Attention-deficit hyperactivity disorder symptoms and brain morphology:Addressing potential selection bias with inverse probability weighting

The goal of this study was to examine what happens to established associations between attention deficit hyperactivity disorder (ADHD) symptoms and cortical surface and thickness regions once we apply inverse probability of censoring weighting (IPCW) to address potential selection bias. Moreover, we illustrate how different factors that predict participation contribute to potential selection bias. Participants were 9- to 11-year-old children from the Generation R study (N = 2707). Cortical area and thickness were measured with magnetic resonance imaging (MRI) and ADHD symptoms with the Child Behavior Checklist. We examined how associations between ADHD symptoms and brain morphology change when we weight our sample back to either follow-up (ages 9–11), baseline (cohort at birth), or eligible (population of Rotterdam at time of recruitment). Weights were derived using IPCW or raking and missing predictors of participation used to estimate weights were imputed. Weighting analyses to baseline and eligible increased beta coefficients for the middle temporal gyrus surface area, as well as fusiform gyrus cortical thickness. Alternatively, the beta coefficient for the rostral anterior cingulate decreased. Removing one group of variables used for estimating weights resulted in the weighted regression coefficient moving closer to the unweighted regression coefficient. In addition, we found considerably different beta coefficients for most surface area regions and all thickness measures when we did not impute missing covariate data. Our findings highlight the importance of using inverse probability weighting (IPW) in the neuroimaging field, especially in the context of mental health-related research. We found that including all variables related to exposure-outcome in the IPW model and combining IPW with multiple imputations can help reduce bias. We encourage future psychiatric neuroimaging studies to define their target population, collect information on eligible but not included participants and use inverse probability of censoring weighting (IPCW) to reduce selection bias.</p

Attention-deficit hyperactivity disorder symptoms and brain morphology:Addressing potential selection bias with inverse probability weighting

The goal of this study was to examine what happens to established associations between attention deficit hyperactivity disorder (ADHD) symptoms and cortical surface and thickness regions once we apply inverse probability of censoring weighting (IPCW) to address potential selection bias. Moreover, we illustrate how different factors that predict participation contribute to potential selection bias. Participants were 9- to 11-year-old children from the Generation R study (N = 2707). Cortical area and thickness were measured with magnetic resonance imaging (MRI) and ADHD symptoms with the Child Behavior Checklist. We examined how associations between ADHD symptoms and brain morphology change when we weight our sample back to either follow-up (ages 9–11), baseline (cohort at birth), or eligible (population of Rotterdam at time of recruitment). Weights were derived using IPCW or raking and missing predictors of participation used to estimate weights were imputed. Weighting analyses to baseline and eligible increased beta coefficients for the middle temporal gyrus surface area, as well as fusiform gyrus cortical thickness. Alternatively, the beta coefficient for the rostral anterior cingulate decreased. Removing one group of variables used for estimating weights resulted in the weighted regression coefficient moving closer to the unweighted regression coefficient. In addition, we found considerably different beta coefficients for most surface area regions and all thickness measures when we did not impute missing covariate data. Our findings highlight the importance of using inverse probability weighting (IPW) in the neuroimaging field, especially in the context of mental health-related research. We found that including all variables related to exposure-outcome in the IPW model and combining IPW with multiple imputations can help reduce bias. We encourage future psychiatric neuroimaging studies to define their target population, collect information on eligible but not included participants and use inverse probability of censoring weighting (IPCW) to reduce selection bias.</p

Par@Graph-a parallel toolbox for the construction and analysis of large complex climate networks

© 2015 Author(s). In this paper, we present Par@Graph, a software toolbox to reconstruct and analyze complex climate networks having a large number of nodes (up to at least 106) and edges (up to at least 1012). The key innovation is an efficient set of parallel software tools designed to leverage the inherited hybrid parallelism in distributed-memory clusters of multi-core machines. The performance of the toolbox is illustrated through networks derived from sea surface height (SSH) data of a global high-resolution ocean model. Less than 8 min are needed on 90 Intel Xeon E5-4650 processors to reconstruct a climate network including the preprocessing and the correlation of 3 × 105 SSH time series, resulting in a weighted graph with the same number of vertices and about 3.2 × 108 edges. In less than 14 min on 30 processors, the resulted graph's degree centrality, strength, connected components, eigenvector centrality, entropy and clustering coefficient metrics were obtained. These results indicate that a complete cycle to construct and analyze a large-scale climate network is available under 22 min Par@Graph therefore facilitates the application of climate network analysis on high-resolution observations and model results, by enabling fast network reconstruct from the calculation of statistical similarities between climate time series. It also enables network analysis at unprecedented scales on a variety of different sizes of input data sets

