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Frontiers in Neuroscience 01 frontiersin.org
fNIRS a novel neuroimaging tool
to investigate olfaction, olfactory
imagery, and crossmodal
interactions: a systematic review
EleanorBoot
1
, AndrewLevy
1,2, GiulianoGaeta
3,
NatalieGunasekara
4, EmiliaParkkinen
3, EmilyKontaris
3,
MurielJacquot
3 and IliasTachtsidis
1,4*
1 Metabolight Ltd., London, United Kingdom, 2 Wellcome Centre for Human Neuroimaging, University
College, London, United Kingdom, 3 Health and Well-being Centre of Excellence, Givaudan UK
Limited, Ashford, United Kingdom, 4 Department of Medical Physics and Biomedical Engineering,
University College London, London, United Kingdom
Olfaction is understudied in neuroimaging research compared to other senses,
but there is growing evidence of its therapeutic benefits on mood and well-
being. Olfactory imagery can provide similar health benefits as olfactory
interventions. Harnessing crossmodal visual-olfactory interactions can
facilitate olfactory imagery. Understanding and employing these cross-modal
interactions between visual and olfactory stimuli could aid in the research and
applications of olfaction and olfactory imagery interventions for health and
wellbeing. This review examines current knowledge, debates, and research
on olfaction, olfactive imagery, and crossmodal visual-olfactory integration.
A total of 56 papers, identified using the PRISMA method, were evaluated to
identify key brain regions, research themes and methods used to determine
the suitability of fNIRS as a tool for studying these topics. The review identified
fNIRS-compatible protocols and brain regions within the fNIRS recording depth
of approximately 1.5 cm associated with olfactory imagery and crossmodal
visual-olfactory integration. Commonly cited regions include the orbitofrontal
cortex, inferior frontal gyrus and dorsolateral prefrontal cortex. The findings of
this review indicate that fNIRS would bea suitable tool for research into these
processes. Additionally, fNIRS suitability for use in naturalistic settings may lead
to the development of new research approaches with greater ecological validity
compared to existing neuroimaging techniques.
KEYWORDS
olfaction, olfactory imagery, crossmodal visual-olfactory integration, systematic
review, neuroimaging, fNIRS
Introduction
e olfactory sense is responsible for the detection, encoding and perception of odours.
Humans have an excellent sense of smell (Porter etal., 2006; Yeshurun and Sobel, 2010), and
are reportedly able to discriminate more than one trillion olfactory stimuli (Bushdid etal.,
2014). Despite these abilities, the human olfactory sense is underappreciated (Boesveldt and
Parma, 2021), with one survey reporting that 53% of youths would rather give up their sense
of smell than give up technology (McCann WorldGroup, 2011). e evolutionary decline of
human olfactory use to allow for greater development of visual systems has even led some to
OPEN ACCESS
EDITED BY
Hasan Ayaz,
Drexel University, UnitedStates
REVIEWED BY
Sara Invitto,
University of Salento, Italy
Nobuyuki Sakai,
Tohoku University, Japan
*CORRESPONDENCE
Ilias Tachtsidis
i.tachtsidis@metabolightltd.co.uk
RECEIVED 25 July 2023
ACCEPTED 02 January 2024
PUBLISHED 31 January 2024
CITATION
Boot E, Levy A, Gaeta G, Gunasekara N,
Parkkinen E, Kontaris E, Jacquot M and
Tachtsidis I (2024) fNIRS a novel
neuroimaging tool to investigate olfaction,
olfactory imagery, and crossmodal
interactions: a systematic review.
Front. Neurosci. 18:1266664.
doi: 10.3389/fnins.2024.1266664
COPYRIGHT
© 2024 Boot, Levy, Gaeta, Gunasekara,
Parkkinen, Kontaris, Jacquot and Tachtsidis.
This is an open-access article distributed
under the terms of the Creative Commons
Attribution License (CC BY). The use,
distribution or reproduction in other forums is
permitted, provided the original author(s) and
the copyright owner(s) are credited and that
the original publication in this journal is cited,
in accordance with accepted academic
practice. No use, distribution or reproduction
is permitted which does not comply with
these terms.
TYPE Review
PUBLISHED 31 January 2024
DOI 10.3389/fnins.2024.1266664
Boot et al. 10.3389/fnins.2024.1266664
Frontiers in Neuroscience 02 frontiersin.org
consider olfaction as nothing more than a vestigial sense (Speed and
Majid, 2018). ese attitudes to olfaction are also mirrored in clinical
settings. Unlike disorders of vision and hearing, olfactory disorders
are not routinely screened for despite olfactory change or impairment
being an early warning sign in many diseases including schizophrenia
(Moberg, 1999; Turetsky et al., 2009; Kamath et al., 2017) and
neurodegenerative conditions (Postuma et al., 2011; Doty, 2012;
Hüttenbrink etal., 2013; Lucassen etal., 2016). Olfactory disorders
have been associated with social isolation, poor mental and emotional
health, decreased ability to detect and avoid environmental hazards,
and an increased nancial burden associated with funding treatment
(Stevenson, 2009; Neuland etal., 2011; Croy etal., 2014; Erskine and
Philpott, 2019). Odours and olfactory cues also inuence health
decision making, food choices and addiction maintenance behaviours
(Tiggemann and Kemps, 2005; Patel etal., 2015; Kleinhans etal., 2020;
Roose and Mulier, 2020; Sehrig etal., 2020). e olfactory sense also
has a strong inuence on emotion and wellbeing (Warrenburg, 2005).
Dierent odours have been demonstrated to modulate mood, and
feelings of stress and anxiety (Lehrner etal., 2000; Fukada etal., 2011;
Kaimal etal., 2020). During and following the years of COVID-19
infection the impact to olfaction due to infection complications
became a signicant metric for long-COVID eects (Kapoor etal.,
2021; Tan etal., 2022; Paranhos etal., 2023). Further research could
advise applications of olfaction interventions in health and wellbeing.
As with olfaction, olfactory imagery can play a role in health-
decision making and addiction maintenance behaviours. Olfactive
imagery is the process of mentalising odours or olfactive experiences.
As with other sensory modalities, forming a mental olfactory image
has been shown to recruit sensory regions involved in olfactory
perception (Djordjevic etal., 2005; Bensa etal., 2007; Rinck etal.,
2008). Along with visual and gustatory imagery, olfactory mental
imagery forms a key component of food cravings (May etal., 2004;
Tiggemann and Kemps, 2005). Olfactory imagery tasks have been
shown to reduce food and cigarette cravings (Kemps and Tiggemann,
2007, 2009; Versland and Rosenberg, 2007). Guided mental imagery
interventions using olfactory mental pictures have also been applied
to improve health and wellbeing in clinical populations. It has been
consistently demonstrated that olfactomotor activity during olfactory
imagery mimics that of odour perception; olfactory imagery is
associated with “sning” behaviours, as well as increased respiratory
volume and depth (Bensa etal., 2003, 2005; Arshamian etal., 2008;
Kleemann etal., 2008). Forming pleasant olfactory mental imagery
has also been shown to improve arterial oxygenation, and reduce the
incidence and extent of atelectasis in patients following open heart
surgery (Rezaei-Nodehi etal., 2018).
Despite many people reporting being able to generate olfactive
images, debate still occurs as to whether olfactive imagery is a “true”
form of imagery (Stevenson and Case, 2005). Whilst the mechanisms
of other forms of mental imagery, such as visual imagery, are well
documented, these do not seem to transfer across to imagery
generation in the olfactory domain. As Stevenson and Case (2005)
describe, formation of a visual mental image comprises the retrieval
of an encoding from long-term memory, instantiation in the short-
term visual store, and the representation of the encoding in a
perceptual form. However, debate still occurs as to whether humans
have an olfactory specic short-term or working memory capacity
(White, 1998; Stevenson and Case, 2005). Evocation of olfactive
imagery is oen described as inconsistent and resource intensive, with
generated images oen being described as eeting (Stevenson and
Case, 2005; Plailly et al., 2011) and extremely vulnerable to
confounding inuences (Herz and von Clef, 2001; González etal.,
2006; Royet etal., 2013a,b). However, olfactive imagery capacity has
demonstrated a degree of plasticity, improving with frequency of use
and expertise in the olfactive domain (Plailly etal., 2011; Royet etal.,
2013a,b). Understanding the mechanisms of olfactory imagery could
allow new approaches to access these health and wellbeing benets
associated with olfactory imagery.
