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Tim Blakely

Tim Blakely

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A connectomic resource for neural cataloguing and circuit dissection of the larval zebrafish brain
Mariela Petkova
Michał Januszewski
Tim Blakely
Kristian J. Herrera
Gregor Schuhknecht
Robert Tiller
Jinhan Choi
Richard L. Schalek
Jonathan Boulanger-Weill
Adi Peleg
Yuelong Wu
Shuohong Wang
Jakob Troidl
Submit Kumar Vohra
Donglai Wei
Stuart Berg
Christopher Knecht
Geoffrey W Meissner
Wyatt Korff
Misha Ahrens
Viren Jain
Jeff W. Lichtman
Florian Engert
bioRxiv (2025)
Preview abstract We present a correlated light and electron microscopy (CLEM) dataset from a 7-day-old larval zebrafish, integrating confocal imaging of genetically labeled excitatory (vglut2a) and inhibitory (gad1b) neurons with nanometer-resolution serial section EM. The dataset spans the brain and anterior spinal cord, capturing >180,000 segmented soma, >40,000 molecularly annotated neurons, and 30 million synapses, most of which were classified as excitatory, inhibitory, or modulatory. To characterize the directional flow of activity across the brain, we leverage the synaptic and cell body annotations to compute region-wise input and output drive indices at single cell resolution. We illustrate the dataset’s utility by dissecting and validating circuits in three distinct systems: water flow direction encoding in the lateral line, recurrent excitation and contralateral inhibition in a hindbrain motion integrator, and functionally relevant targeted long-range projections from a tegmental excitatory nucleus, demonstrating that this resource enables rigorous hypothesis testing as well as exploratory-driven circuit analysis. The dataset is integrated into an open-access platform optimized to facilitate community reconstruction and discovery efforts throughout the larval zebrafish brain. (Full author list included with the paper.) View details
A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution
Alex Shapson-Coe
Michal Januszewski
Daniel R. Berger
Art Pope
Yuelong Wu
Tim Blakely
Richard L. Schalek
Peter Hawley Li
Shuohong Wang
Jeremy Maitin-Shepard
Neha Karlupia
Sven Dorkenwald
Evelina Sjostedt
Laramie J Leavitt
Dongil Lee
Luke Bailey
Angerica Fitzmaurice
Rohin Kar
Benjamin Field
Hank Wu
Julian Wagner-Carena
David Aley
Joanna Lau
Zudi Lin
Donglai Wei
Hanspeter Pfister
Adi Peleg
Viren Jain
Jeff W. Lichtman
Science (2024)
Preview abstract To fully understand how the human brain works, knowledge of its structure at high resolution is needed. Presented here is a computationally intensive reconstruction of the ultrastructure of a cubic millimeter of human temporal cortex that was surgically removed to gain access to an underlying epileptic focus. It contains about 57,000 cells, about 230 millimeters of blood vessels, and about 150 million synapses and comprises 1.4 petabytes. Our analysis showed that glia outnumber neurons 2:1, oligodendrocytes were the most common cell, deep layer excitatory neurons could be classified on the basis of dendritic orientation, and among thousands of weak connections to each neuron, there exist rare powerful axonal inputs of up to 50 synapses. Further studies using this resource may bring valuable insights into the mysteries of the human brain. View details
A connectome and analysis of the adult Drosophila central brain
Louis K Scheffer
C Shan Xu
Michal Januszewski
Zhiyuan Lu
Shin-ya Takemura
Kenneth J Hayworth
Gary B Huang
Kazunori Shinomiya
Jeremy Maitlin-Shepard
Stuart Berg
Jody Clements
Philip M Hubbard
William T Katz
Lowell Umayam
Ting Zhao
David Ackerman
Tim Blakely
John Bogovic
Tom Dolafi
Dagmar Kainmueller
Takashi Kawase
Khaled A Khairy
Laramie Leavitt
Peter H Li
Larry Lindsey
Nicole Neubarth
Donald J Olbris
Hideo Otsuna
Eric T Trautman
Masayoshi Ito
Alexander S Bates
Jens Goldammer
Tanya Wolff
Robert Svirskas
Philipp Schlegel
Erika Neace
Christopher J Knecht
