Nagano, T., Lubling, Y., Stevens, T.J., Schoenfelder, S., Yaffe, E., Dean, W., Laue, E.D., Tanay, A., and Fraser, P. (2013). Nature 502, 59–64. Sander, J.D., and Joung, J.K. (2014). Nat. Biotechnol. 32, 347–355.
Tischler, J., and Surani, M.A. (2013). Curr. Opin. Biotechnol. 24, 69–78. Wu, A.R., Neff, N.F., Kalisky, T., Dalerba, P., Treutlein, B., Rothenberg, M.E., Mburu, F.M., Mantalas, G.L., Sim, S., Clarke, M.F., and Quake, S.R. (2014). Nat. Methods 11, 41–46.
Yan, L.Y., Yang, M.Y., Guo, H.S., Yang, L., Wu, J., Li, R., Liu, P., Lian, Y., Zheng, X.Y., Yan, J., et al. (2013). Nat. Struct. Mol. Biol. 20, 1131–1139. Zong, C.H., Lu, S.J., Chapman, A.R., and Xie, X.S. (2012). Science 338, 1622–1626.
From Connections to Function: The Mouse Brain Connectome Atlas Olaf Sporns1,* and Edward T. Bullmore2,3 1Department
of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA of Psychiatry, Behavioural & Clinical Neuroscience Institute, University of Cambridge, Herchel Smith Building for Brain and Mind Sciences, Robinson Way, Cambridge CB2 0SZ, UK 3GlaxoSmithKline, ImmunoPsychiatry, Alternative Discovery & Development, Stevenage SG1 2NY, UK *Correspondence: [email protected] http://dx.doi.org/10.1016/j.cell.2014.04.023 2Department
Mapping synaptic connections and projections is crucial for understanding brain dynamics and function. In a recent issue of Nature, Oh et al. present a wiring diagram of the whole mouse brain, where standardized labeling, tracing, and imaging of axonal connections reveal new details in the network organization of neuronal connectivity. Nearly 30 years ago a landmark paper reporting the complete pattern of synaptic connectivity among individual neurons of C. elegans, the first and only cell-level network map of any organism’s nervous system (White et al., 1986), stated that ‘‘the functional properties of a nervous system are largely determined by the characteristics of its component neurons and the pattern of synaptic connections between them.’’ This simple sentiment is echoed by a new paper by Hongkui Zeng and colleagues in a recent issue of Nature (Oh et al., 2014), describing a new connectivity map of the mouse brain, which represents an important step toward understanding mammalian brain organization. A comprehensive map of neuronal connectivity—the connectome (Sporns et al., 2005)—is fundamental for understanding not only the anatomical structure of any given nervous system, but also the functional specialization of neurons or brain regions and their arrangement in clusters and communities. The brain’s
wiring diagram, or anatomical connectome, moreover, also provides an important constraint on the possible repertoire of dynamic interactions between populations of neurons that comprise the brain’s spontaneous and evoked activity. This central role of the connectome for understanding brain function has sparked concerted connectivity mapping efforts in a number of species, ranging from invertebrates to the mammalian brain, including that of humans. Oh et al. target their mapping effort at the brain of one of the most important model organisms: the mouse (see also Zingg et al., 2014). They capture shortand long-range interareal and cell-typespecific connections at the mesoscale level, which is a resolution that is intermediate between microscale (singleneuron reconstruction) and macroscale (whole-brain imaging) approaches. The resulting resource, the Allen Mouse Brain Connectivity Atlas, is made freely and publicly available, thus enabling future reanalysis and cumulative refinement.
