A connectomics-based taxonomy of mammals

  1. Laura E Suarez  Is a corresponding author
  2. Yossi Yovel
  3. Martijn P van den Heuvel
  4. Olaf Sporns
  5. Yaniv Assaf
  6. Guillaume Lajoie
  7. Bratislav Misic  Is a corresponding author
  1. Montréal Neurological Institute, McGill University, Canada
  2. Mila - Quebec Artificial Intelligence Institute, Canada
  3. School of Neurobiology, Biochemistry and Biophysics, Tel Aviv University, Israel
  4. Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Netherlands
  5. Psychological and Brain Sciences, Indiana University, United States

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

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
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<104 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<104 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<104 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<104 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
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<104). 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
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<104). 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 0.5λi1.5, 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.

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
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, facilitating fine-grained comparisons at the level of individual regions. These methods implement regional comparisons based on different data modalities including measures of cytoarchitecture (J Garey, 1999; Bianchi et al., 2013), receptor distribution (Levant, 1998), functional and structural connectivity fingerprints (Passingham et al., 2002; Mars et al., 2016), patterns of gene expression (Warrington et al., 2022; Beauchamp et al., 2022), and macroscale gradients of functional activation (Buckner and Margulies, 2019).

It is noteworthy that the relative proportion of edge classes (inter- vs. intra-modular, inter- vs. intra-hemispheric, short- vs. medium- vs. long-range and rich-club vs. feeder vs. peripheral) are preserved across species. This result is reminiscent of recent work on allometric scaling that investigates how white matter connectivity scales with brain size (Bullmore and Sporns, 2012). For example, species with fewer commissural inter-hemispheric connections exhibit lower hemispheric mean shortest path (i.e. stronger intra-hemispheric connectivity), suggesting a similar connectivity conservation principle (Assaf et al., 2020). Likewise, using diffusion-weighted MRI data across 14 different primate species, another study reported negative allometric scaling of cortical surface area with white matter volume and corpus callosum cross-sectional area (Ardesch et al., 2021). This scaling results in less space for white matter connectivity with increasing brain size, translating into larger brains with a relatively higher proportion of short-range connections than long-range connections when compared with smaller brains (Ardesch et al., 2021). These results, however, do not contradict studies showing a positive allometric scaling between white matter and grey matter volume (Zhang and Sejnowski, 2000; Theunissen, 1988; Schlenska, 1974; Frahm et al., 1982); while the proportion of total volume devoted to cerebral white matter is higher in larger brains, it does not keep pace with the rapid cortical expansion that occurs with larger brain size. Collectively these studies highlight that the architecture of neural circuits and their physical embedding are intertwined, and the distribution of connections is such that it retains consistent global architectural features across phylogeny.

The present results contribute to the emerging field of comparative connectomics (van den Heuvel et al., 2016; Mars et al., 2021; Barker, 2021; Tendler et al., 2021). Adopting a harmonized imaging protocol in a large number of mammalian species facilitates a rigorous quantitative comparison of neural circuits. Central to this are network analytic methods that map connectomes to a common space and quantify similarities across local and global levels of organization (de Lange et al., 2014; de Lange et al., 2016; Bassett and Sporns, 2017; Mars et al., 2018b, Mars et al., 2021; Warrington et al., 2022). By comprehensively charting taxonomies of connectome architectures, we may uncover the principles that govern the wiring of neural circuits (Avena-Koenigsberger et al., 2015). In particular, quantitative analysis of connectome architecture across phylogeny may help to link genomics and behaviour (Mišić and Sporns, 2016). Traditionally, taxonomic groups were defined in terms of physical morphology and behavioural repertoire (Burke, 1968), but these are now understood to be driven by speciation events in the genome (Murphy et al., 2021; Zoonomia Consortium, 2020; Álvarez-Carretero et al., 2021). However, we do not yet understand how genes influence neural circuit architecture, which in turn shapes the behavioural repertoire of an organism. By understanding how neural circuits change over phylogeny, we can fill this gap and forge a link from genes to circuits to behaviour. Ultimately, the confluence of genomics, connectomics, and behaviour may help to triangulate towards a more well-rounded view of speciation (Hernández-Hernández et al., 2021), and their simultaneous investigation can further illuminate the link between structure and function in brain networks (Bassett et al., 2010; Stiso and Bassett, 2018; Suárez et al., 2020).

This work must be considered with respect to multiple limitations. First, uniformly parcellating brains of different size into the same number of nodes facilitates comparison of network architecture, but potentially obscures biologically important regional differences. Second, many species are represented by a single individual. Although we focus on orders rather than individual species and there is high within-species reliability (Assaf et al., 2020), the analyses do not capture individual variability within species. Third, all connectomes are reconstructed using diffusion-weighted imaging, which is subject to both systematic false positives and false negatives (Maier-Hein et al., 2017; Schilling et al., 2019). While a uniform, high-resolution ex vivo scanning protocol allows for systematic comparisons among species, and our results recapitulate findings from tract-tracing studies, future work in comparative connectomics will benefit from technological and analytical advances in neural circuit mapping. Fourth, evolutionary circuit modifications may not occur at the level of large-scale white matter, but at finer scales involving smaller nuclei or physiological events not accessible by diffusion imaging, such as up- or downregulation of neurotransmitter receptors (Barker, 2021). Nevertheless, the strikingly consistent taxonomic and phylogenetic relationships revealed by connectome analysis remain and suggest that macroscale connectivity, as measured by diffusion MRI, is informative of species similarities and differences across taxonomic orders.

By encoding connectomes into a common frame of reference, we quantitatively assess neural circuit architecture across the mammalian phylogeny. We find that connectome organization recapitulates previously established taxonomic relationships. Collectively, these findings set the stage for future mechanistic studies to trace the link between genes to neural circuits and ultimately to behaviour, and offer new opportunities to explore how changes in brain network structure across phylogeny translate into changes in function and behaviour.

Materials and methods

Brain samples

Request a detailed protocol

The MaMI database includes a total of 225 ex vivo diffusion and T2- and T1-weighted brain scans of 125 different animal species (Figure 1—figure supplement 1). No animals were deliberately euthanized for this study. All brains were collected based on incidental death of animals in zoos in Israel or natural death collected abroad, and with the permission of the national park authority (approval no. 2012/38645) or its equivalent in the relevant countries. All scans were performed on excised and fixated tissue. Animals’ brains were extracted within 24 hr of death and placed in formaldehyde (10%) for a fixation period of a few days to a few weeks (depending on the brain size). Approximately 24 hr before the MRI scanning session, the brains were placed in phosphate-buffered saline for rehydration. Given the limited size of the bore, small brains were scanned using a 7-T 30/70 BioSpec Avance Bruker system, whereas larger brains were scanned using a 3-T Siemens Prisma system. To minimize image artefacts caused by magnet susceptibility effects, the brains were immersed in fluorinated oil (Flourinert, 3M) inside a plastic bag during the MRI scanning session.

MRI acquisition

Request a detailed protocol

A unified MRI protocol was implemented for all species. The protocol included high-resolution anatomical scans (T2- or T1-weighted MRI), which were used as an anatomical reference, and diffusion MR scans. Diffusion MRI data were acquired using high angular resolution diffusion imaging (HARDI), which consists of a series of diffusion-weighted, spin-echo, echo-planar-imaging images covering the whole brain, scanned in either 60 (in the 7-T scanner) or 64 gradient directions (in the 3-T scanner) with an additional three non-diffusion-weighted images (B0). The b value was 1000 smm-2 in all scans. In the 7-T scans, TR was longer than 12,000 ms (depending on the number of slices), TE was 20 ms, and Δ/δ = 10/4.5 ms. In the 3-T scans, TR was 3500 ms, with a TE of 47 ms and Δ/δ = 17/23 ms.

To linearly scale according to brain size the two-dimensional image pixel resolution (per slice), the size of the matrix remained constant across all species (128 × 96). Due to differences in brain shape, the number of slices varied between 46 and 68. Likewise, the number of scan repetitions and the acquisition time were different for each species, depending on brain size and desired signal-to-noise ratio (SNR) levels. To keep SNR levels above 20, an acquisition time of 48 hr was used for small brains (∼0.15 ml) and 25 min for large brains (>1000 ml). SNR was defined as the ratio of mean signal strength to the standard deviation of the noise (an area in the non-brain part of the image). Full details are provided in Assaf et al., 2020.

