Decoding Olfactory Cognition: EEG Functional Modularity Analysis Reveals Differences in Perception of Positively-Valenced Stimuli

Nida Itrat Abbasi, Sony Saint-Auret, Junji Hamano, Anumita Chaudhury, anastasios Bezerianos, Nitish V. Thakor, Andrei Dragomir

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Investigating the functional modular organisation of the brain provides a deeper insight into the complex network phenomena that govern cognitive processes like olfactory perception. In recent years, understanding the neural mechanisms associated with this unique sensory modality has been gaining traction, due to increasing applications in various clinical and non-clinical research areas. Anatomically distinct, but functionally interconnected brain regions, organized as communities (or functional modules) enable high-order cognitive processes by providing support for the integration of several localized, highly specialized processing functions. In this work, to understand the elicited neuronal communication pathways in response to fragrance stimuli of varying positive valence, graph theoretical network metrics were calculated to quantify differences in brain’s functional networks modular organization estimated from source localised EEG signals. We found that inter-modular connectivity differences in neural responses to olfactory stimuli of different pleasantness levels may be linked to inhibitory processes in the frontal and central-occipital regions. Moreover, our results indicate that significant intra-modular connectivity changes may be linked to emotional processing of fragrance stimuli of varying pleasantness.

Original languageEnglish (US)
Title of host publicationNeural Information Processing - 27th International Conference, ICONIP 2020, Proceedings
EditorsHaiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King
PublisherSpringer Science and Business Media Deutschland GmbH
Pages79-89
Number of pages11
ISBN (Print)9783030638351
DOIs
StatePublished - 2020
Externally publishedYes
Event27th International Conference on Neural Information Processing, ICONIP 2020 - Bangkok, Thailand
Duration: Nov 18 2020Nov 22 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12534 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th International Conference on Neural Information Processing, ICONIP 2020
CountryThailand
CityBangkok
Period11/18/2011/22/20

Keywords

  • Brain networks
  • Functional modularity
  • Graph theory
  • Olfaction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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