ReviewDevelopmental cognitive neuroscience2021
Opportunities for increased reproducibility and replicability of developmental neuroimaging.
Review in Developmental cognitive neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 6 of them syntheses that pooled it.
What it found
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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.
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Who cites it
65 citing papers in PubMed, 6 syntheses or guidelines pooled it, 95 citations in OpenAlex.
- Neuroimaging evidence of acupuncture in cognitive impairment following ischemic stroke: a systematic review.Frontiers in neuroscience · 2025Pooled it
- Pooled it
- A research agenda for understanding how social inequality is linked to brain structure and function.Nature human behaviour · 2024Pooled it
- The brain in social context: A systematic review of substance use and social processing from adolescence to young adulthood.Developmental cognitive neuroscience · 2022Pooled it
- (Un)common space in infant neuroimaging studies: A systematic review of infant templates.Human brain mapping · 2022Pooled it
- Infant social interactions and brain development: A systematic review.Neuroscience and biobehavioral reviews · 2021Pooled it
- A multiverse analysis of the logical memory test and plasma biomarkers of Alzheimer's disease.Cortex; a journal devoted to the study of the nervous system and behavior · 2026Article
- Feature selection leads to divergent neurobiological interpretations of brain-based machine learning biomarkers.Nature human behaviour · 2026Article
- Exploring subthreshold functional network alterations in women with phenylketonuria by higher criticism.BMC research notes · 2026Article
- Can I trust this paper?Psychonomic bulletin & review · 2025Review
- Overlooked features lead to divergent neurobiological interpretations of brain-based machine learning biomarkers.bioRxiv : the preprint server for biology · 2025Article
- Gray matter correlates of childhood maltreatment lack replicability in a multi-cohort brain-wide association study.Nature communications · 2025Article
- Mapping Individual Differences in Intermodal Coupling in Neurodevelopment.bioRxiv : the preprint server for biology · 2025Article
- Interpretable and integrative deep learning for discovering brain-behaviour associations.Scientific reports · 2025Article
- Mapping individual differences in intermodal coupling in neurodevelopment.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Sample size estimation for task-related functional MRI studies using Bayesian updating.Developmental cognitive neuroscience · 2025Article
- Replicability of a resting-state functional connectivity study in profound early blindness.Frontiers in systems neuroscience · 2025Article
- FAIR African brain data: challenges and opportunities.Frontiers in neuroinformatics · 2025Article
- Psychiatric Symptoms, Cognition, and Symptom Severity in Children.JAMA psychiatry · 2024Article
- Imaging-genomic spatial-modality attentive fusion for studying neuropsychiatric disorders.Human brain mapping · 2024Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 5 institutions in 5 countries.
Funding
Abstract
Many workflows and tools that aim to increase the reproducibility and replicability of research findings have been suggested. In this review, we discuss the opportunities that these efforts offer for the field of developmental cognitive neuroscience, in particular developmental neuroimaging. We focus on issues broadly related to statistical power and to flexibility and transparency in data analyses. Critical considerations relating to statistical power include challenges in recruitment and testing of young populations, how to increase the value of studies with small samples, and the opportunities and challenges related to working with large-scale datasets. Developmental studies involve challenges such as choices about age groupings, lifespan modelling, analyses of longitudinal changes, and data that can be processed and analyzed in a multitude of ways. Flexibility in data acquisition, analyses and description may thereby greatly impact results. We discuss methods for improving transparency in developmental neuroimaging, and how preregistration can improve methodological rigor. While outlining challenges and issues that may arise before, during, and after data collection, solutions and resources are highlighted aiding to overcome some of these. Since the number of useful tools and techniques is ever-growing, we highlight the fact that many practices can be implemented stepwise.
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Registered trials
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.