An animal’s nervous system changes as its body grows from birth to adulthood and its behaviours mature1,2,3,4,5,6,7,8. The form and extent of circuit remodelling across the connectome is unknown3,9,10,11,12,13,14,15. Here we used serial-section electron microscopy to reconstruct the full brain of eight isogenic Caenorhabditis elegans individuals across postnatal stages to investigate how it changes with age. The overall geometry of the brain is preserved from birth to adulthood, but substantial changes in chemical synaptic connectivity emerge on this consistent scaffold. Comparing connectomes between individuals reveals substantial differences in connectivity that make each brain partly unique. Comparing connectomes across maturation reveals consistent wiring changes between different neurons. These changes alter the strength of existing connections and create new connections. Collective changes in the network alter information processing. During development, the central decision-making circuitry is maintained, whereas sensory and motor pathways substantially remodel. With age, the brain becomes progressively more feedforward and discernibly modular. Thus developmental connectomics reveals principles that underlie brain maturation.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
What if worms were sentient? Insights into subjective experience from the Caenorhabditis elegans connectome
Biology & Philosophy Open Access 23 August 2023
Nature Communications Open Access 15 July 2023
Nature Communications Open Access 19 April 2023
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
All scripts and files used to generate all figures are available at https://github.com/dwitvliet/nature2021.
Bucher, D., Prinz, A. A. & Marder, E. Animal-to-animal variability in motor pattern production in adults and during growth. J. Neurosci. 25, 1611–1619 (2005).
Kämper, G. & Murphey, R. Maturation of an insect nervous system: Constancy in the face of change. Comp. Biochem. Physiol. A 109, 23–32 (1994).
Gerhard, S., Andrade, I., Fetter, R. D., Cardona, A. & Schneider-Mizell, C. M. Conserved neural circuit structure across Drosophila larval development revealed by comparative connectomics. eLife 6, e29089 (2017).
Kagan, J., Herschkowitz, N. & Herschkowitz, E. C. A Young Mind in a Growing Brain (Lawrence Erlbaum, 2005).
Pujala, A. & Koyama, M. Chronology-based architecture of descending circuits that underlie the development of locomotor repertoire after birth. eLife 8, e42135 (2019).
Hebb, D. O. The Organization of Behavior: A Neuropsychological Theory (John Wiley & Sons, 1949).
Hubel, D. H. & Wiesel, T. N. Brain mechanisms of vision. Sci. Am. 241, 150–162 (1979).
Van Horn, M. R. & Ruthazer, E. S. Glial regulation of synapse maturation and stabilization in the developing nervous system. Curr. Opin. Neurobiol. 54, 113–119 (2019).
White, J. G., Southgate, E., Thomson, J. N. & Brenner, S. The structure of the nervous system of the nematode Caenorhabditis elegans. Phil. Trans. R. Soc. Lond. B 314, 1–340 (1986).
Scheffer, L. K. et al. A connectome and analysis of the adult Drosophila central brain. eLife 9, e57443 (2020).
Karimi, A., Odenthal, J., Drawitsch, F., Boergens, K. M. & Helmstaedter, M. Cell-type specific innervation of cortical pyramidal cells at their apical dendrites. eLife 9, e46876 (2020).
Morgan, J. L. & Lichtman, J. W. An individual interneuron participates in many kinds of inhibition and innervates much of the mouse visual thalamus. Neuron 106, 468–481.e2 (2020).
Randel, N. et al. Inter-individual stereotypy of the Platynereis larval visual connectome. eLife 4, e08069 (2015).
Kornfeld, J. et al. An anatomical substrate of credit assignment in reinforcement learning. Preprint at https://doi.org/10.1101/2020.02.18.954354 (2020).
Schneider-Mizell, C. M. et al. Chandelier cell anatomy and function reveal a variably distributed but common signal. preprint. Preprint at https://doi.org/10.1101/2020.03.31.018952 (2020).
Aicher, C., Jacobs, A. Z. & Clauset, A. Learning latent block structure in weighted networks. J. Complex Netw. 3, 221–248 (2015).
Jin, X., Pokala, N. & Bargmann, C. I. Distinct circuits for the formation and retrieval of an imprinted olfactory memory. Cell 164, 632–643 (2016).
White, J. G., Albertson, D. G. & Anness, M. A. Connectivity changes in a class of motoneurone during the development of a nematode. Nature 271, 764–766 (1978).