Examining longitudinal associations between prenatal exposure to infections and child brain morphology

Background: Maternal infection during pregnancy has been identified as a prenatal risk factor for the later development of psychopathology in exposed offspring. Neuroimaging data collected during childhood has suggested a link between prenatal exposure to maternal infection and child brain structure and function, potentially offering a neurobiological explanation for the emergence of psychopathology. Additionally, preclinical studies utilizing repeated measures of neuroimaging data suggest that effects of prenatal maternal infection on the offspring's brain may normalize over time (i.e., catch-up growth). However, it remains unclear whether exposure to prenatal maternal infection in humans is related to long-term differential neurodevelopmental trajectories. Hence, this study aimed to investigate the association between prenatal exposure to infections on child brain development over time using repeated measures MRI data. Methods: We leveraged data from a population-based cohort, Generation R, in which we examined prospectively assessed self-reported infections at each trimester of pregnancy (N = 2,155). We further used three neuroimaging assessments (at mean ages 8, 10 and 14) to obtain cortical and subcortical measures of the offspring's brain morphology with MRI. Hereafter, we applied linear mixed-effects models, adjusting for several confounding factors, to estimate the association of prenatal maternal infection with child brain development over time. Results:We found that prenatal exposure to infection in the third trimester was associated with a slower decrease in volumes of the pars orbitalis, rostral anterior cingulate and superior frontal gyrus, and a faster increase in the middle temporal gyrus. In the temporal pole we observed a divergent pattern, specifically showing an increase in volume in offspring exposed to more infections compared to a decrease in volume in offspring exposed to fewer infections. We further observed associations in other frontal and temporal lobe structures after exposure to infections in any trimester, though these did not survive multiple testing correction. Conclusions: Our results suggest that prenatal exposure to infections in the third trimester may be associated with slower age-related growth in the regions: pars orbitalis, rostral anterior cingulate and superior frontal gyrus, and faster age-related growth in the middle temporal gyrus across childhood, suggesting a potential sensitive period. Our results might be interpreted as an extension of longitudinal findings from preclinical studies, indicating that children exposed to prenatal infections could exhibit catch-up growth. However, given the lack of differences in brain volume between various infection groups at baseline, there may instead be either a longitudinal deviation or a subtle temporal deviation. Subsequent well-powered studies that extend into the period of full brain development (∼25 years) are needed to confirm whether the observed phenomenon is indeed catch-up growth, a longitudinal deviation, or a subtle temporal deviation.</p

Examining longitudinal associations between prenatal exposure to infections and child brain morphology

Background: Maternal infection during pregnancy has been identified as a prenatal risk factor for the later development of psychopathology in exposed offspring. Neuroimaging data collected during childhood has suggested a link between prenatal exposure to maternal infection and child brain structure and function, potentially offering a neurobiological explanation for the emergence of psychopathology. Additionally, preclinical studies utilizing repeated measures of neuroimaging data suggest that effects of prenatal maternal infection on the offspring's brain may normalize over time (i.e., catch-up growth). However, it remains unclear whether exposure to prenatal maternal infection in humans is related to long-term differential neurodevelopmental trajectories. Hence, this study aimed to investigate the association between prenatal exposure to infections on child brain development over time using repeated measures MRI data. Methods: We leveraged data from a population-based cohort, Generation R, in which we examined prospectively assessed self-reported infections at each trimester of pregnancy (N = 2,155). We further used three neuroimaging assessments (at mean ages 8, 10 and 14) to obtain cortical and subcortical measures of the offspring's brain morphology with MRI. Hereafter, we applied linear mixed-effects models, adjusting for several confounding factors, to estimate the association of prenatal maternal infection with child brain development over time. Results:We found that prenatal exposure to infection in the third trimester was associated with a slower decrease in volumes of the pars orbitalis, rostral anterior cingulate and superior frontal gyrus, and a faster increase in the middle temporal gyrus. In the temporal pole we observed a divergent pattern, specifically showing an increase in volume in offspring exposed to more infections compared to a decrease in volume in offspring exposed to fewer infections. We further observed associations in other frontal and temporal lobe structures after exposure to infections in any trimester, though these did not survive multiple testing correction. Conclusions: Our results suggest that prenatal exposure to infections in the third trimester may be associated with slower age-related growth in the regions: pars orbitalis, rostral anterior cingulate and superior frontal gyrus, and faster age-related growth in the middle temporal gyrus across childhood, suggesting a potential sensitive period. Our results might be interpreted as an extension of longitudinal findings from preclinical studies, indicating that children exposed to prenatal infections could exhibit catch-up growth. However, given the lack of differences in brain volume between various infection groups at baseline, there may instead be either a longitudinal deviation or a subtle temporal deviation. Subsequent well-powered studies that extend into the period of full brain development (∼25 years) are needed to confirm whether the observed phenomenon is indeed catch-up growth, a longitudinal deviation, or a subtle temporal deviation.</p