Connectivity data were acquired using a standardized approach that is applied uniformly across the brain, with injections of an anterograde tracer followed by imaging of axonal fibers using a serial two-photon tomography system. Optical signals were placed into a threedimensional reference model of the mouse brain, thus allowing aggregation of connectivity data across hundreds of injection sites. Important steps along the way include rejecting poor-quality optical images, evaluating sensitivity in capturing both strong and weak axonal projections, and testing reproducibility across repeat injections and across animals. An important caveat of this approach is the difficulty in distinguishing between optical signals from labeled axonal terminals versus those arising from passing fibers. Only the former should be used to derive area-to-area connection weights, whereas the latter add unwanted noise to these estimates. Other methodological issues concern the sizes of injection sites relative to
Cell 157, May 8, 2014 ª2014 Elsevier Inc. 773
Figure 1. Placing the Mouse Connectome among Various Canonical Network Models (A) Schematic diagram of clustering and node degree in a small model network comprising 12 nodes (black circles) and 16 connections (blue lines). The degree corresponds to the number of connections attached at each node, and the clustering coefficient captures how many connected neighbors are also connected among each other. (B) A landscape of network models, defined by the principal dimensions of node degree and clustering coefficient. Regarding degree, ‘‘no hubs’’ refers to networks in which node degrees are largely uniform (e.g., Gaussian), whereas ‘‘hubs’’ refers to networks in which the distribution of node degrees is highly skewed or ‘‘heavy tailed’’ (e.g., in scale-free networks). Regarding clustering coefficient, ‘‘high’’ and ‘‘low’’ refer to networks with, on average, high and low clustering. WS, ER, and BA refer to canonical ‘‘WattsStrogatz’’ (small world), ‘‘Erdo¨s-Re´nyi’’ (random) and ‘‘Baraba´si-Albert’’ (scale-free) network models, respectively.
gray matter regions and the need to perform computational optimization to derive probabilistic estimates for areato-area connection densities. As the authors freely acknowledge, these and other limitations of their approach require additional steps to refine data acquisition and analysis that will need to be incorporated in future work. 295 gray-matter structures covering the entire volume of the mouse brain have been analyzed, and the end result of this effort is a connection matrix representing weighted projection strengths—a function of the number of axons connecting these brain areas. Oh et al. report several important findings. First, the strengths of projections range over at least five orders of magnitude, with a small number of strong projections interspersed among a much larger number of moderate or weak pathways. Although false positives may account for a proportion of the weakest pathways detected in this study, the broad range of connection strengths and its log-normal profile fit with earlier observations in macaque (Markov et al., 2014). Second, detailed analysis of projections in cortico-striatal and cortico-thalamic circuits reveal a topographically precise mapping of cortical projections to subnuclei of
the striatum and thalamus, highlighting the importance of spatial relationships in the organization of the connectome: projections from nearby regions of cortex terminate close to each other in subcortical target regions. Another important observation is that ipsilateral and contralateral projections mirror each other, the latter with an overall weaker strength profile. This suggests that connection profiles of areas, reflecting specific arrangements of circuits and functional specialization, extend across both brain hemispheres. So far, comprehensive maps of interhemispheric connections have been almost entirely lacking. A powerful set of tools for analyzing the topology of connectome data comes from network science and graph theory (Bullmore and Sporns, 2009). Neurons and their synaptic links can be represented as sets of nodes and connections, and measures such as node clustering and the degree to which nodes connect among themselves have proven useful for characterizing neuronal connection patterns (Figure 1). Oh et al. perform a first and thus far rather preliminary topological analysis of the mouse connectome and provide evidence for the existence of high node clustering as well as the presence of hub nodes in the network.
