ArticleDevelopmental cognitive neuroscience2018
Current methods and limitations for longitudinal fMRI analysis across development.
Article in Developmental cognitive neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 37 papers, 1 of them a synthesis that pooled it.
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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
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review.Frontiers in neuroscience · 2019Pooled it
- The effects of acute tryptophan depletion on instrumental reward learning in anorexia nervosa - an fMRI study.Psychological medicine · 2023Trial
- Longitudinal functional network connectivity changes across the clinical stages of C9orf72 hexanucleotide repeat expansion carriers.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Beyond GLM: Inter-Subject Variability as a Complementary Approach to Detect Longitudinal Changes in Emotion Processing in Multiple Sclerosis.Journal of imaging · 2026Article
- Latent Growth Models of Longitudinal Changes in Functional Connectivity during Early Stage Psychosis.Neuroinformatics · 2025Article
- Missing data approaches for longitudinal neuroimaging research: Examples from the Adolescent Brain and Cognitive Development (ABCD) Study®.Developmental cognitive neuroscience · 2025Review
- Ambivalence-dependent alterations in brain function during self-evaluation in adults with depressive symptoms.Social cognitive and affective neuroscience · 2025Article
- A cautionary tale on the effects of different covariance structures in linear mixed effects modeling of fMRI data.Human brain mapping · 2024Article
- Longitudinal regression of covariance matrix outcomes.Biostatistics (Oxford, England) · 2024Article
- Decentralized Mixed Effects Modeling in COINSTAC.Neuroinformatics · 2024Article
- Assembloid learning: opportunities and challenges for personalized approaches to brain functioning in health and disease.Frontiers in artificial intelligence · 2024Article
- A precision neuroscience approach to estimating reliability of neural responses during emotion processing: Implications for task-fMRI.NeuroImage · 2024Article
- BLMM: Parallelised computing for big linear mixed models.NeuroImage · 2022Article
- Age-related change in task-evoked amygdala-prefrontal circuitry: A multiverse approach with an accelerated longitudinal cohort aged 4-22 years.Human brain mapping · 2022Article
- The impact of neighborhood disadvantage on amygdala reactivity: Pathways through neighborhood social processes.Developmental cognitive neuroscience · 2022Article
- Early and late neural correlates of mentalizing: ALE meta-analyses in adults, children and adolescents.Social cognitive and affective neuroscience · 2022Article
- Associations of delay discounting and drinking trajectories from ages 14 to 22.Alcoholism, clinical and experimental research · 2022Article
- A 4-year longitudinal neuroimaging study of cognitive control using latent growth modeling: developmental changes and brain-behavior associations.NeuroImage · 2021Article
- Environmental influences on the pace of brain development.Nature reviews. Neuroscience · 2021Review
- Longitudinal change in hippocampal and dorsal anterior insulae functional connectivity in subjective cognitive decline.Alzheimer's research & therapy · 2021Article
Corrections and comments
- Erratum issuedErratum.2020
Authors and funding
9 authors.
Funding
Abstract
The human brain is remarkably plastic. The brain changes dramatically across development, with ongoing functional development continuing well into the third decade of life and substantial changes occurring again in older age. Dynamic changes in brain function are thought to underlie the innumerable changes in cognition, emotion, and behavior that occur across development. The brain also changes in response to experience, which raises important questions about how the environment influences the developing brain. Longitudinal functional magnetic resonance imaging (fMRI) studies are an essential means of understanding these developmental changes and their cognitive, emotional, and behavioral correlates. This paper provides an overview of common statistical models of longitudinal change applicable to developmental cognitive neuroscience, and a review of the functionality provided by major software packages for longitudinal fMRI analysis. We demonstrate that there are important developmental questions that cannot be answered using available software. We propose alternative approaches for addressing problems that are commonly faced in modeling developmental change with fMRI data.
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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.