Author response: A connectomics-based taxonomy of mammals

Abstract

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Mammalian taxonomies are conventionally defined by morphological traits and genetics. How species differ in terms of neural circuits and whether inter-species differences in neural circuit organization conform to these taxonomies is unknown. The main obstacle to the comparison of neural architectures has been differences in network reconstruction techniques, yielding species-specific connectomes that are not directly comparable to one another. Here, we comprehensively chart connectome organization across the mammalian phylogenetic spectrum using a common reconstruction protocol. We analyse the mammalian MRI (MaMI) data set, a database that encompasses high-resolution ex vivo structural and diffusion MRI scans of 124 species across 12 taxonomic orders and 5 superorders, collected using a unified MRI protocol. We assess similarity between species connectomes using two methods: similarity of Laplacian eigenspectra and similarity of multiscale topological features. We find greater inter-species similarities among species within the same taxonomic order, suggesting that connectome organization reflects established taxonomic relationships defined by morphology and genetics. While all connectomes retain hallmark global features and relative proportions of connection classes, inter-species variation is driven by local regional connectivity profiles. By encoding connectomes into a common frame of reference, these findings establish a foundation for investigating how neural circuits change over phylogeny, forging a link from genes to circuits to behaviour. Editor's evaluation This important article uses an impressively rich data set (obtained and curated by the authors) to compare the structural brain connectomes of many animals spanning six taxonomic orders. The approach is innovative and relies on graph theoretical measures to describe the connectivity, which means it can be done without the need to spatially/functionally match the brains. The authors find compelling evidence that there is more variability between than within order. They attribute this effect to changes in local connectivity features, whereas global patterns are preserved. The approach can potentially be a useful way to study phylogeny and brain evolution. https://doi.org/10.7554/eLife.78635.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Anatomical projections between brain regions form a complex network of polyfunctional neural circuits (Sporns et al., 2005). Signalling on the brain’s connectome is thought to support cognition and the emergence of adaptive behaviour. Advances in imaging technologies have made it increasingly feasible to reconstruct the wiring diagram of biological neural networks. Thanks to extensive international data-sharing efforts, these detailed reconstructions of the nervous system’s connection patterns have been made available in humans and in multiple model organisms (van den Heuvel et al., 2016), including invertebrate (White et al., 1986; Chiang et al., 2011; Towlson et al., 2013; Worrell et al., 2017), avian (Shanahan et al., 2013), rodent (Oh et al., 2014; Bota et al., 2015; Rubinov et al., 2015), feline (Scannell et al., 1995; de Reus and de Reus and van den Heuvel, 2013; Beul et al., 2015), and primate species (Markov et al., 2012; Majka et al., 2016; Liu et al., 2020). The rising availability of connectomics data facilitates cross-species comparative studies that identify commonalities in brain network topology and universal principles of connectome evolution (van den Heuvel et al., 2016; Barker, 2021; Barsotti et al., 2021). A common thread throughout these studies is the existence of non-random topological attributes that theoretically enhance the capacity for information processing (Sporns, 2013). These include a highly clustered architecture with segregated modules that promote specialized information processing (Watts and Strogatz, 1998; Hilgetag and Kaiser, 2004), as well as a densely interconnected core of high-degree hubs that shortens communication pathways (van den Heuvel et al., 2012), promoting the integration of information from distributed specialized domains (Zamora-López et al., 2010; Avena-Koenigsberger et al., 2017). These universal organizational features suggest that connectome evolution has been shaped by two opposing and competitive pressures: maintaining efficient communication while minimizing neural resources used for connectivity (Bullmore and Sporns, 2012). While comparative analysis can focus on commonalities among mammalian connectomes and identify universal wiring principles, it can also be used to systematically explore differences among connectomes that confer specific adaptive advantages. Indeed, despite commonalities, architectural variations are also observed even among closely related species (Barker, 