A stereotaxic breed-averaged, symmetric T2w canine brain atlas including detailed morphological and volumetrical data sets

Nitzsche, Björn; Boltze, Johannes; Ludewig, Eberhard; Flegel, Thomas; Schmidt, Martin J.; Seeger, Johannes; Barthel, Henryk; Brooks, Olivia W.; Gounis, Matthew J.; Stoffel, Michael Hubert; Schulze, Sabine

doi:10.1016/j.neuroimage.2018.01.066

Cited by 58 publications

(106 citation statements)

References 37 publications

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“…These atlases are providing anatomical and cyto-architectonic labels, which give conclusion about the function of this area. Although stereotactic atlases exist in dogs, no functional correlation for these coordinates exists [50]. To overcome this issue, we used cross anatomy to identify functional areas previously localised in dogs in rs-fMRI and task-fMRI.…”

Section: Discussionmentioning

confidence: 99%

Resting state networks of the canine brain under sevoflurane anaesthesia

et al. 2020

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Resting-state functional Magnetic Resonance Imaging (rs-fMRI) has become an established technique in humans and reliably determines several resting state networks (RSNs) simultaneously. Limited data exist about RSN in dogs. The aim of this study was to investigate the RSNs in 10 healthy beagle dogs using a 3 tesla MRI scanner and subsequently perform group-level independent component analysis (ICA) to identify functionally connected brain networks. Rs-fMRI sequences were performed under steady state sevoflurane inhalation anaesthesia. Anaesthetic depth was titrated to the minimum level needed for immobilisation and mechanical ventilation of the patient. This required a sevoflurane MAC between 0.8 to 1.2. Group-level ICA dimensionality of 20 components revealed distributed sensory, motor and higher-order networks in the dogs' brain. We identified in total 7 RSNs (default mode, primary and higher order visual, auditory, two putative motor-somatosensory and one putative somatosensory), which are common to other mammals including humans. Identified RSN are remarkably similar to those identified in awake dogs. This study proves the feasibility of rs-fMRI in anesthetized dogs and describes several RSNs, which may set the basis for investigating pathophysiological characteristics of various canine brain diseases.

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Section: Discussionmentioning

confidence: 99%

Resting state networks of the canine brain under sevoflurane anaesthesia

et al. 2020

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“…Animal models including sheep, ovine, rabbit, dog are demanded for translational research (Hoffmann et al., ; Mehra et al., ). Dogs represent an important model species used in translational cerebrovascular and neurointerventional research, as the anatomy of the arterial blood supply and neurofunction to the brain is very similar to humans (Hoffmann et al., ; Nitzsche et al., ). In humans, there are many reports about variations in the CW; however, in canines, anatomical variations in the CW have not been investigated.…”

Section: Introductionmentioning

confidence: 99%

Anatomical variations in the circle of Willis in canines

Tanaka

Akiyoshi

Mie

2018

Anat Histol Embryol

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The circle of Willis (CW) supplies blood to the brain. In humans, anatomical variations in the CW are an important risk factor for transient ischaemic attack and stroke. In canines, anatomical variations in the CW have not been reported. The aim of this study was to determine anatomical variations in the CW in dogs by magnetic resonance angiography. Normal configuration of the CW was observed in 82%, aplasia of the precommunicating segment of the rostral cerebral artery was observed in 14%, and aplasia of the rostral communicating artery was observed in 4% of dogs included in the study.

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“…Brain regions involved in the respective human resting state networks based on the referred studies and the corresponding regions in the dog resting state networks. The dog brain regions coordinates refer to the stereotaxic, breed-averaged canine brain atlas 36 In case of the default mode network, we found no involvement of lateral parietal regions in dogs.…”

Section: Image Analysismentioning

confidence: 72%

“…Components showing characteristics of susceptibility, motion, vascular of ventricular artefacts were excluded from further analysis. Anatomical labelling was carried out based on relevant anatomical brain atlases [34][35][36] . Table 1.…”

Section: Image Analysismentioning

confidence: 99%

Resting-state fMRI data of awake dogs (Canis familiaris) via group-level independent component analysis reveal multiple, spatially distributed resting-state networks

Szabó

Czeibert

Kettinger

et al. 2018

Preprint

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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 animals of either sex and carried out group-level spatial independent component analysis to explore whole-brain connectivity patterns. Using resting-state functional magnetic resonance imaging (rs-fMRI), in this exploratory study we found multiple resting-state networks in dogs, which resemble the pattern described in humans. We report the following dog resting-state networks: default mode network (DMN), visual network (VIS), sensorimotor network (SMN), combined auditory (AUD)-saliency (SAL) network and cerebellar network (CER). The DMN, similarly to Primates, but unlike previous studies in dogs, showed antero-posterior connectedness with involvement of hippocampal and lateral temporal regions. The results give us insight into the resting-state networks of awake animals from a taxon beyond rodents through a non-invasive method.connectedness of the human default mode network was only found in superorder Euarchontoglires (primates and rodents), while the corresponding networks in superorder Laurasiatheria (described in ferrets 10 and dogs 9, 12 ) were reported to show antero-posterior dissociation. We aimed to investigate whether and if so, what kind of spatially distributed resting-state networks are detectable in a larger sample of awake, unrestrained family dogs in a resting state fMRI setup, following up on previous reports 9, 12 . ResultsGroup-ICA decomposed the data into 20 independent components. ICASSO analysis returned a high stability index of the 20 estimate clusters (mean ±SD = 0.940 ±0.008), indicating high consistency across multiple ICA runs. Examination of the components resulted in retaining 6 components as signal components (spatially distributed networks with low frequency fluctuations, showing high correspondence with bilateral grey matter areas based on their spectral characteristics and spatial maps), while the remaining 14 components were classified as miscellaneous or noise. The excluded components, among others contained ventricular, susceptibility (located mainly in the frontal lobe, due to the extensive sinuses present in the dogs cranium) and motion artefacts. Signal components were primarily restricted to cortical areas. The resulting signal components were as follows:RSN A (Fig. 1): This resting-state network covered parts of the gyrus compositus rostralis, rostro-dorsal regions of the gyrus cinguli and the gyrus rectus; medial, bilateral caudal regions of the gyrus cinguli and gyrus splenialis, included bilaterally the hippocampus and gyrus parahippocampalis, the gyrus compositus caudalis, and caudoventral regions of the cerebellum. All of these regions correspond to regions indicated in the default mo...

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A stereotaxic breed-averaged, symmetric T2w canine brain atlas including detailed morphological and volumetrical data sets

Cited by 58 publications

References 37 publications

Resting state networks of the canine brain under sevoflurane anaesthesia

Resting state networks of the canine brain under sevoflurane anaesthesia

Anatomical variations in the circle of Willis in canines

Resting-state fMRI data of awake dogs (Canis familiaris) via group-level independent component analysis reveal multiple, spatially distributed resting-state networks

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