TY - JOUR
T1 - Saturated Reconstruction of a Volume of Neocortex
AU - Kasthuri, Narayanan
AU - Hayworth, Kenneth Jeffrey
AU - Berger, Daniel Raimund
AU - Schalek, Richard Lee
AU - Conchello, José Angel
AU - Knowles-Barley, Seymour
AU - Lee, Dongil
AU - Vázquez-Reina, Amelio
AU - Kaynig, Verena
AU - Jones, Thouis Raymond
AU - Roberts, Mike
AU - Morgan, Josh Lyskowski
AU - Tapia, Juan Carlos
AU - Seung, H. Sebastian
AU - Roncal, William Gray
AU - Vogelstein, Joshua Tzvi
AU - Burns, Randal
AU - Sussman, Daniel Lewis
AU - Priebe, Carey Eldin
AU - Pfister, Hanspeter
AU - Lichtman, Jeff William
N1 - Funding Information:
We gratefully acknowledged support from the NIH/NINDS (1DP2OD006514-01, TR01 1R01NS076467-01, and 1U01NS090449-01), Conte (1P50MH094271-01), MURI Army Research Office (contract no. W911NF1210594 and IIS-1447786), CRCNS 1R01EB016411), NSF (OIA-1125087 and IIS-1110955), DARPA (HR0011-14-2-0004), the Human Frontier Science Program (RGP0051/2014), the JHU Applied Physics Laboratory, the Research Program for Applied Neuroscience, the Howard Hughes Medical Institute, Nvidia, Intel, and Google. We are also grateful to the Center for the Developing Child at Harvard University for providing tracers, Masconomet Regional High School, and Priya Manavalan and Kunal Lillaney at the Open Connectome Project for help with data management.
Publisher Copyright:
© 2015 Elsevier Inc.
PY - 2015/8/1
Y1 - 2015/8/1
N2 - We describe automated technologies to probe the structure of neural tissue at nanometer resolution and use them to generate a saturated reconstruction of a sub-volume of mouse neocortex in which all cellular objects (axons, dendrites, and glia) and many sub-cellular components (synapses, synaptic vesicles, spines, spine apparati, postsynaptic densities, and mitochondria) are rendered and itemized in a database. We explore these data to study physical properties of brain tissue. For example, by tracing the trajectories of all excitatory axons and noting their juxtapositions, both synaptic and non-synaptic, with every dendritic spine we refute the idea that physical proximity is sufficient to predict synaptic connectivity (the so-called Peters' rule). This online minable database provides general access to the intrinsic complexity of the neocortex and enables further data-driven inquiries.
AB - We describe automated technologies to probe the structure of neural tissue at nanometer resolution and use them to generate a saturated reconstruction of a sub-volume of mouse neocortex in which all cellular objects (axons, dendrites, and glia) and many sub-cellular components (synapses, synaptic vesicles, spines, spine apparati, postsynaptic densities, and mitochondria) are rendered and itemized in a database. We explore these data to study physical properties of brain tissue. For example, by tracing the trajectories of all excitatory axons and noting their juxtapositions, both synaptic and non-synaptic, with every dendritic spine we refute the idea that physical proximity is sufficient to predict synaptic connectivity (the so-called Peters' rule). This online minable database provides general access to the intrinsic complexity of the neocortex and enables further data-driven inquiries.
UR - http://www.scopus.com/inward/record.url?scp=84938233335&partnerID=8YFLogxK
U2 - 10.1016/j.cell.2015.06.054
DO - 10.1016/j.cell.2015.06.054
M3 - Article
C2 - 26232230
AN - SCOPUS:84938233335
SN - 0092-8674
VL - 162
SP - 648
EP - 661
JO - Cell
JF - Cell
IS - 3
ER -