2021). Factors such as the external environment, genetics. and distinct gene expression programs also account for diversity in neural connectivity patterns (Martinez and Sprecher, 2020). Subtle variations in connectome organization may potentially account for species-specific adaptations in behaviour and cognitive function. But how does the connectome vary over phylogeny? Traditionally, mammalian taxonomies were built on morphological differences among species (Darwin, 1959). Besides physical commonalities, species within the same taxonomic group also tend to share similar behavioural repertoires (York, 2018; Bendesky and Bargmann, 2011; Yokoyama et al., 2021). Modern high-throughput whole-genome sequencing has further delineated phylogenetic links and relationships among mammalian species (Murphy et al., 2021; Zoonomia Consortium, 2020; Álvarez-Carretero et al., 2021; Seehausen et al., 2014). In addition to refining the overall classification of mammals, whole-genome comparative analyses have established the genetic basis of phenotypic variation across phylogeny (Murphy et al., 2021). Whether inter-species differences in the organization of connectome wiring conform to this taxonomy remains unknown. How do genes sculpt behaviour across evolution? Could speciation events in the genome leading to variations in connectome architecture be the missing link between genomics and behaviour? Rigorously addressing these questions is challenging due to the lack of methodological consistency in the acquisition and reconstruction of neural circuits, or the limited number of available species. Here, we comprehensively chart connectome organization across the mammalian phylogenetic spectrum. We analyse the mammalian MRI (MaMI) data set, a comprehensive database that encompasses high-resolution ex vivo diffusion and structural (T1- and T2-weighted) MRI scans of 124 species (a total of 225 scans including replicas) (Assaf et al., 2020). All images were acquired using the same scanner and protocol. All connectomes were reconstructed using a uniform parcellation scheme consisting of 200 brain areas, including cortical and subcortical regions. Notably, the MaMI data set spans a wide range of categories across different taxa levels of morphological and phylogenetic mammalian taxonomies (Assaf et al., 2020). Specifically, it includes animal species across 5 different superorders (Afrotheria, Euarchontoglires, Laurasiatheria, Xenarthra, and Marsupialia) and 12 different orders (Cetartiodactyla, Carnivora, Chiroptera, Eulipotyphla, Hyracoidea, Lagomorpha, Marsupialia, Perissodactyla, Primates, Rodentia, Scandentia, and Xenarthra). Taking advantage of the harmonized imaging and reconstruction protocols, we quantitatively assess the similarity of species’ connectomes to construct data-driven phylogenetic relationships based on brain wiring. We compare these inter-species wiring similarities with conventional morphologically and genetically defined mammalian taxonomies. We determine the extent to which connectome topology conforms to established taxonomic classes, and identify network features that are associated with speciation. Results The MaMI data set consists of high-resolution ex vivo diffusion and structural (T1- and T2-weighted) MRI scans of 124 species. Since there is no species-specific template, all connectomes were reconstructed using a uniformly applied 200-node parcellation. Having equally sized networks facilitates graph comparison but also implies a lack of direct correspondence between nodes across species. However, because our focus is on the statistics of connectomes’ topology, this does not impact our analyses. As the size of the network is kept constant across all species, voxel size is normalized to brain volume. Figure 1 shows the distribution of connectomes across 10 mammalian orders (out of the 12 present in the data set). We focus on the 6 orders that contain 5 or more distinct species (within the Laurasiatheria and Euarchontoglires superorders); these include Chiroptera, Rodentia, Cetartiodactyla, Carnivora, Perissodactyla, and Primates, resulting in a total of 111 different animal species and 203 brain scans. A complete list of the animal species included in the data set is provided in Figure 1—figure supplement 1. Figure 1 with 1 supplement see all Download asset Open asset Mammalian MRI (MaMI) data set. The MaMI data set encompasses high-resolution ex vivo structural and diffusion MRI scans of 124 animal species spanning 12 morphologically and phylogenetically defined taxonomic orders: Cetartiodactyla, Carnivora, Chiroptera, Eulipotyphla, Hyracoidea, Lagomorpha, Marsupialia, Perissodactyla, Primates, Rodentia, Scandentia, and Xenarthra. (a) Hierarchical relationships across 10 (out of the 12 included in the data set) morphological and phylogenetic taxonomic orders. Numbers outside the parenthesis correspond to the number of unique species within