MRI economics: Balancing sample size and scan duration in brain wide association studies DOI Creative Commons
Leon Qi Rong Ooi, Csaba Orban, Shaoshi Zhang

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 18, 2024

Abstract A pervasive dilemma in neuroimaging is whether to prioritize sample size or scan time given fixed resources. Here, we systematically investigate this trade-off the context of brain-wide association studies (BWAS) using functional magnetic resonance imaging (fMRI). We find that total duration (sample × per participant) robustly explains individual-level phenotypic prediction accuracy via a logarithmic model, suggesting and are broadly interchangeable up 20-30 min data. However, returns diminish relative size, which explain with principled theoretical derivations. When accounting for overhead costs associated each participant (e.g., recruitment, non-imaging measures), many small-scale some large-scale BWAS might benefit from longer than typically assumed. These results generalize across domains, scanners, acquisition protocols, racial groups, mental disorders, age as well resting-state task-state connectivity. Overall, our study emphasizes importance time, ignored standard power calculations. Standard calculations maximize at expense can result sub-optimal accuracies inefficient use Our empirically informed reference available future design: WEB_APPLICATION_LINK

Language: Английский

Associations Between Prenatal Cannabis Exposure and Childhood Outcomes DOI Open Access
Sarah E. Paul, Alexander S. Hatoum, Jeremy D. Fine

et al.

JAMA Psychiatry, Journal Year: 2020, Volume and Issue: 78(1), P. 64 - 64

Published: Sept. 23, 2020

In light of increasing cannabis use among pregnant women, the US Surgeon General recently issued an advisory against marijuana during pregnancy.To evaluate whether pregnancy is associated with adverse outcomes offspring.In this cross-sectional study, data were obtained from baseline session ongoing longitudinal Adolescent Brain and Cognitive Development Study, which recruited 11 875 children aged 9 to years, as well a parent or caregiver, 22 sites across United States between June 1, 2016, October 15, 2018.Prenatal exposure prior after maternal knowledge pregnancy.Symptoms psychopathology in (ie, psychotic-like experiences [PLEs] internalizing, externalizing, attention, thought, social problems), cognition, sleep, birth weight, gestational age at birth, body mass index, brain structure total intracranial volume, white matter gray volume). Covariates included familial (eg, income psychopathology), prenatal alcohol tobacco), child substance use) variables.Among 489 (5997 boys [52.2%]; mean [SD] age, 9.9 [0.6] years) nonmissing data, 655 (5.7%) exposed prenatally. Relative no exposure, only before (413 [3.6%]) (242 [2.1%]) greater offspring characteristics PLEs thought and, sleep problems, lower cognition volume (all |β| > 0.02; all false discovery rate [FDR]-corrected P < .03). Only was weight volumes relative FDR-corrected .04). When including potentially confounding covariates, remained problems β .02). Exposure did not differ on any when considering variables .70).This study suggests that its correlated factors are risk for middle childhood. Cannabis should be discouraged.

Language: Английский

Citations

245
The Human Connectome Project: A retrospective DOI Creative Commons

Jennifer Stine Elam,

Matthew F. Glasser,

Michael P. Harms

et al.

NeuroImage, Journal Year: 2021, Volume and Issue: 244, P. 118543 - 118543

Published: Sept. 8, 2021

The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances human neuroimaging, particularly for measures of brain connectivity; apply these study a large number healthy young adults; and freely share the data tools with scientific community. NIH awarded grants two consortia; this retrospective focuses on "WU-Minn-Ox" HCP consortium centered at Washington University, University Minnesota, Oxford. In just over 6 years, WU-Minn-Ox succeeded its core objectives by: 1) improving MR scanner hardware, pulse sequence design, image reconstruction methods, 2) acquiring analyzing multimodal MRI MEG unprecedented quality together behavioral from more than 1100 participants, 3) sharing (via ConnectomeDB database) associated analysis visualization tools. To date, 27 Petabytes have been shared, 1538 papers acknowledging use published. "HCP-style" neuroimaging paradigm has emerged set best-practice strategies optimizing acquisition analysis. This article reviews history HCP, including comments key events decisions major project components. We discuss several using data, improved cortical parcellations, analyses connectivity based functional diffusion MRI, brain-behavior relationships. also touch upon our efforts develop variety processing along detailed documentation, tutorials, educational course train next generation neuroimagers. conclude look forward opportunities challenges facing field perspective consortium.