Connectome reconstruction

Request a detailed protocol

The ExploreDTI software was used for diffusion analysis and tractography (Leemans et al., 2009). The following steps were used to reconstruct fibre tracts:

  1. To reduce noise and smooth the data, anisotropic smoothing with a 3-pixel Gaussian kernel was applied.

  2. Motion, susceptibility, and eddy current distortions were corrected in the native space of the HARDI acquisition.

  3. A spherical harmonic deconvolution approach was used to generate fibre-orientation density functions per pixel (Tournier et al., 2004), yielding multiple (n ≥ 1) fibre orientations per voxel. Spherical harmonics of up to fourth order were used.

  4. Whole-brain tractography was performed using a constrained spherical deconvolution (CSD) seed point threshold similar for all samples (0.2) and a step length half the pixel size.

The end result of the tractography analysis is a list of streamlines starting and ending between pairs of voxels. Recent studies have shown that fibre tracking tends to present a bias where the vast majority of end points reside in the white matter (Tournier et al., 2004). To avoid this, the CSD tracking implemented here ensures that approximately 90% of the end points reside in the cortical and subcortical grey matter.

Network generation and analysis

Request a detailed protocol

Before the reconstruction of the networks, certain fibre tracts were removed from the final list of tracts. These include external projection fibres that pass through the cerebral peduncle, as well as cerebellar connections. Inner-hemispheric projections, such as the thalamic radiation, were included in the analysis. Brains were parcellated into 200 nodes using a k-means clustering algorithm. All the fibre end-point positions were used as input, and cluster assignment was done based on the similarity in connectivity profile between pairs of end points. Therefore, vertices with similar connectivity profile have a higher chance of grouping together. The clustering was performed twice, once for each hemisphere. Nodes were defined as the mass centre of the resulting 200 clusters. Connectivity matrices were generated by indexing the number of streamlines between any two nodes (Assaf et al., 2020). The resulting connectivity matrices are hence sparse and weighted adjacency matrices. For the analysis of the Laplacian eigenspectrum, connectivity matrices were binarized by setting connectivity values to 1 if the connection exists and 0 otherwise.

Even though the sizes of the regions differ across species, we opted for a uniform parcellation scheme (i.e. 200 nodes) for several reasons. First, to our knowledge, there is no MRI parcellation for the brains of the majority of the species studied here. Second, how brain regions correspond to one another across species (i.e. homologues) is still not completely understood for many regions and for many species. Third, comparing networks of different sizes introduces numerous analytical biases because most network measures trivially depend on size, making the comparison challenging. We therefore opted to implement a uniform parcellation scheme across species, allowing us to translate connectomes into a common reference feature space in which they can be compared (see ‘Spectral distance and ‘Topological distance’ sections). Note that this approach does not take into account species-specific regional delineations, nor does it capture homologies between nodes across species, which are still not completely understood. To ensure that the results are not idiosyncratic to the choice of parcellation and parcellation scale, we replicated all results using a lower resolution (100 node; Figure 2—figure supplement 7 and Figure 3—figure supplement 9) and higher resolution (300 node; Figure 2—figure supplement 8 and Figure 3—figure supplement 10) parcellation.

Controlling for the scanning resolution and acquisition parameters

Request a detailed protocol

As the size of the matrix was kept constant across species (i.e. 200 nodes), voxel dimensions were linearly scaled with brain volume, thus resulting in different scanning resolutions across the samples. To verify that this was a reasonable assumption, several tests were performed: (1) the diffusion-based connectome of the mouse was previously compared against one derived from tract-tracing (see Assaf et al., 2020 for details), obtaining a strong correlation between both networks. (2) Results on the connectivity conservation principle presented in Assaf et al., 2020 were invariant to different scanning and parcellation parameters across nine different species. (3) The diffusion-weighted imaging method was able to reconstruct specific ground truth fibre systems across brains, and these fibre bundles scaled in size with brain volume.

Spectral distance

Request a detailed protocol

To estimate similarities between species’ connectome organization, we computed the Laplacian eigenspectrum of each graph. The Laplacian eigenspectrum acts like a spectroscopy of the graph and summarizes distinct aspects of the underlying topology (Banerjee and Jost, 2009; Banerjee and Jost, 2008; Newman, 2001; Grone et al., 1990; Grone and Merris, 1994; Das, 2004). We considered the normalized Laplacian matrix L for undirected graphs with binary adjacency matrix A defined as L=I-D-1A, where D is a diagonal matrix with D(i,i)= deg i, and deg i is the binary degree of vertex i.

L(i,j)={1ifi=j1degiifiandjareconnected0otherwise

with i and j representing two vertices of the graph. The Laplacian spectrum is then given by the set of all the eigenvalues of L. Importantly, the eigenspectrum of the normalized Laplacian has the advantage that all eigenvalues are in the range [0,2] (Chung, 1996), facilitating comparison across species. Furthermore, the normalized Laplacian is unitarily equivalent to the symmetric normalized Laplacian (Chung, 1996), that is, Lsymm=I-D-12AD12, thus the eigenvalues of both Laplacians are real. The spectral distance between every pair of animal species was then estimated as 1 minus the cosine similarity of their Laplacian eigenvalue distributions, where eigenvalue distributions were assumed to be vectors in a high-dimensional space.

To allow comparison of our results with previous reports (de Lange et al., 2014), in addition to comparing species using their connectome’s Laplacian eigenspectra straightaway, we smoothed the eigenvalue distribution (i.e. λ1, λ2, …, λn) by convolving eigenvalue frequencies with a Gaussian kernel. The new estimated density is given by

Γ(x)=i=1n12πσ2exp(|xλi|22σ2)

with n being the number of eigenvalues in the approximated distribution, and σ being a smoothing factor of 0.015. We used a step of 0.001, which resulted in a total of n=2000 points. The approximated distribution was normalized such that area under the curve is 1. For the smoothing, we used the KernelDensity function in the neighbors module of the Scikit-learn Python package (Pedregosa et al., 2011). Details of the implementation can be found in the publicly available code repository. As with the Laplacian eigenspectrum, the spectral distance between every pair of animal species was estimated as 1 minus the cosine similarity of their smoothed (normalized) Laplacian eigenvalue distributions. Results of this supplementary analysis can be found in Figure 2—figure supplement 9.

Topological distance

An alternative way to estimate inter-species distances in connectome organization is to compute the correlation between their network features. We estimated a set of local and global graph theory measures of the connectivity matrix. Local measures include node degree, clustering coefficient, node betweenness, and closeness. Global measures include characteristic path length, transitivity, and assortativity. We included both the binary and the weighted versions of these measures. We constructed a vector of local and global topological features for every animal species. Because there are as many local features as nodes in a network, we only used the average and the standard deviation of these measures. Similar to the spectral distance, the topological distance between every pair of animal species was estimated as 1 minus the cosine similarity of their topological feature vectors. All local and global features were estimated using the Python version of the Brain Connectivity Toolbox (https://github.com/aestrivex/bctpy; Sporns et al., 2022; Rubinov and Sporns, 2010). Definitions of these topological metrics can be found below.

Local features

Request a detailed protocol
  • Degree (bin). Number of connections that a node participates in:

    ki=jiaijiN

    where aij=1 if nodes i and j are connected, otherwise aij=0. N corresponds to the set of all nodes in the graph (Rubinov and Sporns, 2010).

  • Degree (wei). Sum of connection weights that a node participates in:

    si=jiwijiN

    where wij corresponds to the connection weight between nodes i and j. N corresponds to the set of all nodes in the graph (Rubinov and Sporns, 2010).

  • Clustering (bin). Proportion of transitive closures (closed triangles) around a node, that is, the fraction of neighbour nodes that are neighbours of each other:

    ci(A)=12jih(i,j)aijaihajh12ki(ki1)=(A3)iiki(ki1)iN

    where A corresponds to the binary adjacency matrix of the graph, (A3)ii is the ith element of the main diagonal of A3=AAA, and ki is the degree of node i (Watts and Strogatz, 1998; Rubinov and Sporns, 2010).

