Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

NLGC: Network Localized Granger Causality with Application to MEG Directional Functional Connectivity Analysis

View ORCID ProfileBehrad Soleimani, View ORCID ProfileProloy Das, I.M. Dushyanthi Karunathilake, View ORCID ProfileStefanie E. Kuchinsky, View ORCID ProfileJonathan Z. Simon, View ORCID ProfileBehtash Babadi
doi: https://doi.org/10.1101/2022.03.09.483683
Behrad Soleimani
aDepartment of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA
bInstitute for Systems Research, University of Maryland, College Park, MD, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Behrad Soleimani
  • For correspondence: behtash@umd.edu
Proloy Das
cDepartment of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Proloy Das
I.M. Dushyanthi Karunathilake
aDepartment of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA
bInstitute for Systems Research, University of Maryland, College Park, MD, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Stefanie E. Kuchinsky
dAudiology and Speech Pathology Center, Walter Reed National Military Medical Center, Bethesda, MD, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Stefanie E. Kuchinsky
Jonathan Z. Simon
aDepartment of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA
bInstitute for Systems Research, University of Maryland, College Park, MD, USA
eDepartment of Biology, University of Maryland College Park, MD, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Jonathan Z. Simon
Behtash Babadi
aDepartment of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA
bInstitute for Systems Research, University of Maryland, College Park, MD, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Behtash Babadi
  • For correspondence: behtash@umd.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Identifying the directed connectivity that underlie networked activity between different cortical areas is critical for understanding the neural mechanisms behind sensory processing. Granger causality (GC) is widely used for this purpose in functional magnetic resonance imaging analysis, but there the temporal resolution is low, making it difficult to capture the millisecond-scale interactions underlying sensory processing. Magnetoencephalography (MEG) has millisecond resolution, but only provides low-dimensional sensor-level linear mixtures of neural sources, which makes GC inference challenging. Conventional methods proceed in two stages: First, cortical sources are estimated from MEG using a source localization technique, followed by GC inference among the estimated sources. However, the spatiotemporal biases in estimating sources propagate into the subsequent GC analysis stage, may result in both false alarms and missing true GC links. Here, we introduce the Network Localized Granger Causality (NLGC) inference paradigm, which models the source dynamics as latent sparse multivariate autoregressive processes and estimates their parameters directly from the MEG measurements, integrated with source localization, and employs the resulting parameter estimates to produce a precise statistical characterization of the detected GC links. We offer several theoretical and algorithmic innovations within NLGC and further examine its utility via comprehensive simulations and application to MEG data from an auditory task involving tone processing from both younger and older participants. Our simulation studies reveal that NLGC is markedly robust with respect to model mismatch, network size, and low signal-to-noise ratio, whereas the conventional two-stage methods result in high false alarms and mis-detections. We also demonstrate the advantages of NLGC in revealing the cortical network-level characterization of neural activity during tone processing and resting state by delineating task- and age-related connectivity changes.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵† The identification of specific products or scientific instrumentation is considered an integral part of the scientific endeavor and does not constitute endorsement or implied endorsement on the part of the author, DoD, or any component agency. The views expressed in this article are those of the author and do not reflect the official policy of the Department of Army/Navy/Air Force, Department of Defense, or U.S. Government.

  • Email addresses: behrad{at}umd.edu (Behrad Soleimani), pdas6{at}mgh.harvard.edu (Proloy Das), dushk{at}umd.edu (I.M. Dushyanthi Karunathilake), stefanie.e.kuchinsky.civ{at}mail.mil (Stefanie E. Kuchinsky), jzsimon{at}umd.edu (Jonathan Z. Simon), behtash{at}umd.edu (Behtash Babadi)

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted May 11, 2022.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
NLGC: Network Localized Granger Causality with Application to MEG Directional Functional Connectivity Analysis
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
NLGC: Network Localized Granger Causality with Application to MEG Directional Functional Connectivity Analysis
Behrad Soleimani, Proloy Das, I.M. Dushyanthi Karunathilake, Stefanie E. Kuchinsky, Jonathan Z. Simon, Behtash Babadi
bioRxiv 2022.03.09.483683; doi: https://doi.org/10.1101/2022.03.09.483683
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
NLGC: Network Localized Granger Causality with Application to MEG Directional Functional Connectivity Analysis
Behrad Soleimani, Proloy Das, I.M. Dushyanthi Karunathilake, Stefanie E. Kuchinsky, Jonathan Z. Simon, Behtash Babadi
bioRxiv 2022.03.09.483683; doi: https://doi.org/10.1101/2022.03.09.483683

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (4384)
  • Biochemistry (9602)
  • Bioengineering (7100)
  • Bioinformatics (24889)
  • Biophysics (12626)
  • Cancer Biology (9968)
  • Cell Biology (14365)
  • Clinical Trials (138)
  • Developmental Biology (7966)
  • Ecology (12116)
  • Epidemiology (2067)
  • Evolutionary Biology (15997)
  • Genetics (10932)
  • Genomics (14746)
  • Immunology (9875)
  • Microbiology (23684)
  • Molecular Biology (9486)
  • Neuroscience (50911)
  • Paleontology (370)
  • Pathology (1540)
  • Pharmacology and Toxicology (2684)
  • Physiology (4022)
  • Plant Biology (8669)
  • Scientific Communication and Education (1510)
  • Synthetic Biology (2397)
  • Systems Biology (6442)
  • Zoology (1346)