Deep learning based feature extraction for prediction and interpretation of sharp-wave ripples in the rodent hippocampus

  1. Andrea Navas-Olive
  2. Rodrigo Amaducci
  3. Maria-Teresa Jurado-Parras
  4. Enrique R Sebastian
  5. Liset M de la Prida  Is a corresponding author
  1. Instituto Cajal, Spain
  2. Universidad Autónoma de Madrid, Spain

Abstract

Local field potential (LFP) deflections and oscillations define hippocampal sharp-wave ripples (SWR), one of the most synchronous events of the brain. SWR reflect firing and synaptic current sequences emerging from cognitively relevant neuronal ensembles. While spectral analysis have permitted advances, the surge of ultra-dense recordings now call for new automatic detection strategies. Here, we show how one-dimensional convolutional networks operating over high-density LFP hippocampal recordings allowed for automatic identification of SWR from the rodent hippocampus. When applied without retraining to new datasets and ultra-dense hippocampus-wide recordings, we discovered physiologically relevant processes associated to the emergence of SWR, prompting for novel classification criteria. To gain interpretability, we developed a method to interrogate the operation of the artificial network. We found it relied in feature-based specialization, which permit identification of spatially segregated oscillations and deflections, as well as synchronous population firing typical of replay. Thus, using deep learning based approaches may change the current heuristic for a better mechanistic interpretation of these relevant neurophysiological events.

Data availability

Data is deposited in the Figshare repository https://figshare.com/projects/cnn-ripple-data/117897. The trained model is accessible at the Github repository for both Python: https://github.com/PridaLab/cnn-ripple, and Matlab: https://github.com/PridaLab/cnn-matlab Code visualization and detection is shown in an interactive notebook https://colab.research.google.com/github/PridaLab/cnn-ripple/blob/main/src/notebooks/cnn-example.ipynb . The online detection Open Ephys plugin is accessible at the Github repository: https://github.com/PridaLab/CNNRippleDetectorOEPlugin

Article and author information

Author details

  1. Andrea Navas-Olive

    Functional and Systems Neuroscience, Instituto Cajal, Madrid, Spain
    Competing interests
    No competing interests declared.
  2. Rodrigo Amaducci

    Grupo de Neurocomputación Biológica, Universidad Autónoma de Madrid, Madrid, Spain
    Competing interests
    No competing interests declared.
  3. Maria-Teresa Jurado-Parras

    Functional and Systems Neuroscience, Instituto Cajal, Madrid, Spain
    Competing interests
    No competing interests declared.
  4. Enrique R Sebastian

    Functional and Systems Neuroscience, Instituto Cajal, Madrid, Spain
    Competing interests
    No competing interests declared.
  5. Liset M de la Prida

    Functional and Systems Neuroscience, Instituto Cajal, Madrid, Spain
    For correspondence
    lmprida@cajal.csic.es
    Competing interests
    Liset M de la Prida, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0160-6472

Funding

Fundacion La Caixa (LCF/PR/HR21/52410030)

  • Liset M de la Prida

Ministerio de Educacion (FPU17/03268)

  • Andrea Navas-Olive

Universidad Autónoma de Madrid (FPI-UAM-2017)

  • Rodrigo Amaducci

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

Reviewing Editor

  1. Adrien Peyrache, McGill University, Canada

Ethics

Animal experimentation: All protocols and procedures were performed according to the Spanish legislation (R.D. 1201/2005 and L.32/2007) and the European Communities Council Directive 2003 (2003/65/CE). Experiments and procedures were approved by the Ethics Committee of the Instituto Cajal and the Spanish Research Council (PROEX131-16 and PROEX161-19). All surgical procedures were performed under isoflurane anesthesia and every effort was made to minimize suffering.

Version history

  1. Received: February 10, 2022
  2. Preprint posted: March 16, 2022 (view preprint)
  3. Accepted: September 2, 2022
  4. Accepted Manuscript published: September 5, 2022 (version 1)
  5. Version of Record published: October 13, 2022 (version 2)

Copyright

© 2022, Navas-Olive 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.

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  1. Andrea Navas-Olive
  2. Rodrigo Amaducci
  3. Maria-Teresa Jurado-Parras
  4. Enrique R Sebastian
  5. Liset M de la Prida
(2022)
Deep learning based feature extraction for prediction and interpretation of sharp-wave ripples in the rodent hippocampus
eLife 11:e77772.
https://doi.org/10.7554/eLife.77772

Share this article

https://doi.org/10.7554/eLife.77772

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