Blog
The latest from Google Research
Releasing the Drosophila Hemibrain Connectome — The Largest Synapse-Resolution Map of Brain Connectivity
Wednesday, January 22, 2020
Posted by Michal Januszewski, Software Engineer and Viren Jain, Research Scientist and Technical Lead, Connectomics at Google
A fundamental way to describe a complex system is to measure its “network” — the way individual parts connect and communicate with each other. For example, biologists study
gene networks
, social scientists study
social networks
and even search engines rely, in part, on
analyzing
the way web pages form a network by linking to one another.
In neuroscience, a long-standing hypothesis is that the connectivity between brain cells plays a major role in the function of the brain. While technical difficulties have historically been a barrier for neuroscientists trying to study brain networks in detail, this is beginning to change. Last year, we
announced
the first nanometer-resolution automated reconstruction of an entire fruit fly brain, which focused on the individual shape of the cells. However, this accomplishment didn't reveal information about their
connectivity
.
Today,
in collaboration
with the
FlyEM
team at HHMI’s
Janelia Research Campus
and several other research partners, we are releasing the “hemibrain” connectome, a highly detailed map of neuronal connectivity in the fly brain, along with a suite of tools for visualization and analysis. The hemibrain is derived from a 3D image of roughly half the fly brain, and contains verified connectivity between ~25,000 neurons that form more than twenty-million connections. To date, this is the largest synapse-resolution map of brain connectivity that has ever been produced, in any species. The goal of this project has been to produce a public resource that any scientist can use to advance their own work, similar to the fly genome, which was
released
twenty years ago and has become a fundamental tool in biology.
Fly brain regions contained within the hemibrain connectome. Also available:
interactive version
(example region:
mushroom body
).
Imaging, Reconstructing, and Proofreading the Hemibrain Connectome
Over a decade of research and development from numerous research partners was required to overcome the challenges in producing the hemibrain connectome. At Janelia, new methods were developed to
stain
the fly brain and then
divide
the tissue into separate 20-micron thick slabs. Each slab was then imaged at 8x8x8nm
3
voxel-resolution using
focused ion beam scanning electron microscopes
customized for months-long continuous operation. Computational methods were developed to stitch and align the raw data into a coherent 26-trillion pixel 3D volume.
However, without an accurate 3D reconstruction of the neurons in a fly brain, producing a connectome from this type of imaging data is impossible. Forming a collaboration with Janelia in 2014, Google began working on the fly brain data, focused on automating 3D reconstruction to jointly work towards producing a connectome. After several
iterations
of technological development we devised a method called
flood-filling networks
(FFNs) and applied it towards reconstructing the entire hemibrain dataset. In the current project, we worked closely with our collaborators to optimize the reconstruction results to be more useful for generating a connectome (i.e., embedded within a proofreading and synaptic detection pipeline), rather than just showing the shape of the neurons.
A
flood-filling network
segmenting (tracing) part of a neuron in the fly hemibrain data.
FFNs were the first automated segmentation technology to yield reconstructions that were sufficiently accurate to enable the overall hemibrain project to proceed. This is because errors in automated reconstruction require correction by expert human “proofreaders,” and previous approaches were estimated to require tens of
millions
of hours of human effort. With FFNs, the hemibrain was proofread using hundreds of thousands of human hours: a two order-of-magnitude improvement. This (still substantial) proofreading effort was performed over two years by a
team
of highly skilled and dedicated annotators, using tools and workflows pioneered at Janelia for this purpose. For example, annotators used VR headsets and custom 3D object-editing tools to examine neuron shapes and fix errors in the automated reconstruction. These revisions were then used to retrain the FFN models, leading to revised and more accurate machine output.
Finally, after proofreading, the reconstruction was combined with
automated synaptic detection
in order to produce the hemibrain connectome. Janelia scientists manually labeled individual synapses and then trained neural network classifiers to automate the task. Generalization was improved through multiple rounds of labeling, and the results from two different network architectures were merged to produce robust classifications across the hemibrain.
Further details about producing the hemibrain can be found in
HHMI’s press release
.
What Is Being Released?
The focus of today’s announcement is a set of inter-related datasets and tools that enable any interested person to visually and programmatically study the fly connectome. Specifically, the following resources are available:
Terabytes of raw data, proofread 3D reconstruction, and synaptic annotations can be interactively
visualized
or
downloaded
in bulk.
A web-based tool
neuPrint
, which can be used to query the connectivity, partners, connection strengths and morphologies of any specified neurons.
A
downloadable
, compact representation of the connectome that is roughly a million-times smaller in bytes than the raw data from which it was derived.
Documentation and video tutorials
explaining
the use of these resources.
A
pre-print
with further details related to the production and analysis of the hemibrain connectome.
Next Steps
Researchers have begun using the hemibrain connectome to develop a more robust understanding of the drosophila nervous system. For example, a major brain circuit of interest is the “central complex” which integrates sensory information and is involved in navigation, motor control, and sleep:
A detailed view of “ring neurons” in the central complex of the fly brain, one of many neural circuits that can be studied using the hemibrain reconstruction and connectome. Interactive version:
ring neurons
and
ellipsoid body
.
Another circuit that is being intensely studied is the “
mushroom body
,” a primary site of learning and memory in the drosophila brain whose detailed structure is contained within the hemibrain connectome (
interactive visualization
).
Acknowledgements
We would like to acknowledge core contributions from Tim Blakely, Laramie Leavitt, Peter Li, Larry Lindsey, Jeremy Maitin-Shepard (Google), Stuart Berg, Gary Huang, Bill Katz, Chris Ordish, Stephen Plaza, Pat Rivlin, Shin-ya Takemura (Janelia collaborators who worked closely with Google’s team), and other amazing collaborators at Janelia and elsewhere who were involved in the hemibrain project.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2021
Jul
Jun
May
Apr
Mar
Feb
Jan
2020
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2019
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
