great dane brain size

Particularly in the case of our network 4, it may be tempting to jump to conclusions about parallels with human cortical regions that are located in approximately the same location and are involved in similar tasks; for example, the fusiform face area, Wernicke's area, or the mirror system. Network 3 includes a distributed network of subcortical regions that are involved movement, eye movement, vision, and spatial navigation, including the lateral geniculate nucleus, pulvinar, hippocampus, cerebellum, oculomotor nucleus, interpeduncular nucleus, ventral tegmental area, and substantia nigra. The precruciate and prorean gyri house premotor and prefrontal cortex, respectively, while the gyrus rectus is part of medial prefrontal cortex. This work was supported by the National Science FoundationDivision of Integrative Organismal Systems (Grant NSF-IOS 1457291). When quantifying linear models we additionally included a lambda parameter to account for phylogenetic signal (Pagel, 1997). It also involves cortical regions, including the medial part of the frontal gyrus (supplementary motor area) and the lateral gyrus (visual cortex). Therefore, shifts in relative brain size may be related to expansion or contraction of specific networks, potentially leading to the presence or absence of correlations between body size and behavior depending on the specific breeds or behaviors being studied. These folds help shorten the distances between any two brain regions, making the brain more efficient. We also additionally rescaled these images to have constant rostral-caudal lengths. To identify regional covariation in gray matter morphology, we used GIFT, a software package for MATLAB (Calhoun et al., 2001). "From Chihuahuas to Great Danes, their head sizes are different, as must be their brain size. A massive natural experiment in this arena has been right under our noses: domestic dogs. In other words, the input to SBM consisted of gray matter maps for each subject, where intensity at each voxel corresponded to the degree of deformation required to come into alignment with the template (i.e., the demeaned log Jacobians). Given these results, we next sought to determine what accounts for this variation by probing the extent to which it is related to body size, head shape, and/or breed group membership. To assess this, we computed an analogous neurocephalic index for each dog (maximum internal cranial cavity length divided by maximum internal cranial cavity width). To overcome inherent difficulties with optimizing OU parameters (Ho and An, 2014), several algorithmic improvements have been proposed. Importantly, this revealed that a large proportion of the brain shows significant gray matter morphological variation across subjects, as illustrated in Figure 1D. Dogs with bigger brains have better self-control after being forbidden from taking a treat, they were able to wait longer before giving in to the temptation of stealing it, even after controlling for their training history. The preprocessing pipeline was implemented using the NiPype workflow engine (Gorgolewski et al., 2011). T2-weighted images underwent bias field correction using ANTS's Atropos N4 tool (Avants et al., 2011) and segmentation into gray matter, white matter, and CSF using FSL's FAST tool (Zhang et al., 2001). This standard OU model has been modified into multiple-regime OU models allowing optima to vary across the phylogeny (Butler and King, 2004). The majority of changes that occur in these components take place on the terminal branches of the phylogenetic tree. This procedure identified six components, each of which were thresholded at Z scores >1.96 or below 1.96. To appreciate this effect, consider the adjacent dachshund and golden retriever images in Figure 1A: the dachshund's brain takes up most of the available endocranial space, whereas the golden retriever shows noticeably larger sinuses. DOI: https://doi.org/10.1523/JNEUROSCI.0303-19.2019, A new look at statistical nodel identification, Behavioral functions of the mesolimbic dopaminergic system: an affective neuroethological perspective, Voice-sensitive regions in the dog and human brain are revealed by comparative fMRI, ANTS: Advanced Open-Source Tools for Normalization And Neuroanatomy, Penn Image Computing and Science Laboratory, University of Pennsylvania, Does size really matter? Both transverse-acquired and sagittally acquired images were available for each dog. This suggests that brain evolution in domestic dog breeds follows a late burst model, with directional changes in brain organization being primarily lineage specific. Importantly, using the tree structure from a recent large-scale genomic analysis (Parker et al., 2017), we were able to determine that the phylogenetic signal of the brain-body allometry is negative; that is, that variation present at the tree's terminal branches is not predicted by the deeper structure of the tree. Our results indicate that skull morphology