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.

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.

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.

Metrics

  • 3,814
    views
  • 604
    downloads
  • 27
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

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. 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

Further reading

    1. Neuroscience
    Sara A Nolin, Mary E Faulkner ... Kristina Visscher
    Research Article

    The brain is organized into systems and networks of interacting components. The functional connections among these components give insight into the brain's organization and may underlie some cognitive effects of aging. Examining the relationship between individual differences in brain organization and cognitive function in older adults who have reached oldest old ages with healthy cognition can help us understand how these networks support healthy cognitive aging. We investigated functional network segregation in 146 cognitively healthy participants aged 85+ in the McKnight Brain Aging Registry. We found that the segregation of the association system and the individual networks within the association system [the fronto-parietal network (FPN), cingulo-opercular network (CON) and default mode network (DMN)], has strong associations with overall cognition and processing speed. We also provide a healthy oldest-old (85+) cortical parcellation that can be used in future work in this age group. This study shows that network segregation of the oldest-old brain is closely linked to cognitive performance. This work adds to the growing body of knowledge about differentiation in the aged brain by demonstrating that cognitive ability is associated with differentiated functional networks in very old individuals representing successful cognitive aging.

    1. Cell Biology
    2. Neuroscience
    Vibhavari Aysha Bansal, Jia Min Tan ... Toh Hean Ch'ng
    Research Article

    The emergence of Aβ pathology is one of the hallmarks of Alzheimer’s disease (AD), but the mechanisms and impact of Aβ in progression of the disease is unclear. The nuclear pore complex (NPC) is a multi-protein assembly in mammalian cells that regulates movement of macromolecules across the nuclear envelope; its function is shown to undergo age-dependent decline during normal aging and is also impaired in multiple neurodegenerative disorders. Yet not much is known about the impact of Aβ on NPC function in neurons. Here, we examined NPC and nucleoporin (NUP) distribution and nucleocytoplasmic transport using a mouse model of AD (AppNL-G-F/NL-G-F) that expresses Aβ in young animals. Our studies revealed that a time-dependent accumulation of intracellular Aβ corresponded with a reduction of NPCs and NUPs in the nuclear envelope which resulted in the degradation of the permeability barrier and inefficient segregation of nucleocytoplasmic proteins, and active transport. As a result of the NPC dysfunction App KI neurons become more vulnerable to inflammation-induced necroptosis – a programmed cell death pathway where the core components are activated via phosphorylation through nucleocytoplasmic shutting. Collectively, our data implicates Aβ in progressive impairment of nuclear pore function and further confirms that the protein complex is vulnerable to disruption in various neurodegenerative diseases and is a potential therapeutic target.