Real-time functional magnetic resonance imaging in obsessive-compulsive disorder

The current literature provides substantial evidence of brain alterations associated with obsessive-compulsive disorder (OCD) symptoms (eg, checking, cleaning/decontamination, counting compulsions; harm or sexual, symmetry/exactness obsessions), and emotional problems (eg, defensive/appetitive emotional imbalance, disgust, guilt, shame, and fear learning/extinction) and cognitive impairments associated with this disorder (eg, inhibitory control, working memory, cognitive flexibility). Building on this evidence, new clinical trials can now target specific brain regions/networks. Real-time functional magnetic resonance imaging (rtfMRI) was introduced as a new therapeutic tool for the self-regulation of brain-mind. In this review, we describe initial trials testing the use of rtfMRI to target brain regions associated with specific OCD symptoms (eg, contamination), and other mind-brain processes (eg, cognitive -working memory, inhibitory control, emotional - defensive, appetitive systems, fear reduction through counter-conditioning) found impaired in OCD patients. While this is a novel topic of research, initial evidence shows the promise of using rtfMRI in training the self-regulation of brain regions and mental processes associated with OCD. Additionally, studies with healthy populations have shown that individuals can regulate brain regions associated with cognitive and emotional processes found impaired in OCD. After the initial "proof-of-concept" stage, there is a need to follow up with controlled clinical trials that could test rtfMRI innovative treatments targeting brain regions and networks associated with different OCD symptoms and cognitive-emotional impairments.info:eu-repo/semantics/publishedVersio

Do Airports green cities? : Von der Airport City zur nachhaltigen Region ; Flughafenstrategien und Regionalentwicklung

Multivariate pattern recognition approaches have become a prominent tool in neuroimaging data analysis. These methods enable the classification of groups of participants (e.g. controls and patients) on the basis of subtly different patterns across the whole brain. This study demonstrates that these methods can be used, in combination with automated morphometric analysis of structural MRI, to determine with great accuracy whether a single subject has been engaged in regular mental training or not. The proposed approach allowed us to identify with 94.87% accuracy (p<0.001) if a given participant is a regular meditator (from a sample of 19 regular meditators and 20 non-meditators). Neuroimaging has been a relevant tool for diagnosing neurological and psychiatric impairments. This study may suggest a novel step forward: the emergence of a new field in brain imaging applications, in which participants could be identified based on their mental experience

Modeling gene expression regulatory networks with the sparse vector autoregressive model

<p>Abstract</p> <p>Background</p> <p>To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems.</p> <p>Results</p> <p>We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets.</p> <p>Conclusion</p> <p>The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any <it>a priori </it>information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.</p

Controle cognitivo associado à indução de irritabilidade: um estudo de RMf usando recordações autobiográficas

