ArticleNeuroImage2021
Independent components of human brain morphology.
Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Generating Novel Brain Morphology by Deforming Learned Templates.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Morphometrics of the preserved post-surgical hemisphere in pediatric drug-resistant epilepsy and implications for post-operative cognition.bioRxiv : the preprint server for biology · 2025Article
- Article
- Multiscale cortical morphometry reveals pronounced regional and scale-dependent variations across the lifespan.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Born too young or too blue tips the scaling of a folded brain.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Cortical scaling of the neonatal brain in typical and altered development.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Brain morphology normative modelling platform for abnormality and centile estimation: Brain MoNoCle.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Attention-Enhanced Fusion of Structural and Functional MRI for Analyzing HIV-Associated Asymptomatic Neurocognitive Impairment.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Article
- In search of subcortical and cortical morphologic alterations of a normal brain through aging: an investigation by computed tomography scan.Anatomy & cell biology · 2024Article
- Cortical folding correlates to aging and Alzheimer's Disease's cognitive and CSF biomarkers.Scientific reports · 2024Article
- Aberrant allometric scaling of cortical folding in preterm-born adults.Brain communications · 2023Article
- Establishing a Baseline for Human Cortical Folding Morphological Variables: A Multisite Study.Frontiers in neuroscience · 2022Article
- Multivariate white matter alterations are associated with epilepsy duration.The European journal of neuroscience · 2021Article
- Morphometrics of the preserved post-surgical hemisphere in pediatric drug-resistant epilepsy and implications for post-operative cognition.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Quantification of brain morphology has become an important cornerstone in understanding brain structure. Measures of cortical morphology such as thickness and surface area are frequently used to compare groups of subjects or characterise longitudinal changes. However, such measures are often treated as independent from each other. A recently described scaling law, derived from a statistical physics model of cortical folding, demonstrates that there is a tight covariance between three commonly used cortical morphology measures: cortical thickness, total surface area, and exposed surface area. We show that assuming the independence of cortical morphology measures can hide features and potentially lead to misinterpretations. Using the scaling law, we account for the covariance between cortical morphology measures and derive novel independent measures of cortical morphology. By applying these new measures, we show that new information can be gained; in our example we show that distinct morphological alterations underlie healthy ageing compared to temporal lobe epilepsy, even on the coarse level of a whole hemisphere. We thus provide a conceptual framework for characterising cortical morphology in a statistically valid and interpretable manner, based on theoretical reasoning about the shape of the cortex.
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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 Socratic graph.