Towards a nanospecific approach for risk assessment.

In the current paper, a new strategy for risk assessment of nanomaterials is described, which builds upon previous project outcomes and is developed within the FP7 NANoREG project. NANoREG has the aim to develop, for the long term, new testing strategies adapted to a high number of nanomaterials where many factors can affect their environmental and health impact. In the proposed risk assessment strategy, approaches for (Quantitative) Structure Activity Relationships ((Q)SARs), grouping and read-across are integrated and expanded to guide the user how to prioritise those nanomaterial applications that may lead to high risks for human health. Furthermore, those aspects of exposure, kinetics and hazard assessment that are most likely to be influenced by the nanospecific properties of the material under assessment are identified. These aspects are summarised in six elements, which play a key role in the strategy: exposure potential, dissolution, nanomaterial transformation, accumulation, genotoxicity and immunotoxicity. With the current approach it is possible to identify those situations where the use of nanospecific grouping, read-across and (Q)SAR tools is likely to become feasible in the future, and to point towards the generation of the type of data that is needed for scientific justification, which may lead to regulatory acceptance of nanospecific applications of these tools.The research leading to these results has been partially funded by the European Union Seventh Framework Programme (FP7/ 2007e2013) under the project NANoREG (A common European approach to the regulatory testing of nanomaterials), grant agreement 310584.info:eu-repo/semantics/publishedVersio

Relevance or Excellence? Setting Research Priorities for Mental Health and Psychosocial Support in Humanitarian Settings

Background: Humanitarian crises are associated with an increase in mental disorders and psychological distress. Despite the emerging consensus on intervention strategies in humanitarian settings, the field of mental health and psychosocial support (MHPSS) in humanitarian settings lacks a consensus-based research agenda. Methods: From August 2009 to February 2010, we contacted policymakers, academic researchers, and humanitarian aid workers, and conducted nine semistructured focus group discussions with 114 participants in three locations (Peru, Uganda, and Nepal), in both the capitals and remote humanitarian settings. Local stakeholders representing a range of academic expertise (psychiatry, psychology, social work, child protection, and medical anthropology) and organizations (governments, universities, nongovernmental organizations, and UN agencies) were asked to identify priority questions for MHPSS research in humanitarian settings, and to discuss factors that hamper and facilitate research. Results: Thematic analyses of transcripts show that participants broadly agreed on prioritized research themes in the following order: (1) the prevalence and burden of mental health and psychosocial difficulties in humanitarian settings, (2) how MHPSS implementation can be improved, (3) evaluation of specific MHPSS interventions, (4) the determinants of mental health and psychological distress, and (5) improved research methods and processes. Rather than differences in research themes across countries, what emerged was a disconnect between different groups of stakeholders regarding research processes: the perceived lack of translation of research findings into actual policy and programs; misunderstanding of research methods by aid workers; different appreciation of the time needed to conduct research; and disputed universality of research constructs. Conclusions: To advance a collaborative research agenda, actors in this field need to bridge the perceived disconnect between the goals of “relevance” and “excellence.” Research needs to be more sensitive to questions and concerns arising from humanitarian interventions, and practitioners need to take research findings into account in designing interventions. (Harv Rev Psychiatry 2012;20:25–36.