is linked to the underlying anatomy of specific, different networks of brain regions; it is possible that this could underlie the reported associations between behavior and head shape (Gcsi et al., 2009; Helton, 2009; McGreevy et al., 2013). To provide a common spatial reference for measuring this variation, we created an unbiased, diffeomorphic template using the ANTS software package (Avants et al., 2009). A simple comparison of regional volumes would be insufficient for several reasons. Specific associations between associated brain networks and behavioral specializations are also apparent. Mel - Dogs are indeed one of the most morphologically diverse mammals on the planet, with the smallest dogs weighing up to 17 times less than the largest dogs. This analysis revealed that the neurocephaliccephalic allometry was thus best explained by a two-grade model (F = 31.19, p < 0.001). For example, component 3, which involves regions involved in movement, eye movement, and spatial navigation, showed a significant correlation with sight hunting, whereas Network 2, which involves regions involved in olfaction and gustation, showed a significant correlation with scent hunting. This is consistent with a previous analysis linking foreshortening of the skull to ventral pitching of the brain and olfactory bulb, resulting in a more spherical brain (Roberts et al., 2010). Cephalic index is a significant predictor of neurocephalic index (pGLS: b = 0.37, t = 3.70, p < 0.01). Does a chihuahuas tiny skull hold the same kind of brain power as all that can fit inside a mastiffs giant noggin? We used permutation testing for statistical hypothesis testing, which is a nonparametric approach appropriate for differing group sizes, but it is still possible that different patterns of variation may have been obtained with a different sample makeup. (2016). We do not retain these email addresses. First, we manually performed skull-stripping on the transverse image. Daniel - While it appears that differences in brain size could make larger dogs more skilled in some areas, its not fair to say that they are massively more intelligent than their smaller counterparts. Consistent with this possibility, one study has already found that border collies and Siberian huskies respond significantly differently to intranasal oxytocin (Kovcs et al., 2016). To maximize the use of all available anatomical information, the transverse and sagittal images were combined as follows. Moreover, we found that these networks differed across breed groups. Figure 2B shows the relationship between neurocephalic and cephalic index. This might be akin to studying language circuitry in a lineage of language-deprived humans: humans almost certainly have some specialized hard-wired adaptations to this circuitry, but experience is required for the anatomical phenotype to fully emerge, and indeed it is difficult to consider language-related neural adaptations divorced from the context of language exposure and learning. Last, we use multiregime Ornstein-Uhlenbeck (OU) approaches to estimate phylogenetic shifts in mean value directly from the data. This component also involves regions of medial frontal cortex, which is involved in downstream or higher-order processing of chemosensation and shows activation in response to olfactory stimulation in awake but not sedated dogs (Jia et al., 2014). Cephalic index is defined as the ratio of skull width to skull length 100. Both sets of scaled template images are shown in Figure 1A. Several years ago, Rona teacher in a public school systemsaw a news piece about Posit 2022 Posit Science. This allowed us to more clearly visualize variation in morphology independent from variation in size. If grade shifts in the brainbody allometry exist, then these would putatively show differences among different breeds. Additional support was provided by NIH OD P51OD11132 to the Yerkes National Primate Research Center. For this reason, even creating the regional outlines for a simple ROI analysis would be problematic. Sagittally acquired images ranged from 0.273 mm2 in-plane resolution and 3.200 mm slice distance to 0.430 mm2 in-plane resolution and 3.200 mm slice distance. We tested this hypothesis by estimating putative grade shifts in the brain to body allometry directly from the data using an OU modeling approach (Khabbazian et al., 2016). In other words, these approaches allow estimating directly from the data where in a phylogeny a shift in mean value of a trait has occurred. This analysis permutes the sign of the log Jacobian and tests the null hypothesis that variation from the mean is random and therefore symmetrically distributed and centered around zero. Midline sagittal images from the raw, native-space scans of selected dogs are shown in Figure 1A. In the case of circuitry that is highly conserved across species, such as circuitry for reward and motivation or fear and anxiety, it is a safe bet that research on other species is a good