Altered topological structure of the brain white matter in maltreated children through topological data analysis.

Childhood maltreatment may adversely affect brain development and consequently influence behavioral, emotional, and psychological patterns during adulthood. In this study, we propose an analytical pipeline for modeling the altered topological structure of brain white matter in maltreated and typically developing children. We perform topological data analysis (TDA) to assess the alteration in the global topology of the brain white matter structural covariance network among children. We use persistent homology, an algebraic technique in TDA, to analyze topological features in the brain covariance networks constructed from structural magnetic resonance imaging and diffusion tensor imaging. We develop a novel framework for statistical inference based on the Wasserstein distance to assess the significance of the observed topological differences. Using these methods in comparing maltreated children with a typically developing control group, we find that maltreatment may increase homogeneity in white matter structures and thus induce higher correlations in the structural covariance; this is reflected in the topological profile. Our findings strongly suggest that TDA can be a valuable framework to model altered topological structures of the brain. The MATLAB codes and processed data used in this study can be found at https://github.com/laplcebeltrami/maltreated.

We employ topological data analysis (TDA) to investigate altered topological structures in the white matter of children who have experienced maltreatment. Persistent homology in TDA is utilized to quantify topological differences between typically developing children and those subjected to maltreatment, using magnetic resonance imaging and diffusion tensor imaging data. The Wasserstein distance is computed between topological features to assess disparities in brain networks. Our findings demonstrate that persistent homology effectively characterizes the altered dynamics of white matter in children who have suffered maltreatment.

brainlife.io: a decentralized and open-source cloud platform to support neuroscience research.

Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.

Examining the reliability of brain age algorithms under varying degrees of participant motion.

Brain age algorithms using data science and machine learning techniques show promise as biomarkers for neurodegenerative disorders and aging. However, head motion during MRI scanning may compromise image quality and influence brain age estimates. We examined the effects of motion on brain age predictions in adult participants with low, high, and no motion MRI scans (Original N = 148; Analytic N = 138). Five popular algorithms were tested: brainageR, DeepBrainNet, XGBoost, ENIGMA, and pyment. Evaluation metrics, intraclass correlations (ICCs), and Bland-Altman analyses assessed reliability across motion conditions. Linear mixed models quantified motion effects. Results demonstrated motion significantly impacted brain age estimates for some algorithms, with ICCs dropping as low as 0.609 and errors increasing up to 11.5 years for high motion scans. DeepBrainNet and pyment showed greatest robustness and reliability (ICCs = 0.956-0.965). XGBoost and brainageR had the largest errors (up to 13.5 RMSE) and bias with motion. Findings indicate motion artifacts influence brain age estimates in significant ways. Furthermore, our results suggest certain algorithms like DeepBrainNet and pyment may be preferable for deployment in populations where motion during MRI acquisition is likely. Further optimization and validation of brain age algorithms is critical to use brain age as a biomarker relevant for clinical outcomes.

Cortisol's diurnal rhythm indexes the neurobiological impact of child adversity in adolescence.

Adverse early life experiences, such as child maltreatment, shapes hypothalamic-pituitary-adrenal (HPA) activity. The impact of social context is often probed through laboratory stress reactivity, yet child maltreatment is a severe form of chronic stress that recalibrates even stable or relatively inflexible stress systems such as cortisol's diurnal rhythm. This study was designed to determine how different social contexts, which place divergent demands on children, shape cortisol's diurnal rhythm. Participants include 120 adolescents (9-14 years), including 42 youth with substantiated child physical abuse. Up to 32 saliva samples were obtained in the laboratory, on days youth stayed home, and on school days. A 3-level hierarchical linear model examined cortisol within each day and extracted the diurnal rhythm at level 1; across days at level 2; and between-individual differences in cortisol and its rhythm at level 3. While cortisol's diurnal rhythm was flattened when youth were in the novel laboratory context, the impact of maltreatment was observed within the home context such that maltreated children had persistently flattened diurnal rhythms. The effect of maltreatment overlapped with current chronic interpersonal family stress. Results are consistent with the idea that maltreatment exerts a robust, detrimental impact on the HPA axis and are interpreted in the context of less flexibility and rhythmicity. The HPA axis adapts by encoding signifiers of relevant harsh or unpredictable environments, and the extreme stress of physical abuse in the family setting may be one of these environments which calibrates the developing child's stress responsive system, even throughout a developmental stage in which the family takes on diminishing importance.

