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

Abstract

Identifying the causal relationships 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, resulting 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, without intermediate 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.

A Joint Subspace Mapping Between Structural and Functional Brain Connectomes

“…Structural connectivity is typically extracted from tractography algorithms applied to diffusion magnetic resonance imaging (MRI) or diffusion tensor imaging (DTI) data [4]. Functional connectivity usually refers to pairwise correlation between activation signals in various brain regions is measured by various functional brain imaging modalities - functional MRI (fMRI) [5], electroencephalography (EEG) [6], magnetoencephalography (MEG) [7] etc. Although several studies [8, 9, 10, 11, 12, 13, 14, 15, 16] have suggested that whole brain functional connectivity is shaped by the underlying structure, the rule by which anatomy constraints brain dynamics remains an open question and an interesting research challenge [10].…”