Automated long-term recording and analysis of neural activity in behaving animals

Abstract

Addressing how neural circuits underlie behavior is routinely done by measuring electrical activity from single neurons in experimental sessions. While such recordings yield snapshots of neural dynamics during specified tasks, they are ill-suited for tracking single-unit activity over longer timescales relevant for most developmental and learning processes, or for capturing neural dynamics across different behavioral states. Here we describe an automated platform for continuous long-term recordings of neural activity and behavior in freely moving rodents. An unsupervised algorithm identifies and tracks the activity of single units over weeks of recording, dramatically simplifying the analysis of large datasets. Months-long recordings from motor cortex and striatum made and analyzed with our system revealed remarkable stability in basic neuronal properties, such as firing rates and inter-spike interval distributions. Interneuronal correlations and the representation of different movements and behaviors were similarly stable. This establishes the feasibility of high-throughput long-term extracellular recordings in behaving animals.

Data availability

The following data sets were generated

Article and author information

Author details

  1. Ashesh K Dhawale

    Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7438-1115
  2. Rajesh Poddar

    Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Steffen BE Wolff

    Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Valentin A Normand

    Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Evi Kopelowitz

    Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Bence P Ölveczky

    Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, United States
    For correspondence
    olveczky@fas.harvard.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2499-2705

Funding

Star Family Challenge Award

  • Bence P Ölveczky

Human Frontier Science Program

  • Steffen B.E Wolff

Life Sciences Research Foundation

  • Ashesh K Dhawale

Charles A. King Trust

  • Ashesh K Dhawale

National Institute of Neurological Disorders and Stroke (R01 NS099323-02)

  • Bence P Ölveczky

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols (#29-15) of the Harvard University. All surgery was performed under isoflurane anesthesia, and every effort was made to minimize suffering.

Reviewing Editor

  1. Andrew J King, University of Oxford, United Kingdom

Publication history

  1. Received: April 11, 2017
  2. Accepted: August 24, 2017
  3. Accepted Manuscript published: September 8, 2017 (version 1)
  4. Version of Record published: September 28, 2017 (version 2)

Copyright

© 2017, Dhawale et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 8,395
    Page views
  • 1,379
    Downloads
  • 66
    Citations

Article citation count generated by polling the highest count across the following sources: Scopus, Crossref, PubMed Central.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Ashesh K Dhawale
  2. Rajesh Poddar
  3. Steffen BE Wolff
  4. Valentin A Normand
  5. Evi Kopelowitz
  6. Bence P Ölveczky
(2017)
Automated long-term recording and analysis of neural activity in behaving animals
eLife 6:e27702.
https://doi.org/10.7554/eLife.27702
  1. Further reading

Further reading

    1. Neuroscience
    Barna Zajzon, David Dahmen ... Renato Duarte
    Research Article

    Information from the sensory periphery is conveyed to the cortex via structured projection pathways that spatially segregate stimulus features, providing a robust and efficient encoding strategy. Beyond sensory encoding, this prominent anatomical feature extends throughout the neocortex. However, the extent to which it influences cortical processing is unclear. In this study, we combine cortical circuit modeling with network theory to demonstrate that the sharpness of topographic projections acts as a bifurcation parameter, controlling the macroscopic dynamics and representational precision across a modular network. By shifting the balance of excitation and inhibition, topographic modularity gradually increases task performance and improves the signal-to-noise ratio across the system. We demonstrate that in biologically constrained networks, such a denoising behavior is contingent on recurrent inhibition. We show that this is a robust and generic structural feature that enables a broad range of behaviorally-relevant operating regimes, and provide an in-depth theoretical analysis unravelling the dynamical principles underlying the mechanism.

    1. Cell Biology
    2. Neuroscience
    Sanja Jasek, Csaba Verasztó ... Gáspár Jékely
    Research Article Updated

    Cells form networks in animal tissues through synaptic, chemical, and adhesive links. Invertebrate muscle cells often connect to other cells through desmosomes, adhesive junctions anchored by intermediate filaments. To study desmosomal networks, we skeletonised 853 muscle cells and their desmosomal partners in volume electron microscopy data covering an entire larva of the annelid Platynereis. Muscle cells adhere to each other, to epithelial, glial, ciliated, and bristle-producing cells and to the basal lamina, forming a desmosomal connectome of over 2000 cells. The aciculae – chitin rods that form an endoskeleton in the segmental appendages – are highly connected hubs in this network. This agrees with the many degrees of freedom of their movement, as revealed by video microscopy. Mapping motoneuron synapses to the desmosomal connectome allowed us to infer the extent of tissue influenced by motoneurons. Our work shows how cellular-level maps of synaptic and adherent force networks can elucidate body mechanics.