  • Clustering (wei). Mean ‘intensity’ of triangles around a node:

    ci(W)=12jih(i,j)wij13wih13wjh1312ki(ki1)=(W[13])ii3ki(ki1)iN

    where W corresponds to the weighted connectivity matrix, (W[13])ii3 is the ith element of the main diagonal of (W[13])3=W13W13W13, and ki is the degree of node i (Onnela et al., 2005; Rubinov and Sporns, 2010).

  • Shortest path length (bin). Minimum geodesic distance between pairs of nodes:

    dij=auvgijauv

    where gij is the shortest geodesic path between nodes i and j. Note that dij= for all disconnected pairs i,j(Rubinov and Sporns, 2010).

  • Shortest path length (wei). Minimum weighted distance between pairs of nodes:

    dijw=auvgijf(wuv)

    where f is a map (e.g. the inverse) from weight to length and gij is the shortest weighted path between nodes i and j (Rubinov and Sporns, 2010).

  • Betweenness (bin). Proportion of shortest (geodesic) paths in the graph that traverse a node:

    bi=1(n1)(n2)jih(i,j)ρhj(i)ρhjiN

    where ρhj is the number of shortest (geodesic) paths between nodes h and j, ρhj(i) is the number of shortest (geodesic) paths between nodes h and j that pass through i, and n is the number of nodes in the graph (Freeman, 1978; Brandes, 2001; Kintali, 2008; Rubinov and Sporns, 2010).

  • Betweenness (wei). Same as betweenness (bin), but shortest paths are estimated on the respective weighted graph (Freeman, 1978; Brandes, 2001; Kintali, 2008; Rubinov and Sporns, 2010).

  • Closeness (bin). Mean shortest (geodesic) path length from a node to all other nodes in the network:

    ei(A)=jih(i,j)aijaih[djh(Ni)]1ki(ki1)iN

    where djh(Ni) is the shortest (geodesic) path length between nodes j and h, which contains only neighbours of i, and is estimated on the corresponding binary adjacency matrix A (Latora and Marchiori, 2001; Rubinov and Sporns, 2010).

  • Closeness (wei). Mean shortest (weighted) path from a node to all other nodes in the network:

    ei(W)=jih(i,j)(wijwih[djhw(Ni)]1)13ki(ki1)iN

    where djhw(Ni) is the length of the shortest (weighted) path between nodes j and h, which contains only neighbours of i, and is estimated on the corresponding weighted adjacency matrix W (Latora and Marchiori, 2001; Rubinov and Sporns, 2010).

Global features

Request a detailed protocol
  • Characteristic path length (bin). Average shortest (geodesic) path length between all pairs of nodes in the graph:

    L=1niNjidijn1

    where dij is the shortest (geodesic) path length between nodes i and j, and n is the number of nodes in the graph (Watts and Strogatz, 1998; Rubinov and Sporns, 2010).

  • Characteristic path length (wei). Average shortest (weighted) path length between all pairs of nodes in the graph:

    Lw=1niNjidijwn1

    where dijw is the shortest (weighted) path length between nodes i and j, and n is the number of nodes in the graph (Watts and Strogatz, 1998; Rubinov and Sporns, 2010).

  • Transitivity (bin). Ratio between the observed number of closed triangles and the maximum possible number of closed triangles:

    T(A)=iNj,hNaijaihajhiNki(ki1)=iN(A3)iiiNki(ki1)

    where A is the binary adjacency matrix, and ki is the degree of node i (Newman, 2003; Rubinov and Sporns, 2010).

  • Transitivity (wei). Ratio between the ‘intensity’ of observed closed triangles and the maximum possible ‘intensity’ of closed triangles:

    T(W)=iNj,hNwij13wih13wjh13ki(ki1)=iN(W[13])ii3iNki(ki1)

    where W is the weighted connectivity matrix, and ki is the degree of node i (Newman, 2003; Rubinov and Sporns, 2010).

  • Assortativity (bin). Correlation coefficient between the degree of a node and the mean degree of its neighbours:

    r=l1(i,j)Lkikj[l1(i,j)L12(ki+kj)]2l1(i,j)L12(ki2+kj2)[l1(i,j)L12(ki+kj)]2

    where l-1 is the inverse of the number of links in the graph, ki is the degree of node i, and L is the set of links in the graph (Newman, 2002; Rubinov and Sporns, 2010).

  • Assortativity (wei). Correlation coefficient between the weighted degree of a node and the mean weighted degree of its neighbour:

    rw=l1(i,j)Lwijsisj[l1(i,j)L12wij(si+sj)]2l1(i,j)L12wij(si2+sj2)[l1(i,j)L12wij(si+sj)]2

    where l-1 is the inverse of the number of links in the graph, si is the weighted degree of node i, and L is the set of links in the graph (Leung and Chau, 2007; Rubinov and Sporns, 2010).

Small-world organization

Request a detailed protocol

We use the index proposed in Humphries and Gurney, 2008 to measure connectomes’ small-worldness level. The index is given by

γ=CCrand
λ=LLrand
S=γλ

where L and C are the corresponding characteristic path length and clustering coefficient of each connectome, respectively, and Lrand and Crand are the corresponding average quantities for a set of 1000 randomly rewired graphs that preserve the degree sequence and distribution of the nodes (Maslov and Sneppen, 2002). A network is said to possess a small-world architecture if S1, that is, if it situates above the identity line in a γ vs. λ plot.

Multi-resolution community detection

Request a detailed protocol

We used the Louvain algorithm to determine the optimal community structure of connectomes (Blondel et al., 2008). Briefly, this algorithm extracts communities from large networks by optimizing a modularity score. Here, we use the Q-metric as the objective function (Blondel et al., 2008; Fortunato and Barthélemy, 2007):

Q(γ)=12mij[Aij-γkikj2m]δ(ci,cj)

where A corresponds to the adjacency matrix of the network, ki is the degree of node i, m is the sum of all connections in the graph, ci is the community affiliation of node i, and δ is the Kronecker delta function (i.e. δ(x,y)=1ifx=y,0otherwise). The size of the partition is controlled by a resolution parameter γ (higher γ values result in a larger number of modules). Because the Louvain method is a greedy algorithm, we first found multiple (250) optimal partitions at γ=1, and then we determined a single partition using a consensus clustering approach (Bassett et al., 2013).

Classification of edges

Connectomes’s connections were classified into different categories based on four criteria. The first criterion is based on the modular structure of the network and classifies connections depending on whether they link brain regions within the same module (i.e. intra-modular) or regions across different modules (i.e. inter-modular); the second criterion is whether connections link brain regions within the same hemisphere (i.e. intra-hemispheric connections) or across hemispheres (inter-hemispheric connections); the third criterion is based on the physical length of the connections (i.e. short-, medium-, and long-range connections); and the fourth criterion is based on the rich-club structure of the network (i.e. rich-club, feeder, and peripheral connections).

Inter- vs. intra-modular connections

Request a detailed protocol

To classify connections as being either inter- or intra-modular, a consensus clustering algorithm was applied on each connectome to determine a partition of the network into different modules (see ‘Community detection’). Once modules are identified, inter-modular connections correspond to those linking brain regions across different modules, whereas intra-modular connections correspond to those linking brain regions belonging to the same module.

Connection length

Request a detailed protocol

Euclidean distance between regions’ centres was used as a proxy for connection length. To subdivide connections into short-, medium-, and long-range, connection lengths were estimated as a percentage with respect to the maximum distance between regions. Short-range connections correspond to those that are less than or equal to 25% of the maximum distance; medium-range connections are above 25% but less than or equal to 75% of the maximum distance; and long-range connections are those above 75% of the maximum distance.

