Soleimani, B. | Search bioRxiv

bioRxiv2023

Changes in Cortical Directional Connectivity during Difficult Listening in Younger and Older Adults

One way to investigate the mechanisms that underlie speech comprehension under difficult listening conditions is via cortical connectivity. The innovative Network Localized Granger Causality (NLGC) framework was applied to magnetoencephalography (MEG) data, obtained from older and younger subjects performing a speech listening task in noisy conditions, in delta and theta frequency bands. Directional connectivity between frontal, temporal, and parietal lobes was analyzed. Both aging- and condition-related changes were found, particularly in theta. In younger adults, as background noise increased, there was a transition from predominantly temporal-to-frontal (bottom-up) connections, to predominantly frontal-to-temporal (top-down). In contrast, older adults showed bidirectional information flow between frontal and temporal cortices even for speech in quiet, not changing substantially with increased noise. Additionally, younger listeners did not show changes in the nature of their cortical links for different listening conditions, whereas older listeners exhibited a switch from predominantly facilitative links to predominantly sharpening, when noise increased. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=169 HEIGHT=200 SRC="FIGDIR/small/541500v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@174c132org.highwire.dtl.DTLVardef@37c5a5org.highwire.dtl.DTLVardef@1910e38org.highwire.dtl.DTLVardef@3c94e0_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIDifferent bands show strong differences in directional functional connectivity patterns C_LIO_LIDirectional functional connectivity patterns altered by listening task difficulty C_LIO_LIAging dramatically alters directional functional connectivity patterns in during listening C_LIO_LINature of functional connectivity, additive vs subtractive, depends on age and task C_LI

neuroscience↗

bioRxiv2022

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 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.

neuroscience↗