Bargmann, C. I. & Marder, E. From the connectome to brain function. Nat. Methods 10, 483–490 (2013).
Varshney, L. R., Chen, B. L., Paniagua, E., Hall, D. H. & Chklovskii, D. B. Structural properties of the Caenorhabditis elegans neuronal network. PLoS Comput. Biol. 7, e1001066 (2011).
Brenner, S. The genetics of Caenorhabditis elegans. Genetics 77, 71–94 (1974).
Mulcahy, B. et al. A pipeline for volume electron microscopy of the Caenorhabditis elegans nervous system. Front. Neural Circuits 12, 94 (2018).
Sulston, J. E. & Horvitz, H. R. Post-embryonic cell lineages of the nematode, Caenorhabditis elegans. Dev. Biol. 56, 110–156 (1977).
Baena, V., Schalek, R. L., Lichtman, J. W. & Terasaki, M. Serial-section electron microscopy using automated tape-collecting ultramicrotome (ATUM). Methods Cell Biol. 152, 41–67 (2019).
Hayworth, K. J. et al. Imaging ATUM ultrathin section libraries with WaferMapper: a multi-scale approach to EM reconstruction of neural circuits. Front. Neural Circuits 8, 68 (2014).
Cardona, A. et al. TrakEM2 software for neural circuit reconstruction. PLoS One 7, e38011 (2012).
Saalfeld, S., Fetter, R., Cardona, A. & Tomancak, P. Elastic volume reconstruction from series of ultra-thin microscopy sections. Nat. Methods 9, 717–720 (2012).
Saalfeld, S., Cardona, A., Hartenstein, V. & Tomancak, P. CATMAID: collaborative annotation toolkit for massive amounts of image data. Bioinformatics 25, 1984–1986 (2009).
Lim, M. A. et al. Neuroendocrine modulation sustains the C. elegans forward motor state. eLife 5, e19887 (2016).
Hung, W. L. et al. Attenuation of insulin signalling contributes to FSN-1-mediated regulation of synapse development. EMBO J. 32, 1745–1760 (2013).
Kasthuri, N. et al. Saturated reconstruction of a volume of neocortex. Cell 162, 648–661 (2015).
Meirovitch, Y. et al. A multi-pass approach to large-scale connectomics. Preprint at https://arxiv.org/abs/1612.02120 (2016).
Matveev, A. et al. A multicore path to connectomics-on-demand. In Proc. 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 267–281 (2017).
Meirovitch, Y. et al. Cross-classification clustering: an efficient multi-object tracking technique for 3-d instance segmentation in connectomics. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, 8425–8435 (2019).
Blumofe, R. D. et al. Cilk: An efficient multithreaded runtime system. J. Parallel Distrib. Comput. 37, 55–69 (1996).
Berger, D. R., Seung, H. S. & Lichtman, J. W. VAST (volume annotation and segmentation tool): efficient manual and semi-automatic labeling of large 3D image stacks. Front. Neural Circuits 12, 88 (2018).
Philbrook, A. et al. Neurexin directs partner-specific synaptic connectivity in C. elegans. eLife 7, e35692 (2018).
Sulston, J., Dew, M. & Brenner, S. Dopaminergic neurons in the nematode Caenorhabditis elegans. J. Comp. Neurol. 163, 215–226 (1975).
Duerr, J. S. et al. The cat-1 gene of Caenorhabditis elegans encodes a vesicular monoamine transporter required for specific monoamine-dependent behaviors. J. Neurosci. 19, 72–84 (1999).
Durbin, R. M. Studies on the Development and Organisation of the Nervous System of Caenorhabditis elegans (Univ. of Cambridge, 1987).
Cook, S. J. et al. Whole-animal connectomes of both Caenorhabditis elegans sexes. Nature 571, 63–71 (2019).
Faskowitz, J., Yan, X., Zuo, X.-N. & Sporns, O. Weighted stochastic block models of the human connectome across the life span. Sci. Rep. 8, 12997 (2018).