Rich-club vs. feeder vs. peripheral connections

Request a detailed protocol

To classify edges as being either rich-club, feeder, or peripheral connections, it is necessary to identify first the rich-club of hubs in the network, that is, the densely interconnected core of nodes that have a disproportionately high number of connections (van den Heuvel and Sporns, 2011; van den Heuvel et al., 2012). To do so, we compute the rich-club coefficients Φ(k) across a range of degree k of the unweighted (binary) connectomes. For binary networks, all nodes that show a degree k are removed from the network, and for the remaining set of nodes (i.e. a sub-graph), the rich-club coefficient is estimated as the ratio of connections present in the sub-graph, to the total number of possible connections that would be present if the resulting sub-graph was fully connected. Formally, the rich-club coefficient is given by Zhou and Mondragon, 2004; Colizza et al., 2006; McAuley et al., 2007

ϕ(k)=2E>kN>k(N>k1)

In random networks, such as the Erdős–Rényi model, nodes with a higher degree have a higher probability of being interconnected by chance alone, thus showing an increasing function of Φ(k). For this reason, the rich-club coefficient is typically normalized relative to a set of m comparable random networks of equal size and node degree sequence and distribution (Maslov and Sneppen, 2002; Colizza et al., 2006; McAuley et al., 2007). The normalized rich-club coefficient is then given by

ϕnorm(k)=ϕ(k)ϕrandom(k)

where ϕrandom corresponds to the average rich-club coefficient over the m random networks. In our particular case, m=1000. An increasing normalized coefficient Φnorm>1 over a range of k reflects the existence of rich-club organization.

To assess the statistical significance of rich-club organization, we used permutation testing (Bassett and Bullmore, 2009; van den Heuvel et al., 2010). Briefly, the population of the m random networks yields a null distribution of rich-club coefficients. For the range of k expressing rich-club organization (i.e. Φnorm>1), we tested whether ϕ(k) significantly exceeds Φrandom(k). Next, we identify the kth level at which the maximum significant Φnorm occurs. Nodes with a degree k are said to belong to the kth-core of the network. Next, we identify the hubs, that is, nodes whose degree is above the average degree of the network plus 1 standard deviation. Therefore, rich-club nodes were identified as those nodes that are both hubs and belong to the kth-core of the network.

Once rich-club nodes are identified, rich-club connections are defined as edges between rich-club nodes; feeder connections are edges connecting rich-club to non-rich-club nodes, and peripheral connections are edges between non-rich-club nodes.

Controlling for replicas

Request a detailed protocol

Because some of the species have multiple scans, this could bias the distribution of intra- and inter-order distances, which could be dominated by those species with a large number of replicas. To account for that, we randomly sample a single connectome per species, and we calculated inter-species distances. We repeated this procedure iteratively 10,000 times. The reported intra- and inter-order distance distributions correspond to the average distances across iterations.

Controlling for density

Request a detailed protocol

Some of the graph features used for the estimation of the topological distance are highly dependent on the density of the network. To regress out the effects of network density on a graph feature, a univariate linear and an exponential model are fitted using density as the explanatory variable, and each feature as response variable. That is,

Linear:y=ax+b
Exponential:y=aebx+c

where y represents network features and x corresponds to density. Network features are then replaced by the residuals of the model. The decision to fit either a linear, an exponential, or no model at all was based on the variance explained by the model or R2. The model with the largest R2 is selected. Only those features with R2>0.1 were controlled to account for density (features in Figure 3—figure supplement 5 with a regression line).

Code availability

Request a detailed protocol

All codes used for data analysis and figure generation are publicly available on GitHub (https://github.com/netneurolab/suarez_connectometaxonomy; Suarez, 2022 copy archived at swh:1:rev:0d8e98f65a51a77784b31ec3ca59176d9119d927) and are built on top of the following open-source Python packages: rnns (https://github.com/estefanysuarez/rnns.git; Suarez, 2021), Netneurotools (https://github.com/netneurolab/netneurotools; Markello et al., 2022), Numpy (Harris et al., 2020; van der Walt et al., 2011; Oliphant, 2006), Scipy (Virtanen et al., 2020), Pandas (McKinney, 2010), Scikit-learn (Pedregosa et al., 2011), bctpy (https://github.com/aestrivex/bctpy; Sporns et al., 2022; Rubinov and Sporns, 2010), Matplotlib (Hunter, 2007), and Seaborn (Waskom et al., 2016).

Data availability

The MaMI data set was originally collected and analyzed by Assaf and colleagues in Assaf et al., 2020. We have included the connectivity matrices used in this study in a public repository available at https://doi.org/10.5281/zenodo.7143143.

The following data sets were generated

References

    1. Burke JD
    (1968)
    A brief history of the taxonomy of mammals
    MCV/Q, Medical College of Virginia Quarterly 4:77–80.
  1. Conference
    1. Chung F
    (1996) Spectral graph theory. Fresno
    Proceedings of the American Mathematical Society.
    https://doi.org/10.1090/cbms/092
    1. Darwin C
    (1959)
    The pressures of population
    What’s New 67:2–5.
    1. Das KC
    (2004) The Laplacian spectrum of a graph
    Computers & Mathematics with Applications 48:715–724.
    https://doi.org/10.1016/j.camwa.2004.05.005
    1. Frahm HD
    2. Stephan H
    3. Stephan M
    (1982)
    Comparison of brain structure volumes in insectivora and primates. I. neocortex
    Journal Fur Hirnforschung 23:375–389.
    1. Grone R
    2. Merris R
    3. Sunder VS
    (1990) The laplacian spectrum of a graph
    SIAM Journal on Matrix Analysis and Applications 11:218–238.
    https://doi.org/10.1137/0611016
  2. Conference
    1. Leemans A
    2. Jeurissen B
    3. Sijbers J
    4. Jones D
    (2009)
    Exploredti: a graphical toolbox for processing, analyzing, and visualizing diffusion mr data
    In Proc Intl Soc Mag Reson Med.
  3. Thesis
    1. Newman MW
    (2001)
    Master’s thesis:The laplacian spectrum of graphs
    University of Manitoba.
  4. Book
    1. Oliphant TE
    (2006)
    A Guide to NumPy
    Trelgol Publishing USA.
    1. Pedregosa F
    2. Varoquaux G
    3. Gramfort A
    4. Michel V
    5. Thirion B
    6. Grisel O
    7. Blondel M
    8. Prettenhofer P
    9. Weiss R
    10. Dubourg V
    (2011)
    Scikit-learn: machine learning in python
    Journal of Machine Learning Research 12:2825–2830.
    1. Scannell JW
    2. Blakemore C
    3. Young MP
    (1995)
    Analysis of connectivity in the cat cerebral cortex
    The Journal of Neuroscience 15:1463–1483.
    1. Schlenska G
    (1974)
    Volumen-und oberflachenmessungen an gehirnen verschiedener saugetiere im vergleich zu einem errechneten modell
    Journal Fur Hirnforschung 15:401–408.
    1. White JG
    2. Southgate E
    3. Thomson JN
    4. Brenner S
    (1986) The structure of the nervous system of the nematode Caenorhabditis elegans
    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 314:1–340.
    https://doi.org/10.1098/rstb.1986.0056

Decision letter

  1. Chris I Baker
    Senior and Reviewing Editor; National Institute of Mental Health, National Institutes of Health, United States
  2. Saad Jbabdi
    Reviewer; University of Oxford, United Kingdom
  3. Katja Heuer
    Reviewer; Institut Pasteur, France

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "A connectomics-based taxonomy of mammals" for consideration by eLife, and sorry for the delay in getting the reviews back to you.

Your article has been reviewed by 3 peer reviewers, including Saad Jbabdi as Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Chris Baker as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Katja Heuer (Reviewer #3).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

The paper was well received. There are several technical comments that need addressing, but the main revision in my opinion concerns the need for more insight as to what is driving the similarities and differences in the connectomes. See the detailed comments below.

Please find below the detailed reviews. I have tried to merge the 3 reviewer comments into a single set of comments, so these might not be in a sensible order.

Reviewer Comments

Reviewers' (Recommendations for the authors):

I think the assumption of the use of the same 200 regions for all species should be shown to be justified.

I would have liked to have seen more detail on how the differences between species is really implemented on the brains, if even in a few species. For instance, some of the authors have published extensively on human specializations in connectivity. It would be nice to show that some of the differences they identified in that work fall under the 'local regional connectivity profile' features that are reported here.

