ArticleScientific data2016
A test-retest dataset for assessing long-term reliability of brain morphology and resting-state brain activity.
Article in Scientific data, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- ACTION: Augmentation and computation toolbox for brain network analysis with functional MRI.NeuroImage · 2025Article
- Sample size requirement for achieving multisite harmonization using structural brain MRI features.NeuroImage · 2022Article
- Identification and Therapeutic Outcome Prediction of Cervical Spondylotic Myelopathy Based on the Functional Connectivity From Resting-State Functional MRI Data: A Preliminary Machine Learning Study.Frontiers in neurology · 2021Article
- Aging-Dependent Genetic Effects Associated to ADHD Predict Longitudinal Changes of Ventricular Volumes in Adulthood.Frontiers in psychiatry · 2020Article
- The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services.Scientific data · 2019Article
- Test-retest reliability of brain morphology estimates.Brain informatics · 2017Article
- Mindboggling morphometry of human brains.PLoS computational biology · 2017Article
- Commentary: A test-retest dataset for assessing long-term reliability of brain morphology and resting-state brain activity.Frontiers in neuroscience · 2017Article
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Abstract
We present a test-retest dataset for evaluation of long-term reliability of measures from structural and resting-state functional magnetic resonance imaging (sMRI and rfMRI) scans. The repeated scan dataset was collected from 61 healthy adults in two sessions using highly similar imaging parameters at an interval of 103-189 days. However, as the imaging parameters were not completely identical, the reliability estimated from this dataset shall reflect the lower bounds of the true reliability of sMRI/rfMRI measures. Furthermore, in conjunction with other test-retest datasets, our dataset may help explore the impact of different imaging parameters on reliability of sMRI/rfMRI measures, which is especially critical for assessing datasets collected from multiple centers. In addition, intelligence quotient (IQ) was measured for each participant using Raven's Advanced Progressive Matrices. The data can thus be used for purposes other than assessing reliability of sMRI/rfMRI alone. For example, data from each single session could be used to associate structural and functional measures of the brain with the IQ metrics to explore brain-IQ association.
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