OBJECTIVE: Despite the relevance of irritability emotions to the treatment, prognosis and classification of psychiatric disorders, the neurobiological basis of this emotional state has been rarely investigated to date. We assessed the brain circuitry underlying personal script-driven irritability in healthy subjects (n = 11) using functional magnetic resonance imaging. METHOD: Blood oxygen level-dependent signal changes were recorded during auditory presentation of personal scripts of irritability in contrast to scripts of happiness or neutral emotional content. Self-rated emotional measurements and skin conductance recordings were also obtained. Images were acquired using a 1,5T magnetic resonance scanner. Brain activation maps were constructed from individual images, and between-condition differences in the mean power of experimental response were identified by using cluster-wise nonparametric tests. RESULTS: Compared to neutral scripts, increased blood oxygen level-dependent signal during irritability scripts was detected in the left subgenual anterior cingulate cortex, and in the left medial, anterolateral and posterolateral dorsal prefrontal cortex (cluster-wise p-value < 0.05). While the involvement of the subgenual cingulate and dorsal anterolateral prefrontal cortices was unique to the irritability state, increased blood oxygen level-dependent signal in dorsomedial and dorsal posterolateral prefrontal regions were also present during happiness induction. CONCLUSION: Irritability induction is associated with functional changes in a limited set of brain regions previously implicated in the mediation of emotional states. Changes in prefrontal and cingulate areas may be related to effortful cognitive control aspects that gain salience during the emergence of irritability.OBJETIVO: Apesar da relevância de emoções de irritabilidade para o tratamento, prognóstico e classificação dos transtornos psiquiátricos, as bases neurobiológicas deste tipo de estado emocional foram raramente investigadas até hoje. Este estudo avaliou os circuitos cerebrais subjacentes à irritabilidade induzida por scripts pessoais em voluntários saudáveis (n = 11) usando ressonância magnética funcional. MÉTODO: Mudanças no sinal dependente do nível de oxigenação sanguínea (blood-oxygen level dependent signal) foram registradas durante a apresentação por via auditiva de scripts pessoais de irritabilidade em contraste com scripts de felicidade ou de conteúdo emocional neutro. Escores em escalas de autoavaliação emocional e medidas de condutância da pele também foram obtidos. A aquisição de imagens foi realizada em aparelho de ressonância magnética de 1,5 T. Os mapas de ativação cerebral foram construídos a partir das imagens individuais, e as diferenças entre as condições experimentais foram investigadas utilizando testes não-paramétricos baseados em permutações. RESULTADOS: Em comparação com scripts neutros, a apresentação de scripts de irritabilidade levou a aumentos de sinal dependente do nível de oxigenação sanguínea na porção subgenual do giro do cíngulo anterior esquerdo e nas porções medial, ântero-lateral e póstero-lateral do córtex pré-frontal dorsal (cluster-wise p-valor < 0,05). Enquanto o envolvimento do cíngulo anterior subgenual e do córtex pré-frontal dorsal antero-lateral surgiu apenas em associação com o estado de irritabilidade, aumentos do sinal dependente do nível de oxigenação sanguínea nas porções dorso-medial e dorsal póstero-lateral do córtex pré-frontal também estiveram presentes durante indução de felicidade. CONCLUSÃO: Indução de irritabilidade está associada a mudanças de atividade funcional num conjunto restrito de regiões cerebrais previamente implicadas na mediação de estados emocionais. Mudanças na atividade de porções do giro do cíngulo e pré-frontais podem estar relacionadas a esforço de controle cognitivo associado à expressão de emoções de irritabilidade

GEDI: a user-friendly toolbox for analysis of large-scale gene expression data

Abstract\ud \ud \ud \ud Background\ud \ud Several mathematical and statistical methods have been proposed in the last few years to analyze microarray data. Most of those methods involve complicated formulas, and software implementations that require advanced computer programming skills. Researchers from other areas may experience difficulties when they attempting to use those methods in their research. Here we present an user-friendly toolbox which allows large-scale gene expression analysis to be carried out by biomedical researchers with limited programming skills.\ud \ud \ud \ud Results\ud \ud Here, we introduce an user-friendly toolbox called GEDI (Gene Expression Data Interpreter), an extensible, open-source, and freely-available tool that we believe will be useful to a wide range of laboratories, and to researchers with no background in Mathematics and Computer Science, allowing them to analyze their own data by applying both classical and advanced approaches developed and recently published by Fujita et al.\ud \ud \ud \ud Conclusion\ud \ud GEDI is an integrated user-friendly viewer that combines the state of the art SVR, DVAR and SVAR algorithms, previously developed by us. It facilitates the application of SVR, DVAR and SVAR, further than the mathematical formulas present in the corresponding publications, and allows one to better understand the results by means of available visualizations. Both running the statistical methods and visualizing the results are carried out within the graphical user interface, rendering these algorithms accessible to the broad community of researchers in Molecular Biology.This research was supported by FAPESP, CAPES, CNPq, FINEP and PRP-USP.This research was supported by FAPESP, CAPES, CNPq, FINEP and PRPUSP

Spectral Lag Relations in GRB Pulses Detected with HETE-2

Using a pulse-fit method, we investigate the spectral lags between the traditional gamma-ray band (50-400 keV) and the X-ray band (6-25 keV) for 8 GRBs with known redshifts (GRB 010921, GRB 020124, GRB 020127, GRB 021211, GRB 030528, GRB 040924, GRB 041006, GRB 050408) detected with the WXM and FREGATE instruments aboard the HETE-2 satellite. We find several relations for the individual GRB pulses between the spectral lag and other observables, such as the luminosity, pulse duration, and peak energy (Epeak). The obtained results are consistent with those for BATSE, indicating that the BATSE correlations are still valid at lower energies (6-25 keV). Furthermore, we find that the photon energy dependence for the spectral lags can reconcile the simple curvature effect model. We discuss the implication of these results from various points of view.Comment: 13 pages, 9 figures, accepted for the publication in PASJ (minor corrections