Adolescent brain cognitive development (ABCD) study: Overview of substance use assessment methods

Medina, Krista Lisdahl; Sher, Kenneth J.; Conway, Kevin P.; González, Raúl; Ewing, Sarah W. Feldstein; Nixon, Sara Jo; Tapert, Susan F.; Bartsch, Hauke; Goldstein, Rita Z.; Heitzeg, Mary M.

doi:10.1016/j.dcn.2018.02.007

Cited by 278 publications

(204 citation statements)

References 265 publications

(353 reference statements)

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“…Currently, we have evidence to suggest recency, frequency, and age of onset of cannabis are likely key variables in predicting not only poor neural health and cognitive functioning, but emotional functioning as well (e.g., depression) (Jacobus et al, ). Larger prospective studies such as the newly launched Adolescent Brain Cognitive Development (ABCD) study will help us better understand the role of preexisting childhood brain differences that contribute to substance use initiation and progression to problematic use, and ultimately poorer health outcomes for adolescents and young adults (Garavan et al, ; Lisdahl et al, ).…”

Section: Discussionmentioning

confidence: 99%

Cannabis and the developing brain: What does the evidence say?

Jacobus

Courtney

Hodgdon

et al. 2019

Birth Defects Research

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Cannabis use during adolescence has been linked to deleterious effects on brain integrity. This article summarizes findings from two prospective investigations (3 and 6 years, on average) on adolescent cannabis use from our laboratory that utilize structural neuroimaging and neurocognitive assessment approaches. Across most studies, findings suggest recency, frequency, and age of onset of cannabis use are likely key variables in predicting poorer neural health outcomes. There is some evidence that preexisting differences in brain architecture may also contribute to vulnerability and outcome differences. Ongoing large‐scale prospective studies of youth will be able to disentangle how both cannabis use as well as pre and postexposure differences play a role in divergent outcomes among youth who use cannabis.

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Section: Discussionmentioning

confidence: 99%

Cannabis and the developing brain: What does the evidence say?

Jacobus

Courtney

Hodgdon

et al. 2019

Birth Defects Research

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“…We illustrate the implications described above by examining alcohol sipping behavior among youth (ages 8 to 11 years) using the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org), a large multisite study of long-term brain development and child health. We focused our analyses on assessing the effects of social and environmental factors on the lifetime occurrence of alcohol sipping behavior measured by the ABCD Substance Use and Culture and Environment modules (see Lisdahl et al, 2018 andZucker et al, 2018 for in-depth descriptions of these modules).…”

Section: Real Data Examplementioning

confidence: 99%

Interpreting interaction effects in generalized linear models of nonlinear probabilities and counts

McCabe¹,

Halvorson²,

King³

et al. 2020

Preprint

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We would like to thank Dr. Tamara Wall for her thoughtful comments on previous versions of this manuscript.Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9-10 and follow them over 10 years into early adulthood. AbstractPsychology studies frequently involve the analysis of binary and count dependent variables. These are typically conducted using generalized linear models (GLMs) that specify non-linear relations between predictors and outcomes. Interactions are central within many research applications of these models. To date, typical practice in evaluating interaction effects in GLMs extends directly from linear approaches, in which the product term coefficient between variables of interest are used to provide evidence of an interaction effect. However, unlike linear models, interaction effects in GLMs are not equal to product terms between predictor variables and are instead a function of all predictors of a model. Here, we applied a partial derivative and finite difference framework to propose solutions for computing and conducting hypothesis testing on interaction effects in binary and count GLMs. We provided concrete guidelines and simulated examples to describe how interaction effects should be estimated and interpreted when GLMs are employed. We further illustrated via simulation how using the product term coefficient as an estimator of the interaction can result in bias and inefficiency of the interaction parameter. We concluded with an example using the Adolescent Brain Cognitive Development Study demonstrating how to correctly evaluate interaction effects in a logistic model. Interaction INTERACTIONS IN GLMS 4Estimating and interpreting interaction effects in generalized linear models of binary and count data 1 Journals were selected to represent various sub-disciplines of psychology, and included Developmental Addictive Behaviors. Databases included PsychInfo and PsychArticles. Boolean search conditions were "KW (interaction OR moderation) OR TX (interacted OR interaction OR moderated OR moderation) AND TX (logistic OR probit OR poisson OR ordinal OR negative binomial) AND TX regression", yielding 1,812 unique publications. Selected articles failed to meet criteria if search terms resulted in false-positives (e.g., moderation was mentioned in-text but was not examined directly in analyses).

