Understanding priorities and needs for child and adolescent mental health in Greece from multiple informants: an open resource dataset.

The Child and Adolescent Mental Health Initiative (CAMHI) aims to enhance mental health care capacity for children and adolescents across Greece. Considering the need for evidence-based policy, the program developed an open-resource dataset for researching the field within the country. A comprehensive, mixed-method, community-based research was conducted in 2022/2023 assessing the current state, needs, barriers, and opportunities according to multiple viewpoints. We surveyed geographically distributed samples of 1,756 caregivers, 1,201 children/adolescents, 404 schoolteachers, and 475 health professionals using validated instruments to assess mental health symptoms, mental health needs, literacy and stigma, service use and access, professional practices, training background, and training needs and preferences. Fourteen focus groups were conducted with informants from diverse populations (including underrepresented minorities) to reach an in-depth understanding of those topics. A dataset with quantitative and qualitative findings is now available for researchers, policymakers, and society [ https://osf.io/crz6h/ and https://rpubs.com/camhi/sdashboard ]. This resource offers valuable data for assessing the needs and priorities for child and adolescent mental health care in Greece. It is now freely available to consult, and is expected to inform upcoming research and evidence-based professional training. This initiative may inspire similar ones in other countries, informing methodological strategies for researching mental health needs.

Probing the content of affective semantic memory following caregiving-related early adversity.

Cognitive science has demonstrated that we construct knowledge about the world by abstracting patterns from routinely encountered experiences and storing them as semantic memories. This preregistered study tested the hypothesis that caregiving-related early adversities (crEAs) shape affective semantic memories to reflect the content of those adverse interpersonal-affective experiences. We also tested the hypothesis that because affective semantic memories may continue to evolve in response to later-occurring positive experiences, child-perceived attachment security will inform their content. The sample comprised 160 children (ages 6-12 at Visit 1; 87F/73 M), 66% of whom experienced crEAs (n = 105). At Visit 1, crEA exposure prior to study enrollment was operationalized as parental-reports endorsing a history of crEAs (abuse/neglect, permanent/significant parent-child separation); while child-reports assessed concurrent attachment security. A false memory task was administered online ∼2.5 years later (Visit 2) to probe the content of affective semantic memories-specifically attachment schemas. Results showed that crEA exposure (vs. no exposure) was associated with a higher likelihood of falsely endorsing insecure (vs. secure) schema scenes. Attachment security moderated the association between crEA exposure and insecure schema-based false recognition. Findings suggest that interpersonal-affective semantic schemas include representations of parent-child interactions that may capture the quality of one's own attachment experiences and that these representations shape how children remember attachment-relevant narrative events. Findings are also consistent with the hypothesis that these affective semantic memories can be modified by later experiences. Moving forward, the approach taken in this study provides a means of operationalizing Bowlby's notion of internal working models within a cognitive neuroscience framework. RESEARCH HIGHLIGHTS: Affective semantic memories representing insecure schema knowledge (child needs + needs-not-met) may be more salient, elaborated, and persistent among youths exposed to early caregiving adversity. All youths, irrespective of early caregiving adversity exposure, may possess affective semantic memories that represent knowledge of secure schemas (child needs + needs-met). Establishing secure relationships with parents following early-occurring caregiving adversity may attenuate the expression of insecure semantic memories, suggesting potential malleability. Affective semantic memories include schema representations of parent-child interactions that may capture the quality of one's own attachment experiences and shape how youths remember attachment-relevant events.

Functional connectivity development along the sensorimotor-association axis enhances the cortical hierarchy.

Human cortical maturation has been posited to be organized along the sensorimotor-association axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the sensorimotor-association axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3355; ages 5-23 years): the Philadelphia Neurodevelopmental Cohort (n = 1207), Nathan Kline Institute-Rockland Sample (n = 397), Human Connectome Project: Development (n = 625), and Healthy Brain Network (n = 1126). Across datasets, the development of functional connectivity systematically varied along the sensorimotor-association axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These consistent and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.

Age, Motion, Medical, and Psychiatric Associations With Incidental Findings in Brain MRI.

