Abstract
.Understanding how cortical networks interact in response to task demands is important both for providing insight into the brain’s processing architecture and for managing neurological diseases and mental disorders. High-density diffuse optical tomography (HD-DOT) is a neuroimaging technique that offers the significant advantages of having a naturalistic, acoustically controllable environment and being compatible with metal implants, neither of which is possible with functional magnetic resonance imaging. We used HD-DOT to study the effective connectivity and assess the modulatory effects of speech intelligibility and syntactic complexity on functional connections within the cortical speech network. To accomplish this, we extend the use of a generalized psychophysiological interaction (PPI) analysis framework. In particular, we apply PPI methods to event-related HD-DOT recordings of cortical oxyhemoglobin activity during auditory sentence processing. We evaluate multiple approaches for selecting cortical regions of interest and for modeling interactions among these regions. Our results show that using subject-based regions has minimal effect on group-level connectivity maps. We also demonstrate that incorporating an interaction model based on estimated neural activity results in significantly stronger effective connectivity. Taken together our findings support the use of HD-DOT with PPI methods for noninvasively studying task-related modulations of functional connectivity.
Highlights
Functional neuroimaging has provided substantial insight into how distributed dynamic neural processes are related in space and time
We examined whether an interaction model based on deconvolution of a canonical hemodynamic response function (HRF) from the hemodynamic activity at the seed location can increase the statistical significance of measures of effective connectivity relative to a no deconvolution routine
To obtain effective connectivity maps, we used a linear model with three types of regressors: one regressor per condition to model the response to the main effect of that condition, one regressor to model task-independent connectivity (TIC) to a selected seed region, and one regressor per condition to model the task-dependent
Summary
Functional neuroimaging has provided substantial insight into how distributed dynamic neural processes are related in space and time. Prior applications of PPI in fMRI data have suggested calculating interactions on the estimated neural activity based on a deconvolution approach.[21] Here, we examined whether an interaction model based on deconvolution of a canonical HRF from the hemodynamic activity at the seed location can increase the statistical significance of measures of effective connectivity relative to a no deconvolution routine (where interactions are modeled at hemodynamic level). To examine these methodological decisions and explore the use of PPI analyses in HD-DOT, we used data from an auditory sentence comprehension task. We hypothesized that processing sentences with increasing levels of linguistic challenge would result in increased effective connectivity within the speech network
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