Exposure to trauma-relevant pictures is associated with tachycardia in victims who had experienced an intense peritraumatic defensive response : the tonic immobility.

Tonic immobility is an involuntary, last-ditch defensive reaction characterized by physical inactivity in a context of inescapable threat that has been described in many species, including humans. The occurrence of this defensive response is a predictor of the severity of psychiatric disorders and may be considered as an index of an intense reaction to a traumatic event. Here, we investigated whether the retrospective reports of peritraumatic tonic immobility reaction in participants exposed to a traumatic event would modify their cardiac responses to pictures related to their trauma. Using a questionnaire of life-threating events, we selected students who experienced violent crime as their most intense trauma and students who had never experienced a violent crime trauma, but experienced other traumatic events. All participants completed a questionnaire that estimated the intensity of tonic immobility during their most intense trauma. Electrocardiographic recordings were collected during exposure to pictures. Participants viewed emotional pictures (human attack with guns) and neutral pictures. These emotional stimuli were selected to be trauma-relevant to the violent crime group and non trauma-relevant to the no violent crime trauma group. Violent crime group showed a positive correlation between heart rate changes after viewing trauma-related pictures and tonic immobility scores. We observed that low tonic immobility scores were associated with bradycardia and high scores with tachycardia in response to trauma-relevant pictures. For the no violent crime group, no significant correlation was detected. These results suggest that the relevance of the stimuli and the magnitude of the defensive response during a previous trauma event were important factors triggering more intense defensive responses

Can Emotional and Behavioral Dysregulation in Youth Be Decoded from Functional Neuroimaging?

High comorbidity among pediatric disorders characterized by behavioral and emotional dysregulation poses problems for diagnosis and treatment, and suggests that these disorders may be better conceptualized as dimensions of abnormal behaviors. Furthermore, identifying neuroimaging biomarkers related to dimensional measures of behavior may provide targets to guide individualized treatment. We aimed to use functional neuroimaging and pattern regression techniques to determine whether patterns of brain activity could accurately decode individual-level severity on a dimensional scale measuring behavioural and emotional dysregulation at two different time points

Brain-behaviour modes of covariation in healthy and clinically depressed young people

Abstract: Understanding how variations in dimensions of psychometrics, IQ and demographics relate to changes in brain connectivity during the critical developmental period of adolescence and early adulthood is a major challenge. This has particular relevance for mental health disorders where a failure to understand these links might hinder the development of better diagnostic approaches and therapeutics. Here, we investigated this question in 306 adolescents and young adults (14–24 y, 25 clinically depressed) using a multivariate statistical framework, based on canonical correlation analysis (CCA). By linking individual functional brain connectivity profiles to self-report questionnaires, IQ and demographic data we identified two distinct modes of covariation. The first mode mapped onto an externalization/internalization axis and showed a strong association with sex. The second mode mapped onto a well-being/distress axis independent of sex. Interestingly, both modes showed an association with age. Crucially, the changes in functional brain connectivity associated with changes in these phenotypes showed marked developmental effects. The findings point to a role for the default mode, frontoparietal and limbic networks in psychopathology and depression