One method that could beemployed to reliably evoke olfactive
imagery is to harness naturally occurring crossmodal interactions. A
crossmodal interaction is where information from two individual
sensory modalities, such as vision and smell, are integrated to create
a sensory percept involving information from both modalities, known
as a multimodal percept. e human brain is inherently geared
towards multimodal sensory processing (Deroy and Spence, 2013);
sensory stimuli are rarely experienced in one single modality. e
crossmodal interaction between two stimuli can bedriven by either
semantic or synaesthetic congruence (Molholm, 2004; Hein etal.,
2007; Spence, 2011). A strong cross-modal interaction occurs between
olfactory and visual information (Gottfried and Dolan, 2003;
Österbauer etal., 2005; Novak etal., 2015; Ripp etal., 2018; Sijben
etal., 2018; Stickel etal., 2019). Visual information has been shown to
aid in the detection, discrimination and labelling of odours (Gottfried
and Dolan, 2003; Novak etal., 2015). e processing of visual stimuli
has been demonstrated to exert a priming eect on secondary and
tertiary olfactory regions (Gottfried and Dolan, 2003). As olfactory
imagery also involves many secondary and tertiary olfactory regions
(Djordjevic et al., 2005; Bensa et al., 2007), these crossmodal
correspondences can also beused to facilitate olfactory imagination.
Understanding and employing these cross-modal interactions
between visual and olfactory stimuli could aid in the research and
applications of olfaction and olfactory imagery.
e olfactory sense has demonstrated extreme inter-individual
variability (Morrot et al., 2012; Yunpeng et al., 2020); olfactory
perceptual abilities can vary as a result of experience (Plailly etal.,
2011; Royet etal., 2013a,b; Nováková etal., 2018), genetic factors
(Keller etal., 2007; Josefsson etal., 2017), age (Doty etal., 1984;
Mobley etal., 2014), gender (Royet etal., 2003), and contextual factors
(Gottfried and Dolan, 2003; Herz, 2003; Laudien etal., 2008). As
olfactory imagination abilities are highly correlated with olfactory
perceptual abilities (Plailly etal., 2011; Royet etal., 2013a,b; Flohr
etal., 2014; Kollndorfer etal., 2015a), it follows that olfactory imagery
abilities are subject to these same confounding inuences. Large
variability in the olfactory sense poses a challenge to the
generalisability of olfactory-based research. Similarly, reproducibility
of olfactory ndings is reliant on either stringent control of sample
characteristics, which may threaten generalisability, or large sample
sizes. However, high instrumentation running costs, and time requires
for data acquisition and analysis, places a constraint on the participant
sample sizes which can be analysed with current cognitive
neuroscience research methodologies (Button etal., 2013; Szűcs and
Ioannidis, 2017).
Current research into olfaction, olfactory imagery and crossmodal
visual-olfactory interactions is also limited in ecological validity
(Reader and Holmes, 2016; Elliott etal., 2021). Restrictive, unnatural
environments required for electroencephalography (EEG) and
functional magnetic resonance imaging (fMRI) are not conducive to
Boot et al. 10.3389/fnins.2024.1266664
Frontiers in Neuroscience 03 frontiersin.org
natural olfactory perception and imagination processes. Poor motion
tolerance can limit participants’ ability to interact with olfactory
stimuli in a naturalistic manner. Methods of odour delivery must also
becarefully designed to ensure instrumentation does not introduce
noise or artefacts in neuroimaging data (Gorodisky et al., 2021).
Neuroimaging environments, particularly the MRI scanner
environment, have been demonstrated to impede perceptual decision-
making and attentional focus (Van Maanen etal., 2015). Olfactory
processing and imagination are cognitively demanding tasks which
require a substantial degree of attentional focus (Zelano etal., 2004;
Keller, 2011; Plailly etal., 2011; Royet etal., 2013a,b; Young, 2019).
Due to the limitations in the ecological validity of current research
methodologies in olfaction, olfactory imagery and crossmodal visual-
olfactory processes, ndings must beinterpreted with caution; the gap
between controlled experimental conditions and natural olfactory-
based experiences may not allow neuroscientic research within these
domains to translate into real-world contexts.
fNIRS is an emergent neuroimaging technique which can provide
real-time insights into brain function during cognitive processes.
Leveraging the unique capabilities of fNIRS could provide solutions
to the current challenges in the research of olfaction, olfactory imagery
and visual-olfactory interactions. fNIRS uses near-infrared (NIR) light
to monitor changes in regional cerebral blood volume and
hemodynamics. Light sources and detectors placed on the scalp direct
NIR light at two discrete wavelengths into the brain, and the intensity
of back-scattered light is recorded to monitor localised changes in
oxygenated (HbO) and deoxygenated (HbR) haemoglobin (for further
information, see Scholkmann etal., 2014). Compared to existing
neuroimaging technologies, fNIRS is relatively cheap, easy to set up,
and does not require a specialist environment (Pinti etal., 2020). e
relative ease and lower costs of performing neuroimaging research
using fNIRS can allow for data collection on a much wider scale than
with fMRI. Applying fNIRS technology to the eld of olfactory
imagery research can allow data collection across a broader sample
size to ensure the generalisability of research within these domains.
Additionally, fNIRS exceptional motion tolerance has allowed for the
application of wearable devices to conduct research in naturalistic
settings (Pinti et al., 2018); using fNIRS could allow olfaction,
olfactory imagery and visual-olfactory integration to bestudied in
naturalistic settings, producing more reliable and ecologically
valid data.
However, fNIRS is limited in its recording depth; the channel
between a source and detector pair interrogates the cerebral tissue
between them at a maximum depth of roughly half the source-
detector separation distance (Quaresima and Ferrari, 2019). e
maximum source-detector separation that can beused to maintain a
detectable signal is 3 cm, resulting in a recording depth of roughly
1.5 cm from the scalp surface. Olfaction is an evolutionarily old sense
in humans, and as such, the functional centres associated with
olfaction are mostly subcortical regions in the evolutionarily early
areas of the brain such as the piriform cortex (PC), amygdala, insula
and hippocampus (Royet etal., 2003; Djordjevic etal., 2005; Plailly
etal., 2007; Han etal., 2019; Zhou etal., 2019). ese regions are too
deep for monitoring using fNIRS. However, several cortical regions
have also been implicated in olfactory, imagery and crossmodal
visual-olfactory processes such as the orbitofrontal cortex (OFC),
middle and inferior frontal gyri (MFG, IFG) and inferior parietal lobe
(IPL) (Plailly etal., 2011; Morrot etal., 2012; Meunier etal., 2014;
Zhou etal., 2019; Iravani etal., 2021). ese regions may beaccessible
for monitoring using fNIRS technology (see Discussion).
is review seeks to evaluate contemporary knowledge, debate
and research themes in the elds of olfaction, olfactive imagery and
crossmodal visual-olfactory correspondences. In particular, this
review aims to identify key brain regions associated with these
cognitive processes, and the common methodological approaches
used, to determine whether neuroimaging with fNIRS would bea
suitable tool for research into olfaction, olfactive imagery and
crossmodal visual-olfactive correspondences. We have recently
summarised and reviewed in Gunasekara etal. (2022), the current
status of using fNIRS in olfaction. Wenow seek to expand this to other
neuroimaging modalities and assess the use of neuroimaging
approaches and paradigms within olfactive imagery and crossmodal
visual-olfactory integration, and advise best practise when applying
fNIRS technology in these domains.
Methods
is review was conducted using the Preferred Reporting Items
for Systematic Reviews and Meta-Analyses (PRISMA) method (Page
etal., 2021). e PRISMA ow chart (Figure1) depicts the literature
identication and screening process.
Articles were identied via a keyword search of the PubMed
database using combinations of the key terms [odour|olfactory
imagery|human olfaction|crossmodal|visual-olfactory|neuroimaging].