Importance: Few investigations have evaluated rates of brain-based magnetic resonance imaging (MRI) incidental findings (IFs) in large lifespan samples, their stability over time, or their associations with health outcomes. Objectives: To examine rates of brain-based IFs across the lifespan, their persistence, and their associations with phenotypic indicators of behavior, cognition, and health; to compare quantified motion with radiologist-reported motion and evaluate its associations with IF rates; and to explore IF consistency across multiple visits. Design, Setting, and Participants: This cross-sectional study included participants from the Nathan Kline Institute-Rockland Sample (NKI-RS), a lifespan community-ascertained sample, and the Healthy Brain Network (HBN), a cross-sectional community self-referred pediatric sample focused on mental health and learning disorders. The NKI-RS enrolled participants (ages 6-85 years) between March 2012 and March 2020 and had longitudinal participants followed up for as long as 4 years. The HBN enrolled participants (ages 5-21 years) between August 2015 and October 2021. Clinical neuroradiology MRI reports were coded for radiologist-reported motion as well as presence, type, and clinical urgency (category 1, no abnormal findings; 2, no referral recommended; 3, consider referral; and 4, immediate referral) of IFs. MRI reports were coded from June to October 2021. Data were analyzed from November 2021 to February 2023. Main Outcomes and Measures: Rates and type of IFs by demographic characteristics, health phenotyping, and motion artifacts; longitudinal stability of IFs; and Euler number in projecting radiologist-reported motion. Results: A total of 1300 NKI-RS participants (781 [60.1%] female; mean [SD] age, 38.9 [21.8] years) and 2772 HBN participants (976 [35.2%] female; mean [SD] age, 10.0 [3.5] years) had health phenotyping and neuroradiology-reviewed MRI scans. IFs were common, with 284 of 2956 children (9.6%) and 608 of 1107 adults (54.9%) having IFs, but rarely of clinical concern (category 1: NKI-RS, 619 [47.6%]; HBN, 2561 [92.4%]; category 2: NKI-RS, 647 [49.8%]; HBN, 178 [6.4%]; category 3: NKI-RS, 79 [6.1%]; HBN, 30 [1.1%]; category 4: NKI-RS: 12 [0.9%]; HBN, 6 [0.2%]). Overall, 46 children (1.6%) and 79 adults (7.1%) required referral for their IFs. IF frequency increased with age. Elevated blood pressure and BMI were associated with increased T2 hyperintensities and age-related cortical atrophy. Radiologist-reported motion aligned with Euler-quantified motion, but neither were associated with IF rates. Conclusions and Relevance: In this cross-sectional study, IFs were common, particularly with increasing age, although rarely clinically significant. While T2 hyperintensity and age-related cortical atrophy were associated with BMI and blood pressure, IFs were not associated with other behavioral, cognitive, and health phenotyping. Motion may not limit clinical IF detection.

Relative brain age is associated with socioeconomic status and anxiety/depression problems in youth.

Brain age, a measure of biological aging in the brain, has been linked to psychiatric illness, principally in adult populations. Components of socioeconomic status (SES) associate with differences in brain structure and psychiatric risk across the lifespan. This study aimed to investigate the influence of SES on brain aging in childhood and adolescence, a period of rapid neurodevelopment and peak onset for many psychiatric disorders. We reanalyzed data from the Healthy Brain Network to examine the influence of SES components (occupational prestige, public assistance enrollment, parent education, and household income-to-needs ratio [INR]) on relative brain age (RBA). Analyses included 470 youth (5-17 years; 61.3% men), self-identifying as White (55%), African American (15%), Hispanic (9%), or multiracial (17.2%). Household income was 3.95 ± 2.33 (mean ± SD) times the federal poverty threshold. RBA quantified differences between chronological age and brain age using covariation patterns of morphological features and total volumes. We also examined associations between RBA and psychiatric symptoms (Child Behavior Checklist [CBCL]). Models covaried for sex, scan location, and parent psychiatric diagnoses. In a linear regression, lower RBA is associated with lower parent occupational prestige (p = .01), lower public assistance enrollment (p = .03), and more parent psychiatric diagnoses (p = .01), but not parent education or INR. Lower parent occupational prestige (p = .02) and lower RBA (p = .04) are associated with higher CBCL anxious/depressed scores. Our findings underscore the importance of including SES components in developmental brain research. Delayed brain aging may represent a potential biological pathway from SES to psychiatric risk. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Connectome-wide structure-function coupling models implicate polysynaptic alterations in autism.