774 Cell 157, May 8, 2014 ª2014 Elsevier Inc.
This combination of clusters and hubs places the mouse brain somewhere between canonical network models representing ‘‘small-world’’ and ‘‘scale-free’’ architectures (Figure 1); these models have been widely studied and are thought to have implications for how different parts of the brain exchange signals. Small-world networks combine high clustering with short path length, which may allow efficient cross-network communication, whereas scale-free networks contain a small number of highly connected hub nodes, which may serve as central way stations for signal traffic. In this context, it is useful to distinguish between ‘‘classic’’ generative models for small-world networks based on random rewiring of lattices (Watts and Strogatz, 1998) and more general descriptive accounts based on the coexistence of high clustering and short path length. Though the former model does not allow for the occurrence of hub nodes (and indeed seems an unlikely candidate for a generative model of brain networks), the latter is compatible with the emergence of hubs as centrally placed connectors that crosslink highly clustered network modules and thus ensure efficient communication—a type of architecture that has been encountered in previous connectome studies. Future analyses that decompose the mouse connectome into structural network communities and more precisely identify highly connected and highly central network hubs are sure to follow soon. In addition, these data also provide new opportunities to understand the contributions of weak connections and the trade-offs between spatial constraints and topological properties (Bullmore and Sporns, 2012). So far, it appears that the mouse connectome exhibits some nonrandom topological features that are consistent with those seen in other species, including C. elegans (e.g., Varshney et al., 2011), Drosophila (e.g., Chiang et al., 2011), cat, and monkey, as well as the human brain. This paper stands out among other connectome mapping efforts for several reasons. First, despite some methodological limitations, the development of a systematically applied mapping approach at the level of cell types and interareal axonal projections paves the way for
similar studies in genetically modified mouse strains or other mammalian species. Second, targeting brain connectivity at the mesoscale avoids the stringent methodological requirements that currently limit microscale studies to small subregions of circuits while overcoming the severe resolution limits and the challenging neurobiological interpretation of macroscale connectomes derived from neuroimaging data. Third, the map generated by the authors charts the brain of one of the most widely used model organisms and will have many important applications for understanding the connectional basis of neural processing, patterns of brain growth and development, and clinical disorders. Fourth, the greater availability of connectome data on multiple species in multiple modalities (microscopy, tract tracing, neuroimaging) will accelerate experimental testing
of the hypothesis that many organizational principles of connectomes are highly conserved across species and invariant across micro, meso, and macro scales. ACKNOWLEDGMENTS
Markov, N.T., Ercsey-Ravasz, M.M., Ribeiro Gomes, A.R., Lamy, C., Magrou, L., Vezoli, J., Misery, P., Falchier, A., Quilodran, R., Gariel, M.A., et al. (2014). Cereb. Cortex 24, 17–36. Oh, S.W., Harris, J.A., Ng, L., Winslow, B., Cain, N., Mihalas, S., Wang, Q., Lau, C., Kuan, L., Henry, A.M., et al. (2014). Nature 508, 207–214.
This work was supported by the James S McDonnell Foundation (O.S.). The Behavioural & Clinical Neuroscience Institute (E.T.B.) is supported by the Wellcome Trust and Medical Research Council.
Sporns, O., Tononi, G., and Ko¨tter, R. (2005). PLoS Comput. Biol. 1, e42.
REFERENCES
Watts, D.J., and Strogatz, S.H. (1998). Nature 393, 440–442.
Bullmore, E., and Sporns, O. (2009). Nat. Rev. Neurosci. 10, 186–198. Bullmore, E., and Sporns, O. (2012). Nat. Rev. Neurosci. 13, 336–349. Chiang, A.S., Lin, C.Y., Chuang, C.C., Chang, H.M., Hsieh, C.H., Yeh, C.W., Shih, C.T., Wu, J.J., Wang, G.T., Chen, Y.C., et al. (2011). Curr. Biol. 21, 1–11.
Varshney, L.R., Chen, B.L., Paniagua, E., Hall, D.H., and Chklovskii, D.B. (2011). PLoS Comput. Biol. 7, e1001066.
White, J.G., Southgate, E., Thomson, J.N., and Brenner, S. (1986). Philos. Trans. R. Soc. Lond. B Biol. Sci. 314, 1–340. Zingg, B., Hintiryan, H., Gou, L., Song, M.Y., Bay, M., Bienkowski, M.S., Foster, N.N., Yamashita, S., Bowman, I., Toga, A.W., and Dong, H.W. (2014). Cell 156, 1096–1111.