Boolean operators “AND,” “OR,” and “NOT” were used to combine
key terms into search terms. Papers published between 2003 and 2023
were retained for review. Using these search terms, a total of 112
papers were identied through the PubMed database. For the
purposes of this review, non-human studies and medical case reports
were excluded. Additionally, papers referring to non-evoked olfactory
experiences such as olfactory hallucinations or olfactive auras
preceding migraines and seizures were excluded. Following screening
for these criteria, 17 papers were excluded. Following review of full
text articles, a further 49 papers were removed. Reasons for removal
included irrelevance and incomplete method reporting. Review
articles were retained or excluded on a case-by-case basis. Additionally,
12 articles were identied through other sources. is resulted in a
total of 58 articles included in this review: 3 review articles and 55
primary research reports. Forty articles reported using neuroimaging
research methodologies, 15 articles reported only using behavioural
methodologies; 23 papers used task-based fMRI, 7 papers used resting
state fMRI, 5 papers used EEG approaches, 2 papers used positron
emission tomography (PET), one paper used transcranial magnetic
stimulation (TMS), one paper used fNIRS, one paper used multimodal
fNIRS and EEG, 11 papers employed behavioural task methods, 4
used questionnaires and 3 performed meta-analyses. Methods are
summarised in Figure2. A total of 36 studies used healthy, non-clinical
participants, 19 used a clinical or specic population. ese population
groups included 6 using anosmic participants, 4 used participants
with post-COVID-19 olfactory dysfunction, 1 using epileptic
participants, 1 using blind participants, 1 used autistic participants, 2
contrasted student and expert perfumers, and 4 compared specic age
groups. e distribution of reviewed publications per year is
summarised in Figure3; the number of publications has remained
mostly consistent over the past 20 years. Publications regarding
Boot et al. 10.3389/fnins.2024.1266664
Frontiers in Neuroscience 04 frontiersin.org
olfactory imagery have remained consistent between 2003 and 2023.
Publications regarding crossmodal visual-olfactory interactions
remained consistently low until 2018 which saw three publications on
this topic, with interest continuing up to 2023. With network-based
and connectivity approaches increasing in popularity, this increase in
publications from 2018 may reect an increasing interest to revisit the
topic of crossmodal interactions using these approaches to characterise
the network and connectivity characteristics which underlie
visual-olfactory integrations. Of the six publications regarding
crossmodal interactions published from 2018 onwards, three used
connectivity-based analyses and two investigated network-based
dynamics during visual-olfactory integration. Publications regarding
olfaction dramatically increased in 2021 with ve publications in one
year followed by a further nine publications across 2022 and 2023. It
is likely that this increased interest in olfaction research is associated
with the coronavirus pandemic, with olfactory loss being a common
FIGURE1
PRISMA flow-chart depicting the literature screening process, including number of articles found via keyword searches and additional sources, number
of articles excluded, and number of articles retained.
FIGURE2
Distribution of research methodologies employed for research into olfaction, olfactive imagery and crossmodal interactions.
Boot et al. 10.3389/fnins.2024.1266664
Frontiers in Neuroscience 05 frontiersin.org
symptom of COVID-19 infection. Seven of the fourteen olfactory
publications from 2021 to 2023 compared normosmic with dysosmic
or anosmic participants. With olfactory dysfunction remaining a
prevalent symptom of COVID-19 and long-covid, it is likely that
olfactory research will continue to see increased interest over the next
few years (Table1).
Olfaction
irty-two of the reviewed articles studied aspects of olfaction; 22
employed neuroimaging techniques, eight used behavioural methods
and two performed meta-analyses. fMRI was the most used
neuroimaging method, with ten neuroimaging papers using a task-
based fMRI method and seven using resting-state fMRI. e bias for
employment of fMRI methodology, which has exceptional spatial
resolution, reects the common research theme of localising olfactory
processes within the brain. As the primary and secondary olfactory
regions have been extensively documented prior to 2002 (see
Figures4, 5), many of the reviewed papers instead seek to localise
specic higher level cognitive olfactive processes. e regions
associated with dierent olfactory-related cognitive processes are
summarised in Table2 and Figure6.
Another common theme involved localisation of olfactory
function and functional changes within specic populations. Eight
studies evaluated participants with olfactory dysfunction, and other
studied population groups included student vs. expert perfumers,
younger and older participants, early and late blind participants, and
participants with temporal lobe epilepsy.
With olfactory regions well established, another common
approach was to characterise the involvement of these regions within
the wider network. e increasing popularity of functional network
analyses within cognitive neuroscience is allowing characterisation of
localised brain region function in cognitive processes which are lost
in subtractive models. Ten of the reviewed studies employed a
functional connectivity or network-based analysis approach, and it is
likely that olfactory research will continue to see an increase in this
approach, as is seen in other cognitive neuroscience domains.
A prominent research theme across the reviewed articles was the
study of hedonics; ten studies considered the impact of odour valence
on olfactory processing. Seven of these articles directly contrasted
odour valence: two behavioural (Hudry etal., 2014; Kärnekull etal.,
2021) and ve neuroimaging (Royet etal., 2003; Bensa etal., 2007;
Callara etal., 2021; Gorodisky etal., 2021; Torske etal., 2022). A
further three papers involved discussion of the impact of odour
hedonicity on olfactory processes, but did not directly manipulate
odour pleasantness (Bensa and Rouby, 2007; Plailly etal., 2007;
Morrot etal., 2012). Royet etal. (2003) further extended this by
exploring the impact of handedness, gender and active hedonic
judgements on hedonic odour processing. e lateral aspect of the le
OFC was implicated in mediating the conscious assessment of odour
pleasantness, with this lateralisation being particularly pronounced in
female participants. All seven of the neuroimaging papers which
included themes of hedonic odour processing also cited OFC
involvement. Other regions commonly cited for their involvement in
hedonic odour processing included the piriform cortex, cingulate
gyrus (CgG), superior temporal gyrus (STG), amygdala and insula.
Royet etal. (2003) identication of dierential involvement of the
le and right OFC in olfactory processing also supports evidence of
the lateralisation of olfactory processing. First proposed by Broman
et al. (2001), the dierential involvement of the le and right
hemispheres in olfactory processing remains a pertinent topic of
discussion within olfactory research. Broman etal. suggested the right
hemisphere is involved in low-level perceptually based odour
processing and encoding, and the le hemisphere is associated with
higher-level cognitive-based odour recognition processes and
semantic interpretation. Royet etal. ndings support this theory, with
the le OFC expressing greater involvement that the right OFC in the
judgement of odour pleasantness. Nine other reviewed articles
discussed or presented evidence to support this lateralisation in
olfactory processing (Royet etal., 2003; Djordjevic etal., 2005; Be nsa
etal., 2007; Plailly etal., 2007; Hudry etal., 2014; Kollndorfer etal.,
2015b; Zhou et al., 2019; Douaud et al., 2022; Kretzmer and
Mennemeier, 2022; Eek etal., 2023).
Hudry etal. (2014) provided further evidence of le hemisphere
dominance in semantic and emotional olfactory processing by
FIGURE3
Distribution of publications related to olfaction, odour imagery and crossmodal visual-olfactory integration by year. For the purposes of this review, the
search range was restricted to 2003–2023.
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TABLE1 Summarising articles reviewed on the topics of olfaction, olfactive imagery and crossmodal olfactory-visual integration.
References Cognitive
process
Participants Method Protocol Conclusions
Amsellem etal. (2018) Visual-olfactory
interactions and
subjective experience
14 participants Behavioural tasks Unimodal or bimodal visual
and olfactory stimulation
Visual and olfactory stimuli are
processed in parallel, interactions
inuence various levels of subjective
experience.
Arnold etal. (2020) Human olfactory
network organisation
728 participants Resting-state fMRI Scanned at rest Identied olfactory functional
network and provided network-level
insights into functional
specialisation and spatial segregation
of the olfactory system.
Arshamian etal. (2020) Dierent sensory
embodiment eects on
imagery across
modalities
61 adults, 120
children
Questionnaire PSI-Q Olfactory imagery does not become
more vivid with age and is dierent
to representations from other senses.
Bensa and Rouby
(2007)
Olfactory and emotional
perception abilities
impact on odour
imagery
40 participants Questionnaire VVIQ, VOIQ, PAS, ETOC Olfactory imagery is related to
emotion and good and bad imagers
dier in experience of emotions and
long term memory of smells.
