Association between Hippocampal Volume and Working Memory in 10,000+ 9-10-Year-Old Children: Sex Differences

Meta-Analysis of Hippocampal Volume and Episodic Memory in Preterm and Term Born Individuals

Preterm birth (< 37 weeks gestation) has been associated with memory deficits, which has prompted investigation of possible alterations in hippocampal volume in this population. However, existing literature reports varying effects of premature birth on hippocampal volume. Specifically, it is unclear whether smaller hippocampal volume in preterm-born individuals is merely reflective of smaller total brain volume. Further, it is not clear if hippocampal volume is associated with episodic memory functioning in preterm-born individuals. Meta-analysis was used to investigate the effects of premature birth on hippocampal volume and episodic memory from early development to young adulthood (birth to 26). PubMed, PsychINFO, and Web of Science were searched for English peer-reviewed articles that included hippocampal volume of preterm and term-born individuals. Thirty articles met the inclusion criteria. Separate meta-analyses were used to evaluate standardized mean differences between preterm and term-born individuals in uncorrected and corrected hippocampal volume, as well as verbal and visual episodic memory. Both uncorrected and corrected hippocampal volume were smaller in preterm-born compared to term-born individuals. Although preterm-born individuals had lower episodic memory performance than term-born individuals, the limited number of studies only permitted a qualitative review of the association between episodic memory performance and hippocampal volume. Tested moderators included mean age, pre/post-surfactant era, birth weight, gestational age, demarcation method, magnet strength, and slice thickness. With this meta-analysis, we provide novel evidence of the effects of premature birth on hippocampal volume.

The impact of poverty and socioeconomic status on brain, behaviour, and development: a unified framework

In this article, we, for the first time, provide a comprehensive overview and unified framework of the impact of poverty and low socioeconomic status (SES) on the brain and behaviour. While there are many studies on the impact of low SES on the brain (including cortex, hippocampus, amygdala, and even neurotransmitters) and behaviours (including educational attainment, language development, development of psychopathological disorders), prior studies did not integrate behavioural, educational, and neural findings in one framework. Here, we argue that the impact of poverty and low SES on the brain and behaviour are interrelated. Specifically, based on prior studies, due to a lack of resources, poverty and low SES are associated with poor nutrition, high levels of stress in caregivers and their children, and exposure to socio-environmental hazards. These psychological and physical injuries impact the normal development of several brain areas and neurotransmitters. Impaired functioning of the amygdala can lead to the development of psychopathological disorders, while impaired hippocampus and cortex functions are associated with a delay in learning and language development as well as poor academic performance. This in turn perpetuates poverty in children, leading to a vicious cycle of poverty and psychological/physical impairments. In addition to providing economic aid to economically disadvantaged families, interventions should aim to tackle neural abnormalities caused by poverty and low SES in early childhood. Importantly, acknowledging brain abnormalities due to poverty in early childhood can help increase economic equity. In the current study, we provide a comprehensive list of future studies to help understand the impact of poverty on the brain.

LATENT SUBGROUP IDENTIFICATION IN IMAGE-ON-SCALAR REGRESSION

Image-on-scalar regression has been a popular approach to modeling the association between brain activities and scalar characteristics in neuroimaging research. The associations could be heterogeneous across individuals in the population, as indicated by recent large-scale neuroimaging studies, for example, the Adolescent Brain Cognitive Development (ABCD) Study. The ABCD data can inform our understanding of heterogeneous associations and how to leverage the heterogeneity and tailor interventions to increase the number of youths who benefit. It is of great interest to identify subgroups of individuals from the population such that: (1) within each subgroup the brain activities have homogeneous associations with the clinical measures; (2) across subgroups the associations are heterogeneous, and (3) the group allocation depends on individual characteristics. Existing image-on-scalar regression methods and clustering methods cannot directly achieve this goal. We propose a latent subgroup image-on-scalar regression model (LASIR) to analyze large-scale, multisite neuroimaging data with diverse sociode-mographics. LASIR introduces the latent subgroup for each individual and group-specific, spatially varying effects, with an efficient stochastic expectation maximization algorithm for inferences. We demonstrate that LASIR outperforms existing alternatives for subgroup identification of brain activation patterns with functional magnetic resonance imaging data via comprehensive simulations and applications to the ABCD study. We have released our reproducible codes for public use with the software package available on Github.