Language: Английский

Citations

203
QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data DOI
Matthew Cieslak, Philip A. Cook, Xiaosong He

et al.

Nature Methods, Journal Year: 2021, Volume and Issue: 18(7), P. 775 - 778

Published: June 21, 2021

Language: Английский

Citations

201
Meaningful associations in the adolescent brain cognitive development study DOI Creative Commons
Anthony Steven Dick, Daniel A. Lopez, Ashley L. Watts

et al.

NeuroImage, Journal Year: 2021, Volume and Issue: 239, P. 118262 - 118262

Published: June 17, 2021

The Adolescent Brain Cognitive Development (ABCD) Study is the largest single-cohort prospective longitudinal study of neurodevelopment and children's health in United States. A cohort n = 11,880 children aged 9–10 years (and their parents/guardians) were recruited across 22 sites are being followed with in-person visits on an annual basis for at least 10 years. approximates US population several key sociodemographic variables, including sex, race, ethnicity, household income, parental education. Data collected include assessments health, mental substance use, culture environment neurocognition, as well geocoded exposures, structural functional magnetic resonance imaging (MRI), whole-genome genotyping. Here, we describe ABCD aims design, issues surrounding estimation meaningful associations using its data, inferences, hypothesis testing, power precision, control covariates, interpretation associations, recommended best practices reproducible research, analytical procedures reporting results.

Language: Английский

Citations

183
Assessment of Neighborhood Poverty, Cognitive Function, and Prefrontal and Hippocampal Volumes in Children DOI Creative Commons
Rita L. Taylor, Shelly R. Cooper,

Joshua J. Jackson

et al.

JAMA Network Open, Journal Year: 2020, Volume and Issue: 3(11), P. e2023774 - e2023774

Published: Nov. 3, 2020

Importance

The association between poverty and unfavorable cognitive outcomes is robust, but most research has focused on individual household socioeconomic status (SES). There increasing evidence that neighborhood context explains unique variance not accounted for by SES.

Objective

To evaluate whether (NP) associated with function prefrontal hippocampal brain structure in ways are dissociable from

Design, Setting, Participants

This cross-sectional study used a baseline sample of the ongoing longitudinal Adolescent Brain Cognitive Development (ABCD) Study. ABCD Study will follow participants assessments each year 10 years. Data were collected at 21 US sites, mostly within urban suburban areas, September 2019 October 2018. School-based recruitment was to create participant reflecting population. analysis conducted March June 2019.

Main Outcomes Measures

NP SES included as factors potentially National Institutes Health Toolbox Battery subtests (dorsolateral cortex [DLPFC], dorsomedial PFC [DMPFC], superior frontal gyrus [SFG]) volumes. Independent variables first considered individually then together mixed-effects models age, sex, intracranial volume covariates. Structural equation modeling (SEM) assess shared task associations. tested hypotheses formulated after data collection.

Results

A total 11 875 children aged 9 years (5678 [47.8%] girls) analyzed. Greater lower scores across all domains (eg, composite: β = −0.18; 95% CI, −0.21 −0.15;P < .001) decreased DLPFC right DLPFC: −0.09; −0.12 −0.07;P .001), DMPFC DMPC: −0.07; −0.09 −0.05;P SFG SFG: −0.05; −0.08 −0.03;P hippocampus (β −0.04; −0.06 −0.01;P .01), even when accounting income. income higher 0.30; 0.28 0.33;P larger regions hippocampus: 0.04; 0.02 0.07;P NP. SEM model good fit domains, being relations language (picture vocabulary: estimate [SE], –0.03 [0.01];P .001; oral reading: –0.02 episodic memory sequence: .008), working (dimensional card sort: flanker inhibitory control: –0.01 .01; list sorting: associations [0.004];P 0.001).

