative selection for TRA. These results demonstrate the existence of a level of self-reactivity to TRA to which the thymus confers no protection and illustrate that organ damage can occur without genetic predisposition to autoimmunity.

Neuroanatomical and neuropsychological features of elderly euthymic depressed patients with early- and late-onset.

BACKGROUND: Whether or not cognitive impairment and brain structure changes are trait characteristics of late-life depression is still disputed. Previous studies led to conflicting data possibly because of the difference in the age of depression onset. In fact, several lines of evidence suggest that late-onset depression (LOD) is more frequently associated with neuropsychological deficits and brain pathology than early-onset depression (EOD). To date, no study explored concomitantly the cognitive profile and brain magnetic resonance imaging (MRI) patterns in euthymic EOD and LOD patients. METHOD: Using a cross-sectional design, 41 remitted outpatients (30 with EOD and 11 with LOD) were compared to 30 healthy controls. Neuropsychological evaluation concerned working memory, episodic memory, processing speed, naming capacity and executive functions. Volumetric estimates of the amygdala, hippocampus, entorhinal and anterior cingulate cortex were obtained using both voxel-based and region of interest morphometric methods. White matter hyperintensities were assessed semiquantitatively. RESULTS: Both cognitive performance and brain volumes were preserved in euthymic EOD patients whereas LOD patients showed a significant reduction of episodic memory capacity and a higher rate of periventricular hyperintensities compared to both controls and EOD patients. CONCLUSION: Our results support the dissociation between EOD thought to be mainly related to psychosocial factors and LOD that is characterized by increasing vascular burden and episodic memory decline.

Constructive thinking skills and impulsivity dimensions in conduct and substance use disorders: differences and relationships in an adolescents' sample.

Impact of conduct disorder (CD) and substance use disorder (SUD) on constructive thinking skills and impulsivity was explored. 71 offending adolescents were assessed for CD and SUD. Furthermore, the constructive thinking inventory, the immediate and delayed memory tasks and the UPPS impulsive behaviour scale were administered. Results showed that youths with CD, independently from SUD, presented higher personality impulsivity (urgency) and altered constructive thinking skills (categorical thinking and personal superstitious thinking). Furthermore, trait-impulsivity explained variation in constructive thinking skills. The implications of these results were discussed.

Can postictal memory predict postoperative memory in patients with temporal lobe epilepsy?

We investigated the contribution of postictal memory testing for lateralizing the epileptic focus and predicting memory outcome after surgery for temporal lobe epilepsy (TLE). Forty-five patients with TLE underwent interictal, postictal, and postoperative assessment of verbal and nonverbal memory. Surgery consisted of anterior temporal lobectomy (36), selective isolated amygdalohippocampectomy (6), or amygdalohippocampectomy coupled to lesionectomy (3). Postictal and postoperative but not interictal memory were significantly lower in left TLE than in right TLE. Nonverbal memory showed no significant difference in left TLE versus right TLE in all conditions. Postictal memory was significantly correlated with postoperative memory, but the effect disappeared when the lateralization of the focus was considered. Postictal verbal memory is a useful bedside tool that can help lateralize the epileptic focus. Larger studies are needed to further estimate its predictive value of the postoperative outcome.

SIMSAFADIM-CLASTIC: A new approach to mathematical 3D forward simulation modelling for terrigenous and carbonate marine sedimentation

Most sedimentary modelling programs developed in recent years focus on either terrigenous or carbonate marine sedimentation. Nevertheless, only a few programs have attempted to consider mixed terrigenous-carbonate sedimentation, and most of these are two-dimensional, which is a major restriction since geological processes take place in 3D. This paper presents the basic concepts of a new 3D mathematical forward simulation model for clastic sediments, which was developed from SIMSAFADIM, a previous 3D carbonate sedimentation model. The new extended model, SIMSAFADIM-CLASTIC, simulates processes of autochthonous marine carbonate production and accumulation, together with clastic transport and sedimentation in three dimensions of both carbonate and terrigenous sediments. Other models and modelling strategies may also provide realistic and efficient tools for prediction of stratigraphic architecture and facies distribution of sedimentary deposits. However, SIMSAFADIM-CLASTIC becomes an innovative model that attempts to simulate different sediment types using a process-based approach, therefore being a useful tool for 3D prediction of stratigraphic architecture and facies distribution in sedimentary basins. This model is applied to the neogene Vallès-Penedès half-graben (western Mediterranean, NE Spain) to show the capacity of the program when applied to a realistic geologic situation involving interactions between terrigenous clastics and carbonate sediments.

