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Mapping the spatiotemporal dynamics of processing task-relevant and task-irrelevant sound feature changes using concurrent EEG-fMRI.

ABSTRACT: The cortical processing of changes in auditory input involves auditory sensory regions as well as different frontoparietal brain networks. The spatiotemporal dynamics of the activation spread across these networks has, however, not been investigated in detail so far. We here approached this issue using concurrent functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), providing us with simultaneous information on both the spatial and temporal patterns of change-related activity. We applied an auditory stimulus categorization task with switching categorization rules, allowing to analyze change-related responses as a function of the changing sound feature (pitch or duration) and the task relevance of the change. Our data show the successive progression of change-related activity from regions involved in early change detection to the ventral and dorsal attention networks, and finally the central executive network. While early change detection was found to recruit feature-specific networks involving auditory sensory but also frontal and parietal brain regions, the later spread of activity across the frontoparietal attention and executive networks was largely independent of the changing sound feature, suggesting the existence of a general feature-independent processing pathway of change-related information. Task relevance did not modulate early auditory sensory processing, but was mainly found to affect processing in frontal brain regions. Hum Brain Mapp 37:3400-3416, 2016. © 2016 Wiley Periodicals, Inc.

SUBMITTER: Puschmann S

PROVIDER: S-EPMC6867321 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Mapping the spatiotemporal dynamics of processing task-relevant and task-irrelevant sound feature changes using concurrent EEG-fMRI.

Puschmann Sebastian S Huster René J RJ Thiel Christiane M CM

Human brain mapping 20160609 10

The cortical processing of changes in auditory input involves auditory sensory regions as well as different frontoparietal brain networks. The spatiotemporal dynamics of the activation spread across these networks has, however, not been investigated in detail so far. We here approached this issue using concurrent functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), providing us with simultaneous information on both the spatial and temporal patterns of change-related act ...[more]

PMID: 27280466

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Project description:Reconstructing EEG sources involves a complex pipeline, with the inverse problem being the most challenging. Multiple inversion algorithms are being continuously developed, aiming to tackle the non-uniqueness of this problem, which has been shown to be partially circumvented by including prior information in the inverse models. Despite a few efforts, there are still current and persistent controversies regarding the inversion algorithm of choice and the optimal set of spatial priors to be included in the inversion models. The use of simultaneous EEG-fMRI data is one approach to tackle this problem. The spatial resolution of fMRI makes fMRI derived spatial priors very convenient for EEG reconstruction, however, only task activation maps and resting-state networks (RSNs) have been explored so far, overlooking the recent, but already accepted, notion that brain networks exhibit dynamic functional connectivity fluctuations. The lack of a systematic comparison between different source reconstruction algorithms, considering potentially more brain-informative priors such as fMRI, motivates the search for better reconstruction models. Using simultaneous EEG-fMRI data, here we compared four different inversion algorithms (minimum norm, MN; low resolution electromagnetic tomography, LORETA; empirical Bayes beamformer, EBB; and multiple sparse priors, MSP) under a Bayesian framework (as implemented in SPM), each with three different sets of priors consisting of: (1) those specific to the algorithm; (2) those specific to the algorithm plus fMRI task activation maps and RSNs; and (3) those specific to the algorithm plus fMRI task activation maps and RSNs and network modules of task-related dFC states estimated from the dFC fluctuations. The quality of the reconstructed EEG sources was quantified in terms of model-based metrics, namely the expectation of the posterior probability P(model|data) and variance explained of the inversion models, and the overlap/proportion of brain regions known to be involved in the visual perception tasks that the participants were submitted to, and RSN templates, with/within EEG source components. Model-based metrics suggested that model parsimony is preferred, with the combination MSP and priors specific to this algorithm exhibiting the best performance. However, optimal overlap/proportion values were found using EBB and priors specific to this algorithm and fMRI task activation maps and RSNs or MSP and considering all the priors (algorithm priors, fMRI task activation maps and RSNs and dFC state modules), respectively, indicating that fMRI spatial priors, including dFC state modules, might contain useful information to recover EEG source components reflecting neuronal activity of interest. Our main results show that providing fMRI spatial derived priors that reflect the dynamics of the brain might be useful to map neuronal activity more accurately from EEG-fMRI. Furthermore, this work paves the way towards a more informative selection of the optimal EEG source reconstruction approach, which may be critical in future studies.

Dataset Information

Mapping the spatiotemporal dynamics of processing task-relevant and task-irrelevant sound feature changes using concurrent EEG-fMRI.

Publications

Mapping the spatiotemporal dynamics of processing task-relevant and task-irrelevant sound feature changes using concurrent EEG-fMRI.

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