Autism spectrum disorder (ASD) is one of the most common neurodevelopmental diagnoses. Although incompletely understood, structural and functional network alterations are increasingly recognized to be at the core of the condition. We utilized multimodal imaging and connectivity modeling to study structure-function coupling in ASD and probed mono- and polysynaptic mechanisms on structurally-governed network function. We examined multimodal magnetic resonance imaging data in 80 ASD and 61 neurotypical controls from the Autism Brain Imaging Data Exchange (ABIDE) II initiative. We predicted intrinsic functional connectivity from structural connectivity data in each participant using a Riemannian optimization procedure that varies the times that simulated signals can unfold along tractography-derived personalized connectomes. In both ASD and neurotypical controls, we observed improved structure-function prediction at longer diffusion time scales, indicating better modeling of brain function when polysynaptic mechanisms are accounted for. Prediction accuracy differences (∆prediction accuracy) were marked in transmodal association systems, such as the default mode network, in both neurotypical controls and ASD. Differences were, however, lower in ASD in a polysynaptic regime at higher simulated diffusion times. We compared regional differences in ∆prediction accuracy between both groups to assess the impact of polysynaptic communication on structure-function coupling. This analysis revealed that between-group differences in ∆prediction accuracy followed a sensory-to-transmodal cortical hierarchy, with an increased gap between controls and ASD in transmodal compared to sensory/motor systems. Multivariate associative techniques revealed that structure-function differences reflected inter-individual differences in autistic symptoms and verbal as well as non-verbal intelligence. Our network modeling approach sheds light on atypical structure-function coupling in autism, and suggests that polysynaptic network mechanisms are implicated in the condition and that these can help explain its wide range of associated symptoms.

Inequalities in the Incidence of Psychotic Disorders Among Racial and Ethnic Groups.

OBJECTIVE: The authors examined recent trends in incidence of psychotic disorders, demographic characteristics, and comorbid psychiatric and medical conditions among six racial/ethnic groups. METHOD: A retrospective cohort study design was used to examine the incidence of psychotic disorders across race/ethnicity groups and comorbid psychiatric and medical conditions among members of Kaiser Permanente Northern California from 2009 to 2019 (N=5,994,758). Poisson regression was used to assess changes in annual incidence, and Cox proportional hazards and logistic regression models adjusted for age and sex were used to test correlates and consequences. RESULTS: Overall, the incidence of nonaffective psychotic disorders decreased slightly over the study period. Compared with White members, the risk of nonaffective psychosis diagnosis was higher among Black (hazard ratio=2.13, 95% CI=2.02-2.24) and American Indian or Alaskan Native (AIAN) (hazard ratio=1.85, 95% CI=1.53-2.23) members and lower among Asian (hazard ratio=0.72, 95% CI=0.68-0.76) and Hispanic (hazard ratio=0.91, 95% CI=0.87-0.96) members, as well as those whose race/ethnicity was categorized as "other" (hazard ratio=0.92, 95% CI=0.86-0.99). Compared with White members, the risk of affective psychosis diagnosis adjusted for age and sex was higher among Black (hazard ratio=1.76, 95% CI=1.62-1.91), Hispanic (hazard ratio=1.09, 95% CI=1.02-1.16), and AIAN (hazard ratio=1.38, 95% CI=1.00-1.90) members and lower among Asian (hazard ratio=0.77, 95% CI=0.71-0.83), Native Hawaiian or other Pacific Islander (hazard ratio=0.69, 95% CI=0.48-0.99), and "other" (hazard ratio=0.86, 95% CI=0.77-0.96) members. Psychotic disorders were associated with significantly higher odds of suicide (odds ratio=2.65, 95% CI=2.15-3.28), premature death (odds ratio=1.30, 95% CI=1.22-1.39), and stroke (odds ratio=1.64, 95% CI=1.55-1.72) and lower odds of health care utilization (odds ratio=0.44, 95% CI=0.42-0.47). CONCLUSIONS: This study demonstrates racial and ethnic variation in incident psychotic disorder diagnoses in the United States, compared with non-Hispanic Whites. Individuals diagnosed with psychosis face a greater burden of other negative health outcomes and lower odds of health care utilization, reflecting personal and economic impacts. Identifying risk factors for elevated rates and protective influences in subgroups can inform strategies for prevention and interventions to ameliorate severe consequences of psychotic syndromes.