Species names sometimes correspond to groups of species. Would it be possible to provide the exact species names? The dataset in Zenodo includes, for example, "Macaque" which I assume may be Rhesus macaques, but for example, "colobus" is a group of monkeys including several species and it would be good to know which species have been included here. Given the vast nature of the dataset, either the exact English names, or the binomial species names, or a csv file which could allow potential users of the data to make the correspondence could be great to facilitate reuse of the data.

On page 6 the authors conclude that given the larger connectome similarity within one taxonomic group, the organisation of the brain connectome may be under selection pressure. However, also the Brownian motion model of evolution – where phenotypes are assumed to vary randomly along the phylogenetic tree – can explain phenotypes that are more similar within one taxonomic group.

When the authors introduce that the "common space" approach was used, maybe they can add a short note on what this means in addition to just the reference?

Methods: in the abstract, the authors say the data was "collected using a single protocol on a single scanner." whereas in the methods section they refer to 2 scanners and 2 field strengths. It seems quite standard to acquire small animal brains on a scanner with higher field strength and different head coils, there is no problem, I just thought maybe there is no need for the 1-scanner statement in the abstract? If the authors wanted to keep something along these lines, it could take up the idea they mention of a "unified MRI protocol was implemented for all species" I guess.

On page 7, the authors say "Consistent with the notion that neural circuit evolution involves local circuit modifications to adapt to specific challenges [10], such as extreme environmental pressures [36, 79, 89], or to support specific behaviours." I would just suggest a slight reformulation, as evolution doesn't do modifications to adapt to challenges or to support behaviour, but rather involves random modifications, that then may have provided an advantage in facing certain challenges or supporting certain behaviours.

On page 8, the authors suggest a "This scaling results in less space for white matter connectivity with increasing brain size." I wonder how this result compares with the reports of a positive allometry of white matter. For example, Zhang and Sejnowski

(https://doi.org/10.1073/pnas.090504197) report a scaling of 1.23 for white matter and build a scaling model for justifying it. There are several references within that paper that go in the same direction. The positive scaling of WM seems also intuitively true, as mice have very little WM compared with monkeys or humans, for example.

Other Comments:

– Visualisation

Some comments / suggestions on the various visualisations in the paper:

– I think a 2D scatter plot using multidimensional scaling would nicely show the between and within order distances (I tried it on your data, it looks nice). This could be complemented by a hierarchical clustering diagram?

– I am not sure why you need to min-max rescale all the distances to be between 0 and 1. In addition, min-max measures are sensitive to outliers. If one group happens to have an outlier, that could drive the entire group to be at the extreme. Is this likely to be happening here? It would also be useful to know at what stage this rescaling is done?

– I don't think the histograms in figures 2 and 3 should be shown using kde smoothing. It hides the data, and actually does a disservice to the data (e.g. in Figure 2B, the diagonal values are visibly lower than the extra diagonal values, but when kde-smoothed the effect appears to be lower than it is).

Another thing about the spectral approach: the lowest eigenvalue is famously important in telling us something about how connected the network is in general (i.e. how easy it is to break it down). Did you see any differences between and within species/orders in this measure?

https://doi.org/10.7554/eLife.78635.sa1

Author response

Reviewers' (Recommendations for the authors):

I think the assumption of the use of the same 200 regions for all species should be shown to be justified.

Please see public review for our response to the comment.

I would have liked to have seen more detail on how the differences between species is really implemented on the brains, if even in a few species. For instance, some of the authors have published extensively on human specializations in connectivity. It would be nice to show that some of the differences they identified in that work fall under the 'local regional connectivity profile' features that are reported here.

We concur with the Reviewer. A similar point was mentioned in the review summary – please see our response on pages 3-6. For convenience, we reproduce the response here.

The parcellations used in this study are data-driven and are not based on anatomical delineations of the cortex, which makes it hard to compare local connectivity profiles across species. This choice was based on two considerations: (1) for most species there exist no MRI parcellations, (2) in the few cases where such parcellations exist, there is incomplete knowledge about which regions (homologues) correspond to one another across species. In the original manuscript, the approach that we took to address this question was to examine how the difference between intra- and inter-order topological distances changes when different sets of topological features are included to estimate inter-species distances, , finding that local features differentiate species across taxonomic orders better than global features.

Nevertheless, we agree with the Reviewers that it is important to further investigate the nature of these local phylogenetic changes with greater anatomical specificity. Inspired by the Reviewers’ suggestion, we focused on a hallmark feature of network connectivity: the relative connectivity profiles of frontal cortex and occipital cortex. Numerous studies have suggested that the organization of the frontal cortex is more highly differentiated across species, whereas the organization of the visual and somatosensory cortices is more conserved [1-3]. Specifically, we selected the 10% most anterior and 10% most posterior nodes in each connectome as a proxy for frontal and occipital cortex. We then compared the binary degree, clustering, betweenness centrality and closeness of the anterior and posterior nodes (i.e. the average local features of those nodes) to assess how frontal and occipital connectivity profiles reconfigure across phylogeny. Figure 3 - figure supplement 2 shows the anterior-posterior differences for the six mammalian orders studied. The key finding is that mammalian orders are largely differentiated by the balance of local connectivity in anterior versus posterior cortex (separate one-way ANOVAs were performed to compare the effect of taxonomic order on anterior-posterior differences in local: (i) degree: F(5)=20.29, p=2.57x10-16, (ii) clustering: F(5)=15.88, p=3.88x10-13, (iii) betweenness: F(5)=17.97, p=1.14x10-14 and (iv) closeness: F(5)=13.54, p=2.36x10-11). The greatest difference in connectivity of the frontal cortex compared to the occipital cortex is observed in the Carnivora and Chiroptera orders, whereas the lowest difference is observed in Rodentia.

Even though there are differences in the balance between anterior and posterior local connectivity across taxonomic orders, we can observe a systematic trend for brain regions that are more anterior, or pertaining to the frontal cortex, to be more central, participating in a larger proportion of communication pathways (i.e. have greater degree and betweenness), and to engage in long-range connections to multiple other systems, presumably allowing them to sample information from multiple sensory domains. Conversely, regions that are more posterior, corresponding to visual cortex in most mammals, tend to have a larger proportion of short-range connections with a more densely clustered architecture, contrary to the more central architecture of anterior regions. These trends can be observed in the exemplars shown in Figure 3 —figure supplement 2, panel b.

Here, by showing just one of the many different ways in which local connectivity has reconfigured across phylogeny, we set the stage to keep developing new methods and analytical tools to explore ways in which local connectivity reconfigures during evolution.

These findings were added as supplementary analysis in Figure 3 —figure supplement 2, and they were introduced in the main text as the last sentence of the first paragraph of the Architectural features differentiate species subsection of the Results section. The new sentence reads:

“An illustration of this principle is depicted in Figure 3 —figure supplement 2 showing that the relative local connectivity of frontal and occipital cortices changes across taxonomic orders [11, 58, 59].”

[11] Barrett, R. L., Dawson, M., Dyrby, T. B., Krug, K., Ptito, M., D'Arceuil, H., … and Catani, M. (2020). Differences in frontal network anatomy across primate species. Journal of Neuroscience, 40(10), 2094-2107.

[58] Krubitzer, L., and Kaas, J. (2005). The evolution of the neocortex in mammals: how is phenotypic diversity generated?. Current opinion in neurobiology, 15(4), 444-453.

[59] Krubitzer, L., and Kahn, D. M. (2003). Nature versus nurture revisited: an old idea with a new twist. Progress in neurobiology, 70(1), 33-52.

In addition, this suggestion from the Reviewers encouraged us to add further discussion and perspective on how future comparative studies can make progress in resolving homologies. The following new paragraph was added after the third paragraph of the Discussion section:

“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, facilitating fine-grained comparisons at the level of individual regions. These methods implement regional comparisons based on different data modalities including measures of cytoarchitecture [21, 46], receptor distribution [64], functional and structural connectivity fingerprints [74, 75, 91], patterns of gene expression [18, 120], and macro scale gradients of functional activation [26].”