each order, and numbers inside the parenthesis correspond to the number of samples (including replicas). (b) Connectivity matrices for five randomly chosen sample species within each of the six orders included in the analyses (i.e. Cetartiodactyla, Carnivora, Chiroptera, Perissodactyla, Primates, and Rodentia). Only orders with at least five different species were included for the analyses. Nodes are organized according to their community affiliation obtained from consensus clustering applied on the connectivity matrix (see ‘Materials and methods’). Communities in (b) correspond to the partition for which the resolution parameter γ=1.0 (Figure 1—figure supplement 1). Connectome-based inter-species distances Similarity between species’ network architectures is estimated using two network-based distance metrics: spectral distance, based on the eigenspectrum of the normalized Laplacian of the connectivity matrix (see Figure 2—figure supplement 1; de Lange et al., 2014), and topological distance, based on a combination of multiscale graph features of the binary and weighted connectivity matrices (Figure 2—figure supplements 2 and 3 show the distribution of individual local and global graph features, respectively; Rubinov and Sporns, 2010). For completeness, Figure 2—figure supplements 4 and 5 show the cumulative distribution of binary and weighted local features, respectively, for individual species. Both methods measure how similar the architectures of two connectomes are. To identify brain connectivity differences across species, we need to be able to analyse data in a shared frame of reference. The normalized Laplacian eigenspectrum and the graph features of the connectivity matrix allow us to translate connectomes into a common feature space in which they are comparable, despite the fact that they come from different species, and that the nodes do not correspond to one another (Mars et al., 2021). To account for the fact that some of the species have more than one scan, we randomly select one sample per species and estimate (spectral and topological) inter-species distances. We repeat this procedure 10,000 times and report the average across iterations. Figure 2a shows the spectral distances between species’ connectomes. In general, we observe smaller distances among members of the same order (outlined in yellow). Figure 2b confirms this intuition by showing that spectral distances within orders (i.e. values along the diagonal) tend to be smaller than distances between orders (i.e. values off the diagonal). Figure 2c shows the distributions of intra- and inter-order distances. The mean/median intra-order distance is significantly smaller than the mean/median inter-order distance (two-sample Welch’s t-test: mean intra- and inter-order distances are 0.43 and 0.55, respectively, p<10−4 two-tailed, and Cohen’s d effect size = 0.67; two-sample Mann–Whitney U-test: median intra- and inter-order distances are 0.44 and 0.55, respectively, p<10−4 two-tailed, and common-language effect size = 68%; Figure 2c). We find comparable results when estimating species similarity using topological distance (two-sample Welch’s t-test: mean intra- and inter-order distances are 0.41 and 0.53, respectively, p<10−4 two-tailed, and Cohen’s d effect size = 0.59; two-sample Mann–Whitney U-test: median intra- and inter-order distances are 0.41 and 0.53, respectively, p<10−4 two-tailed, and common-language effect size = 66%; Figure 2d–f). Figure 2—figure supplement 6 shows the same results as in Figure 2, but using all samples including replicas (i.e. without random resampling). Altogether, results suggest that species with similar genetics, morphology, and behaviour tend to have similar connectome architecture. In other words, variations in connectome architecture reflect phylogeny. Figure 2 with 9 supplements see all Download asset Open asset Spectral and topological distance between orders. (a) Spectral distance between species-specific connectomes. Lower distances indicate greater similarity. Yellow outlines indicate morphologically and genetically defined orders. (b) Median spectral distance within and between all constituent members of each order. (c) Distribution of intra- and inter-order spectral distances. (d) Topological distance between species-specific connectomes. Lower distances indicate greater similarity. Yellow outlines indicate morphologically and genetically defined orders. (e) Median topological distance within and between all constituent members of each order. (f) Distribution of intra- and inter-order topological distances. Effect sizes in (c) and (f) are Cohen’s d estimator corresponding to a two-sample Welch’s t-test (p<10−4). Equivalent conclusions are drawn if common-language effect sizes from the two-sample Mann–Whitney U-test are used. Architectural features differentiate species Next we consider which network features contribute to the differentiation (Figure 