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Can I read Fully Synthetic Neuroimaging Data for Replication and Exploration on EtoBox?
Fully Synthetic Neuroimaging Data for Replication and Exploration by Kenneth I. Vaden; Mulugeta Gebregziabher; Dyslexia Data Consortium; Mark A. Eckert is a Neuroscience article available to read on EtoBox.
What is Fully Synthetic Neuroimaging Data for Replication and Exploration about?
Scientific transparency, data exploration, and education are advanced through data sharing. However, risk for disclosure of personal information and institutional data sharing regulations can impede human subject/patient data sharing and thus limit open science initiatives. Sharing fully synthetic data is an alternative when it is not possible to share real or observed data. Here we describe a data sharing approach that borrows principles and methods from multiple imputation to replace observed values with synthetic values, thereby creating a fully synthetic neuroimaging dataset that accurately represents the covariance structure of the observed dataset. Predictor tables composed of demographic, site, behavioral and total intracranial volume (ICV) variables from 264 pediatric cases were used to create synthetic predictor tables, which were then used to synthesize gray matter images derived from T1-weighted data. The synthetic predictor tables demonstrated pooled variance and statistical estimates that closely approximated the observed data, as reflected in measures of efficiency and statistical bias. Similarly, the synthetic gray matter data accurately represented the variance and
Who reads Fully Synthetic Neuroimaging Data for Replication and Exploration?
It is typically read by researchers, students, and practitioners in Neuroscience.
- Author
- Kenneth I. Vaden; Mulugeta Gebregziabher; Dyslexia Data Consortium; Mark A. Eckert
- Publisher
- Elsevier BV
- Published
- 2020
- Language
- EN
- Field
- Neuroscience (Life Sciences)