Transmission electron microscopy in cell biology: sample preparation techniques and image information

Transmission electron microscopy is a proven technique in the field of cell biology and a very useful tool in biomedical research. Innovation and improvements in equipment together with the introduction of new technology have allowed us to improve our knowledge of biological tissues, to visualizestructures better and both to identify and to locate molecules. Of all the types ofmicroscopy exploited to date, electron microscopy is the one with the mostadvantageous resolution limit and therefore it is a very efficient technique fordeciphering the cell architecture and relating it to function. This chapter aims toprovide an overview of the most important techniques that we can apply to abiological sample, tissue or cells, to observe it with an electron microscope, fromthe most conventional to the latest generation. Processes and concepts aredefined, and the advantages and disadvantages of each technique are assessedalong with the image and information that we can obtain by using each one ofthem.

M-protein and other intrinsic virulence factors of Streptococcus pyogenes are encoded on an ancient pathogenicity island.

BACKGROUND: The increasing number of completely sequenced bacterial genomes allows comparing their architecture and genetic makeup. Such new information highlights the crucial role of lateral genetic exchanges in bacterial evolution and speciation. RESULTS: Here we analyzed the twelve sequenced genomes of Streptococcus pyogenes by a naïve approach that examines the preferential nucleotide usage along the chromosome, namely the usage of G versus C (GC-skew) and T versus A (TA-skew). The cumulative GC-skew plot presented an inverted V-shape composed of two symmetrical linear segments, where the minimum and maximum corresponded to the origin and terminus of DNA replication. In contrast, the cumulative TA-skew presented a V-shape, which segments were interrupted by several steep slopes regions (SSRs), indicative of a different nucleotide composition bias. Each S. pyogenes genome contained up to nine individual SSRs, encompassing all described strain-specific prophages. In addition, each genome contained a similar unique non-phage SSR, the core of which consisted of 31 highly homologous genes. This core includes the M-protein, other mga-related factors and other virulence genes, totaling ten intrinsic virulence genes. In addition to a high content in virulence-related genes and to a peculiar nucleotide bias, this SSR, which is 47 kb-long in a M1GAS strain, harbors direct repeats and a tRNA gene, suggesting a mobile element. Moreover, its complete absence in a M-protein negative group A Streptococcus natural isolate demonstrates that it could be spontaneously lost, but in vitro deletion experiments indicates that its excision occurred at very low rate. The stability of this SSR, combined to its presence in all sequenced S. pyogenes sequenced genome, suggests that it results from an ancient acquisition. CONCLUSION: Thus, this non-phagic SSR is compatible with a pathogenicity island, acquired before S. pyogenes speciation. Its potential excision might bear relevance for vaccine development, because vaccines targeting M-protein might select for M-protein-negative variants that still carry other virulence determinants.

Quantitative genetics of learning ability and resistance to stress in Drosophila melanogaster.

Even though laboratory evolution experiments have demonstrated genetic variation for learning ability, we know little about the underlying genetic architecture and genetic relationships with other ecologically relevant traits. With a full diallel cross among twelve inbred lines of Drosophila melanogaster originating from a natural population (0.75 < F < 0.93), we investigated the genetic architecture of olfactory learning ability and compared it to that for another behavioral trait (unconditional preference for odors), as well as three traits quantifying the ability to deal with environmental challenges: egg-to-adult survival and developmental rate on a low-quality food, and resistance to a bacterial pathogen. Substantial additive genetic variation was detected for each trait, highlighting their potential to evolve. Genetic effects contributed more than nongenetic parental effects to variation in traits measured at the adult stage: learning, odorant perception, and resistance to infection. In contrast, the two traits quantifying larval tolerance to low-quality food were more strongly affected by parental effects. We found no evidence for genetic correlations between traits, suggesting that these traits could evolve at least to some degree independently of one another. Finally, inbreeding adversely affected all traits.

Cerebellar lesions: is there a lateralisation effect on memory deficits?

BACKGROUND: Until recently, neurosurgeons eagerly removed cerebellar lesions without consideration of future cognitive impairment that might be caused by the resection. In children, transient cerebellar mutism after resection has lead to a diminished use of midline approaches and vermis transection, as well as reduced retraction of the cerebellar hemispheres. The role of the cerebellum in higher cognitive functions beyond coordination and motor control has recently attracted significant interest in the scientific community, and might change the neurosurgical approach to these lesions. The aim of this study was to investigate the specific effects of cerebellar lesions on memory, and to assess a possible lateralisation effect. METHODS: We studied 16 patients diagnosed with a cerebellar lesion, from January 1997 to April 2005, in the "Centre Hospitalier Universitaire Vaudois (CHUV)", Lausanne, Switzerland. Different neuropsychological tests assessing short term and anterograde memory, verbal and visuo-spatial modalities were performed pre-operatively. RESULTS: Severe memory deficits in at least one modality were identified in a majority (81%) of patients with cerebellar lesions. Only 1 patient (6%) had no memory deficit. In our series lateralisation of the lesion did not lead to a significant difference in verbal or visuo-spatial memory deficits. FINDINGS: These findings are consistent with findings in the literature concerning memory deficits in isolated cerebellar lesions. These can be explained by anatomical pathways. However, the cross-lateralisation theory cannot be demonstrated in our series. The high percentage of patients with a cerebellar lesion who demonstrate memory deficits should lead us to assess memory in all patients with cerebellar lesions.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

A novel HIV vaccine adjuvanted by IC31 induces robust and persistent humoral and cellular immunity.