We thank V. Laskova for help with electron microscopy samples; B. Harris for help with high-pressure freezing; M. Neubauer, D. Kersen, A. Paulino, M. Suriyalaksh, A. Srajer, M. Chang, S. Ihn and J. Ho for help with imaging; A. Cardona, I. Arganda-Carreras and J. Qian for guidance on electron microscopy alignment; S. Cook, C. Rehaluk and M. Wang for synapse annotation in some datasets; J. Ho, C. Morii-Sciolla, I. So, M. and C.-Y. Ho for generating ground truth and proofreading for volumetric reconstruction; A. Matveev, L. Mi and H. Saribekyan for help with analysis algorithms; J. Wang and D. Cao for help with statistics; A. Lin, C. Tabone and V. Venkatachalam for help with data servers; S. Maeng and D. Fong for help with www.nemanode.org; members of our laboratories for comments; G. Si and L. Varshney for critical reading and suggestions; D. Hall, J. Srinivasan and A. Cardona for early advice. J.K.M. was supported by NSF PoLS (NSF 1806818). B.M. was supported by the Mount Sinai Foundation. J.W.L. was supported by the NIMH, Silvio Conte Center (P50 MH094271), the NIH (U24 NS109102-01) and MURI (GG0008784). J.W.L., A.D.T.S. and M.Z. were supported by the HFSP (RGP0051/2014). A.D.T.S. and M.Z. were supported by the NIH (R01-NS082525-01A1). A.D.T.S. was supported by NIH Brain Initiative (1U01NS111697-01) and NSF BRAIN EAGER (IOS-1452593). M.Z. was supported by CIHR (MOP-123250 and Foundation Scheme 154274), the Radcliffe Institute and the Mount Sinai Foundation.
The authors declare no competing interests.
Peer review information Nature thanks the anonymous reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Electron microscopy reconstruction of cells and synapses in C. elegans brains from birth to adulthood.
a, A representative electron microscopy micrograph of the neuropil (from dataset 3). Presynaptic termini of classical chemical synapses are characterized by a pool of clear synaptic vesicles (red arrows) surrounding an active zone (red arrowhead). Presynaptic termini of chemical synapses of modulatory neurons are characterized by mostly DCVs (orange arrows) distant from the active zone (orange arrowhead). Postsynaptic cells are marked by asterisks. The proportion of dense core and clear synaptic vesicles were not quantified. b, Neurites grow while maintaining overall brain geometry. Correlation of the relative neurite length of each branch between L1 (dataset 1) and adult (dataset 8). The length of each neurite is normalized against the total neurite length of the neuron. P = 9.4 × 10−172, r = 0.75, n = 947, Spearman’s rank correlation. c, Proportion of physical contacts in the brain that harbours at least one chemical synapse at respective developmental time points. d, Most connectivity asymmetry at birth is eliminated during L1. Connectivity asymmetry decreases from birth to adulthood, most significantly during L1. Asymmetry is defined as the coefficient of variation (CV) in synapse number between left-right cell pairs. Error bars indicate s.e.m. e, Total number of missing connections decreases from birth to adulthood, most significantly during L1. One connection refers to a cell making at least one chemical synapse to another cell. A missing connection is defined as a connection absent in only one dataset and from one side of the brain. f, Non-uniform distribution of connections and strengthening of connections across maturation. Distribution of the total number of input and output connections per neuron at birth. g, Non-uniform synapse addition to synaptic inputs and outputs of a cell. Top: neurons with higher number of connections at birth (dataset 1) are more likely to receive new synapses at existing input connections by adulthood (averaging datasets 7 and 8). Bottom: no correlation is observed at existing output connections. Each data point represents one cell. Significance is calculated using two-sided Spearman’s rank correlation (top: P = 1.1 × 10−5, n = 166; bottom: P = 0.017, n = 141). h, Top: neurons with higher number of connections at birth (dataset 1) are more likely to establish new input connections by adulthood (averaging datasets 7 and 8). Bottom: no correlation is observed at new output connections. Each data point represents one cell. Significance is calculated using two-sided Spearman’s rank correlation (top: P = 1.3 × 10−7, n = 166; bottom: P = 0.18, n = 141). i, Upper panels: neurons with more input connections at birth are more likely to strengthen these connections during maturation. Left: the number of input connections at birth (dataset 1) is positively correlated with their synapse number increase by adulthood (average of datasets 7 