indicator of the functional role of these systems in dogs. Additional research is needed to definitively link the function of each network to its adaptive role in response to behavior selection. These findings have relevance to both basic and applied science. Nearly all of the identified variation occurs in the terminal branches of the dog phylogenetic tree, indicating strong, recent selection in individual breeds. These results indicate that through selective breeding, humans have significantly altered the brains of different lineages of domestic dogs in different ways. Nonetheless, we expect the basic finding that this variation exists would remain. Mel - Thanks to Tim and Daniel for their answers. Also, here we questioned whether grade shifts in this allometry exist, putatively showing differences among breeds. Our goal was to determine whether significant nonrandom variation in brain anatomy exists across dogs and, if so, to differentiate between the competing and possibly interacting explanations for this variation. Try Musicnot Junk Food, Your Brain in Love: Part 4 Oxytocin, the Love/Hate Hormone, Your Brain in Love: Part 3 The Neuroscience of Date Night, Your Brain in Love: Part 2 Love and Marriage, https://www.youtube.com/watch?v=eMlg5os1OLM. Having established this basic finding, we then went on to probe the relationship between multiple, potentially interacting factors that might be linked to this variation: the total size of the body or brain, the external and internal morphology of the skull, the structure of the dog phylogenetic tree, and the organization of internal brain networks. The authors declare no competing financial interests. There is substantial diversification of skull shape across dog breeds, and this has been linked to behavioral differences (Drake and Klingenberg, 2010; McGreevy et al., 2013). 1-2. Table 1 lists the breed, breed group, and other data for all dogs included in the study. We also investigated the relationship between these covarying morphological components and the phylogenetic tree. During nonlinear registration, a warp-field is produced that represents the mapping from the original image to the target image. B, Neurocephalic index vesus cephalic index. Graphs represent volumetric quantification of the top five anatomical constituents of each of the two portions of each component. Therefore, the findings reported here should be taken as representative of the innate breed-typical adaptations to brain organization that emerge without the input of specific experience and may actually reflect relaxed or reduced versions of these adaptations. 4). pGLS analyses on gross brain, body, and skull measurements. Finally, on a philosophical level, these results tell us something fundamental about our own place in the larger animal kingdom: we have been systematically shaping the brains of another species. Person to person, the human brain varies a little in size and shapebut not dramatically so. Furthermore, we found that a substantial amount of variation in internal dog brain morphology is related to total brain size, suggesting that evolutionary increases or decreases in relative brain volume may be driven by changes in specific groups of regions. The dataset included T2-weighted MRI scans from 62 purebred dogs of 33 different breeds. First, a significant difference in the volume of, for example, the amygdala in pit bulls versus golden retrievers might seem intuitively meaningful, but to ascertain whether such a difference was truly the result of selection pressure on behavior, the phylogenetic structure of the dog family tree needs to be taken in to account to partition variance attributable to inheritance, and equal statistical priority needs to be given to the alternative hypotheses that observed variation in morphology. Network 6 includes early sensory processing regions for olfaction and vision, including the olfactory peduncle and part of the lateral gyrus, which is the location of primary visual cortex (Evans and de Lahunta, 2013). Independent components analysis revealed six regional networks where morphology covaried significantly across individuals. The resultant t-statistic image was thresholded at p < 0.05, after multiple-comparisons correction was performed using threshold-free cluster enhancement (Smith and Nichols, 2009). Figure 2A shows the relationship between brain volume and body mass. Such multiregime OU models allow modeling trait evolution toward different regimes that each display a different mean trait value. Terms and Conditions | Privacy Policy | Security Policy | Sitemap. To our knowledge, the dogs in the current study were all house pets. Enter multiple addresses on separate lines or separate them with commas. Covarying regional networks in dog brain morphology. If variation in dog brain anatomy is unrelated to behavior, then variation should be randomly distributed across regions. A, MRI images and 3D reconstructions of warped template from 10 selected dogs of