Dataset Information

Biospecimens and the ABCD study: Rationale, methods of collection, measurement and early data.

ABSTRACT: Biospecimen collection in the Adolescent Brain Cognitive Development (ABCD) study - of hair samples, shed deciduous (baby) teeth, and body fluids - will serve dual functions of screening for study eligibility, and providing measures of biological processes thought to predict or correlate with key study outcomes on brain and cognitive development. Biosamples are being collected annually to screen for recency of drug use prior to the neuroimaging or cognitive testing visit, and to store for the following future studies: (1) on the effects of exposure to illicit and recreational drugs (including alcohol and nicotine); (2) of pubertal hormones on brain and cognitive developmental trajectories; (3) on the contribution of genomics and epigenomics to child and adolescent development and behavioral outcomes; and (4) with pre- and post-natal exposure to environmental neurotoxicants and drugs of abuse measured from novel tooth analyses. The present manuscript describes the rationales for inclusion and selection of the specific biospecimens, methodological considerations for each measure, future plans for assessment of biospecimens during follow-up visits, and preliminary ABCD data to illustrate methodological considerations.

SUBMITTER: Uban KA

PROVIDER: S-EPMC6487488 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Biospecimens and the ABCD study: Rationale, methods of collection, measurement and early data.

Uban Kristina A KA Horton Megan K MK Jacobus Joanna J Heyser Charles C Thompson Wesley K WK Tapert Susan F SF Madden Pamela A F PAF Sowell Elizabeth R ER

Developmental cognitive neuroscience 20180316

Biospecimen collection in the Adolescent Brain Cognitive Development (ABCD) study - of hair samples, shed deciduous (baby) teeth, and body fluids - will serve dual functions of screening for study eligibility, and providing measures of biological processes thought to predict or correlate with key study outcomes on brain and cognitive development. Biosamples are being collected annually to screen for recency of drug use prior to the neuroimaging or cognitive testing visit, and to store for the fo ...[more]

PMID: 29606560

Similar Datasets

Project description:BackgroundLarge birth cohort studies are extremely valuable in assessing associations between early life exposures and long-term outcomes. Establishing new birth cohorts is challenging due to declining participation rates. Online methods of data collection may increase feasibility, but have not been evaluated thoroughly.ObjectiveThe primary objective of the ongoing PRegnancy and Infant DEvelopment (PRIDE) Study is to identify exposures during pregnancy and in early life that may affect short-term or long-term health of mother and/or child. In this manuscript, we aimed to evaluate methods of recruitment and online data collection applied.PopulationDutch women aged ≥18 years in early pregnancy.DesignProspective cohort study.MethodsInitially, only prenatal care providers recruited participants, but alternative recruitment methods were added as a result of disappointing participation rates, including collaboration with "Moeders voor Moeders" (organisation that visits women in early pregnancy) and Facebook advertisements. Data on demographic characteristics, obstetric history, maternal health, life style factors, occupational exposures, nutrition, pregnancy complications, and infant outcomes are primarily collected through Web-based questionnaires at multiple time points during and after pregnancy. Additional data collection components include paternal questionnaires, blood and saliva sampling, and linkage to medical records.Preliminary resultsBy September 2019, 9573 women were included in the PRIDE Study, of which 1.3% completed paper-based questionnaires. Mean age of the women analysed was 30.6 years, 71.1% had a high level of education, 57.2% were primiparae, and mean gestational age at enrolment was 9.9 (range 3, 37) weeks, with slight differences between recruitment methods. Pregnancy outcome was known for 89.8%. Retention rate at 6 months after the estimated date of delivery was estimated at 70%. Multiple validation studies conducted within the PRIDE Study indicated high data quality.Conclusion(s)Although challenging and time-consuming, online methods for recruitment and data collection may enable the establishment of new birth cohort studies.

Project description:To understand the neurocognitive mechanisms that underlie heterogeneity in cognitive ageing, recent scientific efforts have led to a growing public availability of imaging cohort data. The Advanced BRain Imaging on ageing and Memory (ABRIM) project aims to add to these existing datasets by taking an adult lifespan approach to provide a cross-sectional, normative database with a particular focus on connectivity, myelinization and iron content of the brain in concurrence with cognitive functioning, mechanisms of reserve, and sleep-wake rhythms. ABRIM freely shares MRI and behavioural data from 295 participants between 18-80 years, stratified by age decade and sex (median age 52, IQR 36-66, 53.20% females). The ABRIM MRI collection consists of both the raw and pre-processed structural and functional MRI data to facilitate data usage among both expert and non-expert users. The ABRIM behavioural collection includes measures of cognitive functioning (i.e., global cognition, processing speed, executive functions, and memory), proxy measures of cognitive reserve (e.g., educational attainment, verbal intelligence, and occupational complexity), and various self-reported questionnaires (e.g., on depressive symptoms, pain, and the use of memory strategies in daily life and during a memory task). In a sub-sample (n = 120), we recorded sleep-wake rhythms using an actigraphy device (Actiwatch 2, Philips Respironics) for a period of 7 consecutive days. Here, we provide an in-depth description of our study protocol, pre-processing pipelines, and data availability. ABRIM provides a cross-sectional database on healthy participants throughout the adult lifespan, including numerous parameters relevant to improve our understanding of cognitive ageing. Therefore, ABRIM enables researchers to model the advanced imaging parameters and cognitive topologies as a function of age, identify the normal range of values of such parameters, and to further investigate the diverse mechanisms of reserve and resilience.