Bensa etal. (2005) Sning patterns during
odour imagery
Exp1: 10
participants
Exp2: 30
participants
Exp3: 40
participants
Behavioural tasks Form auditory, olfactory or
visual mental images
Sning behaviours facilitate odour
imagery and may serve as a reliable
tool for exploring individual
dierences in odour imagery.
Bensa etal. (2007) Hedonic specic
piriform activity in
olfaction and odour
imagery
14 participants Task-based fMRI Smell or imagine odours
following a preparatory cue.
Evidence of activation of primary
sensory olfactory regions during
olfactory imagery.
Callara etal. (2021) Hedonic olfactory
perception
30 participants Task-based EEG Presented with odours of
dierent valence
Interactions with the OFC and brain
regions associated with emotion
recognition and memory
dynamically change with odour
valence.
Djordjevic etal. (2004) Eects of odour and
visual imagery on odour
detection
72 participants Behavioural task Odour, visual or no mental
imagery followed by an
odour detection task
Eect of imagery on detection is
content- and modality-specic.
Djordjevic etal. (2005) Odour imagery
compared with odour
perception
67 behavioural
screening, 12
retained for
scanning
Task-based PET Smell or imagine odours
following a preparatory cue
Neural networks engaged in odour
perception and odour imagery
partially overlap.
Douaud etal. (2022) Brain functional and
structural changes
following COVID-19
infection
401 post-covid, 384
control
Resting-state fMRI Scanned at rest Covid-19 infection associated with
degeneration of olfactory regions
and pathways, and cognitive decline.
Eek etal. (2023) Passive smelling, odour
encoding and odour
recognition
25 participants Task-based fMRI ree odour stimulation
tasks to target passive
smelling, odour encoding
and odour recognition
Identied regions associated with
lower- and higher-order olfactory
functions
Fallon etal. (2020) Eect of visual
congruence on olfactory
habituation
Exp1: 25
participants
Exp2: 25
participants
Task-based EEG Prolonged oral odour
exposure during
presentation of congruent or
incongruent visual stimuli
Congruent visual stimuli enhances
olfactory sensitivity to prolonged
odour stimulation
(Continued)
Boot et al. 10.3389/fnins.2024.1266664
Frontiers in Neuroscience 07 frontiersin.org
TABLE1 (Continued)
References Cognitive
process
Participants Method Protocol Conclusions
Flohr etal. (2014) Odour imagery
following olfactory loss
16 anosmic, 19
healthy control
Task-based fMRI Imagine odours then rate
mental image
Olfactory loss is associated with
diculties performing olfactory
imagery in the conventional way,
and regular exposure to olfactory
information could help maintain
imagery capacity.
González etal. (2006) Word induced olfactory
brain responses
23 participants Task-based fMRI Reading olfactive vs control
words
Reading olfactory words is
associated with activation or
language and olfactory areas.
Gorodisky etal. (2021) Odour induced brain
activity and valence of
odours
20 normosmic, 2
anosmic
Task-based fMRI Passive odour perception
with novel odour canopy
method
Using novel odour canopy method
generates typical olfactory response
in the brain.
Gottfried and Dolan
(2003)
Crossmodal visual
facilitation or olfactory
perception
17 participants Task-based fMRI Unimodal vs bimodal odour
detection task
Human hippocampus mediates
reactivation of crossmodal semantic
associations, even in the absence of
memory processing.
Han etal. (2019) Human olfactory
dysfunction
19 studies Meta-analysis Review of brain regions
associated with olfactory
dysfunction
Summarises structural and
functional alterations associated
with olfactory loss and regain and
new approaches for future clinical
practise.
Han etal. (2022) Eect of generating
odour imagery in
individuals with low
olfactory imagery
abilities
49 participants Task-based fMRI Imagine odours in a long vs
short imagery period
When generating odour images in a
shorter time period, high and low
ability odour imagers may adopt
dierent approaches.
Hörberg etal. (2020) Visual dominance in
visual-olfactory
multisensory integration
30 participants Task-based ERP Bimodal object
categorisation with
competing olfactory and
visual stimuli
Contrary to the idea of visual
dominance, incongruent odours
may uniquely attract mental
processing resources during
perceptual incongruence.
Hucke etal. (2023) Neural spatial
representations of odour
locations
Exp1: 18
participants
Exp2: 14
participants
Task-based EEG
and fNRIS
Monorhinal odour
stimulation presented at
dierent intensities
Trigeminal odour stimulation is
required to create spatial
representation of odour
presentation.
Hudry etal. (2014) Lateralisation of
olfactory processing in
patients with temporal
lobe epilepsy
28 right TLE, 33 le
TLE, 60 control
Behavioural task Odour perception, rating
and naming
Global olfactory impairments in
TLE and evidence for lateralised
olfactory processing.
Infortuna etal. (2022) Motor cortex responses
to pleasant odour
perception and imagery,
impact of personality
and imagery abilities
25 participants Task-based TMS
and EMG
Changes in rMT and MEP
amplitude during odour
perception and imagery.
Perception and imagination of
odours modulates motor cortex
excitability providing evidence for
interactions between olfactory and
motor systems.
Iravani etal. (2021) Functional connectivity
and morphology in
acquired olfactory loss
20 anosmic, 23
healthy control
Resting-state fMRI Scanned at rest Recent sensory loss is associated
with changes in core olfactory areas
and increased dynamic functional
connectivity from olfactory regions
to multisensory integration regions.
(Continued)
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TABLE1 (Continued)
References Cognitive
process
Participants Method Protocol Conclusions
Kärnekull etal. (2021) Verbally induced
olfactory illusions and
visual inuence
17 early blind, 15
late blind, 32
sighted
Behavioural task Odours presented with
negative, neutral and
positive labels.
General mechanisms underlying
verbally induced olfactory illusions
are not caused by visual processing
or visual mental imagery.
Kleemann etal. (2008) Breathing parameters
during odour perception
and olfactory imagery
56 participants Behavioural task Odour perception followed
by mental recall of odour
Olfactory perception and imagery
both have eects on respiratory
prole based on a common
underlying mechanism.
Kollndorfer etal. (2015a) Ability to self-evaluate
olfaction and imagery
abilities
43 anosmic, 16
hyposmic and 16
healthy control
Questionnaires Snin' sticks, self reported
sense of smell (1 to 9), VOIQ
Participants who were able to
perceive odours rely on the vividness
of their mental odour images to
evaluate their olfactory
performance.
Kollndorfer etal.
(2015b)
Olfactory training in
long term anosmia
19 healthy control,
10 anosmic, 7
anosmic followed
up
Task-based fMRI Odour intensity rating
before and aer 12 week
olfactory training
Olfactory training can reorganise
functional networks although no
dierences in spatial distribution
were observed.
Kretzmer and
Mennemeier (2022)
Hemispheric integration
in olfactory stimulation
44 participants Behavioural task Olfactory bilateral vs
unilateral stimulation with
ratio scaling response
Findings consistent with a
summation model of olfactory
integration across le and right
hemispheres.
Leclerc etal. (2019) Olfactory imagery
source memory
48 participants Task-based fMRI Smell or imagine odours, or
hear or imagine words.
Olfactory imagery is susceptible to
source memory errors, and distinct
neural networks underlie auditory
and olfactory imagery involving
dierent areas of the SMA.
Martial etal. (2023) Passive odour
perception and alertness
21 participants, all
male
Resting-state fMRI Lemon or no odour
presented and alertness rated
Higher alertness aer lemon
inhalation versus rest and increased
network integration in olfactory
regions
McNorgan (2012) Multisensory and
modality specic
imagery
65 research reports Meta-analysis ALE and MKDA techniques. Modality-specic imagery regions
overlap but are not conned to
somatosensory and motor execution
areas. e is also a general imagery
network recruited regardless of task.
Meunier etal. (2014) Olfactory memory
networks
16 young, 22 elderly Task-based fMRI Identication of old vs new
odours.
Neural networks involved in odour
recognition memory are organised
into modules and the modular
partitions are linked to behavioural
performance.
Morrot etal. (2012) Individual variability in
olfactory regions
76 participants Task-based fMRI Odour or visual stimuli
detection task.
Low reliability of olfactory
activations means fMRI is not a
suitable diagnostic tool for
neurodegenerative disease in single
subjects.
