Article in Human brain mapping, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Luke W HydeDepartment of Psychology, University of Michigan, Ann Arbor, Michigan, USA.
Funding
Fragile Families and the Transition to AdulthoodR01HD036916 · NICHD · PRINCETON UNIVERSITY · PI Kathryn Edin, Jane Waldfogel · 1999 to 2026
$46.3M
COVID-19 supplement to a computational examination of threat and reward constructs in a predominantly low-income, longitudinal sample at increased risk for internalizing disordersR01MH121079 · NIMH · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI HYDE, LUKE WILLIAMSON, MITCHELL, COLTER M.S. · 2019 to 2024
$7.2M
TRAINING PROGRAM IN DEVELOPMENTAL PSYCHOLOGYT32HD007109 · NICHD · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GELMAN, SUSAN A., MONK, CHRISTOPHER STEPHEN · 1985 to 2025
$6.6M
Mechanisms underlying resilience to neighborhood disadvantage (Administrative Supplement)UH3MH114249 · NIMH · MICHIGAN STATE UNIVERSITY · PI BURT, S. ALEXANDRA, HYDE, LUKE WILLIAMSON · 2018 to 2022
$3.5M
Effects of poverty on affective development: A multi-level, longitudinal studyR01MH103761 · NIMH · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MONK, CHRISTOPHER STEPHEN · 2014 to 2018
$3.3M
Neurobiological pathways underlying maladaptive behaviors in youthR01HD093334 · NICHD · MICHIGAN STATE UNIVERSITY · PI BURT, S. ALEXANDRA, HYDE, LUKE WILLIAMSON · 2017 to 2021
$3.2M
ECONOMIC STATUS, PUBLIC POLICY, AND CHILD NEGLECTR01HD039135 · NICHD · PRINCETON UNIVERSITY · PI PAXSON, CHRISTINA H · 2000 to 2004
$3.1M
The longitudinal impact of the COVID-19 pandemic and related multi-level mitigation and contextual factors on health and socioeconomic outcomes of individuals and families from a vulnerable populationU01HD110063 · NICHD · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI COLTER M.S. MITCHELL, Helen Carmon Spink Meier · 2022 to 2026
$3.0M
Child Care and Parental Employment in Fragile FamiliesR01HD040421 · NICHD · COLUMBIA UNIVERSITY TEACHERS COLLEGE · PI BROOKS-GUNN, JEANNE · 2002 to 2006
$2.8M
Investigating Links Between Racial and Ethnic Discrimination, Neurobiology, and Internalizing SymptomatologyR21MH128793 · NIMH · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI HANSON, JAMIE LARS · 2022 to 2023
$398k
Childhood Socioeconomic Disadvantage and Antisocial Behavior: Investigating the Role of Reward ProcessingF31MH131373 · NIMH · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI WESTERMAN, HEIDI BETH · 2023 to 2025
$126k
Avielle Foundation via The Conway Family Award for Excellence in NeuroscienceEunice Kennedy Shriver National Institute of Child Health and Human Development R01HD093334Eunice Kennedy Shriver National Institute of Child Health and Human Development Developmental Psychology Training T32HD007109Marshall M. Weinberg Fellowship in Cognitive ScienceMichigan State UniversityNARSAD Young Investigator Grant from the Brain and Behavior FoundationNational Institute of Child Health and Human Development R01-HD36916National Institute of Child Health and Human Development R01-HD39135National Institute of Child Health and Human Development R01-HD40421National Institute of Child Health and Human Development U01-HD110063NICHD NIH HHS R01 HD036916NICHD NIH HHS R01 HD039135NICHD NIH HHS R01 HD040421NICHD NIH HHS R01 HD093334NICHD NIH HHS T32 HD007109NICHD NIH HHS U01 HD110063NIMH NIH HHS F31 MH131373NIMH NIH HHS R01 MH103761NIMH NIH HHS R01MH103761NIMH NIH HHS R01 MH121079NIMH NIH HHS R01MH121079NIMH NIH HHS R21 MH128793NIMH NIH HHS R21MH128793NIMH NIH HHS UH3 MH114249NIMH NIH HHS UH3MH114249Office of the Director National Institute of HealthRuth L. Kirschstein National Research Service Award F31MH131373University of MichiganUniversity of Pittsburgh
6 · The paper itself
Abstract
The brain matures rapidly during childhood and adolescence. The environment may calibrate the pace of this process to shape cognition and mental health. Extending its utility as a risk marker from older to younger populations, brain age has been proposed to capture relative brain maturity in youth. Multiple algorithms have been developed to estimate brain age in predominantly White advantaged adults. Whether these models are useful in youth, particularly in more representative cohorts, remains unclear. Here, we systematically compare five influential algorithms (Drobinin, Whitmore, Pyment, Kaufmann, Centile) in two population-based youth cohorts as a benchmark for future applied research. We examined (a) prediction accuracy (correlation with chronological age, mean absolute error), (b) sensitivity to scanning parameters (acquisition sequence, image quality), demographics (sex, puberty), and genetic similarity (intraclass correlations in pairs of monozygotic twins), and (c) strength of convergence between algorithms. In our primary sample of twins recruited from birth records to represent families in disadvantaged neighborhoods (N = 593; 9-19 years), three algorithms (Drobinin, Pyment, Centile) exhibited strong predictions from structural MRI data (correlations with chronological age = 0.51-0.68, mean absolute error = 1.60-3.02). These algorithms also generated correlated brain age values and gaps, and the expected pattern of strong but not identical intraclass correlations in monozygotic twins. Pyment exhibited the strongest correlation with age and was not sensitive to acquisition sequence, image quality, sex, and puberty. In a second sample of predominantly Black, low-income youth with a narrow age range (N = 198; 15-17 years), these five algorithms exhibited weak predictions. This study raises critical questions about what "brain age" means, how it can best be estimated depending on the research question and study population, and whether it can be universally applied across samples with heterogeneous backgrounds and age ranges that are narrow or misaligned with the training data.
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
A Systematic Evaluation of the Performance of Multiple Brain Age Algorithms in Two Cohorts of Youth. · full record | OpenQuestion