Abstract
In this study, we aimed to understand how whole-brain neural networks compute sensory information integration based on the olfactory and visual system. Task-related functional magnetic resonance imaging (fMRI) data was obtained during unimodal and bimodal sensory stimulation. Based on the identification of multisensory integration processing (MIP) specific hub-like network nodes analyzed with network-based statistics using region-of-interest based connectivity matrices, we conclude the following brain areas to be important for processing the presented bimodal sensory information: right precuneus connected contralaterally to the supramarginal gyrus for memory-related imagery and phonology retrieval, and the left middle occipital gyrus connected ipsilaterally to the inferior frontal gyrus via the inferior fronto-occipital fasciculus including functional aspects of working memory. Applied graph theory for quantification of the resulting complex network topologies indicates a significantly increased global efficiency and clustering coefficient in networks including aspects of MIP reflecting a simultaneous better integration and segregation. Graph theoretical analysis of positive and negative network correlations allowing for inferences about excitatory and inhibitory network architectures revealednot significant, but very consistentthat MIP-specific neural networks are dominated by inhibitory relationships between brain regions involved in stimulus processing.
| Dokumententyp: | Zeitschriftenartikel |
|---|---|
| Fakultät: | Medizin |
| Themengebiete: | 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit |
| ISSN: | 1065-9471 |
| Sprache: | Englisch |
| Dokumenten ID: | 65197 |
| Datum der Veröffentlichung auf Open Access LMU: | 19. Jul. 2019, 12:17 |
| Letzte Änderungen: | 04. Nov. 2020, 13:45 |
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