An ontology-based segmentation scheme for tracking postnatal changes in the developing rodent brain with MRI
|dc.identifier.citation||Calabrese, Evan and Johnson, G. Allan and Watson, Charles. 2013. An ontology-based segmentation scheme for tracking postnatal changes in the developing rodent brain with MRI. NeuroImage. 67: pp. 375-384.|
The postnatal period of neurodevelopment has been implicated in a number of brain disorders including autism and schizophrenia. Rodent models have proven to be invaluable in advancing our understanding of the human brain, and will almost certainly play a pivotal role in future studies on postnatal neurodevelopment. The growing field of magnetic resonance microscopy has the potential to revolutionize our understanding of neurodevelopment, if it can be successfully and appropriately assimilated into the vast body of existing neuroscience research. In this study, we demonstrate the utility of a developmental neuro-ontology designed specifically for tracking regional changes in MR biomarkers throughout postnatal neurodevelopment. Using this ontological classification as a segmentation guide, we track regional changes in brain volume in rats between postnatal day zero and postnatal day 80 and demonstrate differential growth rates in axial versus paraxial brain regions. Both the ontology and the associated label volumes are provided as a foundation for future MR-based studies of postnatal neurodevelopment in normal and disease states.
|dc.subject||Diffusion tensor imaging|
|dc.title||An ontology-based segmentation scheme for tracking postnatal changes in the developing rodent brain with MRI|
NOTICE: This is the author’s version of a work that was accepted for publication in NeuroImage. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in, NeuroImage, Vol. 67, (2013).