2—figure supplements 2 and 3 show the distributions of local and global graph features, respectively). To address this question, we recompute inter-species topological distances using different sets of graph features (Figure 3). We find that the difference between intra- and inter-order topological distances tends to be larger when only local (node-level) features are included in the estimation of the topological distance (i.e. degree, clustering coefficient, betweenness, and closeness; Figure 3b and e) compared to when only global features are considered (i.e. characteristic path length, transitivity, and assortativity; Figure 3c and f). This is the case for both the binary and weighted versions of these features (top and bottom rows in Figure 3, respectively). Figure 3—figure supplement 1 shows the same results as in Figure 3, but using all samples including replicas (i.e. without random resampling). These results suggest that differentiation of orders is better explained by differences in local network topology; conversely, global network topology appears to be conserved across species. An illustration of this principle is depicted in Figure 3—figure supplement 2 showing that the relative local connectivity of the anterior and the posterior ends of the cortex changes across taxonomic orders (Barrett et al., 2020; Krubitzer and Kaas, 2005; Krubitzer and Kahn, 2003). Figure 3 with 10 supplements see all Download asset Open asset Contribution of network features. Topological distance can be computed using different combinations of local and global, binary and weighted connectome features. Histograms show intra- and inter-order distance distributions when using (a) all (binary, weighted, local, and global), (b) all local (binary and weighted), (c) all global (binary and weighted), (d) all binary (local and global), (e) only binary local, (f) only binary global, (g) all weighted (local and global), (h) only weighted local, and (i) only weighted global features. Local features include (the average and standard deviation of) degree, clustering, betweenness, and closeness. Global features include characteristic path length, transitivity, and assortativity. For definitions, please see ‘Materials and methods.’ Effect sizes correspond to Cohen’s d estimator from a two-sample Welch’s t-test. Equivalent conclusions are drawn if common-language effect sizes from a two-sample Mann–Whitney U-test are used. In all cases, the difference in the mean and median of intra- and inter-order distance distributions is statistically significant (p<10−4). The same conclusions can be drawn after controlling for network density (Figure 3—figure supplement 6). A similar conclusion can be drawn when the eigenvalue distributions of the (normalized) Laplacian of the connectivity matrices are compared across species (Figure 2—figure supplement 1). In spectral graph theory, the presence of eigenvalues with high multiplicities or eigenvalues symmetric around λi=1 provides information about the network’s local organization that results from the recursive manipulation of connectivity motifs (Banerjee and Jost, 2008; Banerjee and Jost, 2009; de Lange et al., 2014). For instance, node duplication (i.e. the presence of nodes with the same connectivity profile) results in an increase of λi=1. The duplication of edge motifs (i.e. the multiple presence of pairs of connected nodes with the same connectivity profile), on the other hand, produces eigenvalues at equal distances to λi=1. Visually inspecting their Laplacian eigenspectra, one can notice that, across taxonomic orders, species tend to differ mostly around the interval 0.5≤λi≤1.5, both in terms of the multiplicity of λi=1, as well as in the width of the bell-shaped curve around λi=1. While differences in the multiplicity of λi=1 indicate differential amounts of duplicated node motifs present in the network, differences in the value and multiplicity of eigenvalues around λi=1 indicate the presence of distinct edge motifs with disparate numbers of duplications in the network. Therefore, differences across taxonomic orders are most likely due to the presence of different local connectivity fingerprints in the connectivity matrix (Figure 2—figure supplement 1; de Lange et al., 2014; Mars et al., 2018a; Mars et al., 2018b). Determining which are specifically these node and edge motifs cannot be done by simply examining the Laplacian eigenspectra, and is out of the scope of this study. Additional evidence supporting the idea that spectral distance captures mostly differences in local network topology is the fact that the correlation between spectral and topological distance is maximum when only local features are included in the estimation of the topological distance (Figure 3—figure supplement 3). We also observe that that the difference between intra- and inter-order topological distances is greater for binary than for weighted features (Figure 3a–c and d–f, respectively), independently of being local or global. This suggests that the strength of the connections is less important than the binary architecture of the connectivity matrix. Some of the features used for the estimation of the topological distance depend on network density, which varies across taxonomic orders (Figure 3—figure supplement 4). To determine whether the observed differences between intra- and inter-order distances are above and beyond differences due to network density, we perform the same analysis shown in Figure 3, after controlling for density (Figure 3—figure supplement 5). Results, shown in Figure 3—figure supplement 6, suggest that differences between intra- and inter-order topological distances are not driven by differences in network density, but variations in wiring patterns, as captured by topological features, play a role in the observed phylogenetic variations in connectome organization. Altogether, our results show that the subset of features that best differentiate species across taxonomic orders are the binary local topological features. We perform a set of complementary analyses to assess which subset of features produces the best partition of animal species relative to traditional taxonomies. To do so, we (1) project the data on a 2D plane using multidimensional scaling (Figure 3—figure supplement 7) and (2) apply hierarchical clustering to inter-species distance matrices (Figure 3—figure supplement 8). Visual inspection of these results suggests that, consistent with our previous results (Figure 3), local features compared to global features (ignoring panel a, centre vs. right column, respectively, in Figure 3—figure supplements 7 and 8), as well as binary features compared to weighted features (ignoring panel a, centre vs. bottom row, respectively, in Figure 3—figure supplements 7 and 8), yield species partitions that more closely reflect established phylogenetic relationships, further supporting the idea that connectome organization recapitulates traditional taxonomic relationships that are based on morphology and genetics. Conservation of small-world architecture Anatomical brain networks are thought to simultaneously reconcile the opposing demands of functional integration and segregation by combining the presence of functionally specialized clusters with short polysynaptic communication pathways (Tononi et al., 1994; Sporns, 2013; Sporns et al., 2005; Bassett and Bullmore, 2006). Such architecture is often referred to as small-world and is observed in a wide variety of naturally occurring and engineered networks (Watts and Strogatz, 1998). Here, we explore whether these principles of segregation and integration in global connectome organization are consistent across phylogeny. To do so, we estimate for each species the ratio of clustering coefficient to characteristic path length, normalized relative to a set of randomly rewired graphs that preserve the degree sequence of the nodes (Humphries and Gurney, 2008; Maslov and Sneppen, 2002; Rubinov and Sporns, 2010; Figure 4). Consistent with previous reports in individual species’ connectomes (Hilgetag and Kaiser, 2004; Sporns and Zwi, 2004; Bassett and Bullmore, 2006), we find that all connectomes display high and diverse levels of small-worldness, suggesting that simultaneously highly segregated and integrated networks is a global trait conserved across mammalian brains. Figure 4 Download asset Open asset Conservation of small-world architecture. Clustering coefficient vs. characteristic path length normalized relative to a set of 1000 randomly rewired graphs that preserve the degree sequence of the nodes (Maslov and Sneppen, 2002). For definitions of each graph measure, see ‘Materials and methods.’ Each data point represents a different animal species. Data points above the identity line are said to have small-world architecture. The inset on the right bottom corner is a zoom on the abscissa; dots correspond to the median and error bars correspond to the standard deviation across species within the same taxonomic order. Conservation of edge classes across species The topological and spatial arrangement of connections in connectomes is thought to shape the segregation and integration of information and, ultimately, their computational capacity (Faskowitz et al., 2021). To investigate inter-species differences in the topological and spatial distribution of connections, we stratify edges into different classes in four commonly studied partitions. Partitions include inter- and intra-modular connections (Figure 5a), inter- and intra-hemispheric connections (Figure 5b), connection length distribution (short-, medium-, and long-range connections; Figure 5c and Figure 5—figure supplement 1), and rich-club (rich-club, feeder and peripheral connections; Figure 5d). Overall, we find that, along the four partitions, the relative proportions of each