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.

A robust brain network for sustained attention from adolescence to adulthood that predicts later substance use.

Substance use, including cigarettes and cannabis, is associated with poorer sustained attention in late adolescence and early adulthood. Previous studies were predominantly cross-sectional or under-powered and could not indicate if impairment in sustained attention was a consequence of substance-use or a marker of the inclination to engage in such behaviour. This study explored the relationship between sustained attention and substance use across a longitudinal span from ages 14 to 23 in over 1,000 participants. Behaviours and brain connectivity associated with diminished sustained attention at age 14 predicted subsequent increases in cannabis and cigarette smoking, establishing sustained attention as a robust biomarker for vulnerability to substance use. Individual differences in network strength relevant to sustained attention were preserved across developmental stages and sustained attention networks generalized to participants in an external dataset. In summary, brain networks of sustained attention are robust, consistent, and able to predict aspects of later substance use.

Parental education and income are linked to offspring cortical brain structure and psychopathology at 9-11 years.

Background: A child's socioeconomic environment can shape central aspects of their life, including vulnerability to mental disorders. Negative environmental influences in youth may interfere with the extensive and dynamic brain development occurring at this time. Indeed, there are numerous yet diverging reports of associations between parental socioeconomic status (SES) and child cortical brain morphometry. Most of these studies have used single metric- or unimodal analyses of standard cortical morphometry that downplay the probable scenario where numerous biological pathways in sum account for SES-related cortical differences in youth. Methods: To comprehensively capture such variability, using data from 9758 children aged 8.9-11.1 years from the ABCD Study®, we employed linked independent component analysis (LICA) and fused vertex-wise cortical thickness, surface area, curvature and grey-/white-matter contrast (GWC). LICA revealed 70 uni- and multimodal components. We then assessed the linear relationships between parental education, parental income and each of the cortical components, controlling for age, sex, genetic ancestry, and family relatedness. We also assessed whether cortical structure moderated the negative relationships between parental SES and child general psychopathology. Results: Parental education and income were both associated with larger surface area and higher GWC globally, in addition to local increases in surface area and to a lesser extent bidirectional GWC and cortical thickness patterns. The negative relation between parental income and child psychopathology were attenuated in children with a multimodal pattern of larger frontal- and smaller occipital surface area, and lower medial occipital thickness and GWC. Conclusion: Structural brain MRI is sensitive to SES diversity in childhood, with GWC emerging as a particularly relevant marker together with surface area. In low-income families, having a more developed cortex across MRI metrics, appears beneficial for mental health.

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.

Dupilumab-Associated Sezary Syndrome.

Dupilumab is a monoclonal antibody that inhibits interleukin-4 (IL-4) and interleukin-13 (IL-13), approved for the treatment of adults with moderate-to-severe atopic dermatitis (AD). While recent reports have described cases of new-onset mycosis fungoides (MF) following treatment with dupilumab for AD, to our knowledge only one patient has been delineated with the progression to SS. We present an additional case of a patient who was diagnosed with SS following treatment with dupilumab for adult-onset AD and asthma. We examine SS as a possible side effect of dupilumab while also discussing management and theories to explain this phenomenon.

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.

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.

Dupilumab-associated mycosis fungoides: a cross-sectional study.

Treating atopic dermatitis (AD) with dupilumab, a monoclonal antibody that inhibits interleukin-4 (IL-4) and interleukin-13 (IL-13), may be associated with the progression of mycosis fungoides (MF).This study aims to examine the associations between the length of dupilumab treatment, age and sex, and the onset of MF.An institutional data registry and literature search were used for a retrospective cross-sectional study. Only patients with a diagnosis of MF on dupilumab for the treatment of AD and eczematous dermatitis were included.The primary outcome was the length of dupilumab exposure, age, sex, and the onset of MF. Linear correlations (Pearson) and Cox regression analysis were used to assess the correlation and the risk.A total of 25 patients were included in this study. Five eligible patients were identified at our institution. In addition, a PubMed review identified an additional 20 patients. At the time of MF diagnosis, the median age was 58, with 42% female. Disease history was significant for adult-onset AD in most patients (n = 17, 65.4%) or recent flare of AD previously in remission (n = 3, 11.5%). All patients were diagnosed with MF, and one patient progressed to Sézary syndrome while on dupilumab, with an average duration of 13.5 months of therapy prior to diagnosis. Tumor stage at diagnosis of MF was described in 19 of the cases and ranged from an early-stage disease (IA) to advanced disease (IV). Treatment strategies included narrow-band UVB therapy, topical corticosteroids, brentuximab, pralatrexate, and acitretin. Male gender, advanced-stage disease, and older age correlated significantly with the hazard of MF onset and a shorter time to onset during dupilumab treatment.Our results suggest a correlation between the duration of dupilumab treatment and the diagnosis of MF, the higher MF stage at diagnosis, and the shorter the duration of using dupilumab to MF onset. Furthermore, elderly male patients appeared to be more at risk as both male gender and older age correlated with a hazard of MF diagnosis. The results raise the question as to whether the patients had MF misdiagnosed as AD that was unmasked by dupilumab or if MF truly is an adverse effect of treatment with dupilumab. Close monitoring of these patients and further investigation of the relationship between dupilumab and MF can shed more light on this question .

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.

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.

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.

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.