different breeds. Importantly, this grade difference in the neurocephalic to cephalic index aligns with a significant difference in body size (pANOVA: F = 9.73, p < 0.01; average body size 11 kg vs 23 kg in other breeds). However, the neural underpinnings of behavioral differences between breeds remain largely unknown. For many scans in our database, the exterior of the skull was not visible, but a large database of skull measurements is publicly available (Stone et al., 2016). This handles parts of behaviour and planning, so-called executive functions.One way to make the brain more efficient is to have folds, which is why the human brain looks so wrinkly, but a hedgehog or a mouse brain is smooth. Dog lovers, enjoy this scientific peek into your best friends brain! A, Brain volume versus body mass. Alternatively, neuroanatomical variation may be explained primarily by body size rather than breed membership, with different breeds' brains representing minor, random, scaled-up or scaled-down variants of a basic species-wide pattern. Several previous studies have investigated the relationship between dog body size and cognition or behavior, with apparently contradictory results (Helton and Helton, 2010; Stone et al., 2016; cf. (Phylogenetic tree is from Parker et al., 2017.). Next, we determined the smallest ROI that completely covered the brain from the brain mask image. This approach has become a standard approach in comparative biology to model trait change across a phylogeny. The behavioral specialization associated with the most components (four of six) was explicit companionship, and the component associated with the most behavioral specializations (six of 10) was component 4, which involves regions involved in social action and interaction. B, Unbiased group-average template for this dataset. A significant relationship with total brain volume was present for all but component 6, where it was marginal but did not meet significance (component 1: t = 3.663, p = 0.001; component 2: t = 2.608, p = 0.014; component 3: t = 6.219, p < 0.001; component 4: t = 6.325, p < 0.001; component 5: t = 3.938, p < 0.001; component 6: t = 1.845, p = 0.076). The number of sources was estimated using Akaike's information criterion (AIC) (Akaike, 1974); the application of AIC in SBM is described in Xu et al. The AKC groups individual breeds into breed groups, but these breed groups change periodically and some groups contain breeds with disparate behavioral functions: for example, the nonsporting group includes both poodles and Shar-Peis. We assessed the extent to which internal and exterior skull morphology were related to the covarying morphometric networks we identified. Dogs show intraspecific variation in morphology to a degree rarely seen in nature. All Rights Reserved. Components 3, 4, and 6 showed significant relationships with cephalic index, whereas component 1 was marginal (component 1: t = 1.945, p = 0.064; component 3: t = 2.165, p = 0.041; component 4: t = 2.411, p = 0.024; component 6: t = 2.171, p = 0.041; pGLS). A small number of studies have investigated neural variation in dogs, including, for example, the effects of skull shape on brain morphology (Carreira and Ferreira, 2015; Pilegaard et al., 2017) and anatomical correlates of aggression (Jacobs et al., 2007; Vge et al., 2010). But what about dogs? Gray matter segmentations were warped to the study-specific template and modulated by their log Jacobian determinants to produce per-subject maps of the degree of morphological divergence from the study-specific group-average template. For highly conserved structures with clear anatomical boundaries, like the amygdala, this task is surmountable, but very little is known about the organization of higher-order cortical regions in dogs, and some complex behaviors that are the focus of selective breeding, like herding or interspecies communication, almost certainly rely on some of these areas. These regions are involved in the HPA axis, which regulates behavioral and endocrine responses to environmental stressors and threats. It uses independent components analysis to identify spatially distinct, distributed networks of regions that covary across individuals, and computes their statistical relationship to other categorical or continuous variables. We produced a study-specific template representing the average brain morphology across the entire group, equally unbiased toward any particular image. Significant breed differences in temperament, trainability, and social behavior are readily appreciable by the casual observer, and have also been documented quantitatively (Serpell and Hsu, 2005; Tonoike et al., 2015). Here, we use the approach proposed by Khabbazian et al. This approach estimates phenotypic change along individual lineages of a tree and has been shown to provide more accurate estimates than traditional ancestral estimation methods (Smaers and Mongle, 2017).

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