Childhood adversity and COVID-19 outcomes in the UK Biobank.

OBJECTIVES: This study aims to investigate the association between childhood adversity and COVID-19-related hospitalisation and COVID-19-related mortality in the UK Biobank. DESIGN: Cohort study. SETTING: UK. PARTICIPANTS: 151 200 participants in the UK Biobank cohort who had completed the Childhood Trauma Screen were alive at the start of the COVID-19 pandemic (January 2020) and were still active in the UK Biobank when hospitalisation and mortality data were most recently updated (November 2021). MAIN OUTCOME MEASURES: COVID-19-related hospitalisation and COVID-19-related mortality. RESULTS: Higher self-reports of childhood adversity were related to greater likelihood of COVID-19-related hospitalisation in all statistical models. In models adjusted for age, ethnicity and sex, childhood adversity was associated with an odds ratio (OR) of 1.227 of hospitalisation (95% CI 1.153 to 1.306, childhood adversity z=6.49, p<0.005) and an OR of 1.25 of a COVID-19-related death (95% CI 1.11 to 1.424, childhood adversity z=3.5, p<0.005). Adjustment for potential confounds attenuated these associations, although associations remained statistically significant. CONCLUSIONS: Childhood adversity was significantly associated with COVID-19-related hospitalisation and COVID-19-related mortality after adjusting for sociodemographic and health confounders. Further research is needed to clarify the biological and psychosocial processes underlying these associations to inform public health intervention and prevention strategies to minimise COVID-19 disparities.

brainlife.io: A decentralized and open source cloud platform to support neuroscience research.

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research.

Positive parenting moderates associations between childhood stress and corticolimbic structure.

Childhood stress has a deleterious impact on youth behavior and brain development. Resilience factors such as positive parenting (e.g. expressions of warmth and support) may buffer youth against the negative impacts of stress. We sought to determine whether positive parenting buffers against the negative impact of childhood stress on youth behavior and brain structure and to investigate differences between youth-reported parenting and caregiver-reported parenting. Cross-sectional behavioral and neuroimaging data were analyzed from 482 youth (39% female and 61% male, ages 10-17) who participated in an ongoing research initiative, the Healthy Brain Network (HBN). Regression models found that youth-reported positive parenting buffered against the association between childhood stress and youth behavioral problems (ß = -0.10, P = 0.04) such that increased childhood stress was associated with increased youth behavior problems only for youth who did not experience high levels of positive parenting. We also found that youth-reported positive parenting buffered against the association between childhood stress and decreased hippocampal volumes (ß = 0.07, P = 0.02) such that youth who experienced high levels of childhood stress and who reported increased levels of positive parenting did not exhibit smaller hippocampal volumes. Our work identifies positive parenting as a resilience factor buffering youth against the deleterious impact of stressful childhood experiences on problem behaviors and brain development. These findings underscore the importance of centering youth perspectives of stress and parenting practices to better understand neurobiology, mechanisms of resilience, and psychological well-being.

Quantifying numerical and spatial reliability of hippocampal and amygdala subdivisions in FreeSurfer.

On-going, large-scale neuroimaging initiatives can aid in uncovering neurobiological causes and correlates of poor mental health, disease pathology, and many other important conditions. As projects grow in scale with hundreds, even thousands, of individual participants and scans collected, quantification of brain structures by automated algorithms is becoming the only truly tractable approach. Here, we assessed the spatial and numerical reliability for newly deployed automated segmentation of hippocampal subfields and amygdala nuclei in FreeSurfer 7. In a sample of participants with repeated structural imaging scans (N = 928), we found numerical reliability (as assessed by intraclass correlations, ICCs) was reasonable. Approximately 95% of hippocampal subfields had "excellent" numerical reliability (ICCs ≥ 0.90), while only 67% of amygdala subnuclei met this same threshold. In terms of spatial reliability, 58% of hippocampal subfields and 44% of amygdala subnuclei had Dice coefficients ≥ 0.70. Notably, multiple regions had poor numerical and/or spatial reliability. We also examined correlations between spatial reliability and person-level factors (e.g., participant age; T1 image quality). Both sex and image scan quality were related to variations in spatial reliability metrics. Examined collectively, our work suggests caution should be exercised for a few hippocampal subfields and amygdala nuclei with more variable reliability.

