The NIMH Intramural Longitudinal Study of the Endocrine and Neurobiological Events Accompanying Puberty: Protocol and rationale for methods and measures

Contrasting neurofunctional correlates of face- and visuospatial-processing in children and adolescents with Williams syndrome: convergent results from four fMRI paradigms

Understanding neurogenetic mechanisms underlying neuropsychiatric disorders such as schizophrenia and autism is complicated by their inherent clinical and genetic heterogeneity. Williams syndrome (WS), a rare neurodevelopmental condition in which both the genetic alteration (hemideletion of ~ twenty-six 7q11.23 genes) and the cognitive/behavioral profile are well-defined, offers an invaluable opportunity to delineate gene-brain-behavior relationships. People with WS are characterized by increased social drive, including particular interest in faces, together with hallmark difficulty in visuospatial processing. Prior work, primarily in adults with WS, has searched for neural correlates of these characteristics, with reports of altered fusiform gyrus function while viewing socioemotional stimuli such as faces, along with hypoactivation of the intraparietal sulcus during visuospatial processing. Here, we investigated neural function in children and adolescents with WS by using four separate fMRI paradigms, two that probe each of these two cognitive/behavioral domains. During the two visuospatial tasks, but not during the two face processing tasks, we found bilateral intraparietal sulcus hypoactivation in WS. In contrast, during both face processing tasks, but not during the visuospatial tasks, we found fusiform hyperactivation. These data not only demonstrate that previous findings in adults with WS are also present in childhood and adolescence, but also provide a clear example that genetic mechanisms can bias neural circuit function, thereby affecting behavioral traits.

Association between exposure to persistent organic pollutants and pubertal timing in boys and girls: A systematic review and meta-analysis

In recent years, the phenomenon of abnormal pubertal timing in children has become increasingly common worldwide. Persistent organic pollutants (POPs) may be one of the risk factors contributing to this phenomenon, but the relationship between them is unclear based on current evidence. The purpose of this study was to determine the association of POPs exposure with pubertal timing in girls and boys by conducting a systematic review and meta-analysis. We searched PubMed and Embase databases for studies before June 1, 2023. Meta-analysis was performed by pooling relative risk (RR) or odds ratio (OR) or prevalence ratio (PR) or hazard ratio (HR) estimates with 95 % confidence intervals (CIs). Subgroup analysis, publication bias assessment and sensitivity analysis were also carried out. A total of 21 studies were included, involving 2479 boys and 8718 girls. The results of meta-analysis showed that exposure to POPs was significantly associated with delayed pubertal timing in girls (RR: 0.85; 95 % CI: 0.79-0.91; p < 0.001). There was no statistically significant association between exposure to POPs and pubertal timing in boys (RR: 1.18; 95 % CI: 0.99-1.40; p = 0.070). Subgroup analysis showed that there may be gender differences in the effects of exposure to POPs on pubertal timing. Our results suggested that exposure to POPs could delay pubertal timing in girls. However, based on current evidence, no significant association was found between POPs exposure and pubertal timing in boys.

Aging and Pubertal Development Differentially Predict Symptoms of ADHD, Depression, and Impairment in Children and Adolescents: An Eight-Year Longitudinal Study

Activational effects of the reproductive neuroendocrine system may explain why some youths with ADHD are at greater risk for exacerbated ADHD symptoms (hyperactivity, inattention, impulsivity) during adolescence. For youths diagnosed with ADHD, first signs of ADHD symptoms become noticeable by multiple reporters (e.g., teachers, parents) when children enter schools, typically around kindergarten. The current study examined possible sex differences in ADHD, impairment, and comorbidity due to pubertal effects, as the role of pubertal development in ADHD is understudied. ADHD symptoms, depressive symptoms, impairment, and pubertal stage were assessed annually by multiple reporters in a well-characterized community sample of 849 children over-recruited for ADHD over eight years. Ages ranged from 7 to 13 years (38.16% female) at wave 1. Multilevel models indicated that males had higher levels of hyperactivity, impulsivity, and inattention than females, but that females had higher levels of impairment than males. Inattention symptoms did not show marked maturation changes. Hyperactivity and impulsivity declined as youth aged and impairment increased as youth aged. Lastly, depressive symptoms largely increased as youth aged and were higher amongst youth at later pubertal stages. Put together, aging and pubertal development are associated with improved ADHD symptoms but not for youth with high impairment. Findings from this study contributes to understanding the role that aging, pubertal status, and pubertal development plays in ADHD, impairment, and comorbidity in children and adolescents.