[18] Beauchamp, A., Yee, Y., Darwin, B., Raznahan, A., Mars, R. B., and Lerch, J. P. (2022). Whole-brain comparison of rodent and human brains using spatial transcriptomics. bioRxiv.

[21] Bianchi, S., Stimpson, C. D., Bauernfeind, A. L., Schapiro, S. J., Baze, W. B., McArthur, M. J., … and Sherwood, C. C. (2013). Dendritic morphology of pyramidal neurons in the chimpanzee neocortex: regional specializations and comparison to humans. Cerebral cortex, 23(10), 2429-2436.

[26] Buckner, R. L., and Margulies, D. S. (2019). Macroscale cortical organization and a default-like apex transmodal network in the marmoset monkey. Nature communications, 10(1), 1-12.

[46] Garey, L. J. (Ed.). (1999). Brodmann's' localisation in the cerebral cortex'. World Scientific.

[64] Levant, B. (1998). Differential distribution of D3 dopamine receptors in the brains of several mammalian species. Brain research, 800(2), 269-274.

[74] Mars, R. B., Sotiropoulos, S. N., Passingham, R. E., Sallet, J., Verhagen, L., Khrapitchev, A. A., … and Jbabdi, S. (2018). Whole brain comparative anatomy using connectivity blueprints. eLife, 7, e35237.

[75] Mars, R. B., Verhagen, L., Gladwin, T. E., Neubert, F. X., Sallet, J., and Rushworth, M. F. (2016). Comparing brains by matching connectivity profiles. Neuroscience and Biobehavioral Reviews, 60, 90-97.

[91] Passingham, R. E., Stephan, K. E., and Kötter, R. (2002). The anatomical basis of functional localization in the cortex. Nature Reviews Neuroscience, 3(8), 606-616.

[120] Warrington, S., Thompson, E., Bastiani, M., Dubois, J., Baxter, L., Slater, R., … and Sotiropoulos, S. N. (2022). Concurrent mapping of brain ontogeny and phylogeny within a common connectivity space. bioRxiv.

References used in the response:

[1] Barrett, R. L., Dawson, M., Dyrby, T. B., Krug, K., Ptito, M., D'Arceuil, H., … and Catani, M. (2020). Differences in frontal network anatomy across primate species. Journal of Neuroscience, 40(10), 2094-2107.

[2] Krubitzer, L., and Kahn, D. M. (2003). Nature versus nurture revisited: an old idea with a new twist. Progress in neurobiology, 70(1), 33-52.

[3] Krubitzer, L., and Kaas, J. (2005). The evolution of the neocortex in mammals: how is phenotypic diversity generated?. Current opinion in neurobiology, 15(4), 444-453.

Species names sometimes correspond to groups of species. Would it be possible to provide the exact species names? The dataset in Zenodo includes, for example, "Macaque" which I assume may be Rhesus macaques, but for example, "colobus" is a group of monkeys including several species and it would be good to know which species have been included here. Given the vast nature of the dataset, either the exact English names, or the binomial species names, or a csv file which could allow potential users of the data to make the correspondence could be great to facilitate reuse of the data.

We agree with the Reviewer on this point and we have updated Figure 1 —figure supplement 1 in the main manuscript, as well as the file “info.csv” (in the public data repository of the manuscript – url:10.5281/zenodo.6376543) to include the specific species’ names.

On page 6 the authors conclude that given the larger connectome similarity within one taxonomic group, the organisation of the brain connectome may be under selection pressure. However, also the Brownian motion model of evolution – where phenotypes are assumed to vary randomly along the phylogenetic tree – can explain phenotypes that are more similar within one taxonomic group.

We added a sentence to suggest that there exist alternative explanations for the forces driving phenotype evolution based on the Brownian motion model of evolution. The new sentence reads:

“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 [29, 60, 126] for an alternative mechanism of trait evolution characterized by pure drift models based on Brownian motion), analogous to size, weight or color.”

[29] Butler, M. A., and King, A. A. (2004). Phylogenetic comparative analysis: a modeling approach for adaptive evolution. The American Naturalist, 164(6), 683-695.

[60] Lande, R. (1976). Natural selection and random genetic drift in phenotypic evolution. Evolution, 314-334.

[126] Wright, S. (1931). Evolution in Mendelian populations. Genetics, 16(2), 97.

When the authors introduce that the "common space" approach was used, maybe they can add a short note on what this means in addition to just the reference?

We followed the Reviewer’s advice and we have revised the paragraph to clarify what we mean by the term “common space”. The new paragraph reads:

“To identify brain connectivity differences across species, we need to be able to analyze 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 [72].”

[72] Mars, R. B., Jbabdi, S., and Rushworth, M. F. (2021). A common space approach to comparative neuroscience. Annual Review of Neuroscience, 44.

Methods: in the abstract, the authors say the data was "collected using a single protocol on a single scanner." whereas in the methods section they refer to 2 scanners and 2 field strengths. It seems quite standard to acquire small animal brains on a scanner with higher field strength and different head coils, there is no problem, I just thought maybe there is no need for the 1-scanner statement in the abstract? If the authors wanted to keep something along these lines, it could take up the idea they mention of a "unified MRI protocol was implemented for all species" I guess.

We agree with the Reviewer that there is no need for the 1-scanner statement in the Abstract. We have rephrased this sentence in the Abstract to read:

“We analyze the mammalian MRI (MaMI) data set, a database that encompasses high-resolution ex vivo structural and diffusion magnetic resonance imaging (MRI) scans of 124 species across 12 taxonomic orders and 5 superorders, collected using a unified MRI protocol.”

On page 7, the authors say "Consistent with the notion that neural circuit evolution involves local circuit modifications to adapt to specific challenges [10], such as extreme environmental pressures [36, 79, 89], or to support specific behaviours." I would just suggest a slight reformulation, as evolution doesn't do modifications to adapt to challenges or to support behaviour, but rather involves random modifications, that then may have provided an advantage in facing certain challenges or supporting certain behaviours.

We agree with the Reviewer that the way this sentence is written misrepresents the way evolution is thought to operate. We have revised the manuscript and the new sentence reads:

“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 behavioral adaptations, allowing them to face specific challenges [10], such as extreme environmental pressures [41, 90, 101], or to support specific behaviours, such as courtship [9, 39, 40, 56, 71, 88, 99, 129], social bonding [54, 55, 66, 124], or foraging [89, 117].”

[9] Barkan, C. L., Kelley, D. B., and Zornik, E. (2018). Premotor neuron divergence reflects vocal evolution. Journal of Neuroscience, 38(23), 5325-5337.

[10] Barker, A. J. (2021). Brains and speciation: Control of behavior. Current Opinion in Neurobiology, 71, 158-163.

[39] Ding, Y., Berrocal, A., Morita, T., Longden, K. D., and Stern, D. L. (2016). Natural courtship song variation caused by an intronic retroelement in an ion channel gene. Nature, 536(7616), 329-332.

[40] Ding, Y., Lillvis, J. L., Cande, J., Berman, G. J., Arthur, B. J., Long, X., … and Stern, D. L. (2019). Neural evolution of context-dependent fly song. Current biology, 29(7), 1089-1099.

[41] Eigenbrod, O., Debus, K. Y., Reznick, J., Bennett, N. C., Sánchez-Carranza, O., Omerbašić, D., … and Lewin, G. R. (2019). Rapid molecular evolution of pain insensitivity in multiple African rodents. Science, 364(6443), 852-859.

[54] Insel, T. R., and Shapiro, L. E. (1992). Oxytocin receptor distribution reflects social organization in monogamous and polygamous voles. Proceedings of the National Academy of Sciences, 89(13), 5981-5985.

[55] Jaggard, J. B., Lloyd, E., Yuiska, A., Patch, A., Fily, Y., Kowalko, J. E., … and Keene, A. C. (2020). Cavefish brain atlases reveal functional and anatomical convergence across independently evolved populations. Science advances, 6(38), eaba3126.

