Abstract

Because multivariate autoregressive models have failed to adequately account for the complexity of neural signals, researchers have predominantly relied on non-parametric methods when studying the relations between brain and behavior. Using medial temporal lobe (MTL) recordings from 96 neurosurgical patients, we show that time series models with volatility described by a multivariate stochastic latent-variable process and lagged interactions between signals in different brain regions provide new insights into the dynamics of brain function. The implied volatility inferred from our process positively correlates with high-frequency spectral activity, a signal that correlates with neuronal activity. We show that volatility features derived from our model can reliably decode memory states, and that this classifier performs as well as those using spectral features. Using the directional connections between brain regions during complex cognitive process provided by the model, we uncovered perirhinal-hippocampal desynchronization in the MTL regions that is associated with successful memory encoding.

Data availability

The iEEG dataset collected from epileptic patients in this paper is available and to protect patients' confidentiality, can be requested at http://memory.psych.upenn.edu/RAM_Public_Data. The cmlreaders repository for reading in the data is at https://github.com/pennmem/. The main script for the paper is available at https://github.com/tungphan87/MSV_EEG.

Article and author information

Author details

  1. Tung D Phan

    Department of Psychology, University of Pennsylvania, Philadelphia, United States
    For correspondence
    tungphan87@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5957-7566
  2. Jessica A Wachter

    Department of Finance, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Ethan A Solomon

    Department of Bioengineering, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0541-7588
  4. Michael Kahana

    Department of Psychology, University of Pennsylvania, Philadelphia, United States
    For correspondence
    kahana@psych.upenn.edu
    Competing interests
    The authors declare that no competing interests exist.

Funding

Defense Advanced Research Projects Agency (N66001-14-2-4032)

  • Michael Kahana

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

Reviewing Editor

  1. Michael Breakspear, QIMR Berghofer Medical Research Institute, Australia

Ethics

Human subjects: Data were collected at the following centers: Thomas Jefferson University Hospital, Mayo Clinic, Hospital of the University of Pennsylvania, Emory University Hospital, University of Texas Southwestern Medical Center, Dartmouth-Hitchcock Medical Center, Columbia University Medical Center, National Institutes of Health, and University of Washington Medical Center and collected and coordinated via the Data Coordinating Center (DCC) at the University of Pennsylvania. The research protocol for the Data Coordinating Center (DCC) was approved by the University of Pennsylvania IRB (protocol 820553) and informed consent was obtained from each participant.

Version history

  1. Received: October 17, 2018
  2. Accepted: July 29, 2019
  3. Accepted Manuscript published: August 1, 2019 (version 1)
  4. Version of Record published: August 16, 2019 (version 2)

Copyright

© 2019, Phan 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. Tung D Phan
  2. Jessica A Wachter
  3. Ethan A Solomon
  4. Michael Kahana
(2019)
Multivariate Stochastic Volatility Modeling of Neural Data
eLife 8:e42950.
https://doi.org/10.7554/eLife.42950

Share this article

https://doi.org/10.7554/eLife.42950

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