Peer victimization (bullying) on mental health, behavioral problems, cognition, and academic performance in preadolescent children in the ABCD Study

Objective

Peer victimization is a substantial early life stressor linked to psychiatric symptoms and poor academic performance. However, the sex-specific cognitive or behavioral outcomes of bullying have not been well-described in preadolescent children.

Methods

Using the baseline dataset of the Adolescent Brain Cognitive Development (ABCD) Study 2.0.1 data repository (N = 11,875), we evaluated associations between parent-reported bullying victimization, suicidality (suicidal ideation, intent, and/or behavior), and non-suicidal self-injury (NSSI), as well as internalizing and externalizing behavioral problems, cognition, and academic performance.

Results

Of the 11,015 9-10-year-old children included in the analyses (5,263 girls), 15.3% experienced bullying victimization, as reported by the primary caregiver. Of these, boys were more likely to be bullied than girls (odds ratio [OR], 1.2 [95% CI, 1.1-1.3]; p = 0.004). Children who were bullied were more likely to display NSSI or passive suicidality (OR, 2.4 [95% CI, 2.0-2.9]; p < 0.001) and active suicidality (OR, 3.4 [95% CI, 2.7-4.2]; p < 0.001). Bullied children also had lower cognitive scores, greater behavioral problems, and poorer grades (p < 0.001). Across all participants, boys had poorer grades and greater behavioral problems than girls; however, bullied boys had greater behavioral problems than girls in several areas (p < 0.001). Compared to their non-bullied peers, bullied children with greater non-suicidal self-injury or suicidality also had greater behavioral problems and poorer grades (p < 0.001).

Conclusion

These findings highlight the sex-specific effects of bullying, and the negative associations of bullying victimization with cognitive performance, behavioral problems, and academic performance. Future longitudinal studies will identify the natural history and neural correlates of these deficits during adolescence.

The sexual brain, genes, and cognition: A machine-predicted brain sex score explains individual differences in cognitive intelligence and genetic influence in young children

Sex impacts the development of the brain and cognition differently across individuals. However, the literature on brain sex dimorphism in humans is mixed. We aim to investigate the biological underpinnings of the individual variability of sexual dimorphism in the brain and its impact on cognitive performance. To this end, we tested whether the individual difference in brain sex would be linked to that in cognitive performance that is influenced by genetic factors in prepubertal children (N = 9,658, ages 9-10 years old; the Adolescent Brain Cognitive Development study). To capture the interindividual variability of the brain, we estimated the probability of being male or female based on the brain morphometry and connectivity features using machine learning (herein called a brain sex score). The models accurately classified the biological sex with a test ROC-AUC of 93.32%. As a result, a greater brain sex score correlated significantly with greater intelligence (p_fdr  < .001, η p 2 $$ {\eta}_p^2 $$  = .011-.034; adjusted for covariates) and higher cognitive genome-wide polygenic scores (GPSs) (p_fdr  < .001, η p 2 $$ {\eta}_p^2 $$  < .005). Structural equation models revealed that the GPS-intelligence association was significantly modulated by the brain sex score, such that a brain with a higher maleness score (or a lower femaleness score) mediated a positive GPS effect on intelligence (indirect effects = .006-.009; p = .002-.022; sex-stratified analysis). The finding of the sex modulatory effect on the gene-brain-cognition relationship presents a likely biological pathway to the individual and sex differences in the brain and cognitive performance in preadolescence.