Muccioli etal. (2023) Cognitive and
functional connectivity
impairment in post-
COVID-19 olfactory
dysfunction
19 hyposmia, 26
control
Resting-state fMRI Scanned at rest Persistent OD following COVID-19
is associated with altered olfactory
network connectivity which
correlates with severity.
(Continued)
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TABLE1 (Continued)
References Cognitive
process
Participants Method Protocol Conclusions
Novak etal. (2015) Subthreshold negative
emotion perception
from olfactory-visual
integration
16 participants Task-based fMRI Rating of valence in odours
and sub-threshold emotional
faces.
Findings conrm involvement of
multisensory convergence areas and
unique areas in olfaction-related
integration and support inverse
eectiveness principle.
Österbauer etal. (2005) Odour responses in
human brain with co-
occurring colour stimuli
9 participants Task-based fMRI Unimodal or bimodal visual
and olfactory stimulation.
Neuronal correlates of olfactory
response are modulated by colour
cues in brain areas previously
associated with hedonic value of
odours.
Palmiero etal. (2013) Imaginative vs semantic
processing
87 participants Behavioural task Two experiments comparing
imaginative and semantic
processing in vision,
audition and olfaction.
Visual and auditory imaginative
processing can bedierentiated
from semantic processing, though
imagery relies heavily on semantic
representations.
Perszyk etal. (2023) Odour imagery,
perception and food cue
reactivity
45 participants Task-based fMRI Oodur perception and
imagination task.
Accuracy of decoding imagined but
not real odour quality correlated
with odour imagery ability and
greater adiposity mediated by cue-
potentiated craving and food intake.
Plailly etal. (2007) Odour discrimination 16 participants Task-based PET Odour detection and odour
discrimination task.
Successively discriminating between
odours activates a le lateralised
frontotemporal network involving
olfactory regions and working
memory regions.
Plailly etal. (2011) Functional
reorganisation of brain
regions involved in
odour imagery in
experts
14 student and 14
expert perfumers
Task-based fMRI Odour imagery task and
passive odour perception
task.
Olfactory expertise is associated
with a functional reorganisation of
olfactory and memory brain regions
allowing increased ability to imagine
odours and create fragrances.
Raj etal. (2023) Cognitive inuence on
odour identication
errors in age related
smell loss
2479 older adults Behavioural Odour naming task from a
set of target and distractor
names
Odour identication errors are
partially explained by semantic and
perceptual similarities.
Rekow etal. (2022) Crossmodal olfactory
facilitation in visual
categorisation
26 participants Task-based EEG Ambiguous and
unambiguous visual stimuli
presented with or without a
congruent odour
Congruent body odour facilitate
rapid, automatic visual
categorisation of ambiguous face
stimuli.
Ripp etal. (2018) Multisensory olfactory-
visual integration
18 participants Task-based fMRI Unimodal or bimodal visual
and olfactory stimulation.
Identied a multisensory integration
processing specic network involved
in olfactory-visual integration.
Royet etal. (2003) Emotional responses to
odours
28 participants Task-based fMRI Pleasant and unpleasant
odour perception.
Lateralised processing of odours
varies with handedness and gender.
Le hemisphere is involved in
judgements of odour pleasantness.
Royet etal. (2013a,b) Odour mental imagery
in non-experts
14 student and 14
expert perfumers
Task-based fMRI Reanalysis of data from
Plailly etal. (2011)
Evidence of odour imagery
capabilities in non-experts, however
the neurophysiological and cognitive
processes vary with expertise.
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TABLE1 (Continued)
References Cognitive
process
Participants Method Protocol Conclusions
Schicker etal. (2022) Removing a modality
during visual-olfactory
stimulation
20 middle aged, 13
older adults
Task-based fMRI Unimodal vs bimodal visual
olfactory stimulation with
removal of a modality at the
end of bimodal trials
Removal of a modality from a
bimodal presentation results in
additional brain activity associated
with attention, memory, and
searching for the missing stimulus.
Schienle etal. (2017) Emotion-specic
nocebo eects
29 participants, all
female
Task-based fMRI Aective image task whilst
wearing odourless patch
under nose.
Nocebo elicited an aversive odour
response to visually induced disgust,
and modulated OFC activation and
connectivity.
Schlintl etal. (2022) Olfactory imagery for
autobiographical
memory retrieval
296 participants, all
female
Behavioural Asked to generate non-
specic odour mental
imagery
Odour imagery more eective than
visual imagery in retrieving
unpleasant adulthood memories or
pleasant childhood memories but
evoked less diverse emotions.
Seo etal. (2010) Cross-modal integration
between odours and
abstract symbols
Exp1: 120
participants
Exp2: 42
participants
Task-based EEG Pleasant or unpleasant odour
presented with congruent,
incongruent or no abstract
shapes
Congruent shapes increased
pleasantness and unpleasantness
ratings of odours and modulated N1
amplitude and latency. Evidence of
abstract shapes modulating odour
perceptual experience.
Sijben etal. (2018) Semantic congruence in
olfactory-visual
perception
19 participants Task-based fMRI Congruent, semi congruent
or incongruent visual and
olfactory stimuli.
Identied le IFG involvement in
multisensory integration across
dierent congruence levels which
would not have been possible with a
subtractive design.
Stickel etal. (2019) Audio-visual and
olfactory-visual
integration in autistic vs
healthy controls
18 autistic and 17
healthy controls
Task-based fMRI Unimodal vs bimodal
olfactory-visual or audio-
visual stimuli.
Multisensory integration has shared
neural sources across olfactory-
visual and audio-visual stimulation
in patients and controls. Enhanced
recruitment of the IPS modulates
changes between areas relevant to
sensory perception.
Tomasino etal. (2022) Multisensory mental
imagery following
covid-19
55 with olfactory or
gustatory
dysfunction, 20
without following
Covid-19
Questionnaire PSI-Q, VOIQ and two
custom questionnaires.
COVID-19 infection frequently
causes hyposmia and dysgeusia, and
may also alter mental
representations responsible for
olfactory and gustatory perception.
Tomiczek and Stevenson
(2009)
Eects of odour naming
on imagery ability
31 participants, all
female
Behavioural task Repetition priming and
recognition naming task
Trying to form an odour image
facilitates performance by producing
a generic state of activation, which
only benets existing odour-name
associations.
Torske etal. (2022) Functional anatomy of
the olfactory system
81 research reports Meta-analysis ALE technique Identied olfactory brain areas with
signicant peaks across all reviewed
brain areas, and regions specic to
dierent odour categories.
Wingrove etal. (2023) Olfactory network
functional connectivity
in post-COVID-19 OD
57 participants,
grouped based on
antibody and
chemosensory
status
Resting-state fMRI Scanned at rest Identies functional dierences in
olfactory, sensory processing and
cognitive functional areas associated
with post-COVID OD
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studying participants with unilateral temporal lobe epilepsy (TLE).
eir ndings highlighted the privileged role of the le hemisphere
for emotional and semantic processing, with le TLE participants
judging odours as less pleasant and exhibiting greater diculty with
identication. Furthermore, the reported advantage for judging odour
familiarity during right nostril stimulation validates the role of the
right hemisphere in encoding the sensory percept of an odour;
familiarity ratings largely reect the clarity of perceptual processing
(Broman etal., 2001; Royet, 2004).
Kollndorfer etal., 2015b evaluated three functional networks
involved in olfactory processing labelled as the olfactory network, the
somatosensory network, and the integrative network. ey reported
the olfactory network was relatively symmetrical across both
hemispheres, whereas the somatosensory network expressed
signicantly greater right hemisphere recruitment and the integrative
expressed a clear le hemisphere bias. Zhou etal. (2019) performed a
laterality index analysis to quantify functional asymmetry of the
olfactory processing. Similarly to Kollndorfer et al., they did not
identify any signicant asymmetry across the primary olfactory
network. ese ndings, suggest that odours are perceived equally by
both hemispheres, but that each hemisphere proceeds to encode
dierent aspects of the odour: the right hemisphere encoding the
TABLE1 (Continued)
References Cognitive
process
Participants Method Protocol Conclusions
Yamashita etal. (2022) Harmony between
colours and odours
5 participants Task-based fNIRS Participants smelled odours
in synaesthetically or
semantically congruent or
incongruent coloured
lighting
Synaesthetic-driven crossmodal
interactions are more congruent
than semantic-driven
Yunpeng etal. (2020) Individual dierences in
olfactory brain
activations in
normosmia/dysosmia
22 dysosmic, 16
normosmic
Task-based fMRI Presented with alternating
blocks of coee smell or
odourless air
Large inter-individual variabilities
for odour-induced brain activation
means it appears problematic to
diagnose olfactory dysfunction on
an individual level using fMRI.
Zhou etal. (2019) Functional pathways in
human olfactory system
25 participants Resting-state fMRI At rest, breathing through
nose
Results provide insight into the
functional and anatomical
organisation of the human olfactory
system.
ALE, activation likelihood estimate; EEG, electroencephalography; EMG, electromyography; ETOC, European Test of Olfactory Capabilities; fMRI, functional magnetic resonance imaging;
fNIRS, functional near-infrared spectroscopy; MEP, motor-evoked potential; MKDA, multilevel kernel density analysis; PAS, physical anhedonia scale; PET, positron emission tomography;
PSI-Q, Plymouth sensory inventory questionnaire; VOIQ, vividness of olfactory imagery questionnaire; VVIQ, vividness of visual imagery questionnaire.
FIGURE4
A schematic view of the human olfactory system. The primary and secondary olfactory regions are represented in blue and green, respectively. Amy,
amygdala; Ento, entorhinal cortex; Hipp, hippocampus; OFC, orbitofrontal cortex; PC, piriform cortex; Thal, thalamus. Retrieved from Saive etal.
(2014).
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FIGURE5
A schematic view of the human olfactory system. The primary, secondary and tertiary olfactory regions are represented in blue, purple and green
respectively.
olfactory perceptual experience, and the le hemisphere encoding
emotional and semantic interpretations of the odour.
e most notable theme within the reviewed olfactory research
was the characterisation of olfactory memory. Fourteen of the
reviewed papers weighed in on the debate regarding olfactory memory
processes. Whilst only three of the reviewed papers actively studied
olfactory memory (Plailly etal., 2007; Meunier etal., 2014; Eek etal.,
2023), eleven papers were able to apply their ndings to contribute
further knowledge to the discussion of olfactory memory (Djordjevic
etal., 2004; Bensa and Rouby, 2007; Plailly etal., 2011; Hudry etal.,
2014; Kollndorfer etal., 2015a; Zhou etal., 2019; Callara etal., 2021;
Infortuna etal., 2022; Torske etal., 2022; Muccioli etal., 2023; Perszyk
etal., 2023). Memory of odours and olfactory experiences presents a
unique case of memory encoding and recall compared to other
sensory modalities (Stevenson and Case, 2005; White, 2009; Eek etal.,
2023). As such, it is understandable that there is great interest in the
study of olfactory memory, and it remains such a prominent topic of
research within olfactory research.
Olfactory memories are highly resistant to forgetting over time
and oen experienced with higher emotional intensity (Roediger
etal., 2017). As shown by Callara etal. (2021) and Zhou etal. (2019),
the olfactory system has connections with both the amygdala and
hippocampus. Whilst other sensory systems must relay through the
thalamus (Eek et al., 2023), the olfactory system is directly
communicating with centres associated with emotion and memory.
Callara etal. (2021) identied the OFC as the node with the highest
inow during olfactory stimulation, noting its key role in olfactory
perception. ey also identied strong interactions between the OFC
and brain regions associated with emotion and memory. ey
conclude that these connections may beresponsible for the enhanced
encoding and emotional intensity of olfactory memory.
Discourse surrounding odour memory is inherently associated
with the lateralisation of olfactory processing. e same le-right
dichotomy in odour processing appears to bemirrored within odour
memory encoding and recall (Broman etal., 2001; Royet, 2004; Hudry
et al., 2014). e “dual process theory” describes two memory
processes contributing to stimulus recognition: “familiarity,” described
as perceptual recognition of an odour related to implicit or
unconscious memory, and “recollection,” described as conceptually
driven recognition along with contextual information retrieval
involving explicit or conscious memory (Royet, 2004; Eek etal., 2023).
ese memory processes are associated with the right and le
hemispheres respectively, mirroring the described laterality of
olfactory processing (Royet, 2004; Hudry etal., 2014). Hudry etal.
(2014) study particularly highlights the complex interplay between the
hemispheres in the recollection and familiarity of odours; odour
identication was impaired in participants with le TLE, whereas
odour familiarity ratings were associated with a clear right-
nostril advantage.
Another point of discussion within olfactory memory research
pertains to the existence of an olfactory working memory capacity
(White, 1998; Stevenson and Case, 2005). e reviewed literature
presents a general consensus to support the existence of a working
memory. One method to interrogate olfactory working-memory is
through odour discrimination; discrimination between successive
odour stimuli requires working memory involvement to hold the
perceptual trace of the rst stimuli for comparison with the subsequent
odour presentation. Plailly et al. (2007) employed an odour
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discrimination paradigm inspired by the n-back task, a common
paradigm used to investigate working memory, to evaluate olfactory
working-memory. Authors identied activations in the le IFG and
OFC associated with the maintenance of the rst odour perceptual
trace, demonstrating the existence of an olfactory working-
memory capacity.
Working-memory capacity in the olfactory domain can also
beinvestigated via tasks which require the maintenance of a neural
representation of olfactory stimuli. For example, Hucke etal. (2023)
used a combined EEG and fNIRS methodology to investigate the
requirement of trigeminal stimulation for neural representation of
odour source localisation. e involvement of somatosensory cortices
during localisation of odour stimuli indicates the dorsal network
involvement in processing where a stimulus occurs, as has been
extensively documented during visual processing, also extends to
olfactory processing (Frasnelli etal., 2012; Hucke etal., 2023). ese
results also support the sensorimotor recruitment models of working
memory whereby the systems involved in the sensory perception of
stimuli can also hold a short-term representation of sensory
information (D’Esposito and Postle, 2015). is provides further
support for the existence of an olfactory working-memory capacity
which mirrors that of other sensory modalities.
e prominence of memory discussion in olfactory processing
also appears to beclosely related to the research theme of odour
hedonics, as hedonic judgements are mainly driven by memory and
semantic smell identication (Schleidt etal., 1988; Sucker etal., 2007).
e performance of hedonic odour judgement, particularly of
unpleasant odours was consistently associated with le hemisphere
involvement within the reviewed literature. Given the evidence
surrounding the lateralisation of odour memory, it appears that this
le hemisphere bias is indicative of sematic and contextually driven
odour recollection processes, mediated by the le hemisphere.
All the reviewed neuroimaging literature reported activation
within at least one of the documented olfactory processing regions.
e reviewed literature presents a consensus as to the lateralisation of
olfactory function, with the right hemisphere associated with low-level
olfactory perceptual processing, and the le hemisphere associated
with higher level cognitive olfactory processing including hedonic
judgements, odour naming, semantic interpretation and olfactory
memory. e three most prominent themes within the reviewed
literature; hedonic odour perception, lateralisation of odour
processing and olfactory memory, all appear to bevery closely related.
Hedonic odour perception was associated with mostly le-lateralised
regions including le OFC, CgG, STG, piriform, and amygdala, and
bilateral insulae. Multiple studies reported activation in regions
associated with memory recall and working memory, including the
PrC, SPL and IFG. e reviewed studies appear to provide support for
the existence of an olfactory-specic working-memory capacity. is
supports the notion that olfactory imagery is mediated by the same
mechanisms underlying other imagery modalities, and hence is a
“true” form of sensory imagery.
Olfactory imagery
Twenty-two articles studied olfactive imagery. Eleven papers
employed neuroimaging methods, ten employed behavioural methods
and one performed a meta-analytic review. Nine neuroimaging papers
used task-based fMRI, one used PET and one used TMS and
EMG. Once again, the dominance of fMRI in olfactory imagery
research appears to reect a common aim of localising olfactory
imagery regions within the brain. A summary of brain regions
associated with olfactory imagery is presented in Table3 and Figure7.