and 8). P = 1.6 × 10−17, n = 166 by the Spearman’s rank correlation. Right: the number of output connections at birth does not predict the synapse number increase at input connections by adulthood. P = 0.32, n = 120 by the Spearman’s rank correlation. Lower panels: Neither input connection (left) nor output connection (right) at birth predicts the synapse number increase at output connections by adulthood. left: P = 0.16, n = 120; right: P = 0.12, n = 141 by the two-sided Spearman’s rank correlation. Each point represents one cell. j, Upper panels: neurons with higher number of input connections (left) or output connections (right) at birth (dataset 1) are more likely to establish new input connections by adulthood (average of datasets 7 and 8). Left: P = 5.4 × 10−4, n = 166; right: P = 1.7 × 10−4, n = 120 by the Spearman’s rank correlation. Lower panels: Neither the input (left) or output (right) connection number at birth predicts the likelihood to establish new output connections by adulthood. Left: P = 1.00, n = 120; right: P = 0.08, n = 141 by the two-sided Spearman’s rank correlation. Each data point represents one cell. k, Relative number of synapses added to existing connections is correlated between outputs of the same cell compared to connections to and from different cells. Relative number of synapses added represents the fold increase of synapse number from birth (dataset 1) to adulthood (average of datasets 7 and 8). ns (not significant) P = 0.48, **P = 4.5 × 10−3, ***P = 4.9 × 10−5, two-sided Mann–Whitney U test, FDR adjusted using Benjamini–Hochberg correction (noutputs = 753, ninputs = 1203, nother = 90709). Centre line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range; outliers not shown. Top: each data point represents the mean coefficient of variation (CV) in the number of synapses for different sets of connections. The CV of output connections from the same cell is maintained. The CV of input connections to the same cell increases over time, at the same rate as connections to and from different cells. Error bars indicate s.e.m. Bottom: the difference between the mean CV for output and input connections relative to connections between different cells grows over time. *** P = 5.3 × 10−7, r = 0.99, two-sided Spearman’s rank correlation.
Wiring diagrams for an adult connectome (dataset 8). Each circle represents a cell. Circle colour denotes cell type. Each line represents a connection with at least one chemical synapse between two cells. Line width indicates synapse number. Straight lines direct information from sensory to muscle layers whereas curved lines direct information in reverse. Cell coordinates are represented as in Fig. 1b, with overlapping cells manually separated.
Extended Data Fig. 3 A physical contact matrix between neurites and muscle fibres in seven volumetrically reconstructed C. elegans brains.
Cells are pooled by left-right pairs. The physical contact size is represented by the largest value from the seven datasets. Statistical significance calculated by two-sided Spearman’s rank correlation.
a, 3D reconstructions of one neuron class (AIZL and AIZR) across maturation. The overall geometry was maintained, whereas the number of spine-like protrusions (grey arrows) increased over time. b, Proportion of postsynaptic spine-like protrusions increases across maturation. *** P = 6.5 × 10−5, two-sided Spearman’s rank correlation. c, Total number of spine-like protrusions in the brain increases across maturation. *** P = 5.3 × 10−7, two-sided Spearman’s rank correlation. d, Proportion of synapses with at least one spine-like protrusion postsynaptic partner increases across maturation. *** P = 1.8 × 10−4, two-sided Spearman’s rank correlation. e, Distribution of spine-like protrusions by location, with the entry of the neurite into the brain as the most proximal, and the exit or terminal end of the neurite the most distal. f, Number of spine-like protrusions that oppose a presynaptic terminal per neuron at birth (average of datasets 1 and 2) and in adulthood (average of datasets 7 and 8). g, Proportion of presynaptic inputs onto spine-like protrusions per neuron in adulthood (average of datasets 7 and 8), grouped by their cell type. h, Proportion of synapses with spine-like protrusions that comprise stable, variable, and developmentally dynamic connections. Developmentally dynamic connections have the highest proportion. *** (stable-dev. dynamic) P = 3.7 × 10−34, *** (variable-dev. dynamic) P = 5.1 × 10−25, two-tailed Z-test, FDR adjusted using Benjamini–Hochberg correction (nstable = 10,059, nvariable = 2,169, ndev.dynamic = 1,611).
