1. Neuroscience
Download icon

Cortico-hippocampal network connections support the multidimensional quality of episodic memory

  1. Rose A Cooper  Is a corresponding author
  2. Maureen Ritchey
  1. Boston College, United States
Research Article
  • Cited 13
  • Views 3,889
  • Annotations
Cite this article as: eLife 2019;8:e45591 doi: 10.7554/eLife.45591


Episodic memories reflect a bound representation of multimodal features that can be reinstated with varying precision. Yet little is known about how brain networks involved in memory, including the hippocampus and posterior-medial (PM) and anterior-temporal (AT) systems, interact to support the quality and content of recollection. Participants learned color, spatial, and emotion associations of objects, later reconstructing the visual features using a continuous color spectrum and 360-degree panorama scenes. Behaviorally, dependencies in memory were observed for the gist but not precision of event associations. Supporting this integration, hippocampus, AT, and PM regions showed increased connectivity and reduced modularity during retrieval compared to encoding. These inter-network connections tracked a multidimensional, objective measure of memory quality. Moreover, distinct patterns of connectivity tracked item color and spatial memory precision. These findings demonstrate how hippocampal-cortical connections reconfigure during episodic retrieval, and how such dynamic interactions might flexibly support the multidimensional quality of remembered events.

Data availability

Data and code have been made available via GitHub: https://github.com/memobc/paper-orbitfmri

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Rose A Cooper

    Department of Psychology, Boston College, Chestnut Hill, United States
    For correspondence
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1521-8371
  2. Maureen Ritchey

    Department of Psychology, Boston College, Chestnut Hill, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5957-3642


National Institutes of Health (R00MH103401)

  • Maureen Ritchey

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


Human subjects: Informed consent was obtained from all participants prior to the experiment. Procedures were approved by the Boston College Institutional Review Board (17.026).

Reviewing Editor

  1. Muireann Irish, University of Sydney, Australia

Publication history

  1. Received: January 28, 2019
  2. Accepted: March 22, 2019
  3. Accepted Manuscript published: March 22, 2019 (version 1)
  4. Version of Record published: April 5, 2019 (version 2)


© 2019, Cooper & Ritchey

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.


  • 3,889
    Page views
  • 574
  • 13

Article citation count generated by polling the highest count across the following sources: Crossref, Scopus, 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)

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

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

Further reading

    1. Neuroscience
    Antonio HO Fonseca et al.
    Research Article Updated

    Mice emit ultrasonic vocalizations (USVs) that communicate socially relevant information. To detect and classify these USVs, here we describe VocalMat. VocalMat is a software that uses image-processing and differential geometry approaches to detect USVs in audio files, eliminating the need for user-defined parameters. VocalMat also uses computational vision and machine learning methods to classify USVs into distinct categories. In a data set of >4000 USVs emitted by mice, VocalMat detected over 98% of manually labeled USVs and accurately classified ≈86% of the USVs out of 11 USV categories. We then used dimensionality reduction tools to analyze the probability distribution of USV classification among different experimental groups, providing a robust method to quantify and qualify the vocal repertoire of mice. Thus, VocalMat makes it possible to perform automated, accurate, and quantitative analysis of USVs without the need for user inputs, opening the opportunity for detailed and high-throughput analysis of this behavior.

    1. Neuroscience
    Giulio Bondanelli et al.
    Research Article Updated

    Across sensory systems, complex spatio-temporal patterns of neural activity arise following the onset (ON) and offset (OFF) of stimuli. While ON responses have been widely studied, the mechanisms generating OFF responses in cortical areas have so far not been fully elucidated. We examine here the hypothesis that OFF responses are single-cell signatures of recurrent interactions at the network level. To test this hypothesis, we performed population analyses of two-photon calcium recordings in the auditory cortex of awake mice listening to auditory stimuli, and compared them to linear single-cell and network models. While the single-cell model explained some prominent features of the data, it could not capture the structure across stimuli and trials. In contrast, the network model accounted for the low-dimensional organization of population responses and their global structure across stimuli, where distinct stimuli activated mostly orthogonal dimensions in the neural state-space.