[56] Khallaf, M. A., Auer, T. O., Grabe, V., Depetris-Chauvin, A., Ammagarahalli, B., Zhang, D. D., … and Knaden, M. (2020). Mate discrimination among subspecies through a conserved olfactory pathway. Science advances, 6(25), eaba5279.

[66] Loomis, C., Peuß, R., Jaggard, J. B., Wang, Y., McKinney, S. A., Raftopoulos, S. C., … and Duboue, E. R. (2019). An adult brain atlas reveals broad neuroanatomical changes in independently evolved populations of Mexican cavefish. Frontiers in neuroanatomy, 13, 88.

[71] Markow, T. A., and O'Grady, P. M. (2005). Evolutionary genetics of reproductive behavior in Drosophila: connecting the dots. Annual review of genetics, 39(1), 263-291.

[88] O’Grady, P. M., and DeSalle, R. (2018). Phylogeny of the genus Drosophila. Genetics, 209(1), 1-25.

[89] Pantoja, C., Larsch, J., Laurell, E., Marquart, G., Kunst, M., and Baier, H. (2020). Rapid effects of selection on brain-wide activity and behavior. Current Biology, 30(18), 3647-3656.

[90] Park, T. J., Lu, Y., Jüttner, R., Smith, E. S. J., Hu, J., Brand, A., … and Lewin, G. R. (2008). Selective inflammatory pain insensitivity in the African naked mole-rat (Heterocephalus glaber). PLoS biology, 6(1), e13.

[99] Seeholzer, L. F., Seppo, M., Stern, D. L., and Ruta, V. (2018). Evolution of a central neural circuit underlies Drosophila mate preferences. Nature, 559(7715), 564-569.

[101] Smith, E. S. J., Omerbašić, D., Lechner, S. G., Anirudhan, G., Lapatsina, L., and Lewin, G. R. (2011). The molecular basis of acid insensitivity in the African naked mole-rat. Science, 334(6062), 1557-1560.

[117] Vanwalleghem, G. C., Ahrens, M. B., and Scott, E. K. (2018). Integrative whole-brain neuroscience in larval zebrafish. Current opinion in neurobiology, 50, 136-145.

[124] Winslow, J. T., Hastings, N., Carter, C. S., Harbaugh, C. R., and Insel, T. R. (1993). A role for central vasopressin in pair bonding in monogamous prairie voles. Nature, 365(6446), 545-548.

[129] York, R. A., Byrne, A., Abdilleh, K., Patil, C., Streelman, T., Finger, T. E., and Fernald, R. D. (2019). Behavioral evolution contributes to hindbrain diversification among Lake Malawi cichlid fish. Scientific reports, 9(1), 1-9.

On page 8, the authors suggest a "This scaling results in less space for white matter connectivity with increasing brain size." I wonder how this result compares with the reports of a positive allometry of white matter. For example, Zhang and Sejnowski

(https://doi.org/10.1073/pnas.090504197) report a scaling of 1.23 for white matter, and build a scaling model for justifying it. There are several references within that paper that go in the same direction. The positive scaling of WM seems also intuitively true, as mice have very little WM compared with monkeys or humans, for example.

The sentence cited here by the Reviewer makes reference to the negative allometric scaling results reported in [1] between cortical surface area and the total volume devoted to cerebral white matter. In contrast, the observed positive allometric scaling exponents reported in [2] and other studies [3-5] are between white matter volume and gray matter volume. These results are complementary rather than contradictory.

To maintain connectivity,arger brains require longer fibers to communicate between distant cortical areas. This is supported by the positive allometric scaling reported in [2] between white matter volume and gray matter volume, which indicates that the volume of the white matter that comprises long axons increases disproportionately faster compared to the volume of the gray matter corresponding to cell bodies, dendrites and axons for local information processing. On the other hand, the negative allometric scaling reported in [1] between cortical surface area and white matter volume suggests that, while the proportion of total volume devoted to cerebral white matter is higher in larger brains, it does not keep pace with the rapid cortical expansion that occurs with larger brain size. Therefore, as it is mentioned in the sentence cited by the Reviewer, there is less and less space available for white matter connectivity with increasing brain size, which translates into larger brains with a relatively higher proportion of short-range connections than long-range connections when compared with smaller brains. Synthesizing these results, we conclude that, despite the higher proportion -in comparison to gray matter-, white matter volume is relatively smaller in larger brains compared to smaller brains.

To avoid confusion, we have revised the manuscript and added the following sentence to clarify that these results are not in contradiction with previous studies. We:

“These results, however, do not contradict studies showing a positive allometric scaling between white matter and gray matter volume [44, 97, 109, 131]; while the proportion of total volume devoted to cerebral white matter is higher in larger brains, it does not keep pace with the rapid cortical expansion that occurs with larger brain size.”

[44] Frahm, H. D., Stephan, H., and Stephan, M. (1982). Comparison of brain structure volumes in Insectivora and Primates. I. Neocortex. Journal fur Hirnforschung, 23(4), 375-389.

[97] Schlenska, G. (1974). Volumen-und oberflachenmessungen an gehirnen verschiedener saugetiere im vergleich zu einem errechneten modell.

[109] Theunissen, B. (1989). The Debate. In Eugène Dubois and the Ape-Man from Java (pp. 79-127). Springer, Dordrecht.

[131] Zhang, K., and Sejnowski, T. J. (2000). A universal scaling law between gray matter and white matter of cerebral cortex. Proceedings of the National Academy of Sciences, 97(10), 5621-5626.

References used in the response:

[1] Ardesch, D. J., Scholtens, L. H., De Lange, S. C., Roumazeilles, L., Khrapitchev, A. A., Preuss, T. M., … and Van Den Heuvel, M. P. (2022). Scaling principles of white matter connectivity in the human and nonhuman primate brain. Cerebral Cortex, 32(13), 2831-2842.

[2] Zhang, K., and Sejnowski, T. J. (2000). A universal scaling law between gray matter and white matter of cerebral cortex. Proceedings of the National Academy of Sciences, 97(10), 5621-5626.

[3] Theunissen, B. (1989). The Debate. In Eugène Dubois and the Ape-Man from Java (pp. 79-127). Springer, Dordrecht.

[4] Schlenska, G. (1974). Volumen-und oberflachenmessungen an gehirnen verschiedener saugetiere im vergleich zu einem errechneten modell.

[5] Frahm, H. D., Stephan, H., and Stephan, M. (1982). Comparison of brain structure volumes in Insectivora and Primates. I. Neocortex. Journal fur Hirnforschung, 23(4), 375-389.

Other Comments:

– Visualisation

Some comments / suggestions on the various visualisations in the paper:

– I think a 2D scatter plot using multidimensional scaling would nicely show the between and within order distances (I tried it on your data, it looks nice). This could be complemented by a hierarchical clustering diagram?

We appreciate the Reviewer’s suggestion, and we have added as supplementary figures the 2D scatter plots using multidimensional scaling (Figure 3 —figure supplement 7), as well as the hierarchical clustering diagrams (Figure 3 —figure supplement 8). For the 2D projection of the data, we used all data samples including replicas, whereas for the hierarchical clustering algorithm we used the average inter-species distance matrix obtained after randomly resampling one sample per species.

We added a new paragraph at the end of the Architectural features differentiate species subsection of the Results section to introduce these new supplementary analyses. The new paragraph reads:

“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 (a) project the data on a 2D plane using multidimensional scaling (Figure 3 —figure supplement 7), and (b) 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, center vs right column, respectively, in Figure 3 —figure supplement 7 and Figure 3 —figure supplement 8), as well as binary features compared to weighted features (ignoring panel a, center vs bottom row, respectively, in Figure 3 —figure supplement 7 and Figure 3 —figure supplement 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.”

– I am not sure why you need to min-max rescale all the distances to be between 0 and 1. In addition, min-max measures are sensitive to outliers. If one group happens to have an outlier, that could drive the entire group to be at the extreme. Is this likely to be happening here? It would also be useful to know at what stage this rescaling is done?