Altered Topological Structure of the Brain White Matter in Maltreated Children through Topological Data Analysis.

Childhood maltreatment may adversely affect brain development and consequently influence behavioral, emotional, and psychological patterns during adulthood. In this study, we propose an analytical pipeline for modeling the altered topological structure of brain white matter in maltreated and typically developing children. We perform topological data analysis (TDA) to assess the alteration in the global topology of the brain white-matter structural covariance network among children. We use persistent homology, an algebraic technique in TDA, to analyze topological features in the brain covariance networks constructed from structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). We develop a novel framework for statistical inference based on the Wasserstein distance to assess the significance of the observed topological differences. Using these methods in comparing maltreated children to a typically developing control group, we find that maltreatment may increase homogeneity in white matter structures and thus induce higher correlations in the structural covariance; this is reflected in the topological profile. Our findings strongly suggest that TDA can be a valuable framework to model altered topological structures of the brain. The MATLAB codes and processed data used in this study can be found at https://github.com/laplcebeltrami/maltreated.

Probing multiple algorithms to calculate brain age: Examining reliability, relations with demographics, and predictive power.

The calculation of so-called "brain age" from structural MRIs has been an emerging biomarker in aging research. Data suggests that discrepancies between chronological age and the predicted age of the brain may be predictive of mortality and morbidity (for review, see Cole, Marioni, Harris, & Deary, 2019). However, with these promising results come technical complexities of how to calculate brain age. Various groups have deployed methods leveraging different statistical approaches, often crafting novel algorithms for assessing this biomarker derived from structural MRIs. There remain many open questions about the reliability, collinearity, and predictive power of different algorithms. Here, we complete a rigorous systematic comparison of three commonly used, previously published brain age algorithms (XGBoost, brainageR, and DeepBrainNet) to serve as a foundation for future applied research. First, using multiple datasets with repeated structural MRI scans, we calculated two metrics of reliability (intraclass correlations and Bland-Altman bias). We then considered correlations between brain age variables, chronological age, biological sex, and image quality. We also calculated the magnitude of collinearity between approaches. Finally, we used machine learning approaches to identify significant predictors across brain age algorithms related to clinical diagnoses of cognitive impairment. Using a large sample (N = 2557), we find all three commonly used brain age algorithms demonstrate excellent reliability (r > .9). We also note that brainageR and DeepBrainNet are reasonably correlated with one another, and that the XGBoost brain age is strongly related to image quality. Finally, and notably, we find that XGBoost brain age calculations were more sensitive to the detection of clinical diagnoses of cognitive impairment. We close this work with recommendations for future research studies focused on brain age.

Trajectories of cortical structures associated with stress across adolescence: a bivariate latent change score approach.

BACKGROUND: Stress exposure in childhood and adolescence has been linked to reductions in cortical structures and cognitive functioning. However, to date, most of these studies have been cross-sectional, limiting the ability to make long-term inferences, given that most cortical structures continue to develop through adolescence. METHODS: Here, we used a subset of the IMAGEN population cohort sample (N = 502; assessment ages: 14, 19, and 22 years; mean age: 21.945 years; SD = 0.610) to understand longitudinally the long-term interrelations between stress, cortical development, and cognitive functioning. To these ends, we first used a latent change score model to examine four bivariate relations - assessing individual differences in change in the relations between adolescent stress exposure and volume, surface area, and cortical thickness of cortical structures, as well as cognitive outcomes. Second, we probed for indirect neurocognitive effects linking stress to cortical brain structures and cognitive functions using rich longitudinal mediation modeling. RESULTS: Latent change score modeling showed that greater baseline adolescence stress at age 14 predicted a small reduction in the right anterior cingulate volume (Std. ß = -.327, p = .042, 95% CI [-0.643, -0.012]) and right anterior cingulate surface area (Std. ß = -.274, p = .038, 95% CI [-0.533, -0.015]) across ages 14-22. These effects were very modest in nature and became nonsignificant after correcting for multiple comparisons. Our longitudinal analyses found no evidence of indirect effects in the two neurocognitive pathways linking adolescent stress to brain and cognitive outcomes. CONCLUSION: Findings shed light on the impact of stress on brain reductions, particularly in the prefrontal cortex that have consistently been implicated in the previous cross-sectional studies. However, the magnitude of effects observed in our study is smaller than that has been reported in past cross-sectional work. This suggests that the potential impact of stress during adolescence on brain structures may likely be more modest than previously noted.

