ArticleBMC bioinformatics2019
A controlled comparison of thickness, volume and surface areas from multiple cortical parcellation packages.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 20 citations in OpenAlex.
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- Longitudinal stability of cortical grey matter measures varies across brain regions, imaging metrics, and testing sites in the ABCD study.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Manual and automated analysis of atrophy patterns in dementia with Lewy bodies on MRI.BMC neurology · 2022Article
- Diagnosis of early mild cognitive impairment using a multiobjective optimization algorithm based on T1-MRI data.Scientific reports · 2022Article
- Changes in Volume of Subregions Within Basal Ganglia in Obsessive-Compulsive Disorder: A Study With Atlas-Based and VBM Methods.Frontiers in neuroscience · 2022Article
- Metric magnetic resonance imaging analysis reveals pronounced substantia-innominata atrophy in dementia with Lewy bodies with a psychiatric onset.Frontiers in aging neuroscience · 2022Article
- Spatial Stability of Functional Networks: A Measure to Assess the Robustness of Graph-Theoretical Metrics to Spatial Errors Related to Brain Parcellation.Frontiers in neuroscience · 2021Article
- Neuroimaging and DNA Methylation: An Innovative Approach to Study the Effects of Early Life Stress on Developmental Plasticity.Frontiers in psychology · 2021Article
- From a deep learning model back to the brain-Identifying regional predictors and their relation to aging.Human brain mapping · 2020Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
backgroundCortical parcellation is an essential neuroimaging tool for identifying and characterizing morphometric and connectivity brain changes occurring with age and disease. A variety of software packages have been developed for parcellating the brain's cortical surface into a variable number of regions but interpackage differences can undermine reproducibility. Using a ground truth dataset (Edinburgh_NIH10), we investigated such differences for grey matter thickness (GM
resultsCorresponding gyral definitions and morphometry approaches were not identical across the packages. As expected, there were differences in the bordering landmarks of each gyrus as well as in the manner in which variability was addressed. Rostral and caudal SFG and SMG boundaries differed, and in the event of a double CG occurrence, its upper fold was not always addressed. This led to a knock-on effect that was visible at the neighbouring gyri (e.g., knock-on effect at the SFG following CG definition) as well as gyral morphometric measurements of the affected gyri. Statistical analysis showed that the most consistent approaches were FreeSurfer's Desikan-Killiany-Tourville (DKT) protocol for GM
conclusionsGiven the significance and implications that a parcellation protocol will have on the classification, and sometimes treatment, of subjects, it is essential to select the protocol which accurately represents their regions of interest and corresponding morphometrics, while embracing cortical variability.
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