The effects of puberty and sex on adolescent white matter development: A systematic review

Adolescence, the transition between childhood and adulthood, is characterized by rapid brain development in white matter (WM) that is attributed in part to rising levels in adrenal and gonadal hormones. The extent to which pubertal hormones and related neuroendocrine processes explain sex differences in WM during this period is unclear. In this systematic review, we sought to examine whether there are consistent associations between hormonal changes and morphological and microstructural properties of WM across species and whether these effects are sex-specific. We identified 90 (75 human, 15 non-human) studies that met inclusion criteria for our analyses. While studies in human adolescents show notable heterogeneity, results broadly demonstrate that increases in gonadal hormones across pubertal development are associated with macro- and microstructural changes in WM tracts that are consistent with the sex differences found in non-human animals, particularly in the corpus callosum. We discuss limitations of the current state of the science and recommend important future directions for investigators in the field to consider in order to advance our understanding of the neuroscience of puberty and to promote forward and backward translation across model organisms.

A Combined Region- and Pixel-Based Deep Learning Approach for Quantifying Abdominal Adipose Tissue in Adolescents Using Dixon Magnetic Resonance Imaging

BACKGROUND

The development of adipose tissue during adolescence may provide valuable insights into obesity-associated diseases. We propose an automated convolutional neural network (CNN) approach using Dixon-based magnetic resonance imaging (MRI) to quantity abdominal subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) in children and adolescents.

METHODS

474 abdominal Dixon MRI scans of 136 young healthy volunteers (aged 8-18) were included in this study. For each scan, an axial fat-only Dixon image located at the L2-L3 disc space and another image at the L4-L5 disc space were selected for quantification. For each image, an outer and an inner region around the abdomen wall, as well as SAT and VAT pixel masks, were generated by expert readers as reference standards. A standard U-Net CNN architecture was then used to train two models: one for region segmentation and one for fat pixel classification. The performance was evaluated using the dice similarity coefficient (DSC) with fivefold cross-validation, and by Pearson correlation and the Student's t-test against the reference standards.

RESULTS

For the DSC results, means and standard deviations of the outer region, inner region, SAT, and VAT comparisons were 0.974 ± 0.026, 0.997 ± 0.003, 0.981 ± 0.025, and 0.932 ± 0.047, respectively. Pearson coefficients were 1.000 for both outer and inner regions, and 1.000 and 0.982 for SAT and VAT comparisons, respectively (all p = NS).

CONCLUSION

These results show that our method not only provides excellent agreement with the reference SAT and VAT measurements, but also accurate abdominal wall region segmentation. The proposed combined region- and pixel-based CNN approach provides automated abdominal wall segmentation as well as SAT and VAT quantification with Dixon MRI and enables objective longitudinal assessment of adipose tissues in children during adolescence.

Developmental coupling of cerebral blood flow and fMRI fluctuations in youth

The functions of the human brain are metabolically expensive and reliant on coupling between cerebral blood flow (CBF) and neural activity, yet how this coupling evolves over development remains unexplored. Here, we examine the relationship between CBF, measured by arterial spin labeling, and the amplitude of low-frequency fluctuations (ALFF) from resting-state magnetic resonance imaging across a sample of 831 children (478 females, aged 8-22 years) from the Philadelphia Neurodevelopmental Cohort. We first use locally weighted regressions on the cortical surface to quantify CBF-ALFF coupling. We relate coupling to age, sex, and executive functioning with generalized additive models and assess network enrichment via spin testing. We demonstrate regionally specific changes in coupling over age and show that variations in coupling are related to biological sex and executive function. Our results highlight the importance of CBF-ALFF coupling throughout development; we discuss its potential as a future target for the study of neuropsychiatric diseases.

Beyond linearity in neuroimaging: Capturing nonlinear relationships with application to longitudinal studies

The ubiquitous adoption of linearity for quantitative predictors in statistical modeling is likely attributable to its advantages of straightforward interpretation and computational feasibility. The linearity assumption may be a reasonable approximation especially when the variable is confined within a narrow range, but it can be problematic when the variable's effect is non-monotonic or complex. Furthermore, visualization and model assessment of a linear fit are usually omitted because of challenges at the whole brain level in neuroimaging. By adopting a principle of learning from the data in the presence of uncertainty to resolve the problematic aspects of conventional polynomial fitting, we introduce a flexible and adaptive approach of multilevel smoothing splines (MSS) to capture any nonlinearity of a quantitative predictor for population-level neuroimaging data analysis. With no prior knowledge regarding the underlying relationship other than a parsimonious assumption about the extent of smoothness (e.g., no sharp corners), we express the unknown relationship with a sufficient number of smoothing splines and use the data to adaptively determine the specifics of the nonlinearity. In addition to introducing the theoretical framework of MSS as an efficient approach with a counterbalance between flexibility and stability, we strive to (a) lay out the specific schemes for population-level nonlinear analyses that may involve task (e.g., contrasting conditions) and subject-grouping (e.g., patients vs controls) factors; (b) provide modeling accommodations to adaptively reveal, estimate and compare any nonlinear effects of a predictor across the brain, or to more accurately account for the effects (including nonlinear effects) of a quantitative confound; (c) offer the associated program 3dMSS to the neuroimaging community for whole-brain voxel-wise analysis as part of the AFNI suite; and (d) demonstrate the modeling approach and visualization processes with a longitudinal dataset of structural MRI scans.