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A Methotology for Analyzing Curvature in the Developing Brain from Preterm to Adult

Publisher:
Int J Imaging Syst Technol
Publication Date:
Jun-2008
Volume Number:
18
Issue Number:
1
Pages:
42-68
Citation:
Int J Imaging Syst Technol. 2008 Jun 1;18(1):42-68.
PubMed ID:
19936261
PMCID:
PMC2779548
Keywords:
Gyral Folding, Brain Development, Principal Curvature Analysis, FreeSurfer, Neonate, Preterm, Surface Reconstruction, Gaussian Curvature, Bending Energy, MRI
Appears in Collections:
NA-MIC
Sponsors:
P41 RR014075/RR/NCRR NIH HHS/United States
R01 RR016594/RR/NCRR NIH HHS/United States
U24 RR021382/RR/NCRR NIH HHS/United States
R01 EB001550/EB/NIBIB NIH HHS/United States
R01 NS052585/NS/NINDS NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Pienaar R., Fischl B., Caviness V., Makris N., Grant P.E. A Methotology for Analyzing Curvature in the Developing Brain from Preterm to Adult. Int J Imaging Syst Technol. 2008 Jun 1;18(1):42-68. PMID: 19936261. PMCID: PMC2779548.
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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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