Cell 157, May 8, 2014 ª2014 Elsevier Inc. 775
Tischler, J., and Surani, M.A. (2013). Curr. Opin. Biotechnol. 24, 69–78. Wu, A.R., Neff, N.F., Kalisky, T., Dalerba, P., Treutlein, B., Rothenberg, M.E., Mburu, F.M., Mantalas, G.L., Sim, S., Clarke, M.F., and Quake, S.R. (2014). Nat. Methods 11, 41–46.
Yan, L.Y., Yang, M.Y., Guo, H.S., Yang, L., Wu, J., Li, R., Liu, P., Lian, Y., Zheng, X.Y., Yan, J., et al. (2013). Nat. Struct. Mol. Biol. 20, 1131–1139. Zong, C.H., Lu, S.J., Chapman, A.R., and Xie, X.S. (2012). Science 338, 1622–1626.
From Connections to Function: The Mouse Brain Connectome Atlas Olaf Sporns1,* and Edward T. Bullmore2,3 1Department
of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA of Psychiatry, Behavioural & Clinical Neuroscience Institute, University of Cambridge, Herchel Smith Building for Brain and Mind Sciences, Robinson Way, Cambridge CB2 0SZ, UK 3GlaxoSmithKline, ImmunoPsychiatry, Alternative Discovery & Development, Stevenage SG1 2NY, UK *Correspondence: [email protected] http://dx.doi.org/10.1016/j.cell.2014.04.023 2Department
Mapping synaptic connections and projections is crucial for understanding brain dynamics and function. In a recent issue of Nature, Oh et al. present a wiring diagram of the whole mouse brain, where standardized labeling, tracing, and imaging of axonal connections reveal new details in the network organization of neuronal connectivity. Nearly 30 years ago a landmark paper reporting the complete pattern of synaptic connectivity among individual neurons of C. elegans, the first and only cell-level network map of any organism’s nervous system (White et al., 1986), stated that ‘‘the functional properties of a nervous system are largely determined by the characteristics of its component neurons and the pattern of synaptic connections between them.’’ This simple sentiment is echoed by a new paper by Hongkui Zeng and colleagues in a recent issue of Nature (Oh et al., 2014), describing a new connectivity map of the mouse brain, which represents an important step toward understanding mammalian brain organization. A comprehensive map of neuronal connectivity—the connectome (Sporns et al., 2005)—is fundamental for understanding not only the anatomical structure of any given nervous system, but also the functional specialization of neurons or brain regions and their arrangement in clusters and communities. The brain’s
wiring diagram, or anatomical connectome, moreover, also provides an important constraint on the possible repertoire of dynamic interactions between populations of neurons that comprise the brain’s spontaneous and evoked activity. This central role of the connectome for understanding brain function has sparked concerted connectivity mapping efforts in a number of species, ranging from invertebrates to the mammalian brain, including that of humans. Oh et al. target their mapping effort at the brain of one of the most important model organisms: the mouse (see also Zingg et al., 2014). They capture shortand long-range interareal and cell-typespecific connections at the mesoscale level, which is a resolution that is intermediate between microscale (singleneuron reconstruction) and macroscale (whole-brain imaging) approaches. The resulting resource, the Allen Mouse Brain Connectivity Atlas, is made freely and publicly available, thus enabling future reanalysis and cumulative refinement.