Variation in spatial dependencies across the cortical mantle discriminates the functional behaviour of primary and association cortex.

Recent theories of cortical organisation suggest features of function emerge from the spatial arrangement of brain regions. For example, association cortex is located furthest from systems involved in action and perception. Association cortex is also 'interdigitated' with adjacent regions having different patterns of functional connectivity. It is assumed that topographic properties, such as distance between regions, constrains their functions, however, we lack a formal description of how this occurs. Here we use variograms, a quantification of spatial autocorrelation, to profile how function changes with the distance between cortical regions. We find function changes with distance more gradually within sensory-motor cortex than association cortex. Importantly, systems within the same type of cortex (e.g., fronto-parietal and default mode networks) have similar profiles. Primary and association cortex, therefore, are differentiated by how function changes over space, emphasising the value of topographical features of a region when estimating its contribution to cognition and behaviour.

An open-access dataset of naturalistic viewing using simultaneous EEG-fMRI.

In this work, we present a dataset that combines functional magnetic imaging (fMRI) and electroencephalography (EEG) to use as a resource for understanding human brain function in these two imaging modalities. The dataset can also be used for optimizing preprocessing methods for simultaneously collected imaging data. The dataset includes simultaneously collected recordings from 22 individuals (ages: 23-51) across various visual and naturalistic stimuli. In addition, physiological, eye tracking, electrocardiography, and cognitive and behavioral data were collected along with this neuroimaging data. Visual tasks include a flickering checkerboard collected outside and inside the MRI scanner (EEG-only) and simultaneous EEG-fMRI recordings. Simultaneous recordings include rest, the visual paradigm Inscapes, and several short video movies representing naturalistic stimuli. Raw and preprocessed data are openly available to download. We present this dataset as part of an effort to provide open-access data to increase the opportunity for discoveries and understanding of the human brain and evaluate the correlation between electrical brain activity and blood oxygen level-dependent (BOLD) signals.

A reproducible and generalizable software workflow for analysis of large-scale neuroimaging data collections using BIDS Apps.

Neuroimaging research faces a crisis of reproducibility. With massive sample sizes and greater data complexity, this problem becomes more acute. Software that operates on imaging data defined using the Brain Imaging Data Structure (BIDS) - BIDS Apps - have provided a substantial advance. However, even using BIDS Apps, a full audit trail of data processing is a necessary prerequisite for fully reproducible research. Obtaining a faithful record of the audit trail is challenging - especially for large datasets. Recently, the FAIRly big framework was introduced as a way to facilitate reproducible processing of large-scale data by leveraging DataLad - a version control system for data management. However, the current implementation of this framework was more of a proof of concept, and could not be immediately reused by other investigators for different use cases. Here we introduce the BIDS App Bootstrap (BABS), a user-friendly and generalizable Python package for reproducible image processing at scale. BABS facilitates the reproducible application of BIDS Apps to large-scale datasets. Leveraging DataLad and the FAIRly big framework, BABS tracks the full audit trail of data processing in a scalable way by automatically preparing all scripts necessary for data processing and version tracking on high performance computing (HPC) systems. Currently, BABS supports jobs submissions and audits on Sun Grid Engine (SGE) and Slurm HPCs with a parsimonious set of programs. To demonstrate its scalability, we applied BABS to data from the Healthy Brain Network (HBN; n=2,565). Taken together, BABS allows reproducible and scalable image processing and is broadly extensible via an open-source development model.

ReX: an integrative tool for quantifying and optimizing measurement reliability for the study of individual differences.

Characterizing multifaceted individual differences in brain function using neuroimaging is central to biomarker discovery in neuroscience. We provide an integrative toolbox, Reliability eXplorer (ReX), to facilitate the examination of individual variation and reliability as well as the effective direction for optimization of measuring individual differences in biomarker discovery. We also illustrate gradient flows, a two-dimensional field map-based approach to identifying and representing the most effective direction for optimization when measuring individual differences, which is implemented in ReX.

The science of child and adolescent mental health in Greece: a nationwide systematic review.

