ArticleNeuroImage2018
Quantitative assessment of structural image quality.
Article in NeuroImage, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 285 papers, 3 of them syntheses 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.
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.
Who cites it
285 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Shared and specific associations of amygdala nuclei volumes with PTSD symptom domains and childhood trauma: An ENIGMA-PGC PTSD mega-analysis.Molecular psychiatry · 2026Pooled it
- Genetic insights into human cortical organization and development through genome-wide analyses of 2,347 neuroimaging phenotypes.Nature genetics · 2023Pooled it
- Remodeling of the Cortical Structural Connectome in Posttraumatic Stress Disorder: Results From the ENIGMA-PGC Posttraumatic Stress Disorder Consortium.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2022Pooled it
- Daily biofeedback to modulate heart rate oscillations affects structural volume in hippocampal subregions targeted by the locus coeruleus in older adults but not younger adults.Neurobiology of aging · 2023Trial
- Heart rate variability (HRV) changes and cortical volume changes in a randomized trial of five weeks of daily HRV biofeedback in younger and older adults.International journal of psychophysiology : official journal of the International Organization of Psychophysiology · 2022Trial
- Localized surface-based morphometric differences in painful temporomandibular disorder.Neuroimage. Reports · 2026Article
- Mapping topological abnormalities in cortical similarity networks to schizophrenia-associated gene expression.Dialogues in clinical neuroscience · 2026Article
- Major Depressive Disorder With Non-Suicidal Self-Injury in Adolescents: Altered Morphological Inverse Divergence Gradient in Brain Structure and Gene Expression Patterns.Human brain mapping · 2026Article
- Quality assessment and control of unprocessed anatomical, functional and diffusion MRI of the human brain using MRIQC.Nature protocols · 2026Review
- Presence, Severity, and Functional Associations of Incomplete Hippocampal Inversion in 22q11.2 Deletion Syndrome.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Article
- Anatomical white matter tracts span the cortical hierarchy to support cognitive diversity.Nature human behaviour · 2026Article
- The shifting landscape of brain-wide myelination across the human lifespan.Nature communications · 2026Article
- Brain-cognition relationships and treatment outcome in treatment-resistant late-life depression.Nature communications · 2026Article
- Dynamic fMRI networks of human emotion.eLife · 2026Article
- Early-Life Cumulative Intake of Ultra-Processed Foods and Subcortical Brain Volume at Age Six Years: A Prospective Cohort Study.The American journal of clinical nutrition · 2026Article
- A longitudinal resource for mapping interindividual variation in the aging connectome.Scientific data · 2026Article
- Early deviations from normative brain morphology and cortical microstructure in schizophrenia spectrum disorders.Translational psychiatry · 2026Article
- Characterizing progressive cortical thinning in multiple system atrophy: a longitudinal MRI study.NPJ Parkinson's disease · 2026Article
- Validation of five automated structural MRI quality assessment tools against expert ratings.Neuroradiology · 2026Article
- Genetic pathways linking oxytocin-vasotocin hypothalamic subunit architecture with psychiatric and metabolic traits.Molecular psychiatry · 2026Article
225 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
20 authors.
Funding
Abstract
Data quality is increasingly recognized as one of the most important confounding factors in brain imaging research. It is particularly important for studies of brain development, where age is systematically related to in-scanner motion and data quality. Prior work has demonstrated that in-scanner head motion biases estimates of structural neuroimaging measures. However, objective measures of data quality are not available for most structural brain images. Here we sought to identify quantitative measures of data quality for T1-weighted volumes, describe how these measures relate to cortical thickness, and delineate how this in turn may bias inference regarding associations with age in youth. Three highly-trained raters provided manual ratings of 1840 raw T1-weighted volumes. These images included a training set of 1065 images from Philadelphia Neurodevelopmental Cohort (PNC), a test set of 533 images from the PNC, as well as an external test set of 242 adults acquired on a different scanner. Manual ratings were compared to automated quality measures provided by the Preprocessed Connectomes Project's Quality Assurance Protocol (QAP), as well as FreeSurfer's Euler number, which summarizes the topological complexity of the reconstructed cortical surface. Results revealed that the Euler number was consistently correlated with manual ratings across samples. Furthermore, the Euler number could be used to identify images scored "unusable" by human raters with a high degree of accuracy (AUC: 0.98-0.99), and out-performed proxy measures from functional timeseries acquired in the same scanning session. The Euler number also was significantly related to cortical thickness in a regionally heterogeneous pattern that was consistent across datasets and replicated prior results. Finally, data quality both inflated and obscured associations with age during adolescence. Taken together, these results indicate that reliable measures of data quality can be automatically derived from T1-weighted volumes, and that failing to control for data quality can systematically bias the results of studies of brain maturation.
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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.