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“…For the current report, we analyzed data acquired in the subset of ABCD participants (see (Bjork et al, 2017;Casey et al, 2018;Lisdahl et al, 2018;Volkow et al, 2017)…”

Section: Abcd -Multiband Datasetmentioning

confidence: 99%

Correction of respiratory artifacts in MRI head motion estimates

Fair

Miranda-Domínguez

Snyder³

et al. 2018

Preprint

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Head motion represents one of the greatest technical obstacles for brain MRI. Accurate detection of artifacts induced by head motion requires precise estimation of movement.However, this estimation may be corrupted by factitious effects owing to main field fluctuations generated by body motion. In the current report, we examine head motion estimation in multiband resting state functional connectivity MRI (rs-fcMRI) data from the Adolescent Brain and Cognitive Development (ABCD) Study and a comparison 'single-shot' dataset from OregonHealth & Science University. We show unequivocally that respirations contaminate movement estimates in functional MRI and that respiration generates apparent head motion not associated with degraded quality of functional MRI. We have developed a novel approach using a bandstop filter that accurately removes these respiratory effects. Subsequently, we demonstrate that utilizing this filter improves post-processing data quality. Lastly, we demonstrate the real-time implementation of motion estimate filtering in our FIRMM (Framewise Integrated Real-Time MRI Monitoring) software package.(FIRMM) . FIRMM provides near instantaneous analysis of head motion (framewise displacement = FD) during scanning. This feature enables the duration of resting state fMRI scanning to be dynamically determined, that is, for as long as is necessary to acquire a prescribed quantity of data meeting a fixed quality assurance criterion. Thus, FIRMM helps to ensure that an adequate quantity of resting state data is acquired in most participants.It also reduces the need for 'overscanning' all participants in order to ensure adequate acquisitions across subjects. Moreover, subjects who cannot suppress excessive motion can be identified promptly and efficiently excluded from the study. Additionally, FIRMM alerts the scanner operator to changes in participant behavior (e.g., sleep, or increased movement because of discomfort), and also can be utilized to give motion feedback to participants themselves. These advances have been thoroughly detailed in prior reports Greene et al., 2018).Here, we address new issues that have arisen when calculating head motion estimates (FD) fMRI data. Multiband imaging (simultaneous multi-slice sequences [SMS]) is a recently developed technique that substantially speeds up fMRI by exciting multiple slices simultaneously (Feinberg and Yacoub, 2012;Moeller et al., 2010;Uğurbil et al., 2013;Xu et al., 2013). Thus, the time needed to acquire one whole brain volume can be reduced to well below 1 sec. While multiband sequences offer several clear advantages and are now becoming widely used, they come at the cost of reducing the temporal signal to noise ratio (tSNR; (Chen et al., 2015)) and, potentially, new types of 'slice-leakage' artifacts (Barth et al., 2016;Todd et al., 2016).Another unanticipated consequence of the improved temporal resolution of SMS sequences is corruption of head motion estimation (FD) arising from the interaction between echo-planar imaging (EPI) and small perturbatio...

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Adolescent brain cognitive development (ABCD) study: Overview of substance use assessment methods

Cited by 278 publications

References 265 publications

Cannabis and the developing brain: What does the evidence say?

Cannabis and the developing brain: What does the evidence say?

Interpreting interaction effects in generalized linear models of nonlinear probabilities and counts

Correction of respiratory artifacts in MRI head motion estimates

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