Five neuroimaging papers sought to localise regions associated
with olfactory imagery by contrasting imagery and perception
(Djordjevic etal., 2005; Bensa etal., 2007; Plailly etal., 2011; Leclerc
etal., 2019; Perszyk etal., 2023). Leclerc etal. (2019) identied a
largely le lateralised network including le DLPFC, IFG, IPS, angular
gyrus and pre-SMA, and right frontal pole and IFG which was more
active during odour imagery than during odour perception. is
appears to mirror the prominent discourse around the lateralisation
of olfactory processes within olfaction research; the ndings of a
mostly le-lateralised network associated with olfactory imagery
further corroborates the le hemisphere is involvement in the higher
level cognitive olfactory processes including odour memory and
semantic labelling. Somewhat conversely, Djordjevic etal. (2005)
compared olfactory perception and olfactory imagery, nding odour
imagery eciency scores were signicantly correlated with rCBF
increases in right anterior and posterior OFC. e authors concluded
that this positive correlation suggests successful odour imagery occurs
when the brain treats odour images the same as perceived odours.
Another approach to localise olfactory imagery regions contrasted
olfactive imagery with imagery in other sensory modalities such as
visual or auditory imagery. Four papers contrasted sensory specic
imagery with modality general imagery regions and identied the le
lateralised imagery network is modality general, but that there are also
regions associated with olfactory-specic imagery (McNorgan, 2012;
Flohr etal., 2014; Leclerc etal., 2019; Han etal., 2022). McNorgan
(2012) performed a meta-analysis of articles studying uni- and multi-
sensory imagery to localise imagery general and modality specic
brain regions. ey analysed 65 research reports across olfaction,
audition, gustatory, motor, tactile, visual-colour, visual-form and
visual-motion. Analysis identied a general imagery network of eight,
mostly le lateralised regions. Four le-lateralised clusters exclusively
associated with olfaction were identied in the anterior cingulate,
hippocampus, amygdala and SPL. Similarly, Han et al. (2022)
identied olfactory imagery was associated with greater activation in
bilateral PrC (SPL) and superior occipital cortices, le hippocampus
and right SFG than visual imagery. e authors concluded this
increased involvement of the PrC, superior occipital regions (cuneus)
and hippocampus in the odour imagery condition suggest that odour
imagery may rely more on memory retrieval processes than visual
imagery (Figure8).
Most of the reviewed papers evaluated olfactory imagery in a
healthy, non-clinical population. Due to the wide heterogeneity in
olfactory imagery abilities across the general population, it is
understandable that many reviewed studies still seek to characterise
olfactory imagery in the general population, rather than focusing on
subgroups with potentially atypical olfactory imagery. However, six
papers did investigate specic populations. Studies which included
specic populations included dysosmic or anosmic participants (Flohr
etal., 2014; Kollndorfer etal., 2015a; Tomasino etal., 2022), young vs
adult participants (Arshamian etal., 2020) and student vs expert
perfumers (Plailly et al., 2011; Royet etal., 2013a,b). A common
research theme within these papers was to consider the impact of
olfactory exposure on olfactory imagery abilities. All the reviewed
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TABLE2 Summarising key regions associated with olfaction.
Subcortical Frontal Parietal Temporal
References Cognitive process of
interest
PC Amyg CgG Insula OFC DLPFC IFG SMA/preSMA PrCG PostCG PrC AG STG ITG
Arnold etal. (2020) Human olfactory network
organisation
x x x x
Bensa etal. (2007) Hedonic specic piriform activity in
olfaction and odour imagery
x x x
Callara etal. (2021) Hedonic olfactory perception x x x x
Djordjevic etal. (2005) Odour imagery compared with
odour perception
x x x x x x
Douaud etal. (2022) Brain functional and structural
changes following COVID-19
infection
x x x x x
Eek etal. (2023) Passive smelling, odour encoding
and odour recognition
x x x x x x
Gorodisky etal. (2021) Odour induced brain activity and
valence of odours
x x x
Han etal. (2019) Human olfactory dysfunction x x x x x
Hucke etal. (2023) Neural spatial representations of
odour locations
x x x x x
Iravani etal. (2021) Functional connectivity and
morphology in acquired olfactory
loss
x x x x x
Kollndorfer etal.
(2015b)
Olfactory training in long term
anosmia
x x x x x
Martial etal. (2023) Passive odour perception and
alertness
x x x
Meunier etal. (2014) Olfactory memory networks x x x x x x
Morrot etal. (2012) Individual variability in olfactory
regions
x x x x x x
Muccioli etal. (2023) Cognitive and functional
connectivity impairment in post-
COVID-19 olfactory dysfunction
x x x x
Perszyk etal. (2023) Odour imagery, perception and food
cue reactivity
x x x x x x x x x
Plailly etal. (2007) Odour discrimination x x x x x x
(Continued)
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papers agreed that varying expertise was associated with the
recruitment of dierent brain regions for olfactory imagination.
One potential reason for this dierence in regions may bedue to
dierences in imagery generation techniques. Han etal. (2022) unique
paradigm investigated dierences odour imagery generation across
varying olfactory imagery abilities by employing short vs. long odour
imagery generation times. Participants with lower olfactory imagery
expressed stronger activation in the le SMA and right SFG in the
short olfactory imagery condition than in the long olfactory imagery
condition, brain regions involved in modality general mental imagery
(McNorgan, 2012; Zvyagintsev etal., 2013). is increased activation
of multisensory regions may indicate participants with lower olfactory
imagery abilities formed mental images including other sensory
modalities to facilitate olfactory imagination.
Another possible reason for these dierences may bedierences
in retrieval eort of olfactory memories. Plailly et al. (2011)
identied a bilateral network of regions including the right MFG
which expressed reduced imagery-induced activation with expertise.
ey concluded that the activation decrease associated with
increased olfactory imagery performance are reective of the
“retrieval eort” (Tulving, 1985); student perfumers at the beginning
of their career must deploy a greater level of processing resources to
retrieve the olfactive image than expert perfumers. ese ndings
are further extended by Flohr etal. (2014) who identied increasing
activation in bilateral DLPFC (MFG) associated with olfactory loss,
and that the degree of DLPFC activation varies with longevity of
olfactory dysfunction. Flohr et al. hypothesise this varying
recruitment of DLPFC is the result of greater recruitment of working
memory resources based on similar observations amongst the
visually impaired (Dulin et al., 2011), also providing further
evidence of an olfactory working memory capacity, and supporting
the conclusion of Plailly etal. (2011) that increasing activation
within these regions is indicative of greater retrieval eort correlating
with lower olfactory expertise.
Royet etal. (2013a,b) re-analysed Plailly etal. (2011) data to
identify changes to functional coactivation between 22 ROIs identied
by Plailly et al., hypothesising that increasing olfactory expertise
would beassociated with greater connection across olfactory memory
regions. ey identied professional perfumers demonstrated
signicantly greater coactivations between MFG and the rest of the
olfactory imagery network, and signicantly lower coactivation
between the PrC and rest of the imagery network than student
perfumers. ey concluded these changes to connectivity reect
dierences in the recall mechanisms underlying olfactory imagery
between student and expert perfumers. According to “multiple trace
theory” of memory consolidation, retrieval-related activation of the
hippocampus reduces over time, with more involvement of prefrontal
cortex regions in the recall of more mature memories; retrieval of
some distant memories can have no hippocampal involvement. e
increased connectivity of the middle frontal gyrus with olfactory and
memory regions in the expert group is likely indicative of post
hippocampal memory recall in the expert group. In contrast, the
increased coactivation of the PrC with memory and olfactory regions
in the student group is indicative of allocation of top-down attentional
resources to memory retrieval. Involvement of the superior parietal
lobe, including the PrC, during memory recall has also been associated
with lower condence in the accuracy of mental imagery (Ciaramelli
etal., 2008).
Subcortical Frontal Parietal Temporal
References Cognitive process of
interest
PC Amyg CgG Insula OFC DLPFC IFG SMA/preSMA PrCG PostCG PrC AG STG ITG
Plailly etal. (2011) Functional reorganisation of regions
involved in odour imagery in
experts
x x x x
Royet etal. (2003) Emotional responses to odours x x x x x x x x
Torske etal. (2022) Functional anatomy of the olfactory
system
x x x x x x x x x
Wingrove etal. (2023) Olfactory network functional
connectivity in post-COVID-19 OD
x x x
Yunpeng etal. (2020) Individual dierences in olfactory
brain activations in normosmia/
dysnomia
x x x
Zhou etal. (2019) Functional pathways in human
olfactory system
x x x x x x x x x x
Cortical areas which may besuitable for monitoring using fNIRS are shaded grey (see Discussion). PC, piriform cortex; Amyg, amygdala; CgG, Cingulate gyrus; OFC, orbitofrontal cortex; DLPFC, dorsolateral prefrontal cortex; IFG, Inferior frontal gyrus; SMA,
supplementary motor area; pre-SMA, pre-supplementary motor area; PrCG, precentral gyrus; PostCG, postcentral gyrus; PrC, precuneus; AG, angular gyrus; STG, Superior temporal gyrus; ITG, Inferior temporal gyrus.
