AUTHOR=Johnson Philippa J. , Pascalau Raluca , Luh Wen-Ming , Raj Ashish , Cerda-Gonzalez Sofia , Barry Erica F. TITLE=Stereotaxic Diffusion Tensor Imaging White Matter Atlas for the in vivo Domestic Feline Brain JOURNAL=Frontiers in Neuroanatomy VOLUME=14 YEAR=2020 URL=https://www.frontiersin.org/journals/neuroanatomy/articles/10.3389/fnana.2020.00001 DOI=10.3389/fnana.2020.00001 ISSN=1662-5129 ABSTRACT=

The cat brain is a useful model for neuroscientific research and with the increasing use of advanced neuroimaging techniques there is a need for an open-source stereotaxic white matter brain atlas to accompany the cortical gray matter atlas, currently available. A stereotaxic white matter atlas would facilitate anatomic registration and segmentation of the white matter to aid in lesion localization or standardized regional analysis of specific regions of the white matter. In this article, we document the creation of a stereotaxic feline white matter atlas from diffusion tensor imaging (DTI) data obtained from a population of eight mesaticephalic felines. Deterministic tractography reconstructions were performed to create tract priors for the major white matter projections of Corpus callosum (CC), fornix, cingulum, uncinate, Corona Radiata (CR), Corticospinal tract (CST), inferior longitudinal fasciculus (ILF), Superior Longitudinal Fasciculus (SLF), and the cerebellar tracts. T1-weighted, fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD) population maps were generated. The volume, mean tract length and mean FA, MD, AD and RD values for each tract prior were documented. A structural connectome was then created using previously published cortical priors and the connectivity metrics for all cortical regions documented. The provided white matter atlas, diffusivity maps, tract priors and connectome will be a valuable resource for anatomical, pathological and translational neuroimaging research in the feline model. Multi-atlas population maps and segmentation priors are available at Cornell’s digital repository: https://ecommons.cornell.edu/handle/1813/58775.2.