connection class are conserved across taxonomic orders, despite differences in connection density. Collectively, this is consistent with the results from the previous sections showing that global architectural features of connectomes are consistent across phylogeny. Figure 5 with 1 supplement see all Download asset Open asset Contribution of edge types. Mean proportion of (a) inter- and intra-modular connections, (b) inter- and intra-hemispheric connections, (c) short- (length ≤ 25%), medium- (25% < length ≤ 75%) and long-range connections (length ≥ 75%), and (d) rich-club (connecting two rich-club nodes), feeder (connecting one rich-club and one non-rich-club node) and peripheral (connecting two non-rich-club nodes) connections. Error bars indicate 95% confidence intervals. Discussion In this study, we chart the organization of whole-brain neural circuits across 111 mammalian species and 5 superorders. We find that connectome organization recapitulates to a large extent traditional taxonomies. While all connectomes retain hallmark global features and relative proportions of edge classes, inter-species variation is driven by local regional connection profiles. Conventional mammalian taxonomies are delineated based on the concept that a species is a group of organisms that can reproduce naturally with one another and create fertile offspring (Mallet, 1995). As a result, classical taxonomies based on animal morphology have largely been reconciled with emerging evidence from whole-genome sequencing Baker and Bradley, 2006; namely, organisms with similar genomes display similar physical characteristics and behaviour. Our work shows that inter-species similarity – as defined by morphology, behaviour, and genetics – is concomitant with the organization of neural circuits. Specifically, species that are part of the same taxonomic order tend to display similar connectome architecture, suggesting that brain network organization is under selection pressure (see also Butler and King, 2004; Lande, 1976; Wright, 1931 for an alternative mechanism of trait evolution characterized by pure drift models based on Brownian motion), analogous to size, weight, or colour. Which network features drive differences across taxonomic orders? Interestingly, all connectomes display consistent global hallmarks that were previously documented in tract-tracing studies, including high clustering and near-minimal path length characteristic of small-world organization, as well as segregated network communities and densely interconnected hub nodes (van den Heuvel et al., 2016). The conservation in global wiring and organizational principles is further supported by a reduced difference between intra- and inter-order topological distances estimated exclusively from global features compared to the case in which only local features are considered. Thus, relative differences between connectomes across taxonomic orders are mainly driven by local regional features. These results are in line with the idea that a brain region’s functional fingerprint – the specific computation or function that it performs by virtue of its unique firing patterns and dynamics – is determined by its underlying cortico-cortical connectional fingerprint (Mars et al., 2021; Mars et al., 2018b; Mars et al., 2016; Passingham et al., 2002). Accordingly, inter-species differences in functional and behavioural repertoire are likely supported by changes in local connectivity patterns. Along the same lines, our results are also consistent with the notion that neural circuit evolution involves random local circuit modifications that may have provided species with behavioural adaptations, allowing them to face specific challenges (Barker, 2021), such as extreme environmental pressures Park et al., 2008; Smith et al., 2011; Eigenbrod et al., 2019, or to support specific behaviours, such as courtship (O’Grady and DeSalle, 2018; Markow and O’Grady, 2005; Ding et al., 2019; Seeholzer et al., 2018; Khallaf et al., 2020; Barkan et al., 2018; Ding et al., 2016; York et al., 2019), social bonding (Insel and Shapiro, 1992; Winslow et al., 1993; Jaggard et al., 2020; Loomis et al., 2019), or foraging (Vanwalleghem et al., 2018; Pantoja et al., 2020). How computations and cognitive functions emerge from these species-specific circuit modifications remains a key question in the field (Buckner and Krienen, 2013; Suárez et al., 2021). These results highlight the importance of developing species-specific, anatomical-based parcellations, as well as new ways to align connectomes from different species. Understanding how homologous regions correspond to one another will allow further investigation of regional inter-species differences in connectome topology, which is a fundamental step for advancing comparative connectomics. A variety of emerging methods are contributing to further resolve correspondence between brain regions across species, facilitati