Distinct Mechanism of Audiovisual Integration With Informative and Uninformative Sound in a Visual Detection Task: A DCM Study

Li, Qi; Yang, Xi; Zhang, Mengchao; Liu, Lin; Tang, Xiaoyu

doi:10.3389/fncom.2019.00059

Cited by 8 publications

(7 citation statements)

References 65 publications

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“…10) TC-TC: There are 24 publications, [17], [30], [37], [52], [68], [74][75], [77], [89][90], [101], [106], [122], [133], [135], [137][138], [156], [162][163], [165][166][167][168], studied the interactions between TC and TC. As in the previous case, seven task types were presented and the most relevant were sensory tasks, speech tasks, and disease diagnosis.…”

Section: Resultsmentioning

confidence: 99%

Dynamic Causal Modeling on the Identification of Interacting Networks in the Brain: A Systematic Review

Wang

Liang

2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Dynamic causal modeling (DCM) has long been used to characterize effective connectivity within networks of distributed neuronal responses. Previous reviews have highlighted the understanding of the conceptual basis behind DCM and its variants from different aspects. However, no detailed summary or classification research on the task-related effective connectivity of various brain regions has been made formally available so far, and there is also a lack of application analysis of DCM for hemodynamic and electrophysiological measurements. This review aims to analyze the effective connectivity of different brain regions using DCM for different measurement data. We found that, in general, most studies focused on the networks between different cortical regions, and the research on the networks between other deep subcortical nuclei or between them and the cerebral cortex are receiving increasing attention, but far from the same scale. Our analysis also reveals a clear bias towards some task types. Based on these results, we identify and discuss several promising research directions that may help the community to attain a clear understanding of the brain network interactions under different tasks.

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Section: Resultsmentioning

confidence: 99%

Dynamic Causal Modeling on the Identification of Interacting Networks in the Brain: A Systematic Review

Wang

Liang

2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…It would be interesting to reproduce the analysis reported above using nuanced models of the sort used in dynamic causal models based on the canonical microcircuit. Provisional work in this area has already considered A/V sensory integration (Li, Xi, Zhang, Liu, & Tang, 2019); however, it has not yet addressed the cardinal issue of inter‐individual variability.…”

Section: Discussionmentioning

confidence: 99%

Biophysical mechanisms governing large‐scale brain network dynamics underlying individual‐specific variability of perception

Kumar

Dutta

Talwar

et al. 2020

Eur J of Neuroscience

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Perception necessitates interaction among neuronal ensembles, the dynamics of which can be conceptualized as the emergent behavior of coupled dynamical systems. Here, we propose a detailed neurobiologically realistic model that captures the neural mechanisms of inter-individual variability observed in cross-modal speech perception. From raw EEG signals recorded from human participants when they were presented with speech vocalizations of McGurk-incongruent and congruent audiovisual (AV) stimuli, we computed the global coherence metric to capture the neural variability of large-scale networks. We identified that participants' McGurk susceptibility was negatively correlated to their alpha band global coherence. The proposed biophysical model conceptualized the global coherence dynamics emerge from coupling between the interacting neural masses-representing the sensory-specific auditory/visual areas and modality nonspecific associative/integrative regions. Subsequently, we could predict that an extremely weak direct AV coupling results in a decrease in alpha band global coherence-mimicking the cortical dynamics of participants with higher McGurk susceptibility. Source connectivity analysis also showed decreased connectivity between sensory-specific regions in participants more susceptible to McGurk effect, thus establishing an empirical validation to the prediction. Overall, our study provides an outline to link variability in structural and functional connectivity metrics to variability of performance that can be useful for several perception and action task paradigms.

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“…Future studies should employ advanced electrophysiological instrumentation, such as electroencephalography (EEG), functional near-infrared spectroscopy (FNIRS), and functional magnetic resonance imaging (fMRI) techniques, to explore whether the occurrence of audiovisual interaction differs in specific event-related potential (ERP) components or brain regions (Li et al, 2019;Xi et al, 2020a,b).…”

Section: Limitationsmentioning

confidence: 99%

Effects of audiovisual interactions on working memory: Use of the combined N-back + Go/NoGo paradigm

Yang²,

He³

et al. 2023

Front. Psychol.

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BackgroundApproximately 94% of sensory information acquired by humans originates from the visual and auditory channels. Such information can be temporarily stored and processed in working memory, but this system has limited capacity. Working memory plays an important role in higher cognitive functions and is controlled by central executive function. Therefore, elucidating the influence of the central executive function on information processing in working memory, such as in audiovisual integration, is of great scientific and practical importance.PurposeThis study used a paradigm that combined N-back and Go/NoGo tasks, using simple Arabic numerals as stimuli, to investigate the effects of cognitive load (modulated by varying the magnitude of N) and audiovisual integration on the central executive function of working memory as well as their interaction.MethodsSixty college students aged 17–21 years were enrolled and performed both unimodal and bimodal tasks to evaluate the central executive function of working memory. The order of the three cognitive tasks was pseudorandomized, and a Latin square design was used to account for order effects. Finally, working memory performance, i.e., reaction time and accuracy, was compared between unimodal and bimodal tasks with repeated-measures analysis of variance (ANOVA).ResultsAs cognitive load increased, the presence of auditory stimuli interfered with visual working memory by a moderate to large extent; similarly, as cognitive load increased, the presence of visual stimuli interfered with auditory working memory by a moderate to large effect size.ConclusionOur study supports the theory of competing resources, i.e., that visual and auditory information interfere with each other and that the magnitude of this interference is primarily related to cognitive load.

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Distinct Mechanism of Audiovisual Integration With Informative and Uninformative Sound in a Visual Detection Task: A DCM Study

Cited by 8 publications

References 65 publications

Dynamic Causal Modeling on the Identification of Interacting Networks in the Brain: A Systematic Review

Dynamic Causal Modeling on the Identification of Interacting Networks in the Brain: A Systematic Review

Biophysical mechanisms governing large‐scale brain network dynamics underlying individual‐specific variability of perception

Effects of audiovisual interactions on working memory: Use of the combined N-back + Go/NoGo paradigm

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