Connectivity matrix including connections observed in eight C. elegans brains. Cells are pooled by left-right pairs. Each connection size represents the largest synapse number in any dataset. Stable, developmentally dynamic, and variable connections are colour-coded (Methods).
a, Composition of stable, developmentally dynamic, and variable connections in each dataset classified by synapse size. b, Prevalence of variable connections is not caused by over-annotation of ambiguous synapses. High proportions of both variable and non-variable (stable and developmentally dynamic) connections form at non-variable physical contacts. A physical contact is defined as variable when it is absent from more than one of the seven datasets. c, Synapses that constitute non-variable and variable connections, sorted by electron microscopy section numbers that the presynaptic active zone encompasses. All synapses in seven volumetrically segmented datasets are included. Synapses comprising variable connections are marginally smaller that those comprising non-variable connections, but no threshold can be set to remove exclusively the variable connections. d, Proportion of synapses that form a polyadic synapse with synapses of the stable connections. A marginally smaller portion of synapses that comprise variable connections (78%) than those comprising non-variable connections (93%) reside in this configuration. Therefore, variable connections are not fortuitous accidents of synapse annotation. e, Synapses comprising non-variable and variable connections sorted by the number of post-synaptic partners. They exhibit similar distributions from monoadic to polyadic. Non-variable connections have marginally more polyadic synapses than variable connections (20% vs 28% for dyadic, and 61% vs 54% for triadic synapses, respectively). No threshold by postsynaptic partner number can be set to filter variable connections. f, Proportion of postsynaptic contact area occupied by each postsynaptic partner at each synapse. Synapses comprising variable connections on average occupy less postsynaptic area than synapses comprising non-variable connections, but no threshold can be set to only exclude variable connections. g, Any threshold removes both variable and non-variable connections. Total number of non-variable (stable and developmentally dynamic) and variable connections in adulthood (average of datasets 7 and 8) upon thresholding by different synapse numbers. No synapse number provides a filter for specific removal of variable connections: all removes both variable and stable connections. h, Thresholding connections by synapse number leaves substantial proportion of variable connections for all cell types. Non-uniform distribution of variable connections remains when connections with low synapse numbers are removed. i, Non-uniform distribution of variable and developmentally dynamic connections. Wiring diagrams for variable, stable, and developmentally dynamic connections. Each line represents a connection observed in at least one dataset. Line width indicates the largest number of synapses observed for a connection across datasets. Each circle represents a cell. Cell coordinates are represented as in Fig. 1b. Comparison of the proportion of variable and non-variable connections for each cell type. Non-variable connections include stable and developmentally changing connections. Cell types with significantly higher or lower proportions of variable connections are indicated. Upper panel: *P (modulatory-inter) = 2.2 × 10−2, *P (modulatory-sensory) = 6.5 × 10−3, ***P (sensory-motor) = 4.7 × 10−8, ***P (modulatory-motor) = 5.2 × 10−8, ***P (inter-motor) = 1.7 × 10−7, lower panel: ***P (sensory-muscle) = 6.9 × 10−9, ***P (modulatory-muscle) = 1.3 × 10−7, ***P (inter-muscle) = 3.6 × 10−5, ***P (motor-muscle) = 8.1 × 10−7. n = 22-57, two-sided Mann–Whitney U test, FDR adjusted using Benjamini–Hochberg correction. Centre line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range; outliers not shown.
Extended Data Fig. 7 Stability of interneuron connections and strengthening of feedforward connections are revealed by assessing connection strength by synapse size.
a, Proportion of developmentally dynamic connections by cell type, when connection strength changes were evaluated by either synapse number (left) or synapse size (middle). Connections between interneurons are the most stable regardless of how synapse weight was evaluated. Right panel: Developmental stability of connections is not correlated with the extend of synapse number increase from birth (average of datasets 1 and 2) to adulthood (average of datasets 7 and 8). b, Increase in both feedforward signal flow and modularity across maturation. The number of synapses for stable connections in adults (datasets 7 and 8) relative to birth (datasets 1 and 2). Stable feedforward connections are strengthened more than stable feedback and recurrent connections. ns (not significant) P = 0.13, **P (feedforward-recurrent) = 0.0015, **P (feedforward-feedback) = 0.0012, two-sided Mann–Whitney U test, FDR adjusted using Benjamini–Hochberg correction (nfeedforward = 301, nrecurrent = 229, nfeedback = 107). Centre line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range; outliers not shown. c, Proportions of feedforward, feedback, and recurrent connections for stable and developmentally dynamic connections. **P (stable-strenghened) = 0.0015, **P (stable-weakened) = 0.0032, two-tailed Z-test of the proportion of feedforward connections, FDR adjusted using Benjamini–Hochberg correction (nstable = 737, nadded = 198, nweakened = 18).
a, The log-likelihood score for each WSBM model (Methods). b, Optimal number of modules detected by WSBM using subsets of connections.
