Background and aims: Dogs have recently become an important model species for comparative social and cognitive neuroscience. Brain template-related label maps are essential for functional magnetic resonance imaging (fMRI) data analysis, to localize neural responses. In this study, we present a detailed, individual-based, T1-weighted MRI-based brain label map used in dog neuroimaging analysis. Methods: A typical, medium-headed dog (a 7.5-year-old male Golden Retriever) was selected from a cohort of 22 dogs, based on brain morphology (shape, size, and gyral pattern), to serve as the template for a label map. Results: Eighty-six 3-dimensional labels were created to highlight the main cortical (cerebral gyri on the lateral and medial side) and subcortical (thalamus, caudate nucleus, amygdala, and hippocampus) structures of the prosencephalon and diencephalon, and further main parts of brainstem (mesencephalon and rhombencephalon). Discussion: Importantly, this label map is (a) considerably more detailed than any available dog brain template; (b) it is easy to use with freeware and commercial neuroimaging software for MRI and fMRI analysis; and (c) it can be registered to other existing templates, including a recent average-based dog brain template. Using the coordinate system and label map proposed here can enhance precision and standard localization during future canine neuroimaging studies.This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited, a link to the CC License is provided, and changesif anyare indicated. (SID_1)
Resting-state networks are spatially distributed, functionally connected brain regions. Studying these networks gives us information about the large-scale functional organization of the brain and alternations in these networks are considered to play a role in a wide range of neurological conditions and aging. To describe resting-state networks in dogs, we measured 22 awake, unrestrained individuals of both sexes and carried out group-level spatial independent component analysis to explore whole-brain connectivity patterns. In this exploratory study, using resting-state functional magnetic resonance imaging (rs-fMRI), we found several such networks: a network involving prefrontal, anterior cingulate, posterior cingulate and hippocampal regions; sensorimotor (SMN), auditory (AUD), frontal (FRO), cerebellar (CER) and striatal networks. The network containing posterior cingulate regions, similarly to Primates, but unlike previous studies in dogs, showed antero-posterior connectedness with involvement of hippocampal and lateral temporal regions. The results give insight into the resting-state networks of awake animals from a taxon beyond rodents through a non-invasive method.
Most common methods that directly show macro- or microscopic anatomy of the brain usually require the removal of the organ from the neurocranium. However, the brain can be revealed in situ by using proper sectioning techniques. Our aim was to both improve the cryosectioning method, test its limits and create a high-resolution macro-anatomical image series of a Beagle brain, which at the time of the study did not exist. A two-year-old female Beagle has been scanned with CT and MRI ante and post mortem, then the arteries of the head were filled with red resin. After freezing to -80°C, a neurocranium block was created and was embedded into a water-gelatin mix. Using a special milling device and a DSLR camera, 1112 consecutive RGB-color cryosections were made with a 100 μm layer thickness and captured in high resolution (300 dpi, 24-bit color, and pixel size was 19.5 x 19.5 μm). Image post-processing was done with Adobe Photoshop CS3 and Thermo Scientific Amira 6.0 softwares, and as a result of the proper alignment and coregistration, visualization and comparing was possible with all the applied imaging modalities (CT, MRI, cryosectioning) in any arbitrary plane. Surface models from the arteries, veins, brain and skull were also generated after segmentation in the same coordinate system, giving a unique opportunity for comparing the two-dimensional and three-dimensional anatomy. This is the first study which focuses directly to this high-definition multimodal visualization of the canine brain, and it provides the most accurate results compared to previous cryosectioning studies, as using an improved method, higher image quality, more detailed image, proper color fidelity and lower artefact formation were achieved. Based on the methodology we described, it can serve as a base for future multimodal (CT, MR, augmented- or virtual reality) imaging atlases for medical, educational and scientific purposes.
Computed tomography (CT) is one of the most useful techniques for digitizing bone structures and making endocranial models from the neurocranium. The resulting digital endocasts reflect the morphology of the brain and the associated structures. Our first aim was to document the methodology behind creating detailed digital endocasts of canine skulls. We created digital endocasts of the skulls of 24 different dog breeds and 4 wild canids for visualization and teaching purposes. We used CT scanning with 0.323 mm × 0.322 mm × 0.6 mm resolution. The imaging data were segmented with 3D Slicer software and refined with Autodesk Meshmixer. Images were visualized in 3D Slicer and surface models were converted to 3D PDFs to provide easier interactive access, and 3D prints were also generated for visualization purposes. Our second aim was to analyze how skull length and width relate to the surface areas of the prepiriform rhinencephalic, prefrontal, and non-prefrontal cerebral convexity areas of the endocasts. The rhinencephalic area ratio decreased with a larger skull index. Our results open the possibility to analyze the relationship between the skull and brain morphology, and to link certain features to behavior, and cognition in dogs.
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