Project description:BackgroundAdversity occurring during development is associated with detrimental health and quality of life outcomes, not just following exposure but throughout the lifespan. Despite increased research, there exists both overlapping and distinct definitions of early life adversity exposure captured by over 30 different empirically validated tools. A data-driven approach to defining and cataloging exposure is needed to better understand associated outcomes and advance the field.MethodsWe utilized baseline data on 11,566 youth enrolled in the ABCD Study to catalog youth and caregiver-reported early life adversity exposure captured across 14 different measures. We employed an exploratory factor analysis to identify the factor domains of early life adversity exposure and conducted a series of regression analyses to examine its association with problematic behavioral outcomes.ResultsThe exploratory factor analysis yielded a 6-factor solution corresponding to the following distinct domains: 1) physical and sexual violence; 2) parental psychopathology; 3) neighborhood threat; 4) prenatal substance exposure; 5) scarcity; and 6) household dysfunction. The prevalence of exposure among 9-and 10-year-old youth was largely driven by the incidence of parental psychopathology. Sociodemographic characteristics significantly differed between youth with adversity exposure and controls, depicting a higher incidence of exposure among racial and ethnic minoritized youth, and among those identifying with low socioeconomic status. Adversity exposure was significantly associated with greater problematic behaviors and largely driven by the incidence of parental psychopathology, household dysfunction and neighborhood threat. Certain types of early life adversity exposure were more significantly associated with internalizing as opposed to externalizing problematic behaviors.ConclusionsWe recommend a data-driven approach to define and catalog early life adversity exposure and suggest the incorporation of more versus less data to capture the nuances of exposure, e.g., type, age of onset, frequency, duration. The broad categorizations of early life adversity exposure into two domains, such as abuse and neglect, or threat and deprivation, fail to account for the routine co-occurrence of exposures and the duality of some forms of adversity. The development and use of a data-driven definition of early life adversity exposure is a crucial step to lessening barriers to evidence-based treatments and interventions for youth.

Project description:BackgroundMachine learning models often use passively recorded sensor data streams as inputs to train machine learning models that predict outcomes captured through ecological momentary assessments (EMA). Despite the growth of mobile data collection, challenges in obtaining proper authorization to send notifications, receive background events, and perform background tasks persist.ObjectiveWe investigated challenges faced by mobile sensing apps in real-world settings in order to develop design guidelines. For active data, we compared 2 prompting strategies: setup prompting, where the app requests authorization during its initial run, and contextual prompting, where authorization is requested when an event or notification occurs. Additionally, we evaluated 2 passive data collection paradigms: collection during scheduled background tasks and persistent reminders that trigger passive data collection. We investigated the following research questions (RQs): (RQ1) how do setup prompting and contextual prompting affect scheduled notification delivery and the response rate of notification-initiated EMA? (RQ2) Which authorization paradigm, setup or contextual prompting, is more successful in leading users to grant authorization to receive background events? and (RQ3) Which polling-based method, persistent reminders or scheduled background tasks, completes more background sessions?MethodsWe developed mobile sensing apps for iOS and Android devices and tested them through a 30-day user study asking college students (n=145) about their stress levels. Participants responded to a daily EMA question to test active data collection. The sensing apps collected background location events, polled for passive data with persistent reminders, and scheduled background tasks to test passive data collection.ResultsFor RQ1, setup and contextual prompting yielded no significant difference (ANOVA F1,144=0.0227; P=.88) in EMA compliance, with an average of 23.4 (SD 7.36) out of 30 assessments completed. However, qualitative analysis revealed that contextual prompting on iOS devices resulted in inconsistent notification deliveries. For RQ2, contextual prompting for background events was 55.5% (χ21=4.4; P=.04) more effective in gaining authorization. For RQ3, users demonstrated resistance to installing the persistent reminder, but when installed, the persistent reminder performed 226.5% more background sessions than traditional background tasks.ConclusionsWe developed design guidelines for improving mobile sensing on consumer mobile devices based on our qualitative and quantitative results. Our qualitative results demonstrated that contextual prompts on iOS devices resulted in inconsistent notification deliveries, unlike setup prompting on Android devices. We therefore recommend using setup prompting for EMA when possible. We found that contextual prompting is more efficient for authorizing background events. We therefore recommend using contextual prompting for passive sensing. Finally, we conclude that developing a persistent reminder and requiring participants to install it provides an additional way to poll for sensor and user data and could improve data collection to support adaptive interventions powered by machine learning.

Dataset Information

Biospecimens and the ABCD study: Rationale, methods of collection, measurement and early data.

Publications

Biospecimens and the ABCD study: Rationale, methods of collection, measurement and early data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets