Mining the Mind Research Network: A Novel Framework for Exploring Large Scale, Heterogeneous Translational Neuroscience Research Data Sources

A neuroinformatics (NI) system is critical to brain imaging research in order to shorten the time between study conception and results. Such a NI system is required to scale well when large numbers of subjects are studied. Further, when multiple sites participate in research projects organizational issues become increasingly difficult. Optimized NI applications mitigate these problems. Additionally, NI software enables coordination across multiple studies, leveraging advantages potentially leading to exponential research discoveries. The web-based, Mind Research Network (MRN), database system has been designed and improved through our experience with 200 research studies and 250 researchers from seven different institutions. The MRN tools permit the collection, management, reporting and efficient use of large scale, heterogeneous data sources, e.g., multiple institutions, multiple principal investigators, multiple research programs and studies, and multimodal acquisitions. We have collected and analyzed data sets on thousands of research participants and have set up a framework to automatically analyze the data, thereby making efficient, practical data mining of this vast resource possible. This paper presents a comprehensive framework for capturing and analyzing heterogeneous neuroscience research data sources that has been fully optimized for end-users to perform novel data mining

Data Sharing in Neuroimaging Research

Significant resources around the world have been invested in neuroimaging studies of brain function and disease. Easier access to this large body of work should have profound impact on research in cognitive neuroscience and psychiatry, leading to advances in the diagnosis and treatment of psychiatric and neurological disease. A trend toward increased sharing of neuroimaging data has emerged in recent years. Nevertheless, a number of barriers continue to impede momentum. Many researchers and institutions remain uncertain about how to share data or lack the tools and expertise to participate in data sharing. The use of electronic data capture (EDC) methods for neuroimaging greatly simplifies the task of data collection and has the potential to help standardize many aspects of data sharing. We review here the motivations for sharing neuroimaging data, the current data sharing landscape, and the sociological or technical barriers that still need to be addressed. The INCF Task Force on Neuroimaging Datasharing, in conjunction with several collaborative groups around the world, has started work on several tools to ease and eventually automate the practice of data sharing. It is hoped that such tools will allow researchers to easily share raw, processed, and derived neuroimaging data, with appropriate metadata and provenance records, and will improve the reproducibility of neuroimaging studies. By providing seamless integration of data sharing and analysis tools within a commodity research environment, the Task Force seeks to identify and minimize barriers to data sharing in the field of neuroimaging

Data sharing in neuroimaging research

Significant resources around the world have been invested in neuroimaging studies of brain function and disease. Easier access to this large body of work should have profound impact on research in cognitive neuroscience and psychiatry, leading to advances in the diagnosis and treatment of psychiatric and neurological disease. A trend toward increased sharing of neuroimaging data has emerged in recent years. Nevertheless, a number of barriers continue to impede momentum. Many researchers and institutions remain uncertain about how to share data or lack the tools and expertise to participate in data sharing. The use of electronic data capture (EDC) methods for neuroimaging greatly simplifies the task of data collection and has the potential to help standardize many aspects of data sharing. We review here the motivations for sharing neuroimaging data, the current data sharing landscape, and the sociological or technical barriers that still need to be addressed. The INCF Task Force on Neuroimaging Datasharing, in conjunction with several collaborative groups around the world, has started work on several tools to ease and eventually automate the practice of data sharing. It is hoped that such tools will allow researchers to easily share raw, processed, and derived neuroimaging data, with appropriate metadata and provenance records, and will improve the reproducibility of neuroimaging studies. By providing seamless integration of data sharing and analysis tools within a commodity research environment, the Task Force seeks to identify and minimize barriers to data sharing in the field of neuroimaging

Juvenile Idiopathic Arthritis Subtype- and Sex-specific Associations with Genetic Variants in the PSMA6/PSMC6/PSMA3 Gene Cluster

BackgroundThe ubiquitin proteasome system plays an exceptional biological role in the antigen processing and immune response and it could potentially be involved in pathogenesis of many immunity-related diseases, including juvenile idiopathic arthritis (JIA).MethodsThe PSMB5 (rs11543947), PSMA6 (rs2277460, rs1048990), PSMC6 (rs2295826, rs2295827), and PSMA3 (rs2348071) proteasomal genes were genotyped on JIA subtype- and sex-specific association; plasma proteasome levels was measured in patients having risk and protective four-locus genotypes and eventual functional significance of allele substitutions was evaluated in silico.ResultsLoci rs11543947 and rs1048990 were identified as disease neutral and other loci as disease susceptible (p < 0.05). The rs2277460, rs2295826, and rs2295827 loci had the strongest association with oligoarthritis [odds ratio (OR) = 2.024, 95% confidence interval (CI) 1.101–3.722; OR = 2.371, 95% CI 1.390–4.044; OR = 2.183, 95% CI 1.272–2.737, respectively), but the rs2348071 locus was associated with polyarthritis in females (OR = 3.438, 95% CI 1.626–7.265). A strong (p < 0.001) association was detected between the rs2277460/rs2295826/rs2295827/rs2348071 four-locus genotypes and the healthy phenotype when all loci were homozygous on common alleles (OR 0.439, 95% CI 0.283–0.681) and with the disease phenotype when the rs2348071 and the rs2295826 and/or rs2295827 loci were represented by risk genotypes simultaneously (OR 4.674, 95% CI 2.096–10.425). Rarely observed in controls, the double rs2277460/rs2348071 heterozygotes were rather frequent in affected males and more strongly associated with polyarthritis (p < 0.05). Haplotypes carrying the rare rs2295826/rs2295827 and rs2277460 alleles showed a strong (p < 0.001) association with oligo- and polyarthritis, respectively. The plasma proteasome level was found to be significantly higher in females having four-locus risk genotypes compared with protective genotypes (p < 0.001). Sequence affinity to transcription factors and similarity to splicing signals, microRNAs and/or hairpin precursors potentially depend on allele substitutions in disease susceptible loci.ConclusionWe demonstrate for the first time evidence of a sex-specific association of PSMA6/PSMC6/PSMA3 genetic variants with subtypes of JIA and plasma proteasome concentrations. Theoretical models of the functional significance of allele substitutions are discussed

Juvenile Idiopathic Arthritis Subtype- and Sex-specific Associations with Genetic Variants in the PSMA6/PSMC6/PSMA3 Gene Cluster

BackgroundThe ubiquitin proteasome system plays an exceptional biological role in the antigen processing and immune response and it could potentially be involved in pathogenesis of many immunity-related diseases, including juvenile idiopathic arthritis (JIA).MethodsThe PSMB5 (rs11543947), PSMA6 (rs2277460, rs1048990), PSMC6 (rs2295826, rs2295827), and PSMA3 (rs2348071) proteasomal genes were genotyped on JIA subtype- and sex-specific association; plasma proteasome levels was measured in patients having risk and protective four-locus genotypes and eventual functional significance of allele substitutions was evaluated in silico.ResultsLoci rs11543947 and rs1048990 were identified as disease neutral and other loci as disease susceptible (p < 0.05). The rs2277460, rs2295826, and rs2295827 loci had the strongest association with oligoarthritis [odds ratio (OR) = 2.024, 95% confidence interval (CI) 1.101–3.722; OR = 2.371, 95% CI 1.390–4.044; OR = 2.183, 95% CI 1.272–2.737, respectively), but the rs2348071 locus was associated with polyarthritis in females (OR = 3.438, 95% CI 1.626–7.265). A strong (p < 0.001) association was detected between the rs2277460/rs2295826/rs2295827/rs2348071 four-locus genotypes and the healthy phenotype when all loci were homozygous on common alleles (OR 0.439, 95% CI 0.283–0.681) and with the disease phenotype when the rs2348071 and the rs2295826 and/or rs2295827 loci were represented by risk genotypes simultaneously (OR 4.674, 95% CI 2.096–10.425). Rarely observed in controls, the double rs2277460/rs2348071 heterozygotes were rather frequent in affected males and more strongly associated with polyarthritis (p < 0.05). Haplotypes carrying the rare rs2295826/rs2295827 and rs2277460 alleles showed a strong (p < 0.001) association with oligo- and polyarthritis, respectively. The plasma proteasome level was found to be significantly higher in females having four-locus risk genotypes compared with protective genotypes (p < 0.001). Sequence affinity to transcription factors and similarity to splicing signals, microRNAs and/or hairpin precursors potentially depend on allele substitutions in disease susceptible loci.ConclusionWe demonstrate for the first time evidence of a sex-specific association of PSMA6/PSMC6/PSMA3 genetic variants with subtypes of JIA and plasma proteasome concentrations. Theoretical models of the functional significance of allele substitutions are discussed

Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge.

Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods

Compendio de métodos para caracterizar la geometría de los tejidos cerebrales a partir de imágenes de resonancia magnética por difusión del agua.

221 p.FIDMAG Hermanas Hospitalarias Research Foundation; CIBERSAM:Centro de Investigación Biomédica en Re

Cognitive Evaluation of Bupropion Sustained Release in Heavy Tobacco Smokers Using Event-Related Potentials

Objective. The aim of this study was to investigate the effects of bupropion sustained release (SR) on cognitive function, evaluated by event-related potentials (ERPs), in heavy tobacco smokers