Default mode network deactivation during odor–visual association

Abstract

Default mode network (DMN) deactivation has been shown to be functionally relevant for goal-directed cognition. In this study, we investigated the DMN’s role during olfactory processing using two complementary functional magnetic resonance imaging (fMRI) paradigms with identical timing, visual-cue stimulation and response monitoring protocols. Twenty-nine healthy, non-smoking, right-handed adults (mean age = 26±4 yrs., 16 females) completed an odor-visual association fMRI paradigm that had two alternating odor+visual and visual-only trial conditions. During odor+visual trials, a visual cue was presented simultaneously with an odor, while during visual-only trial conditions the same visual cue was presented alone. Eighteen of the 29 participants (mean age = 27.0 ± 6.0 yrs.,11 females) also took part in a control no-odor fMRI paradigm that consisted of visual-only trial conditions which were identical to the visual-only trials in the odor-visual association paradigm. We used Independent Component Analysis (ICA), extended unified structural equation modeling (euSEM), and psychophysiological interaction (PPI) to investigate the interplay between the DMN and olfactory network. In the odor-visual association paradigm, DMN deactivation was evoked by both the odor+visual and visual-only trial conditions. In contrast, the visual-only trials in the no-odor paradigm did not evoke consistent DMN deactivation. In the odor-visual association paradigm, the euSEM and PPI analyses identified a directed connectivity between the DMN and olfactory network which was significantly different between odor+visual and visual-only trial conditions. The results support a strong interaction between the DMN and olfactory network and highlights DMN’s role in task-evoked brain activity and behavioral responses during olfactory processing.

Disruptions of the olfactory and default mode networks in Alzheimer's disease

“…fMRI preprocessing was done using the SPM8 software (http://www.fil.ion.ucl.ac.uk/spm). Group Independent Component Analysis (gICA) of concatenated data of CN, MCI and AD, was used to identify ON and DMN (Karunanayaka et al., , ). The gICA method implemented in this paper consisted of: (a) preprocessing steps (i.e., mean centering and Principal Components Analysis [PCA]) at both individual (40 components) and group (50 components) levels, and (b) ICA decomposition (using the FastICA algorithm), followed by hierarchical agglomerative clustering.…”

“…Impairment in olfaction and memory are among the first clinical symptoms of Alzheimer's disease (AD) (Doty, Reyes, & Gregor, ; Waldton, ). This is likely due to the close anatomical and functional associations between olfaction and memory systems (Karunanayaka et al., ). Impaired odor identification in particular has been found to be predictive of progressive memory loss and dementia characteristic of AD, however, the relationships between olfactory deficits, dementia, and neurodegeneration remain unclear (Doty et al., ; Moberg et al., ; Murphy, ; Rezek, ; Roberts et al., ).…”

“…In Karunanayaka et al. (), they showed a significant suppression (deactivation) in the DMN during an olfactory fMRI activation task, suggesting that odor processing may be drawing cognitive, attentional, and memory resources (Karunanayaka et al., ). Therefore, we predicted that the functional connectivity (FC) between the ON and DMN can provide (a) a neural basis to relate olfactory and memory deficits in AD and (b) a sensitive predictor of olfactory deficits in early AD.…”

18F-FDG-PET Hyperactivity in Alzheimer’s Disease Cerebellum and Primary Olfactory Cortex

“…Our prior olfactory fMRI studies demonstrated significant reductions in brain activity and connectivity in the primary olfactory cortex (POC) and related structures in AD and MCI subjects [21,26,27]. Thus, the olfactory deficits and neurodegeneration of the central olfactory system should play a central role in AD initiation and progression.…”

Functional connectome analyses reveal a highly optimized human olfactory network

“…The largely subcortical composition of the olfactory system, with many loci at the air-tissue interface, has presented a serious challenge to olfactory network identification, especially for unguided, whole-brain rs-fMRI connectivity analysis. However, important insights into the olfactory network have been gained by targeting olfactory regions of interest (ROIs) (Plailly et al, 2008;Krusemark and Li, 2012;Krusemark et al, 2013;Sunwoo et al, 2015;Kollndorfer et al, 2015;Novak et al, 2015;Karunanayaka et al, 2017;Milardi et al, 2017;Fjaeldstad et al, 2017;Cecchetto et al, 2019), especially in combination with network-science analysis (Meunier et al, 2014;Royet et al, 2011;Ripp et al, 2018;Zhou et al, 2019). The olfactory ROIs are fairly reliably identified, but inconsistencies in network composition and connections also abound in this literature (Fjaeldstad et al, 2017;Cecchetto et al, 2019).…”