Impact of Socioeconomic Status on Amygdala and Hippocampus Subdivisions in Children and Adolescents.

Socioeconomic status (SES) in childhood can impact behavioral and brain development. Past work has consistently focused on the amygdala and hippocampus, two brain areas critical for emotion and behavioral responding. While there are SES differences in amygdala and hippocampal volumes, there are many unanswered questions in this domain connected to neurobiological specificity, and for whom these effects may be more pronounced. We may be able to investigate some anatomical subdivisions of these brain areas, as well as if relations with SES vary by participant age and sex. No work to date has however completed these types of analyses. To overcome these limitations, here, we combined multiple, large neuroimaging datasets of children and adolescents with information about neurobiology and SES (N=2,765). We examined subdivisions of the amygdala and hippocampus and found multiple amygdala subdivisions, as well as the head of the hippocampus, were related to SES. Greater volumes in these areas were seen for higher-SES youth participants. Looking at age- and sex-specific subgroups, we tended to see stronger effects in older participants, for both boys and girls. Paralleling effects for the full sample, we see significant positive associations between SES and volumes for the accessory basal amygdala and head of the hippocampus. We more consistently found associations between SES and volumes of the hippocampus and amygdala in boys (compared to girls). We discuss these results in relation to conceptions of "sex-as-a-biological variable" and broad patterns of neurodevelopment across childhood and adolescence. These results fill in important gaps on the impact of SES on neurobiology critical for emotion, memory, and learning.

Cognitive variations following exposure to childhood adversity: Evidence from a pre-registered, longitudinal study.

Background: Different methodological approaches to studying the effects and timing of childhood adversity have been proposed and tested. While childhood adversity has primarily been operationalized through specificity (effects of individual adversity types) and cumulative risk (sum of all adversities reported by an individual) models, dimensional models (probeable through latent class and other cluster analyses) have recently gained traction given that it can overcome some of the limitations of the specificity and cumulative risk approaches. On the other hand, structured lifecourse modelling is a new statistical approach that examines the effects of the timing of adversity exposure on health outcomes by comparing sensitive periods and accumulation hypotheses. In this study, we apply these sets of methodological approaches and theoretical models to better understand the complex effects of childhood adversity on cognitive outcomes. Methods: We analysed data obtained from the Avon Longitudinal Study of Parents and Children for 2965 participants (Male = 1125; Female = 1840). This included parental report of 11 types of childhood adversity when participants were between 8 months and 8.7 years, and performance on inhibition, working memory and emotion recognition neurocognitive tasks when participants were 24 years of age (April 1, 1992-October 31, 2017). We used latent class analysis to classify the participants into subgroups, while we used Kruskal-Wallis test to examine differences in cognitive performance among the adversity subgroups. Additionally, to test whether sensitive period or accumulation models better explain the effects of childhood adversity on cognitive functioning, we carried out separate analyses using structured lifecourse modelling approaches. Findings: Latent class analysis showed evidence of 5 classes, namely: low adversity (71.6%), dysfunctional family (9.58%); parental deprivation (9.65%); family poverty (6.07%) and global adversity (3.1%). We observed group differences in cognitive performance among the adversity classes in an inhibition control task, χ2(4) = 15.624, p = 0.003 and working memory task, χ2(4) = 15.986, p = 0.003. Pairwise comparison tests showed that participants in the family poverty class performed significantly worse than those in the low adversity class, for the inhibition control task (p = 0.007) while participants in the global adversity class significantly performed worse than participants in the low adversity class (p = 0.026) and dysfunctional family class (p = 0.034) on the working memory task. A further analysis revealed that the associations between each individual adversity type and cognitive outcomes were mostly consistent with the observed class performance in which they co-occurred. Follow-up analyses suggested that adversity during specific sensitive periods, namely very early childhood and early childhood, explained more variability in these observed associations, compared to the accumulation of adversities. Interpretation: These findings suggest that dimensional approaches e.g., latent class analysis or cluster analysis could be good alternatives to studying childhood adversity. Using latent class analysis for example, can help reveal the population distribution of co-occurring adversity patterns among participants who may be at the greatest health risk and thus, enable a targeted intervention. In addition, this approach could be used to investigate specific pathways that link adversity classes to different developmental outcomes that could further complement the specificity or cumulative risk approaches to adversity. On the other hand, findings from a separate analysis using structured lifecourse modelling approaches also highlight the vital developmental timeframes in childhood during which the impact of adversity exposure on cognitive outcomes is greatest, suggesting the need to provide comprehensive academic and mental health support to individuals exposed during those specific timeframes. Funding: T.N. received funding from Cambridge Trust (University of Cambridge).