Conclusions Relevance

In this study, volume. These findings demonstrate importance including broader environmental influences conceptualizing early life adversity.

Language: Английский

Citations

167
Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study DOI Creative Commons
Jianzhong Chen, Angela Tam, Valeria Kebets

et al.

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: April 25, 2022

Abstract How individual differences in brain network organization track behavioral variability is a fundamental question systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the level. However, most studies focus on single traits, thus not capturing broader relationships across behaviors. In large sample of 1858 typically developing children from Adolescent Brain Cognitive Development (ABCD) study, we show predictive features are distinct domains cognitive performance, personality scores mental health assessments. On other hand, within each domain predicted by similar features. Predictive models generalize to measures same domain. Although tasks known modulate connectome, between resting task states. Overall, our findings reveal shared account for variation broad behavior childhood.

Language: Английский

Citations

162
Sleep duration, brain structure, and psychiatric and cognitive problems in children DOI
Wei Cheng, Edmund T. Rolls, Weikang Gong

et al.

Molecular Psychiatry, Journal Year: 2020, Volume and Issue: 26(8), P. 3992 - 4003

Published: Feb. 3, 2020

Language: Английский

Citations

154
Associations Between Neighborhood Disadvantage, Resting-State Functional Connectivity, and Behavior in the Adolescent Brain Cognitive Development Study: The Moderating Role of Positive Family and School Environments DOI
Divyangana Rakesh, Caio Seguin, Andrew Zalesky

et al.

Biological Psychiatry Cognitive Neuroscience and Neuroimaging, Journal Year: 2021, Volume and Issue: 6(9), P. 877 - 886

Published: March 23, 2021

Language: Английский

Citations

139
Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity DOI Creative Commons
Jingwei Li, Danilo Bzdok, Jianzhong Chen

et al.

Science Advances, Journal Year: 2022, Volume and Issue: 8(11)

Published: March 16, 2022

Algorithmic biases that favor majority populations pose a key challenge to the application of machine learning for precision medicine. Here, we assessed such bias in prediction models behavioral phenotypes from brain functional magnetic resonance imaging. We examined using two independent datasets (preadolescent versus adult) mixed ethnic/racial composition. When predictive were trained on data dominated by white Americans (WA), out-of-sample errors generally higher African (AA) than WA. This toward WA corresponds more WA-like brain-behavior association patterns learned models. AA only, compared training only or an equal number and participants, accuracy improved but stayed below Overall, results point need caution further research regarding current minority populations.

Language: Английский

Citations

112
A practical guide for researchers and reviewers using the ABCD Study and other large longitudinal datasets DOI Creative Commons
Natalie M. Saragosa‐Harris, Natasha Chaku, Niamh MacSweeney

et al.

Developmental Cognitive Neuroscience, Journal Year: 2022, Volume and Issue: 55, P. 101115 - 101115

Published: May 20, 2022

As the largest longitudinal study of adolescent brain development and behavior to date, Adolescent Brain Cognitive Development (ABCD) Study® has provided immense opportunities for researchers across disciplines since its first data release in 2018. The size scope also present a number hurdles, which range from becoming familiar with design structure employing rigorous reproducible analyses. current paper is intended as guide reviewers working ABCD data, highlighting features (and strengths limitations therein) well relevant analytical methodological considerations. Additionally, we explore justice, equity, diversity, inclusion efforts they pertain Study other large-scale datasets. In doing so, hope increase both accessibility transparency within field developmental cognitive neuroscience.

Language: Английский

Citations

102