NLGC: Network localized Granger causality with application to MEG directional functional connectivity analysis
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 t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-10-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811922006127 |
| _version_ | 1811216606373609472 |
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| author | Behrad Soleimani Proloy Das I.M. Dushyanthi Karunathilake Stefanie E. Kuchinsky Jonathan Z. Simon Behtash Babadi |
| author_facet | Behrad Soleimani Proloy Das I.M. Dushyanthi Karunathilake Stefanie E. Kuchinsky Jonathan Z. Simon Behtash Babadi |
| author_sort | Behrad Soleimani |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T06:42:00Z |
| format | Article |
| id | doaj.art-f3b1fc2b0c374bb2a76c11fc2ce25afa |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-04-12T06:42:00Z |
| publishDate | 2022-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-f3b1fc2b0c374bb2a76c11fc2ce25afa2022-12-22T03:43:41ZengElsevierNeuroImage1095-95722022-10-01260119496NLGC: Network localized Granger causality with application to MEG directional functional connectivity analysisBehrad Soleimani0Proloy Das1I.M. Dushyanthi Karunathilake2Stefanie E. Kuchinsky3Jonathan Z. Simon4Behtash Babadi5Corresponding authors.; Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA; Institute for Systems Research, University of Maryland, College Park, MD, USADepartment of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USADepartment of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA; Institute for Systems Research, University of Maryland, College Park, MD, USAAudiology and Speech Pathology Center, Walter Reed National Military Medical Center, Bethesda, MD, USADepartment 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, USACorresponding authors.; Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA; Institute for Systems Research, University of Maryland, College Park, MD, USAIdentifying 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.http://www.sciencedirect.com/science/article/pii/S1053811922006127MEGGranger causalitySource localizationStatistical inferenceFunctional connectivity analysisAuditory processing |
| spellingShingle | Behrad Soleimani Proloy Das I.M. Dushyanthi Karunathilake Stefanie E. Kuchinsky Jonathan Z. Simon Behtash Babadi NLGC: Network localized Granger causality with application to MEG directional functional connectivity analysis NeuroImage MEG Granger causality Source localization Statistical inference Functional connectivity analysis Auditory processing |
| title | NLGC: Network localized Granger causality with application to MEG directional functional connectivity analysis |
| title_full | NLGC: Network localized Granger causality with application to MEG directional functional connectivity analysis |
| title_fullStr | NLGC: Network localized Granger causality with application to MEG directional functional connectivity analysis |
| title_full_unstemmed | NLGC: Network localized Granger causality with application to MEG directional functional connectivity analysis |
| title_short | NLGC: Network localized Granger causality with application to MEG directional functional connectivity analysis |
| title_sort | nlgc network localized granger causality with application to meg directional functional connectivity analysis |
| topic | MEG Granger causality Source localization Statistical inference Functional connectivity analysis Auditory processing |
| url | http://www.sciencedirect.com/science/article/pii/S1053811922006127 |
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