Automatic detection of synaptic partners in a whole-brain Drosophila electron microscopy data set

被引：72

作者：

Buhmann, Julia ^{[1
,2
,3
]}

Sheridan, Arlo ^{[1
]}

Malin-Mayor, Caroline ^{[1
]}

Schlegel, Philipp ^{[4
]}

Gerhard, Stephan ^{[5
,6
]}

Kazimiers, Tom ^{[1
]}

Krause, Renate ^{[1
,2
,3
]}

Nguyen, Tri M. ^{[5
]}

Heinrich, Larissa ^{[1
]}

Lee, Wei-Chung Allen ^{[7
]}

Wilson, Rachel ^{[5
]}

Saalfeld, Stephan ^{[1
]}

Jefferis, Gregory S. X. E. ^{[4
]}

Bock, Davi D. ^{[8
]}

Turaga, Srinivas C. ^{[1
]}

Cook, Matthew ^{[2
,3
]}

Funke, Jan ^{[1
]}

机构：

[1] HHMI, Janelia Res Campus, Ashburn, VA 20147 USA

[2] Univ Zurich, Inst Neuroinformat, Zurich, Switzerland

[3] Swiss Fed Inst Technol, Zurich, Switzerland

[4] MRC Lab Mol Biol, Cambridge, England

[5] Harvard Med Sch, Boston, MA 02115 USA

[6] UniDesign Solut GmbH, Zurich, Switzerland

[7] Boston Childrens Hosp, FM Kirby Neurobiol Ctr, Boston, MA USA

[8] Univ Vermont, Burlington, VA USA

来源：

NATURE METHODS | 2021年 / 18卷 / 07期

基金：

英国惠康基金; 瑞士国家科学基金会; 英国医学研究理事会;

关键词：

D O I：

10.1038/s41592-021-01183-7

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

A deep-learning-based approach enables automatic identification of synaptically connected neurons in electron microscopy datasets of the fly brain. We develop an automatic method for synaptic partner identification in insect brains and use it to predict synaptic partners in a whole-brain electron microscopy dataset of the fruit fly. The predictions can be used to infer a connectivity graph with high accuracy, thus allowing fast identification of neural pathways. To facilitate circuit reconstruction using our results, we develop CIRCUITMAP, a user interface add-on for the circuit annotation tool CATMAID.

引用

页码：771 / +

页数：11

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