Extended Data Fig. 9 Comparison of multiple adult connectomes reveals extensive variability in connectivity.
a, Shared and unique connections for three adult connectomes: dataset 7, dataset 8, and N2U (a) annotated by White et al. 1986 (ref. 40), illustrated in the Venn diagram. Connections of all synapse numbers are included for comparison (Methods). b, Re-annotation of N2U increased its variability. Re-annotation of the N2U adult connectome (ref. 41) added 1109 new connections that disproportionally enlarged its pool of unique connections (Methods). Only 16% contributed to connections shared by three connectomes. This suggests the use of different annotation criteria from the original annotation. c, Propensity of forming variable connections correlates with cell type. Comparison between the proportion of adult connectome-defined variable and non-variable connections for each cell type. Adult-defined non-variable connections include the connections that are present in both of our adult datasets as well as the original connectome annotated by White et al. 1986. Cell types with significantly higher or lower proportions of variable connections are denoted; upper panel: ***P (sensory-motor) = 5.1 × 10−5, ***P (modulatory-motor) = 1.7 × 10−6, ***P (inter-motor) = 7.5 × 10−5, lower panel: ***P (sensory-muscle) = 2.6 × 10−6, ***P (modulatory-muscle) = 9.9 × 10−9, ***P (inter-muscle) = 4.7 × 10−4, ***P (motor-muscle) = 2.6 × 10−6; two-sided Mann–Whitney U test, FDR adjusted using Benjamini–Hochberg correction. Centre line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range; outliers not shown. d, The low variability of connections from motor neurons to muscles cannot be simply explained by saturation of their physical contacts by synapses. Physical contacts are not saturated for connections for any cell type. Motor neurons, which have the lowest proportion of variable connections (Extended Data Fig. 6j), are not restricted by few available potential synaptic partners. Centre line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range; outliers not shown. e, Higher variability for certain cell types is not explained by a fixed probability of an erroneous connection by neurons with abundant synapse formation. Top: the number of synapses for stable output connections by cell types. Modulatory neurons, which exhibit a higher proportion of variable connections than other cell types (Extended Data Fig. 6j), do not exhibit more synapses per stable connection. Centre line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range; outliers not shown. Bottom: The number of variable connections formed by a cell does not correlate with the strength of its stable output connections. Each data point represents one cell. ns (not significant) P = 0.08, r = 0.15, n = 139, two-sided Spearman’s rank correlation coefficient. f, Top: the relative number of synapses added to existing stable output connections by cell types. Connections from modulatory neurons, which have a higher proportion of variable connections than other cell types (Extended Data Fig. 6j), do not exhibit higher increase in synapse number than connections from other cell types. Centre line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range; outliers not shown. Bottom: The number of variable connections formed by a cell does not correlate with the number of synapses added to existing stable output connections from birth to adulthood. The relative number of synapses added is quantified as the fold increase of synapse number from birth (dataset 1) to adulthood (averaged of datasets 7 and 8). Each data point represents one cell. ns (not significant) P = 0.56, r = 0.05, n = 139, two-sided Spearman’s rank correlation coefficient. For panels d–f, the synapse number for the adult brain (averaged of datasets 7 and 8) is shown.
This file contains extended discussions, supplementary figures s1 – s2 and supplementary references.
Synapse number connectivity matrices.
Synapse size connectivity matrices.
Physical contact matrices.
Synapses onto spine-like protrusions.
Fly-through of an adult EM dataset.
Volumetric reconstruction of an adult dataset.
Individual neurons across maturation.
Modules in the adult brain.
About this article
Cite this article
Witvliet, D., Mulcahy, B., Mitchell, J.K. et al. Connectomes across development reveal principles of brain maturation. Nature 596, 257–261 (2021). https://doi.org/10.1038/s41586-021-03778-8
This article is cited by
Nature Communications (2023)
Nature Methods (2023)
Nature Reviews Neuroscience (2023)
Nature Communications (2023)
What if worms were sentient? Insights into subjective experience from the Caenorhabditis elegans connectome
Biology & Philosophy (2023)