Developmental Premorbid Body Mass Index Trajectories of Adolescents With Eating Disorders in a Longitudinal Population Cohort

Objective: To examine whether childhood body mass index (BMI) trajectories are prospectively associated with later eating disorder (ED) diagnoses. / Method: Using a subsample from the Avon Longitudinal Study of Parents and Children (N = 1,502), random-coefficient growth models were used to compare premorbid BMI trajectories of individuals who later developed anorexia nervosa (n = 243), bulimia nervosa (n = 69), binge-eating disorder (n = 114), and purging disorder (n = 133) and a control group without EDs or ED symptoms (n = 966). BMI was tracked longitudinally from birth to 12.5 years of age and EDs were assessed at 14, 16, and 18 years of age. / Results: Distinct developmental trajectories emerged for EDs at a young age. The average growth trajectory for individuals with later anorexia nervosa veered significantly below that of the control group before 4 years of age for girls and 2 years for boys. BMI trajectories were higher than the control trajectory for all other ED groups. Specifically, the mean bulimia nervosa trajectory veered significantly above that of controls at 2 years for girls, but boys with later bulimia nervosa did not exhibit higher BMIs. The mean binge-eating disorder and purging disorder trajectories significantly diverged from the control trajectory at no older than 6 years for girls and boys. / Conclusion: Premorbid metabolic factors and weight could be relevant to the etiology of ED. In anorexia nervosa, premorbid low weight could represent a key biological risk factor or early manifestation of an emerging disease process. Observing children whose BMI trajectories persistently and significantly deviate from age norms for signs and symptoms of ED could assist the identification of high-risk individuals

ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression

We applied several regression and deep learning methods to predict fluid intelligence scores from T1-weighted MRI scans as part of the ABCD Neurocognitive Prediction Challenge (ABCD-NP-Challenge) 2019. We used voxel intensities and probabilistic tissue-type labels derived from these as features to train the models. The best predictive performance (lowest mean-squared error) came from Kernel Ridge Regression (KRR; $\lambda=10$), which produced a mean-squared error of 69.7204 on the validation set and 92.1298 on the test set. This placed our group in the fifth position on the validation leader board and first place on the final (test) leader board.Comment: Winning entry in the ABCD Neurocognitive Prediction Challenge at MICCAI 2019. 7 pages plus references, 3 figures, 1 tabl

Explicating the Conditions Under Which Multilevel Multiple Imputation Mitigates Bias Resulting from Random Coefficient-Dependent Missing Longitudinal Data

Random coefficient dependent (RCD) missingness is a non-ignorable mechanism through which missing data can arise in longitudinal designs. RCD, for which we cannot test, is a problematic form of missingness that occurs if subject-specific random effects correlate with propensity for missingness or dropout. Particularly when covariate missingness is a problem, investigators typically handle missing longitudinal data by using single-level multiple imputation procedures implemented with long-format data, which ignores within-person dependency entirely, or implemented with wide-format (i.e., multivariate) data, which ignores some aspects of within-person dependency. When either of these standard approaches to handling missing longitudinal data is used, RCD missingness leads to parameter bias and incorrect inference. We explain why multilevel multiple imputation (MMI) should alleviate bias induced by a RCD missing data mechanism under conditions that contribute to stronger determinacy of random coefficients. We evaluate our hypothesis with a simulation study. Three design factors are considered: intraclass correlation (ICC; ranging from .25 to .75), number of waves (ranging from 4 to 8), and percent of missing data (ranging from 20% to 50%). We find that MMI greatly outperforms the single-level wide-format (multivariate) method for imputation under a RCD mechanism. For the MMI analyses, bias was most alleviated when the ICC is high, there were more waves of data, and when there was less missing data. Practical recommendations for handling longitudinal missing data are suggested

Evolving stories of child career development

Herein, the contributions to this special issue and positions the field of child career development in terms of its past, present, and future are considered. There is an initial brief overview of past developments in the field, specifically as described in seminal reviews. The article then considers the present status of and future agenda for the field in relation to four identified themes: advances in child career development theory; innovations in practice and assessment related to child career development; child career development in diverse settings; and policy implications of child career development theory, research and practice. The article concludes by proposing seven directions for future research in child career development

ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and Kernel ridge regression

We applied several regression and deep learning methods to predict fluid intelligence scores from T1-weighted MRI scans as part of the ABCD Neurocognitive Prediction Challenge 2019. We used voxel intensities and probabilistic tissue-type labels derived from these as features to train the models. The best predictive performance (lowest mean-squared error) came from kernel ridge regression ($$\lambda =10$$), which produced a mean-squared error of 69.7204 on the validation set and 92.1298 on the test set. This placed our group in the fifth position on the validation leader board and first place on the final (test) leader board