Chelsea X Alvarado
Dennis A Bailey
Samantha Ballinger
Jolanta A Borycz
Brandon S Canino
Natasha Cheatham
Michael Cook
Marisa Dreher
Octave Duclos
Bryon Eubanks
Kelli Fairbanks
Samantha Finley
Nora Forknall
Audrey Francis
Gary Patrick Hopkins
Emily M Joyce
SungJin Kim
Nicole A Kirk
Julie Kovalyak
Shirley Lauchie
Alanna Lohff
Charli Maldonado
Emily A Manley
Sari McLin
Caroline Mooney
Miatta Ndama
Omotara Ogundeyi
Nneoma Okeoma
Christopher Ordish
Nicholas Padilla
Christopher M Patrick
Tyler Paterson
Elliott E Phillips
Emily M Phillips
Neha Rampally
Caitlin Ribeiro
Madelaine K Robertson
Jon Thomson Rymer
Sean M Ryan
Megan Sammons
Anne K Scott
Ashley L Scott
Aya Shinomiya
Claire Smith
Kelsey Smith
Natalie L Smith
Margaret A Sobeski
Alia Suleiman
Jackie Swift
Satoko Takemura
Iris Talebi
Dorota Tarnogorska
Emily Tenshaw
Temour Tokhi
John J Walsh
Tansy Yang
Jane Anne Horne
Feng Li
Ruchi Parekh
Patricia K Rivlin
Vivek Jayaraman
Marta Costa
Gregory SXE Jefferis
Kei Ito
Stephan Saalfeld
Reed George
Ian Meinertzhagen
Gerald M Rubin
Harald F Hess
Viren Jain
Stephen M Plaza
eLife, 9 (2020)
Preview abstract The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly's brain. View details
Automated Reconstruction of a Serial-Section EM Drosophila Brain with Flood-Filling Networks and Local Realignment
Peter Li
Larry Lindsey
Michal Januszewski
Zhihao Zheng
Alexander Shakeel Bates
István Taisz
Mike Tyka
Matthew Nichols
Feng Li
Eric Perlman
Jeremy Maitin-Shepard
Tim Blakely
Laramie J Leavitt
Gregory S.X.E. Jefferis
Davi Bock
Viren Jain
bioRxiv (2019)
Preview abstract Reconstruction of neural circuitry at single-synapse resolution is an attractive target for improving understanding of the nervous system in health and disease. Serial section transmission electron microscopy (ssTEM) is among the most prolific imaging methods employed in pursuit of such reconstructions. We demonstrate how Flood-Filling Networks (FFNs) can be used to computationally segment a forty-teravoxel whole-brain Drosophila ssTEM volume. To compensate for data irregularities and imperfect global alignment, FFNs were combined with procedures that locally re-align serial sections as well as dynamically adjust and synthesize image content. The proposed approach produced a largely merger-free segmentation of the entire ssTEM Drosophila brain, which we make freely available. As compared to manual tracing using an efficient skeletonization strategy, the segmentation enabled circuit reconstruction and analysis workflows that were an order of magnitude faster. View details
High-Precision Automated Reconstruction of Neurons with Flood-Filling Networks
Michał Januszewski
Jörgen Kornfeld
Peter H. Li
Art Pope
Tim Blakely
Larry Lindsey
Jeremy B Maitin-Shepard
Mike Tyka
Winfried Denk
Viren Jain
Nature Methods (2018)
Preview abstract Reconstruction of neural circuits from volume electron microscopy data requires the tracing of cells in their entirety, including all their neurites. Automated approaches have been developed for tracing, but their error rates are too high to generate reliable circuit diagrams without extensive human proofreading. We present flood-filling networks, a method for automated segmentation that, similar to most previous efforts, uses convolutional neural networks, but contains in addition a recurrent pathway that allows the iterative optimization and extension of individual neuronal processes. We used flood-filling networks to trace neurons in a dataset obtained by serial block-face electron microscopy of a zebra finch brain. Using our method, we achieved a mean error-free neurite path length of 1.1 mm, and we observed only four mergers in a test set with a path length of 97 mm. The performance of flood-filling networks was an order of magnitude better than that of previous approaches applied to this dataset, although with substantially increased computational costs. View details
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