Positive valence systems in youth anxiety development: A scoping review.

Research on pathophysiological mechanisms supporting anxiety development in youth has traditionally focused on the role of threat systems. However, emerging research suggests that the positive valence system (PVS) may also play a strong and unique role in the development and maintenance of anxiety during childhood and adolescence. To better understand the connection between the PVS and anxiety, this scoping review describes current research spanning multiple units of analysis (i.e., self-report, behavior, neural circuits) linking child and adolescent anxiety and risk for anxiety to various PVS constructs (i.e., positive affect, reward responsiveness, reward learning and decision-making). After screening, 78 peer-reviewed articles and dissertations published between 1998 and May 2021 were included in a qualitative review. Though some consistencies in the literature were found, such as high neural reactivity to incentive anticipation in youth at temperamental risk for social anxiety and blunted positive affect in youth with social anxiety disorder, the literature is largely inconsistent. Inconsistencies could be related to the small number of similar studies, small and homogenous study samples, and variability in methodologies employed in this research. It cannot be confirmed whether findings linking PVS constructs to anxiety are unique to anxiety symptoms or better accounted for by co-occurring depressive symptoms. This review concludes with recommendations for robust future research in this area.

Developmental connections between socioeconomic status, self-regulation, and adult externalizing problems.

Children from low socioeconomic status (SES) backgrounds are at particularly heightened risk for developing later externalizing problems. A large body of research has suggested an important role for self-regulation in this developmental linkage. Self-regulation has been conceptualized as a mediator as well as a moderator of these connections. Using data from the Child Development Project (CDP, N = 585), we probe these contrasting (mediating/moderating) conceptualizations, using both Frequentist and Bayesian statistical approaches, in the linkage between early SES and later externalizing problems in a multi-decade longitudinal study. Connecting early SES, physiology (i.e., heart rate reactivity) and inhibitory control (a Stroop task) in adolescence, and externalizing symptomatology in early adulthood, we found the relation between SES and externalizing problems was moderated by multiple facets of self-regulation. Participants from lower early SES backgrounds, who also had high heart rate reactivity and lower inhibitory control, had elevated levels of externalizing problems in adulthood relative to those with low heart rate reactivity and better inhibitory control. Such patterns persisted after controlling for externalizing problems earlier in life. The present results may aid in understanding the combinations of factors that contribute to the development of externalizing psychopathology in economically marginalized youth.

Low household income and neurodevelopment from infancy through adolescence.

Despite advancements in the study of brain maturation at different developmental epochs, no work has linked the significant neural changes occurring just after birth to the subtler refinements in the brain occurring in childhood and adolescence. We aimed to provide a comprehensive picture regarding foundational neurodevelopment and examine systematic differences by family income. Using a nationally representative longitudinal sample of 486 infants, children, and adolescents (age 5 months to 20 years) from the NIH MRI Study of Normal Brain Development and leveraging advances in statistical modeling, we mapped developmental trajectories for the four major cortical lobes and constructed charts that show the statistical distribution of gray matter and reveal the considerable variability in regional volumes and structural change, even among healthy, typically developing children. Further, the data reveal that significant structural differences in gray matter development for children living in or near poverty, first detected during childhood (age 2.5-6.5 years), evolve throughout adolescence.