Connectivity data were acquired using a standardized approach that is applied uniformly across the brain, with injections of an anterograde tracer followed by imaging of axonal fibers using a serial two-photon tomography system. Optical signals were placed into a threedimensional reference model of the mouse brain, thus allowing aggregation of connectivity data across hundreds of injection sites. Important steps along the way include rejecting poor-quality optical images, evaluating sensitivity in capturing both strong and weak axonal projections, and testing reproducibility across repeat injections and across animals. An important caveat of this approach is the difficulty in distinguishing between optical signals from labeled axonal terminals versus those arising from passing fibers. Only the former should be used to derive area-to-area connection weights, whereas the latter add unwanted noise to these estimates. Other methodological issues concern the sizes of injection sites relative to
Cell 157, May 8, 2014 ª2014 Elsevier Inc. 773
Figure 1. Placing the Mouse Connectome among Various Canonical Network Models (A) Schematic diagram of clustering and node degree in a small model network comprising 12 nodes (black circles) and 16 connections (blue lines). The degree corresponds to the number of connections attached at each node, and the clustering coefficient captures how many connected neighbors are also connected among each other. (B) A landscape of network models, defined by the principal dimensions of node degree and clustering coefficient. Regarding degree, ‘‘no hubs’’ refers to networks in which node degrees are largely uniform (e.g., Gaussian), whereas ‘‘hubs’’ refers to networks in which the distribution of node degrees is highly skewed or ‘‘heavy tailed’’ (e.g., in scale-free networks). Regarding clustering coefficient, ‘‘high’’ and ‘‘low’’ refer to networks with, on average, high and low clustering. WS, ER, and BA refer to canonical ‘‘WattsStrogatz’’ (small world), ‘‘Erdo¨s-Re´nyi’’ (random) and ‘‘Baraba´si-Albert’’ (scale-free) network models, respectively.
gray matter regions and the need to perform computational optimization to derive probabilistic estimates for areato-area connection densities. As the authors freely acknowledge, these and other limitations of their approach require additional steps to refine data acquisition and analysis that will need to be incorporated in future work. 295 gray-matter structures covering the entire volume of the mouse brain have been analyzed, and the end result of this effort is a connection matrix representing weighted projection strengths—a function of the number of axons connecting these brain areas. Oh et al. report several important findings. First, the strengths of projections range over at least five orders of magnitude, with a small number of strong projections interspersed among a much larger number of moderate or weak pathways. Although false positives may account for a proportion of the weakest pathways detected in this study, the broad range of connection strengths and its log-normal profile fit with earlier observations in macaque (Markov et al., 2014). Second, detailed analysis of projections in cortico-striatal and cortico-thalamic circuits reveal a topographically precise mapping of cortical projections to subnuclei of
the striatum and thalamus, highlighting the importance of spatial relationships in the organization of the connectome: projections from nearby regions of cortex terminate close to each other in subcortical target regions. Another important observation is that ipsilateral and contralateral projections mirror each other, the latter with an overall weaker strength profile. This suggests that connection profiles of areas, reflecting specific arrangements of circuits and functional specialization, extend across both brain hemispheres. So far, comprehensive maps of interhemispheric connections have been almost entirely lacking. A powerful set of tools for analyzing the topology of connectome data comes from network science and graph theory (Bullmore and Sporns, 2009). Neurons and their synaptic links can be represented as sets of nodes and connections, and measures such as node clustering and the degree to which nodes connect among themselves have proven useful for characterizing neuronal connection patterns (Figure 1). Oh et al. perform a first and thus far rather preliminary topological analysis of the mouse connectome and provide evidence for the existence of high node clustering as well as the presence of hub nodes in the network.