The HIV vaccine strategy that, to date, generated immune protection consisted of a prime-boost regimen using a canarypox vector and an HIV envelope protein with alum, as shown in the RV144 trial. Since the efficacy was weak, and previous HIV vaccine trials designed to generate antibody responses failed, we hypothesized that generation of T cell responses would result in improved protection. Thus, we tested the immunogenicity of a similar envelope-based vaccine using a mouse model, with two modifications: a clade C CN54gp140 HIV envelope protein was adjuvanted by the TLR9 agonist IC31®, and the viral vector was the vaccinia strain NYVAC-CN54 expressing HIV envelope gp120. The use of IC31® facilitated immunoglobulin isotype switching, leading to the production of Env-specific IgG2a, as compared to protein with alum alone. Boosting with NYVAC-CN54 resulted in the generation of more robust Th1 T cell responses. Moreover, gp140 prime with IC31® and alum followed by NYVAC-CN54 boost resulted in the formation and persistence of central and effector memory populations in the spleen and an effector memory population in the gut. Our data suggest that this regimen is promising and could improve the protection rate by eliciting strong and long-lasting humoral and cellular immune responses.

Architectural features of bacterial genomes and their applications

Résumé -Caractéristiques architecturales des génomes bactériens et leurs applications Les bactéries possèdent généralement un seul chromosome circulaire. A chaque génération, ce chromosome est répliqué bidirectionnellement, par deux complexes enzymatiques de réplication se déplaçant en sens opposé depuis l'origine de réplication jusqu'au terminus, situé à l'opposé. Ce mode de réplication régit l'architecture du chromosome -l'orientation des gènes par rapport à la réplication, notamment - et est en grande partie à l'origine des pressions qui provoquent la variation de la composition en nucléotides du génome, hors des contraintes liées à la structure et à la fonction des protéines codées sur le chromosome. Le but de cette thèse est de contribuer à quantifier les effets de la réplication sur l'architecture chromosomique, en s'intéressant notamment aux gènes des ARN ribosomiques, cruciaux pour la bactérie. D'un autre côté, cette architecture est spécifique à l'espèce et donne ainsi une «identité génomique » aux gènes. Il est démontré ici qu'il est possible d'utiliser des marqueurs «naïfs » de cette identité pour détecter, notamment dans le génome du staphylocoque doré, des îlots de pathogénicité, qui concentrent un grand nombre de facteurs de virulence de la bactérie. Ces îlots de pathogénicité sont mobiles, et peuvent passer d'une bactérie à une autre, mais conservent durant un certain temps l'identité génomique de leur hôte précédent, ce qui permet de les reconnaître dans leur nouvel hôte. Ces méthodes simples, rapides et fiables seront de la plus haute importance lorsque le séquençage des génomes entiers sera rapide et disponible à très faible coût. Il sera alors possible d'analyser instantanément les déterminants pathogéniques et de résistance aux antibiotiques des agents pathogènes. Summary The bacterial genome is a highly organized structure, which may be referred to as the genome architecture, and is mainly directed by DNA replication. This thesis provides significant insights in the comprehension of the forces that shape bacterial chromosomes, different in each genome and contributing to confer them an identity. First, it shows the importance of the replication in directing the orientation of prokaryotic ribosomal RNAs, and how it shapes their nucleotide composition in a tax on-specific manner. Second, it highlights the pressure acting on the orientation of the genes in general, a majority of which are transcribed in the same direction as replication. Consequently, apparent infra-arm genome rearrangements, involving an exchange of the leading/lagging strands and shown to reduce growth rate, are very likely artifacts due to an incorrect contig assembly. Third, it shows that this genomic identity can be used to detect foreign parts in genomes, by establishing this identity for a given host and identifying the regions that deviate from it. This property is notably illustrated with Staphylococcus aureus: known pathogenicity islands and phages, and putative ancient pathogenicity islands concentrating many known pathogenicity-related genes are highlighted; the analysis also detects, incidentally, proteins responsible for the adhesion of S. aureus to the hosts' cells. In conclusion, the study of nucleotide composition of bacterial genomes provides the opportunity to better understand the genome-level pressures that shape DNA sequences, and to identify genes and regions potentially related to pathogenicity with fast, simple and reliable methods. This will be of crucial importance when whole-genome sequencing will be a rapid, inexpensive and routine tool.

