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Pienaar R, Fischl B, Caviness V, et al. A METHODOLOGY FOR ANALYZING CURVATURE IN THE DEVELOPING BRAIN FROM PRETERM TO ADULT. Int J Imaging Syst Technol. 2008;18(1):42-68doi: 10.1002/ima.v18:1.
Pienaar, R., Fischl, B., Caviness, V., Makris, N., & Grant, P. E. (2008). A METHODOLOGY FOR ANALYZING CURVATURE IN THE DEVELOPING BRAIN FROM PRETERM TO ADULT. International journal of imaging systems and technology, 18(1), 42-68. https://doi.org/10.1002/ima.v18:1
Pienaar, R, et al. "A METHODOLOGY FOR ANALYZING CURVATURE IN THE DEVELOPING BRAIN FROM PRETERM TO ADULT." International journal of imaging systems and technology vol. 18,1 (2008): 42-68. doi: https://doi.org/10.1002/ima.v18:1
Pienaar R, Fischl B, Caviness V, Makris N, Grant PE. A METHODOLOGY FOR ANALYZING CURVATURE IN THE DEVELOPING BRAIN FROM PRETERM TO ADULT. Int J Imaging Syst Technol. 2008 Jun 01;18(1):42-68. doi: 10.1002/ima.v18:1. PMID: 19936261; PMCID: PMC2779548.
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Int J Imaging Syst Technol. 2008 Jun 01;18(1):42-68. doi: 10.1002/ima.v18:1.

A METHODOLOGY FOR ANALYZING CURVATURE IN THE DEVELOPING BRAIN FROM PRETERM TO ADULT.

International journal of imaging systems and technology

R Pienaar, B Fischl, V Caviness, N Makris, P E Grant

PMID: 19936261 PMCID: PMC2779548 DOI: 10.1002/ima.v18:1

Abstract

The character and timing of gyral development is one manifestation of the complex orchestration of human brain development. The ability to quantify these changes would not only allow for deeper understanding of cortical development, but also conceivably allow for improved detection of pathologies. This paper describes a FreeSurfer based image-processing analysis "pipeline" or methodology that inputs an MRI volume, corrects possible contrast defects, creates surface reconstructions, and outputs various curvature-based function analyses. A technique of performing neonate reconstructions using FreeSurfer, which has not been possible previously due to inverted image contrast in pre-myelinated brains, is described. Once surfaces are reconstructed, the analysis component of the pipeline incorporates several surface-based curvature functions found in literature (principle curvatures, Gaussian, mean curvature, "curvedness", and Willmore Bending Energy). We consider the problem of analyzing curvatures from different sized brains by introducing a Gaussian-curvature based variable-radius filter. Segmented volume data is also analyzed for folding measures: a gyral folding index (gyrification-white index GWI), and a gray-white matter junction folding index (WMF). A very simple curvature-based classifier is proposed that has the potential to discriminate between certain classes of subjects. We also present preliminary results of this curvature analysis pipeline on nine neonate subjects (30.4 weeks through 40.3 weeks Corrected Gestational Age), 3 children (2, 3, and 7 years) and 3 adults (33, 37, and 39 years). Initial results demonstrate that curvature measures and functions across our subjects peaked at term, with a gradual decline through early childhood and further decline continuing through to adults. We can also discriminate older neonates, children, and adults based on curvature analysis. Using a variable radius Gaussian-curvature filter, we also observed that the per-unit bending energy of neonate brain surfaces was also much higher than the children and adults.

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