EXPRESSION OF THE DEVELOPMENT OF P27/KIP1 OF CHICK RETINA

P27/Kip1 is a protein that inhibits cell cycle and that is also involved in cell migration and differentiation. In the present work, we characterized the expression and localization of p27/Kip1 during the development of the chick retina in vivo and in vitro. The expression of p27/Kip1 was analysed by western blotting and immunocytochemistry. P27/Kip1 content in retinal monolayer cultures obtained from 7- day-old chick embryos increased during cell differentiation in the cultures. The expression of this protein increased after culture day 2 (C2) (163 ± 9,9% of the expression in C0, n=7), attaining the maximal level of expression of 261 ± 14,4% (n=4) in C4. This level of expression was constant until C9 (273 ± 15,8%, n=4). Immunoreactivity for p27/Kip1 (IR) was observed only in neurons until C7. During the in vivo ontogeny, the expression of this protein was maximal in retinas from E12 embryos and, although decreasing by 30% after this stage, p27/Kip1 levels remained high until the adult period. High IR was observed in cell bodies located in the future ganglion cell layer at the beginning of development. As ontogeny proceedes, 2 populations with different labeling intensities were detected. The IR of low intensity was observed in some elongated neuroblasts located in the future nuclear layers. The high intensity IR was detected in more differentiated cell bodies and in migrating cell bodies in the Inner Plexiform Layer. During synaptogenesis, labeled processes of cells located in the ganglion cell layer and amacrines, directed to the Inner Plexiform Layer were identified. In retinas from 60-day-old animals, high IR was observed in part of the cell bodies from all the layers of the tissue, except in the cell bodies of glial cells (n=5). Our data indicate tha p27/Kip1 is involved in the processes of cell cycle exit, differentiation and migration during development. Its presence in the adult retina suggests that it has a role in the mantainance of the cell differentiated state in this tissue.Fundação de Amparo a Pesquisa do Estado do Rio de JaneiroA proteína p27/Kip1 é um inibidor do ciclo celular que também está envolvida com fenômenos de migração e diferenciação celular. Neste trabalho, caracterizamos a expressão e a localização de p27/Kip1 durante o desenvolvimento da retina de pinto in vivo e in vitro. A expressão de p27/Kip1 foi caracterizada por western blotting e imunocitoquímica. O conteúdo de p27/kip1 em culturas em monocamadas de células de retinas de embriões de pinto com 7 dias (E7) aumentava durante o decorrer da diferenciação das células. A expressão desta proteína aumentou a partir do segundo dia de cultivo (C2), atingindo um nível máximo de 261 ± 14,4% (n=4) em C4. Esta expressão permaneceu elevada até C9 (273 ± 15,8%, n=4). Imunorreatividade (IR) para p27/kip1 foi observada apenas em neurônios até C7. Na ontogênese in vivo, a expressão desta proteína foi máxima em retinas de E12 e, apesar de uma diminuição de 30%, permaneceu elevada até retinas adultas. Alta IR para p27/Kip1 foi verificada em corpos celulares localizados na camada de células ganglionares prospectiva nos estágios precoces do desenvolvimento. Com o decorrer da ontogênese in vivo, 2 populações com intensidade de marcação diferente foram detectadas. A IR de pouca intensidade foi observada em alguns neuroblastos alongados localizados nas futuras camadas nucleares. Já a IR de alta intensidade foi verificada nos corpos de células com aspecto mais diferenciado e nos corpos de células em migração pela camada plexiforme interna. No período de sinaptogênese, processos marcados de células da camada de ganglionares e de amácrinas, direcionados para a camada plexiforme interna, foram identificados. Em retinas obtidas de pintos com 60 dias, alta IR foi observada em parte dos corpos celulares de todas as camadas deste tecido, exceto nos corpos de células gliais (n=5). Nossos dados indicam que p27/Kip1 participa dos processos de saída do ciclo celular, diferenciação e migração durante o desenvolvimento. Sua permanência na retina adulta sugere um papel na manutenção do estado diferenciado das células

Análise bacteriológica de biofilmes de superfícies dentárias radiculares apresentando diferentes estágios de atividade de cárie

This study evaluated the numbers and determined the proportion of mutans streptococci and Lactobacillus spp., which are possible relevant cariogenic organisms, in biofilms recovered from lesions at root surfaces with active caries lesions (ARC), inactive caries lesions, and sound root surfaces (SRS). Samples were cultured in MSB agar for mutans streptococci counts, Rogosa agar for Lactobacillus spp. counts, and brain-heart infusion agar for total viable anaerobic counts. After incubation, the number of colony-forming units (CFUs) was determined and compared between groups by the Mann-Whitney U test with a significance level set at 95%. The proportion of counts of mutans streptococci and Lactobacillus spp. in the total viable microorganisms was also analyzed by Chi-square test. Ninety samples (30 from each surface) from 37 patients were cultured and analyzed. The CFU was similar between mutans streptococci and Lactobacillus spp. These species were present in at least half of the samples and no difference was found in the frequency of isolation of these species. Only 6 samples showed a proportion of more than 10% of mutans streptococci; 4 of the samples were from ARC. Most (93%) SRS samples did not contain viable Lactobacillus spp. The data indicate the low counts of mutans streptococci and Lactobacillus spp. in root surfaces, regardless of the activity of caries lesions.O estudo analisou contagens e proporções de mutans streptococci e Lactobacillus spp., que podem ser microorganismos importantes em lesões de cárie radicular com diferentes atividades. Biofilmes foram coletados em três locais: ARC – superfície radicular com lesão ativa de cárie; IRC – superfícies radiculares com lesão inativa de cárie; SRS – superfícies de raizes hígidas. As amostras foram cultivadas em agar MSB para contagens de mutans streptococci; agar Rogosa para Lactobacillus spp., e agar BHI para contagens de microrganismos viáveis anaeróbicos totais. Após a incubação, o número de unidades formadoras de colônias (UFCs) foi determinado e comparado entre os grupos pelo teste de Mann-Whitney U test. O nível de significância foi estabelecido em 95%. A proporção de contagem de mutans streptococci e Lactobacillus spp. no total de microrganismos viáveis também foi analisado através do teste de qui-quadrado. Um total de 90 amostras de 37 pacientes foram cultivadas e analisadas: 30 amostras de ARC, 30 de IRC e 30de SRS. Números de UFC foram semelhantes entre os grupos para ambos, mutans streptococci e Lactobacillus spp. Estas espécies estavam presentes em pelo menos metade de todas as amostras e nenhuma diferença foi encontrada na frequência de isolamento dessas espécies dentro dos grupos. Apenas 6 amostras apresentaram mais de 10% de mutans streptococci e 4 foram de ARC. Em relação aos Lactobacillus spp., 93% das amostras não apresentaram proporção dessas bactérias nas SRS. Mutans streptococci e Lactobacillus spp. estão presentes em baixa proporção nas superfícies radiculares, independentemente da atividade das lesões de cárie

Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models

Pattern recognition models have been increasingly applied to neuroimaging data over the last two decades. These applications have ranged from cognitive neuroscience to clinical problems. A common limitation of these approaches is that they do not incorporate previous knowledge about the brain structure and function into the models. Previous knowledge can be embedded into pattern recognition models by imposing a grouping structure based on anatomically or functionally defined brain regions. In this work, we present a novel approach that uses group sparsity to model the whole brain multivariate pattern as a combination of regional patterns. More specifically, we use a sparse version of Multiple Kernel Learning (MKL) to simultaneously learn the contribution of each brain region, previously defined by an atlas, to the decision function. Our application of MKL provides two beneficial features: (1) it can lead to improved overall generalisation performance when the grouping structure imposed by the atlas is consistent with the data; (2) it can identify a subset of relevant brain regions for the predictive model. In order to investigate the effect of the grouping in the proposed MKL approach we compared the results of three different atlases using three different datasets. The method has been implemented in the new version of the open-source Pattern Recognition for Neuroimaging Toolbox (PRoNTo)

Sparse network-based models for patient classification using fMRI

Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from healthy participants. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. Many psychiatric disorders, such as depression and schizophrenia, are thought to be brain connectivity disorders. Therefore, pattern recognition based on network models might provide deeper insights and potentially more powerful predictions than whole-brain voxel-based approaches. Here, we build a novel sparse network-based discriminative modeling framework, based on Gaussian graphical models and L1-norm regularized linear Support Vector Machines (SVM). In addition, the proposed framework is optimized in terms of both predictive power and reproducibility/stability of the patterns. Our approach aims to provide better pattern interpretation than voxel-based whole-brain approaches by yielding stable brain connectivity patterns that underlie discriminative changes in brain function between the groups. We illustrate our technique by classifying patients with major depressive disorder (MDD) and healthy participants, in two (event- and block-related) fMRI datasets acquired while participants performed a gender discrimination and emotional task, respectively, during the visualization of emotional valent faces

Predicting anxiety from wholebrain activity patterns to emotional faces in young adults: a machine learning approach

BACKGROUND: It is becoming increasingly clear that pathophysiological processes underlying psychiatric disorders categories are heterogeneous on many levels, including symptoms, disease course, comorbidity and biological underpinnings. This heterogeneity poses challenges for identifying biological markers associated with dimensions of symptoms and behaviour that could provide targets to guide treatment choice and novel treatment. In response, the research domain criteria (RDoC) (Insel et al., 2010) was developed to advocate a dimensional approach which omits any disease definitions, disorder thresholds, or cut-points for various levels of psychopathology to understanding the pathophysiological processes underlying psychiatry disorders. In the present study we aimed to apply pattern regression analysis to identify brain signatures during dynamic emotional face processing that are predictive of anxiety and depression symptoms in a continuum that ranges from normal to pathological levels, cutting across categorically-defined diagnoses. METHODS: The sample was composed of one-hundred and fifty-four young adults (mean age=21.6 and s.d.=2.0, 103 females) consisting of eighty-two young adults seeking treatment for psychological distress that cut across categorically-defined diagnoses and 72 matched healthy young adults. Participants performed a dynamic face task involving fearful, angry and happy faces (and geometric shapes) while undergoing functional Magnetic Resonance Imaging (fMRI). Pattern regression analyses consisted of Gaussian Process Regression (GPR) implemented in the Pattern Recognition for Neuroimaging toolbox (PRoNTo). Predicted and actual clinical scores were compared using Pearson's correlation coefficient (r) and normalized mean squared error (MSE) to evaluate the models' performance. Permutation test was applied to estimate significance levels. RESULTS: GPR identified patterns of neural activity to dynamic emotional face processing predictive of self-report anxiety in the whole sample, which covered a continuum that ranged from healthy to different levels of distress, including subthreshold to fully-syndromal psychiatric diagnoses. Results were significant using two different cross validation strategies (two-fold: r=0.28 (p-value=0.001), MSE=4.47 (p-value=0.001) and five fold r=0.28 (p-value=0.002), MSE=4.62 (p-value=0.003). The contributions of individual regions to the predictive model were very small, demonstrating that predictions were based on the overall pattern rather than on a small combination of regions. CONCLUSIONS: These findings represent early evidence that neuroimaging techniques may inform clinical assessment of young adults irrespective of diagnoses by allowing accurate and objective quantitative estimation of psychopathology