Different transcriptional control of metabolism and extracellular matrix in visceral and subcutaneous fat of obese and rimonabant treated mice.

BACKGROUND: The visceral (VAT) and subcutaneous (SCAT) adipose tissues play different roles in physiology and obesity. The molecular mechanisms underlying their expansion in obesity and following body weight reduction are poorly defined. METHODOLOGY: C57Bl/6 mice fed a high fat diet (HFD) for 6 months developed low, medium, or high body weight as compared to normal chow fed mice. Mice from each groups were then treated with the cannabinoid receptor 1 antagonist rimonabant or vehicle for 24 days to normalize their body weight. Transcriptomic data for visceral and subcutaneous adipose tissues from each group of mice were obtained and analyzed to identify: i) genes regulated by HFD irrespective of body weight, ii) genes whose expression correlated with body weight, iii) the biological processes activated in each tissue using gene set enrichment analysis (GSEA), iv) the transcriptional programs affected by rimonabant. PRINCIPAL FINDINGS: In VAT, "metabolic" genes encoding enzymes for lipid and steroid biosynthesis and glucose catabolism were down-regulated irrespective of body weight whereas "structure" genes controlling cell architecture and tissue remodeling had expression levels correlated with body weight. In SCAT, the identified "metabolic" and "structure" genes were mostly different from those identified in VAT and were regulated irrespective of body weight. GSEA indicated active adipogenesis in both tissues but a more prominent involvement of tissue stroma in VAT than in SCAT. Rimonabant treatment normalized most gene expression but further reduced oxidative phosphorylation gene expression in SCAT but not in VAT. CONCLUSION: VAT and SCAT show strikingly different gene expression programs in response to high fat diet and rimonabant treatment. Our results may lead to identification of therapeutic targets acting on specific fat depots to control obesity.

Persistence of cognitive impairment and its negative impact on psychosocial functioning in lithium-treated, euthymic bipolar patients: a 6-year follow-up study.

BACKGROUND: Previous cross-sectional studies report that cognitive impairment is associated with poor psychosocial functioning in euthymic bipolar patients. There is a lack of long-term studies to determine the course of cognitive impairment and its impact on functional outcome. Method A total of 54 subjects were assessed at baseline and 6 years later; 28 had DSM-IV TR bipolar I or II disorder (recruited, at baseline, from a Lithium Clinic Program) and 26 were healthy matched controls. They were all assessed with a cognitive battery tapping into the main cognitive domains (executive function, attention, processing speed, verbal memory and visual memory) twice over a 6-year follow-up period. All patients were euthymic (Hamilton Rating Scale for Depression score lower than 8 and Young mania rating scale score lower than 6) for at least 3 months before both evaluations. At the end of follow-up, psychosocial functioning was also evaluated by means of the Functioning Assessment Short Test. RESULTS: Repeated-measures multivariate analysis of covariance showed that there were main effects of group in the executive domain, in the inhibition domain, in the processing speed domain, and in the verbal memory domain (p<0.04). Among the clinical factors, only longer illness duration was significantly related to slow processing (p=0.01), whereas strong relationships were observed between impoverished cognition along time and poorer psychosocial functioning (p<0.05). CONCLUSIONS: Executive functioning, inhibition, processing speed and verbal memory were impaired in euthymic bipolar out-patients. Although cognitive deficits remained stable on average throughout the follow-up, they had enduring negative effects on psychosocial adaptation of patients.

Neurocognitive deficits and personality traits among euthymic patients with mood disorders in late life.

BACKGROUND: Previous studies revealed that acute depressive episodes are associated with both cognitive deficits and modified personality patterns in late life. Whether or not these psychological changes are present after remission remains a matter of debate. To date, no study provided concomitant assessment of cognition and psychological functions in this particular clinical setting. METHOD: Using a cross-sectional design, 58 remitted outpatients (36 with unipolar early-onset depression (EOD) and 22 with bipolar disorder (BD)) were compared to 62 healthy controls. Assessment included detailed neurocognitive measures and evaluation of the five factor personality dimensions (NEO-Personality Inventory). RESULTS: Group comparisons revealed significant slower processing speed, working and episodic memory performances in BD patients. EOD patients showed cognitive abilities comparable to those of elderly controls. In NEO PI assessment, both BD and EOD patients displayed higher Depressiveness facet scores. In addition, the EOD but not BD group had lower Extraversion factor, and Warmth and Positive Emotion facet scores than controls. CONCLUSIONS: After remission from acute affective symptoms, older BD patients show significant impairment in several cognitive functions while neuropsychological performances remained intact in elderly patients with EOD. Supporting a long-lasting psychological vulnerability, EOD patients are more prone to develop emotion-related personality trait changes than BD patients.