ArticleCerebral cortex (New York, N.Y. : 1991)2019
Evaluating the Prediction of Brain Maturity From Functional Connectivity After Motion Artifact Denoising.
Article in Cerebral cortex (New York, N.Y. : 1991), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 1 of them a synthesis that pooled it.
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Who cites it
67 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Quantitative assessment of neurodevelopmental maturation: a comprehensive systematic literature review of artificial intelligence-based brain age prediction in pediatric populations.Frontiers in neuroinformatics · 2024Pooled it
- Functional brain organization is stable within individuals across years.bioRxiv : the preprint server for biology · 2026Article
- Multivariate relationships between social cognitive performance and functional connectivity during task and rest across schizophrenia spectrum disorders and healthy controls.Molecular psychiatry · 2026Article
- Amygdala clustering coefficients modulate the effect of neuroticism on the sensation of boredom.Brain structure & function · 2026Article
- Neuroimaging markers of cognition in late childhood associated with moderate to late preterm birth.Pediatric research · 2026Article
- Dense Phenotyping of Human Brain Network Organization Using Precision fMRI.Annual review of psychology · 2026Review
- Brain network efficiency during social interaction is associated with real-world social network size.Behavioral and brain functions : BBF · 2025Article
- Motion impact score for detecting spurious brain-behavior associations.Nature communications · 2025Article
- Sources of outdoor air pollution exposure and child brain network development across the United States.bioRxiv : the preprint server for biology · 2025Article
- Precision functional mapping reveals less inter-individual variability in the child vs. adult human brain.bioRxiv : the preprint server for biology · 2025Article
- Connectome-based predictive modeling of brain pathology and cognition in autosomal dominant Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Segregation of three resting-state brain networks predicts reappraisal success across the lifespan.Social cognitive and affective neuroscience · 2025Article
- Joint Aging Patterns in Brain Function and Structure Revealed Using 27,793 Samples.Research (Washington, D.C.) · 2025Article
- Multimodal brain age prediction using machine learning: combining structural MRI and 5-HT2AR PET-derived features.GeroScience · 2024Article
- Brain age as a biomarker for pathological versus healthy ageing - a REMEMBER study.Alzheimer's research & therapy · 2024Article
- Atypical functional connectivity between the amygdala and visual, salience regions in infants with genetic liability for autism.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- In the face of ambiguity: intrinsic brain organization in development predicts one's bias toward positivity or negativity.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Network-level enrichment provides a framework for biological interpretation of machine learning results.Network neuroscience (Cambridge, Mass.) · 2024Article
- Resting-state functional network segregation of the default mode network predicts valence bias across the lifespan.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Simulating rigid head motion artifacts on brain magnitude MRI data-Outcome on image quality and segmentation of the cerebral cortex.PloS one · 2024Article
7 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
Abstract
The ability to make individual-level predictions from neuroanatomy has the potential to be particularly useful in child development. Previously, resting-state functional connectivity (RSFC) MRI has been used to successfully predict maturity and diagnosis of typically and atypically developing individuals. Unfortunately, submillimeter head motion in the scanner produces systematic, distance-dependent differences in RSFC and may contaminate, and potentially facilitate, these predictions. Here, we evaluated individual age prediction with RSFC after stringent motion denoising. Using multivariate machine learning, we found that 57% of the variance in individual RSFC after motion artifact denoising was explained by age, while 4% was explained by residual effects of head motion. When RSFC data were not adequately denoised, 50% of the variance was explained by motion. Reducing motion-related artifact also revealed that prediction did not depend upon characteristics of functional connections previously hypothesized to mediate development (e.g., connection distance). Instead, successful age prediction relied upon sampling functional connections across multiple functional systems with strong, reliable RSFC within an individual. Our results demonstrate that RSFC across the brain is sufficiently robust to make individual-level predictions of maturity in typical development, and hence, may have clinical utility for the diagnosis and prognosis of individuals with atypical developmental trajectories.
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