Neural Profile of Callous Traits in Children: A Population-Based Neuroimaging Study

BACKGROUND: Callous traits during childhood, e.g., lack of remorse and shallow affect, are a key risk marker for antisocial behavior. Although callous traits have been found to be associated with structural and functional brain alterations, evidence to date has been almost exclusively limited to small, high-risk samples of boys. We characterized gray and white matter brain correlates of callous traits in over 2000 children from the general population. METHODS: Data on mother-reported callous traits and brain imaging were collected at age 10 years from participants of the Generation R Study. Structural magnetic resonance imaging was used to investigate brain morphology using volumetric indices and whole-brain analyses (n = 2146); diffusion tensor imaging was used to assess global and specific white matter microstructure (n = 2059). RESULTS: Callous traits were associated with lower global brain (e.g., total brain) volumes as well as decreased cortical surface area in frontal and temporal regions. Global mean diffusivity was negatively associated with callous traits, suggesting higher white matter microstructural integrity in children with elevated callous traits. Multiple individual tracts, including the uncinate and cingulum, contributed to this global association. Whereas no gender differences were observed for global volumetric indices, white matter associations were present only in girls. CONCLUSIONS: This is the first study to provide a systematic characterization of the structural neural profile of callous traits in the general pediatric population. These findings extend previous work based on selected samples by demonstrating that childhood callous traits in the general population are characterized by widespread macrostructural and microstructural differences across the brain

ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology

We predicted residual fluid intelligence scores from T1-weighted MRI data available as part of the ABCD NP Challenge 2019, using morphological similarity of grey-matter regions across the cortex. Individual structural covariance networks (SCN) were abstracted into graph-theory metrics averaged over nodes across the brain and in data-driven communities/modules. Metrics included degree, path length, clustering coefficient, centrality, rich club coefficient, and small-worldness. These features derived from the training set were used to build various regression models for predicting residual fluid intelligence scores, with performance evaluated both using cross-validation within the training set and using the held-out validation set. Our predictions on the test set were generated with a support vector regression model trained on the training set. We found minimal improvement over predicting a zero residual fluid intelligence score across the sample population, implying that structural covariance networks calculated from T1-weighted MR imaging data provide little information about residual fluid intelligence.Comment: 8 pages plus references, 3 figures, 2 tables. Submission to the ABCD Neurocognitive Prediction Challenge at MICCAI 201

Maternal prepregnancy body mass index and offspring white matter microstructure: results from three birth cohorts

Prepregnancy maternal obesity is a global health problem and has been associated with offspring metabolic and mental ill-health. However, there is a knowledge gap in understanding potential neurobiological factors related to these associations. This study explored the relation between maternal prepregnancy body mass index (BMI) and offspring brain white matter microstructure at the age of 6, 10, and 26 years in three independent cohorts. Maternal BMI was associated with higher FA and lower MD in multiple brain tracts in offspring aged 10 and 26 years, but not at 6 years of age. Future studies should examine whether our observations can be replicated and explore the potential causal nature of the findings.This work was supported by the European Union’s Horizon 2020 research and innovation program [grant agreement no. 633595 DynaHEALTH] and no. 733206 LifeCycle], the Netherlands Organization for Health Research and Development [ZONMW Vici project 016.VICI.170.200]. The PREOBE cohort was funded by Spanish Ministry of Innovation and Science. Junta de Andalucía: Excellence Projects (P06-CTS-02341) and Spanish Ministry of Economy and Competitiveness (BFU2012-40254-C03-01). The first phase of the Generation R Study is made possible by financial support from the Erasmus Medical Centre, the Erasmus University, and the Netherlands Organization for Health Research and Development (ZonMW, grant ZonMW Geestkracht 10.000.1003). The Northern Finland Birth Cohort 1986 is funded by University of Oulu, University Hospital of Oulu, Academy of Finland (EGEA), Sigrid Juselius Foundation, European Commission (EURO-BLCS, Framework 5 award QLG1-CT-2000-01643), NIH/NIMH (5R01MH63706:02

White Matter Development in Early Puberty: A Longitudinal Volumetric and Diffusion Tensor Imaging Twin Study

White matter microstructure and volume show synchronous developmental patterns in children. White matter volume increases considerably during development. Fractional anisotropy, a measure for white matter microstructural directionality, also increases with age. Development of white matter volume and development of white matter microstructure seem to go hand in hand. The extent to which the same or different genetic and/or environmental factors drive these two aspects of white matter maturation is currently unknown. We mapped changes in white matter volume, surface area and diffusion parameters in mono- and dizygotic twins who were scanned at age 9 (203 individuals) and again at age 12 (126 individuals). Over the three-year interval, white matter volume (+6.0%) and surface area (+1.7%) increased, fiber bundles expanded (most pronounced in the left arcuate fasciculus and splenium), and fractional anisotropy increased (+3.0%). Genes influenced white matter volume (heritability ∼85%), surface area (∼85%), and fractional anisotropy (locally 7% to 50%) at both ages. Finally, volumetric white matter growth was negatively correlated with fractional anisotropy increase (r = –0.62) and this relationship was driven by environmental factors. In children who showed the most pronounced white matter growth, fractional anisotropy increased the least and vice-versa. Thus, white matter development in childhood may reflect a process of both expansion and fiber optimization

ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology

We predicted fluid intelligence from T1-weighted MRI data available as part of the ABCD NP Challenge 2019, using morphological similarity of grey-matter regions across the cortex. Individual structural covariance networks (SCN) were abstracted into graph-theory metrics averaged over nodes across the brain and in data-driven communities/modules. Metrics included degree, path length, clustering coefficient, centrality, rich club coefficient, and small-worldness. These features derived from the training set were used to build various regression models for predicting residual fluid intelligence scores, with performance evaluated both using cross-validation within the training set and using the held-out validation set. Our predictions on the test set were generated with a support vector regression model trained on the training set. We found minimal improvement over predicting a zero residual fluid intelligence score across the sample population, implying that structural covariance networks calculated from T1-weighted MR imaging data provide little information about residual fluid intelligence

Maternal prepregnancy body mass index and offspring white matter microstructure:results from three birth cohorts

Abstract Background and aims: Prepregnancy maternal obesity is a global health problem and has been associated with offspring metabolic and mental ill-health. However, there is a knowledge gap in understanding potential neurobiological factors related to these associations. This study explored the relation between maternal prepregnancy body mass index (BMI) and offspring brain white matter microstructure at the age of 6, 10, and 26 years in three independent cohorts. Subjects and methods: The study used data from three European birth cohorts (n = 116 children aged 6 years, n = 2466 children aged 10 years, and n = 437 young adults aged 26 years). Information on maternal prepregnancy BMI was obtained before or during pregnancy and offspring brain white matter microstructure was measured at age 6, 10, or 26 years. We used magnetic resonance imaging-derived fractional anisotropy (FA) and mean diffusivity (MD) as measures of white matter microstructure in the brainstem, callosal, limbic, association, and projection tracts. Linear regressions were fitted to examine the association of maternal BMI and offspring white matter microstructure, adjusting for several socioeconomic and lifestyle-related confounders, including education, smoking, and alcohol use. Results: Maternal BMI was associated with higher FA and lower MD in multiple brain tracts, for example, association and projection fibers, in offspring aged 10 and 26 years, but not at 6 years. In each cohort maternal BMI was related to different white matter tract and thus no common associations across the cohorts were found. Conclusions: Maternal BMI was associated with higher FA and lower MD in multiple brain tracts in offspring aged 10 and 26 years, but not at 6 years of age. Future studies should examine whether our observations can be replicated and explore the potential causal nature of the findings