Evidence-based information is essential for effective mental health care, yet the extent and accessibility of the scientific literature are critical barriers for professionals and policymakers. To map the necessities and make validated resources accessible, we undertook a systematic review of scientific evidence on child and adolescent mental health in Greece encompassing three research topics: prevalence estimates, assessment instruments, and interventions. We searched Pubmed, Web of Science, PsycINFO, Google Scholar, and IATPOTEK from inception to December 16th, 2021. We included studies assessing the prevalence of conditions, reporting data on assessment tools, and experimental interventions. For each area, manuals informed data extraction and the methodological quality were ascertained using validated tools. This review was registered in protocols.io [68583]. We included 104 studies reporting 533 prevalence estimates, 223 studies informing data on 261 assessment instruments, and 34 intervention studies. We report the prevalence of conditions according to regions within the country. A repository of locally validated instruments and their psychometrics was compiled. An overview of interventions provided data on their effectiveness. The outcomes are made available in an interactive resource online [ https://rpubs.com/camhi/sysrev_table ]. Scientific evidence on child and adolescent mental health in Greece has now been cataloged and appraised. This timely and accessible compendium of up-to-date evidence offers valuable resources for clinical practice and policymaking in Greece and may encourage similar assessments in other countries.

Omnipresence of the sensorimotor-association axis topography in the human connectome.

Low-dimensional representations are increasingly used to study meaningful organizational principles within the human brain. Most notably, the sensorimotor-association axis consistently explains the most variance in the human connectome as its so-called principal gradient, suggesting that it represents a fundamental organizational principle. While recent work indicates these low dimensional representations are relatively robust, they are limited by modeling only certain aspects of the functional connectivity structure. To date, the majority of studies have restricted these approaches to the strongest connections in the brain, treating weaker or negative connections as noise despite evidence of meaningful structure among them. The present work examines connectivity gradients of the human connectome across a full range of connectivity strengths and explores the implications for outcomes of individual differences, identifying potential dependencies on thresholds and opportunities to improve prediction tasks. Interestingly, the sensorimotor-association axis emerged as the principal gradient of the human connectome across the entire range of connectivity levels. Moreover, the principal gradient of connections at intermediate strengths encoded individual differences, better followed individual-specific anatomical features, and was also more predictive of intelligence. Taken together, our results add to evidence of the sensorimotor-association axis as a fundamental principle of the brain's functional organization, since it is evident even in the connectivity structure of more lenient connectivity thresholds. These more loosely coupled connections further appear to contain valuable and potentially important information that could be used to improve our understanding of individual differences, diagnosis, and the prediction of treatment outcomes.

Association between COVID-19 risk-mitigation behaviors and specific mental disorders in youth.

BACKGROUND: Although studies of adults show that pre-existing mental disorders increase risk for COVID-19 infection and severity, there is limited information about this association among youth. Mental disorders in general as well as specific types of disorders may influence the ability to comply with risk-mitigation strategies to reduce COVID-19 infection and transmission. METHODS: Youth compliance (rated as "Never," "Sometimes," "Often," or "Very often/Always") with risk mitigation was reported by parents on the CoRonavIruS Health Impact Survey (CRISIS) in January 2021. The sample comprised 314 female and 514 male participants from the large-scale Child Mind Institute Healthy Brain Network, a transdiagnostic self-referred, community sample of children and adolescents (ages 5-21). Responses were summarized using factor analysis of risk mitigation, and their associations with lifetime mental disorders (assessed via structured diagnostic interviews) were identified with linear regression analyses (adjusted for covariates). All analyses used R Project for Statistical Computing for Mac (v.4.0.5). RESULTS: A two-factor model was the best-fitting solution. Factor 1 (avoidance behaviors) included avoiding groups, indoor settings, and other peoples' homes; avoidance scores were higher among youth with any anxiety disorder (p = .01). Factor 2 (hygiene behaviors) included using hand sanitizer, washing hands, and maintaining social distance; hygiene scores were lower among youth with ADHD (combined type) (p = .02). Mask wearing was common (90%), did not load on either factor, and was not associated with any mental health disorder. CONCLUSION AND RELEVANCE: Although most mental disorders examined were not associated with risk mitigation, youth with ADHD characterized by hyperactivity plus inattention may need additional support to consistently engage in risk-mitigation behaviors. Enhancing risk-mitigation strategies among at-risk groups of youth may help reduce COVID-19 infection and transmission.

Deviations from a typical development of the cerebellum in youth are associated with psychopathology, executive functions and educational outcomes.