774 Cell 157, May 8, 2014 ª2014 Elsevier Inc.
This combination of clusters and hubs places the mouse brain somewhere between canonical network models representing ‘‘small-world’’ and ‘‘scale-free’’ architectures (Figure 1); these models have been widely studied and are thought to have implications for how different parts of the brain exchange signals. Small-world networks combine high clustering with short path length, which may allow efficient cross-network communication, whereas scale-free networks contain a small number of highly connected hub nodes, which may serve as central way stations for signal traffic. In this context, it is useful to distinguish between ‘‘classic’’ generative models for small-world networks based on random rewiring of lattices (Watts and Strogatz, 1998) and more general descriptive accounts based on the coexistence of high clustering and short path length. Though the former model does not allow for the occurrence of hub nodes (and indeed seems an unlikely candidate for a generative model of brain networks), the latter is compatible with the emergence of hubs as centrally placed connectors that crosslink highly clustered network modules and thus ensure efficient communication—a type of architecture that has been encountered in previous connectome studies. Future analyses that decompose the mouse connectome into structural network communities and more precisely identify highly connected and highly central network hubs are sure to follow soon. In addition, these data also provide new opportunities to understand the contributions of weak connections and the trade-offs between spatial constraints and topological properties (Bullmore and Sporns, 2012). So far, it appears that the mouse connectome exhibits some nonrandom topological features that are consistent with those seen in other species, including C. elegans (e.g., Varshney et al., 2011), Drosophila (e.g., Chiang et al., 2011), cat, and monkey, as well as the human brain. This paper stands out among other connectome mapping efforts for several reasons. First, despite some methodological limitations, the development of a systematically applied mapping approach at the level of cell types and interareal axonal projections paves the way for
similar studies in genetically modified mouse strains or other mammalian species. Second, targeting brain connectivity at the mesoscale avoids the stringent methodological requirements that currently limit microscale studies to small subregions of circuits while overcoming the severe resolution limits and the challenging neurobiological interpretation of macroscale connectomes derived from neuroimaging data. Third, the map generated by the authors charts the brain of one of the most widely used model organisms and will have many important applications for understanding the connectional basis of neural processing, patterns of brain growth and development, and clinical disorders. Fourth, the greater availability of connectome data on multiple species in multiple modalities (microscopy, tract tracing, neuroimaging) will accelerate experimental testing
of the hypothesis that many organizational principles of connectomes are highly conserved across species and invariant across micro, meso, and macro scales. ACKNOWLEDGMENTS
Markov, N.T., Ercsey-Ravasz, M.M., Ribeiro Gomes, A.R., Lamy, C., Magrou, L., Vezoli, J., Misery, P., Falchier, A., Quilodran, R., Gariel, M.A., et al. (2014). Cereb. Cortex 24, 17–36. Oh, S.W., Harris, J.A., Ng, L., Winslow, B., Cain, N., Mihalas, S., Wang, Q., Lau, C., Kuan, L., Henry, A.M., et al. (2014). Nature 508, 207–214.
This work was supported by the James S McDonnell Foundation (O.S.). The Behavioural & Clinical Neuroscience Institute (E.T.B.) is supported by the Wellcome Trust and Medical Research Council.
Sporns, O., Tononi, G., and Ko¨tter, R. (2005). PLoS Comput. Biol. 1, e42.
REFERENCES
Watts, D.J., and Strogatz, S.H. (1998). Nature 393, 440–442.
Bullmore, E., and Sporns, O. (2009). Nat. Rev. Neurosci. 10, 186–198. Bullmore, E., and Sporns, O. (2012). Nat. Rev. Neurosci. 13, 336–349. Chiang, A.S., Lin, C.Y., Chuang, C.C., Chang, H.M., Hsieh, C.H., Yeh, C.W., Shih, C.T., Wu, J.J., Wang, G.T., Chen, Y.C., et al. (2011). Curr. Biol. 21, 1–11.
Varshney, L.R., Chen, B.L., Paniagua, E., Hall, D.H., and Chklovskii, D.B. (2011). PLoS Comput. Biol. 7, e1001066.
White, J.G., Southgate, E., Thomson, J.N., and Brenner, S. (1986). Philos. Trans. R. Soc. Lond. B Biol. Sci. 314, 1–340. Zingg, B., Hintiryan, H., Gou, L., Song, M.Y., Bay, M., Bienkowski, M.S., Foster, N.N., Yamashita, S., Bowman, I., Toga, A.W., and Dong, H.W. (2014). Cell 156, 1096–1111.
Cell 157, May 8, 2014 ª2014 Elsevier Inc. 775
