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The Gini coefficient: A methodological pilot study to assess fetal brain development employing postmortem diffusion MR

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Riffert,  Till
Methods and Development Group MEG and EEG - Cortical Networks and Cognitive Functions, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Dhital,  Bibek
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Knösche,  Thomas R.
Methods and Development Group MEG and EEG - Cortical Networks and Cognitive Functions, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Anwander,  Alfred
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

Viehweger, A., Riffert, T., Dhital, B., Knösche, T. R., Anwander, A., Stepan, H., et al. (2014). The Gini coefficient: A methodological pilot study to assess fetal brain development employing postmortem diffusion MR. Pediatric Radiology, 44(10), 1290-1301. doi:10.1007/s00247-014-3002-4.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0018-D830-A
Abstract
Background Diffusion-weighted imaging (DWI) is important in the assessment of fetal brain development. However, it is clinically challenging and time-consuming to prepare neuromorphological examinations to assess real brain age and to detect abnormalities. Objective To demonstrate that the Gini coefficient can be a simple, intuitive parameter for modelling fetal brain development. Materials and methods Postmortem fetal specimens(n = 28) were evaluated by diffusion-weighted imaging (DWI) on a 3-T MRI scanner using 60 directions, 0.7-mm isotropic voxels and b-values of 0, 150, 1,600 s/mm2. Constrained spherical deconvolution (CSD) was used as the local diffusion model. Fractional anisotropy (FA), apparent diffusion coefficient (ADC) and complexity (CX) maps were generated. CX was defined as a novel diffusion metric. On the basis of those three parameters, the Gini coefficient was calculated. Results Study of fetal brain development in postmortem specimens was feasible using DWI. The Gini coefficient could be calculated for the combination of the three diffusion parameters. This multidimensional Gini coefficient correlated well with age (Adjusted R2 = 0.59) between the ages of 17 and 26 gestational weeks. Conclusions We propose a new method that uses an economics concept, the Gini coefficient, to describe the whole brain with one simple and intuitive measure, which can be used to assess the brain’s developmental state.