Biology subjects

O'Leary, D.

Publications and source records attributed to O'Leary, D..

2 recordsLinked to original sources

bioRxiv2022

Temporal dynamics of oscillatory brain activity during the observation of communicative interactions between point-light agents

Predicting actions from nonverbal cues and using them to optimize ones response behavior (i.e., interpersonal predictive coding) is essential in everyday social interactions. We aimed to investigate the neural correlates of different cognitive processes evolving over time during interpersonal predictive coding. Thirty-nine participants watched two agents depicted by moving point-light stimuli while an electroencephalogram (EEG) was recorded. One well-recognizable agent performed either a communicative or an individual action. The second agent either was blended into a cluster of noise dots (i.e., present), or was entirely replaced by noise dots (i.e., absent), which participants had to differentiate. EEG amplitude and coherence analyses for theta, alpha and beta frequency bands revealed a dynamic pattern unfolding over time: Watching communicative actions was associated with enhanced coupling within medial anterior regions involved in social and mentalizing processes and with dorsolateral prefrontal activation indicating a higher deployment of cognitive resources. Trying to detect the agent in the cluster of noise dots without having seen communicative cues was related to enhanced coupling in posterior regions for social perception and visual processing. Observing an expected outcome was modulated by motor system activation. Finally, when the agent was detected correctly, activation in posterior areas for visual processing of socially-relevant features was increased. Taken together, our results demonstrate that it is crucial to consider the temporal dynamics of social interactions and of their neural correlates to better understand interpersonal predictive coding. This could lead to optimized treatment approaches for individuals with problems in social interactions.

neuroscience↗

bioRxiv2022

Explicitly Nonlinear Dynamic Functional Network Connectivity In Resting-State fMRI Data

Most dynamic functional connectivity in fMRI data is focused on linear correlations, and to our knowledge, no study has studied whole brain explicitly nonlinear dynamic relationships within the data. While some approaches have attempted to study overall connectivity more generally using flexible models, we are particularly interested in whether the non-linear relationships, above and beyond linear, are capturing unique information. This study thus proposes an approach to assess the explicitly nonlinear dynamic functional network connectivity derived from the relationship among independent component analysis time courses. Linear relationships were removed at each time point to evaluate, typically ignored, explicitly nonlinear dFNC using normalized mutual information. Simulations showed the proposed method accurately estimated NMI over time, even within relatively short windows of data. Results on fMRI data included 151 schizophrenia patients, and 163 healthy controls showed three unique, highly structured, mostly long-range, functional states that also showed significant group differences. This analysis identifies a higher level of explicitly nonlinear dependencies in transient connectivity within the visual network in healthy controls compared to schizophrenia patients. In particular, nonlinear relationships tend to be more widespread than linear ones. We also find highly significant differences in the relative co-occurrence of linear and explicitly nonlinear states in HC and SZ, suggesting these may be an important aspect of the disorder. Overall, this work suggests that quantifying nonlinear dependencies of dynamic functional connectivity may provide a complementary and potentially valuable tool for studying brain function by exposing relevant variation that is typically ignored.

neuroscience↗