The growing number of neuroimaging studies of cynomolgus macaques require extending existing templates to facilitate species-specific application of voxel-wise neuroimaging methodologies. This study aimed to create population-averaged structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) templates for the cynomolgus macaques and apply the templates in fully automated voxel-wise analyses. We presented the development of symmetric and asymmetric MRI and DTI templates from a sample of 63 young male cynomolgus monkeys with the use of optimized template creation approaches. We also generated the associated average tissue probability maps and Diffeomorphic Anatomical Registration using Exponentiated Lie Algebra templates for use with the Statistical Parametric Mapping (SPM), as well as the average fractional anisotropy/skeleton targets for incorporation into tract-based spatial statistics (TBSS) framework. Both asymmetric and symmetric templates in a standardized coordinate space demonstrated low bias and high contrast. Fully automated processing using SPM was accomplished for all native MRI datasets and demonstrated outstanding performance regarding skull-stripping, segmentation, and normalization when using the MRI templates. Automated normalization to the DTI template was excellently achieved for all native DTI images using the TBSS pipeline. The cynomolgus MRI and DTI templates are anticipated to provide a common platform for precise single-subject data analysis and facilitate comparison of neuroimaging findings in cynomolgus monkeys across studies and sites. It is also hoped that the procedures of template creation and fully-automated voxel-wise frameworks will provide a straightforward avenue for investigating brain function, development, and neuro-psychopathological disorders in non-human primate models.
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http://dx.doi.org/10.1007/s12021-021-09545-4 | DOI Listing |
Imaging Neurosci (Camb)
March 2024
Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
We present MMORF-FSL's MultiMOdal Registration Framework-a newly released nonlinear image registration tool designed primarily for application to magnetic resonance imaging (MRI) images of the brain. MMORF is capable of simultaneously optimising both displacement and rotational transformations within a single registration framework by leveraging rich information from multiple scalar and tensor modalities. The regularisation employed in MMORF promotes local rigidity in the deformation, and we have previously demonstrated how this effectively controls both shape and size distortion, leading to more biologically plausible warps.
View Article and Find Full Text PDFProc SPIE Int Soc Opt Eng
February 2024
Dept. of Electrical and Computer Engineering, Vanderbilt University, Nashville, USA.
Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction.
View Article and Find Full Text PDFImaging Neurosci (Camb)
August 2024
Cornell College of Veterinary Medicine, Department of Clinical Sciences, Cornell University, Ithaca, NY, United States.
Hum Brain Mapp
September 2024
Institute of Diagnostic and Interventional Neuroradiology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
Med Image Anal
October 2024
Massachusetts General Hospital and Harvard Medical School, Department of Radiation Oncology, Division of Radiation Biophysics, 100 Blossom St, Boston, MA 02114, USA.
Diffusion tensor imaging (DTI) is used in tumor growth models to provide information on the infiltration pathways of tumor cells into the surrounding brain tissue. When a patient-specific DTI is not available, a template image such as a DTI atlas can be transformed to the patient anatomy using image registration. This study investigates a model, the invariance under coordinate transform (ICT), that transforms diffusion tensors from a template image to the patient image, based on the principle that the tumor growth process can be mapped, at any point in time, between the images using the same transformation function that we use to map the anatomy.
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