BACKGROUND: Understanding deviations from typical brain development is a promising approach to comprehend pathophysiology in childhood and adolescence. We investigated if cerebellar volumes different than expected for age and sex could predict psychopathology, executive functions and academic achievement. METHODS: Children and adolescents aged 6-17 years from the Brazilian High-Risk Cohort Study for Mental Conditions had their cerebellar volume estimated using Multiple Automatically Generated Templates from T1-weighted images at baseline (n = 677) and at 3-year follow-up (n = 447). Outcomes were assessed using the Child Behavior Checklist and standardized measures of executive functions and school achievement. Models of typically developing cerebellum were based on a subsample not exposed to risk factors and without mental-health conditions (n = 216). Deviations from this model were constructed for the remaining individuals (n = 461) and standardized variation from age and sex trajectory model was used to predict outcomes in cross-sectional, longitudinal and mediation analyses. RESULTS: Cerebellar volumes higher than expected for age and sex were associated with lower externalizing specific factor and higher executive functions. In a longitudinal analysis, deviations from typical development at baseline predicted inhibitory control at follow-up, and cerebellar deviation changes from baseline to follow-up predicted changes in reading and writing abilities. The association between deviations in cerebellar volume and academic achievement was mediated by inhibitory control. CONCLUSIONS: Deviations in the cerebellar typical development are associated with outcomes in youth that have long-lasting consequences. This study highlights both the potential of typical developing models and the important role of the cerebellum in mental health, cognition and education.

IQ Modulates Coupling Between Diverse Dimensions of Psychopathology in Children and Adolescents.

OBJECTIVE: Correlations between cognitive ability and psychopathology are well recognized, but prior research has been limited by focusing on individuals with intellectual disability, single-diagnosis psychiatric populations, or few measures of psychopathology. Here, we quantify relationships between full-scale IQ and multiple dimensions of psychopathology in a diverse care-seeking population, with a novel focus on differential coupling between psychopathology dimensions as a function of IQ. METHOD: A total of 70 dimensional measures of psychopathology, plus IQ and demographic data, were collated for 2,752 children and adolescents from the Healthy Brain Network dataset. We first examined univariate associations between IQ and psychopathology, and then characterized how the correlational architecture of psychopathology differs between groups at extremes of the IQ distribution. RESULTS: Associations with IQ vary in magnitude between different domains of psychopathology: IQ shows the strongest negative correlations with attentional and social impairments, but is largely unrelated to affective symptoms and psychopathy. Lower IQ is associated with stronger coupling between internalizing problems and aggression, repetitive behaviors, and hyperactivity/inattentiveness. CONCLUSION: Our analyses reveal that variation in general cognitive ability is associated not only with significant and selective shifts in severity of psychopathology, but also in the coupling between different dimensions of psychopathology. These findings have relevance for the clinical assessment of mental health in populations with varying IQ, and may also inform ongoing efforts to improve the measurement of psychopathology and to understand how relationships between cognition and behavior are reflected in brain organization. DIVERSITY & INCLUSION STATEMENT: We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure sex balance in the selection of non-human subjects. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper received support from a program designed to increase minority representation in science. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science.

Curation of BIDS (CuBIDS): A workflow and software package for streamlining reproducible curation of large BIDS datasets.

The Brain Imaging Data Structure (BIDS) is a specification accompanied by a software ecosystem that was designed to create reproducible and automated workflows for processing neuroimaging data. BIDS Apps flexibly build workflows based on the metadata detected in a dataset. However, even BIDS valid metadata can include incorrect values or omissions that result in inconsistent processing across sessions. Additionally, in large-scale, heterogeneous neuroimaging datasets, hidden variability in metadata is difficult to detect and classify. To address these challenges, we created a Python-based software package titled "Curation of BIDS" (CuBIDS), which provides an intuitive workflow that helps users validate and manage the curation of their neuroimaging datasets. CuBIDS includes a robust implementation of BIDS validation that scales to large samples and incorporates DataLad--a version control software package for data--as an optional dependency to ensure reproducibility and provenance tracking throughout the entire curation process. CuBIDS provides tools to help users perform quality control on their images' metadata and identify unique combinations of imaging parameters. Users can then execute BIDS Apps on a subset of participants that represent the full range of acquisition parameters that are present, accelerating pipeline testing on large datasets.