Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published January 6, 2022 | Submitted + Supplemental Material
Journal Article Open

Transforming representations of movement from body- to world-centric space

Abstract

When an animal moves through the world, its brain receives a stream of information about the body's translational velocity from motor commands and sensory feedback signals. These incoming signals are referenced to the body, but ultimately, they must be transformed into world-centric coordinates for navigation. Here we show that this computation occurs in the fan-shaped body in the brain of Drosophila melanogaster. We identify two cell types, PFNd and PFNv, that conjunctively encode translational velocity and heading as a fly walks. In these cells, velocity signals are acquired from locomotor brain regions and are multiplied with heading signals from the compass system. PFNd neurons prefer forward–ipsilateral movement, whereas PFNv neurons prefer backward–contralateral movement, and perturbing PFNd neurons disrupts idiothetic path integration in walking flies. Downstream, PFNd and PFNv neurons converge onto hΔB neurons, with a connectivity pattern that pools together heading and translation direction combinations corresponding to the same movement in world-centric space. This network motif effectively performs a rotation of the brain's representation of body-centric translational velocity according to the current heading direction. Consistent with our predictions, we observe that hΔB neurons form a representation of translational velocity in world-centric coordinates. By integrating this representation over time, it should be possible for the brain to form a working memory of the path travelled through the environment.

Additional Information

© 2021 Nature Publishing Group. Received 22 December 2020; Accepted 28 October 2021; Published 15 December 2021. This study benefited from the public release of the hemibrain connectome by the FlyEM Team at Janelia. We thank I. S. Haber and A. A. Li for tracing and annotation in the full adult female brain dataset (FAFB)25; N. Eckstein, A. S. Bates, J. Funke and G. X. E. Jefferis for neurotransmitter predictions based on those data; J. Omoto for assistance with behavioural experiments; W. B. Dickson for sharing modified FicTrac software and machining help; T. Wolff, G. M. Rubin, V. Jayaraman, G. Card, B. D. Pfeiffer, D. J. Anderson and H. Amrein for providing fly stocks; H. H. Yang, M. A. Basnak, M. J. Marquis, Y. E. Fisher, T. Okubo, A. Rayshubskiy, C. D. Harvey, B. L. de Bivort, J. Drugowitsch, B. el Jundi, C. Pehlevan, J. A. Assad and the Wilson laboratory for discussions. This work was supported by the Harvard Medical School Neurobiology Imaging Facility (NINDS P30 #NS072030), the HMS Research Computing Group O2 cluster, and the HMS Research Instrumentation Core Facility. This study was supported by NIH grants T32 GM007753, F30 DC017698 (to J.L.), R01 EB028171 (to S.D.), and U19 NS104655 (to M.H.D., S.D. and R.I.W.). R.I.W. and G.M. are HHMI Investigators. Data availability: The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. Code availability: Code for implementing the computational model is available at https://github.com/druckmann-lab/Translational-velocity-and-heading-model. These authors contributed equally: Amir H. Behbahani, Lydia Hamburg, Elena A. Westeinde. Author Contributions: J.L. and R.I.W. conceived the project and coordinated the work. J.L. designed and performed imaging experiments and analyses. A.H.B. and M.H.D. designed and performed behavioural experiments and analyses; M.H.D. also provided hardware and software support. L.H. and S.D. designed, implemented and analysed the computational model. E.A.W. designed and performed electrophysiological experiments and analyses. P.M.D. performed MCFO experiments. C.L. and G.M. provided the hΔB split-Gal4 line prior to publication. J.L. and R.I.W. analysed data and wrote the paper with input from all authors. 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.

Attached Files

Submitted - 2020.12.22.424001v1.full.pdf

Supplemental Material - 41586_2021_4191_Fig10_ESM.webp

Supplemental Material - 41586_2021_4191_Fig11_ESM.webp

Supplemental Material - 41586_2021_4191_Fig12_ESM.webp

Supplemental Material - 41586_2021_4191_Fig13_ESM.webp

Supplemental Material - 41586_2021_4191_Fig14_ESM.webp

Supplemental Material - 41586_2021_4191_Fig5_ESM.webp

Supplemental Material - 41586_2021_4191_Fig6_ESM.webp

Supplemental Material - 41586_2021_4191_Fig7_ESM.webp

Supplemental Material - 41586_2021_4191_Fig8_ESM.webp

Supplemental Material - 41586_2021_4191_Fig9_ESM.webp

Supplemental Material - 41586_2021_4191_MOESM1_ESM.pdf

Files

2020.12.22.424001v1.full.pdf
Files (5.3 MB)
Name Size Download all
md5:7976a4d48e64551ceb3ac18f26a7fd41
152.6 kB Download
md5:046bd5eb5bd21334149e647ffcaa2e2a
3.1 MB Preview Download
md5:d81b24c8c2415b14d88efc8ee1778922
256.3 kB Download
md5:0b1afadf428bb8875cdf07a92b198d23
193.9 kB Download
md5:522a939c5760ab9cdd3464ee92e18353
259.4 kB Download
md5:7a652af5e2d91d24b56d14afe111cf62
189.1 kB Download
md5:934c8923fa1628c65fac7e1d4c0e0e8c
259.7 kB Download
md5:fc76b3df97808cda6278158697b91820
88.0 kB Download
md5:7193dc4410ec45190611d9e056fb26b6
234.0 kB Download
md5:5fc56ad21807352c7945b8c408b58d3e
370.8 kB Download
md5:08f8d6a29bc4c4bc7bbe15fc00728b6e
160.9 kB Download
md5:a16ea5dff556066a40f43cf151344684
84.2 kB Preview Download

Additional details

Created:
August 22, 2023
Modified:
December 22, 2023