Min-max scaling was applied at the last stage for visualization purposes, since scaling distance values between 0 and 1 facilitates the interpretation of the results. Because min-max scaling is linear, relative distances between (inter-species distance) values are preserved after scaling and hence neither the results are altered in any way, nor are they sensitive to outliers. Author response image 1 shows the same results as in Figure 2 without applying min-max scaling. Because interpreting and comparing values is easier when the minimum and maximum reference values are 0 and 1, respectively, we decided to keep the scaling to show the results in the manuscript.

Author response image 1
Inter-species spectral distance without min-max scaling.

– I don't think the histograms in figures 2 and 3 should be shown using kde smoothing. It hides the data, and actually does a disservice to the data (e.g. in Figure 2B, the diagonal values are visibly lower than the extra diagonal values, but when kde-smoothed the effect appears to be lower than it is).

We followed the Reviewer’s suggestion to display the distributions in Figure 2 and Figure 3 as histograms, instead of using kde smoothing. Figure 2 and Figure 3 in the original manuscript were replaced by revised Figure 2 and Figure 3.

Another thing about the spectral approach: the lowest eigenvalue is famously important in telling us something about how connected the network is in general (i.e. how easy it is to break it down). Did you see any differences between and within species/orders in this measure?

It has been established theoretically that the smallest eigenvalue of the normalized Laplacian is necessarily zero regardless of the topology of the graph, i.e. λ1=0 [1]. We verified that for all samples that was actually the case. The second eigenvalue, however, indicates the connectedness of the graph. Specifically, if λ2>0, then the graph is connected [1]. Because connectomes represent brain networks, we would expect that this was the case for all animal species, otherwise this would suggest that there are disconnected or isolated brain regions. We examined the second eigenvalue of the Laplacian eigenspectra, and we found that for all species it is the case that λ2>0, except for four samples belonging to the taxonomic order Chiroptera, namely the Artibeus Jamacien, Myotis Emargenitus, Pkuhlii (3) and Tadarida Teniotis (1), which had 2/200, 5/200, 2/200 and 2/200 disconnected nodes, respectively. Despite the fact that these four connectomes have disconnected components, they were still included in the analyses but care was taken when estimating global topological features that might have been affected by this, such as shortest path length (wei) and shortest path length (bin) (by invoking function arguments that ignore infinite-length paths); local features are not significantly impacted since the average across the 200 nodes was used. Figure R5 shows the distribution of the second eigenvalue across taxonomic orders.

Author response image 2
Distribution of the second eigenvalue of the (normalized) Laplacian eigenspectrum across taxonomic orders.

References used in the response:

[1] Chung, F. R., and Graham, F. C. (1997). Spectral graph theory (Vol. 92). American Mathematical Soc..

https://doi.org/10.7554/eLife.78635.sa2

Article and author information

Author details

  1. Laura E Suarez

    1. Montréal Neurological Institute, McGill University, Montreal, Canada
    2. Mila - Quebec Artificial Intelligence Institute, Montreal, Canada
    Contribution
    Conceptualization, Software, Formal analysis, Investigation, Methodology, Writing – original draft
    For correspondence
    laura.suarez@mail.mcgill.ca
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0700-1500
  2. Yossi Yovel

    School of Neurobiology, Biochemistry and Biophysics, Tel Aviv University, Tel Aviv, Israel
    Contribution
    Data curation, Writing - review and editing
    Competing interests
    No competing interests declared
  3. Martijn P van den Heuvel

    Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
    Contribution
    Writing - review and editing
    Competing interests
    No competing interests declared
  4. Olaf Sporns

    Psychological and Brain Sciences, Indiana University, Bloomington, United States
    Contribution
    Writing - review and editing
    Competing interests
    No competing interests declared
  5. Yaniv Assaf

    School of Neurobiology, Biochemistry and Biophysics, Tel Aviv University, Tel Aviv, Israel
    Contribution
    Data curation, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6941-1535
  6. Guillaume Lajoie

    Mila - Quebec Artificial Intelligence Institute, Montreal, Canada
    Contribution
    Conceptualization, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  7. Bratislav Misic

    Montréal Neurological Institute, McGill University, Montreal, Canada
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Validation, Visualization, Writing – original draft, Project administration
    For correspondence
    bratislav.misic@mcgill.ca
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0307-2862

Funding

Natural Sciences and Engineering Research Council of Canada

  • Bratislav Misic
  • Guillaume Lajoie

Canadian Institutes of Health Research

  • Bratislav Misic

Fondation Brain Canada (Future Leaders Fund)

  • Bratislav Misic

Canada Research Chairs

  • Bratislav Misic
  • Guillaume Lajoie

Michael J. Fox Foundation for Parkinson's Research

  • Bratislav Misic

Healthy Brains for Healthy Lives

  • Bratislav Misic

Canadian Institute for Advanced Research

  • Guillaume Lajoie

National Science Foundation - BSF

  • Yaniv Assaf

National Institute of Mental Health (R01-MH122957)

  • Olaf Sporns

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

BM acknowledges support from the Natural Sciences and Engineering Research Council of Canada (NSERC), Canadian Institutes of Health Research (CIHR), Brain Canada Foundation Future Leaders Fund, the Canada Research Chairs (CRC) Program and the Healthy Brains for Healthy Lives (HBHL) initiative. GL acknowledges support from NSERC, the CRC Program, and the Canada CIFAR AI Cahir Program.

Senior and Reviewing Editor

  1. Chris I Baker, National Institute of Mental Health, National Institutes of Health, United States

Reviewers

  1. Saad Jbabdi, University of Oxford, United Kingdom
  2. Katja Heuer, Institut Pasteur, France

Publication history

  1. Preprint posted: March 12, 2022 (view preprint)
  2. Received: March 14, 2022
  3. Accepted: November 4, 2022
  4. Accepted Manuscript published: November 7, 2022 (version 1)
  5. Version of Record published: November 22, 2022 (version 2)

Copyright

© 2022, Suarez et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 365
    Page views
  • 102
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Laura E Suarez
  2. Yossi Yovel
  3. Martijn P van den Heuvel
  4. Olaf Sporns
  5. Yaniv Assaf
  6. Guillaume Lajoie
  7. Bratislav Misic
(2022)
A connectomics-based taxonomy of mammals
eLife 11:e78635.
https://doi.org/10.7554/eLife.78635
  1. Further reading

Further reading

    1. Neuroscience
    Maria Cecilia Martinez, Camila Lidia Zold ... Mariano Andrés Belluscio
    Research Article

    The automatic initiation of actions can be highly functional. But occasionally these actions cannot be withheld and are released at inappropriate times, impulsively. Striatal activity has been shown to participate in the timing of action sequence initiation and it has been linked to impulsivity. Using a self-initiated task, we trained adult male rats to withhold a rewarded action sequence until a waiting time interval has elapsed. By analyzing neuronal activity we show that the striatal response preceding the initiation of the learned sequence is strongly modulated by the time subjects wait before eliciting the sequence. Interestingly, the modulation is steeper in adolescent rats, which show a strong prevalence of impulsive responses compared to adults. We hypothesize this anticipatory striatal activity reflects the animals’ subjective reward expectation, based on the elapsed waiting time, while the steeper waiting modulation in adolescence reflects age-related differences in temporal discounting, internal urgency states, or explore–exploit balance.

    1. Computational and Systems Biology
    2. Neuroscience
    Sergio Oscar Verduzco-Flores, Erik De Schutter
    Research Article Updated

    How dynamic interactions between nervous system regions in mammals performs online motor control remains an unsolved problem. In this paper, we show that feedback control is a simple, yet powerful way to understand the neural dynamics of sensorimotor control. We make our case using a minimal model comprising spinal cord, sensory and motor cortex, coupled by long connections that are plastic. It succeeds in learning how to perform reaching movements of a planar arm with 6 muscles in several directions from scratch. The model satisfies biological plausibility constraints, like neural implementation, transmission delays, local synaptic learning and continuous online learning. Using differential Hebbian plasticity the model can go from motor babbling to reaching arbitrary targets in less than 10 min of in silico time. Moreover, independently of the learning mechanism, properly configured feedback control has many emergent properties: neural populations in motor cortex show directional tuning and oscillatory dynamics, the spinal cord creates convergent force fields that add linearly, and movements are ataxic (as in a motor system without a cerebellum).