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

NeuroImage (Oct 2022)

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

Affiliations

Behrad Soleimani: Corresponding authors.; Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA; Institute for Systems Research, University of Maryland, College Park, MD, USA
Proloy Das: Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
I.M. Dushyanthi Karunathilake: Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA; Institute for Systems Research, University of Maryland, College Park, MD, USA
Stefanie E. Kuchinsky: Audiology and Speech Pathology Center, Walter Reed National Military Medical Center, Bethesda, MD, USA
Jonathan Z. Simon: Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA; Institute for Systems Research, University of Maryland, College Park, MD, USA; Department of Biology, University of Maryland College Park, MD, USA
Behtash Babadi: Corresponding authors.; Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA; Institute for Systems Research, University of Maryland, College Park, MD, USA

Journal volume & issue: Vol. 260
p. 119496

Abstract

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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.

Published in NeuroImage

ISSN: 1053-8119 (Print); 1095-9572 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry
Website: https://www.journals.elsevier.com/neuroimage

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