TABLE2 (Continued)
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A key nding across the reviewed literature is the involvement of
key memory retrieval and working memory regions in olfactory
imagery. is supports the argument that olfactory imagery is a true
form of sensory imagery. e involvement of memory regions is also
demonstrated to vary with varying levels of olfactory expertise. It is
hypothesised that this reects dierences in the mechanisms of
retrieval, and use of polymodal imagery to facilitate odour imagery
generation. Localisation of olfactory imagery regions compared to
olfactory perception reveals a largely le lateralised olfactory imagery
network including le DLPFC, IFG, IPS, angular gyrus and
pre-SMA. is reects the lateralisation of olfactory function as
proposed by Broman etal. (2001) and prominently discussed within
olfactory research that the right hemisphere is associated with the
sensory perception of odours, and the le hemisphere is involved in
the higher level cognitive olfactory processes including odour memory
and semantic labelling. However, many of the regions identied in this
network appear to be modality-general imagery regions. When
contrasted with imagery in other sensory modalities, olfactory specic
activity is observed in the anterior cingulate, hippocampus, amygdala
and SPL, regions which have also been implicated in memory recall.
It is likely that the enhanced involvement of memory recall regions
within olfactory imagery when compared to other modalities is the
result of greater retrieval eort required to form an olfactory mental
image, and top-down attention direction towards the intended
modality within an involuntary polymodal mental image formed to
facilitate olfactory imagery generation.
Crossmodal interactions
irteen studies investigated crossmodal interactions between
vision and olfaction. Twelve of these papers employed neuroimaging
techniques and one used only behavioural measures. e most
common imaging modality was fMRI, employed by seven of the
reviewed papers. is is likely reective of a strong research aim of
characterising the regions involved in crossmodal interactions. One
study used fNIRS to investigate crossmodal colour-odour
correspondances. e use of fNIRS in this study allowed the
investigation of colour-odour correspondances using a unique
paradigm which has not been used in previous neuroimaging
investigation of crossmodal visual-odour correspondances. Regions
associated with crossmodal interactions are summarised in Table4
and Figure9.
e most commonly used paradigm involved presenting
participants with unimodal vs bimodal visual and olfactory stimuli.
is protocol was used by six studies, ve fMRI studies (Gottfried and
Dolan, 2003; Österbauer etal., 2005; Ripp etal., 2018; Stickel etal.,
2019; Schicker etal., 2022) and one behavioural study (Amsellem
et al., 2018). Within the bimodal condition, all of these studies
presented the bimodal stimuli as congruent or incongruent pairs.
Additionally, Amsellem etal. (2018) included semi-congruent and
semi-incongruent conditions. ey achieved this by selecting two
target odours and creating three additional blended fragrances with
varying ratios of the two target odours. rough this, they were able
to demonstrate that congruence between visual and olfactory stimuli
is not a dichotomy, but rather that participants were able to detect the
nuances of varying degrees of congruence, which impacted upon
pleasantness ratings.
In addition to unimodal vs bimodal conditions, three papers
(Gottfried and Dolan, 2003; Ripp etal., 2018; Stickel etal., 2019) also
included pleasant and unpleasant valence conditions. is resulted in
four bimodal, four unimodal and one baseline condition. From this
Gottfried and Dolan (2003) contrasted these conditions to identify
brain regions associated with olfaction, pleasant and unpleasant odour
perception, olfactory-visual interactions and congruence of olfactory-
visual stimuli. Both Ripp et al. (2018) and Stickel et al. (2019)
performed connectivity analyses. Stickel etal. (2019) investigated
multisensory integration using DCM to analyse information exchange
during bimodal olfactory-visual or auditory-visual stimulation. Using
three key regions identied from the unimodal visual (cuneus),
unimodal olfactory (amygdala) and bimodal congruent (IPS)
conditions, Stickel etal. modelled the network linked to integration of
visual and olfactory stimuli. eir model composed of a driving input
of bimodal olfactory-visual stimulation to the IPS and nonlinear
modulations from IPS to the reciprocal cuneus ↔ amygdala
connection (Figure10). eir results showed an overlapping network
FIGURE6
A schematic representation of commonly cited regions involved in olfaction as identified in this review.
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TABLE3 Summarising key regions associated with olfactory imagery.
Subcortical Frontal Parietal Temporal
References Cognitive process
of interest
PC Hippo Amyg Insula FP OFC SFG MFG/
DLPFC
IFG/
VLPFC
SMA/
preSMA
PrCG PostCG PrC IPS AG ITG
Bensa etal.
(2007)
Hedonic specic piriform
activity in olfaction and
odour imagery
x x x
Djordjevic etal.
(2005)
Odour imagery compared
with odour perception
x x x x x x
Flohr etal.
(2014)
Odour imagery following
olfactory loss
x x x x x x x
González etal.
(2006)
Word induced olfactory
brain responses
x x x x
Han etal. (2022) Eect of generating odour
imagery in individuals
with low olfactory imagery
abilities
x x x x x x
Leclerc etal.
(2019)
Olfactory imagery source
memory
x x x x x x x
McNorgan
(2012)
Multisensory and
modality specic imagery
x x x x
Perszyk etal.
(2023)
Odour imagery,
perception and food cue
reactivity
x x x x x x
Plailly etal.
(2011)
Functional reorganisation
of brain regions involved
in odour imagery in
experts
x x x x x x x x x x x
Royet etal.
(2013a,b)
Odour mental imagery in
non-experts
x x x x x x x x x x
Schienle etal.
(2017)
Emotion-specic nocebo
eects
x x x x
Cortical areas which may besuitable for monitoring using fNIRS are shaded grey (see Discussion). PC, piriform cortex; Amyg, amygdala; FP, Frontal pole; OFC, orbitofrontal cortex; SFG, superior frontal gyrus; MFG, middle frontal gyrus; DLPFC, dorsolateral
prefrontal cortex; IFG, Inferior frontal gyrus; VLPFC, ventrolateral prefrontal cortex; SMA, supplementary motor area; pre-SMA, pre-supplementary motor area; PrCG, precentral gyrus; PostCG, postcentral gyrus; PrC, precuneus; IPS, Intraparietal sulcus; AG, angular
gyrus; ITG, Inferior temporal gyrus.
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of brain regions involved in multisensory integration of olfactory-
visual and audio-visual information. ey also demonstrate the IPS
modulates changes between areas relevant to sensory multisensory
perception by exerting top-down control over primary sensory regions.
Ripp etal. (2018) also used a unimodal vs bimodal paradigm with
congruent and incongruent, and pleasant and unpleasant conditions,
and a graph theoretical network based functional connectivity
analysis. Ripp et al. (2018) identied six nodes which expressed
signicantly stronger functional connectivity in the bimodal condition
than the combination of unimodal conditions. Bimodal presentation
of odour and pictures, collapsed across valence, was associated with
signicantly greater functional connectivity between the right
putamen ↔ right insula, PrC ↔ le SMG and le MOG ↔ le
IFG. Involvement of the right insula and putamen has been observed
in previous studies of multisensory integration, regardless of sensory
modality (Banati, 2000; Bushara et al., 2001; Olson et al., 2002;
Naghavi etal., 2007; Renier et al., 2009), leading the authors to
conclude that this connectivity between the insula and right putamen
is part of a functional multisensory integration specic network.
Involvement of the PrC, as cited in Royet etal. (2013a,b), is likely
indicative of top-down facilitation of memory retrieval. Ripp etal.
proposed that the increased connectivity between the PrC and SMG
is indicative of memory retrieval and maintenance of the retrieved
memory within a phonological store, once again supporting the
evidence for an olfactory working memory. e le IFG has also been
shown to be associated with odour working memory, semantic
interpretation and odour naming (Djordjevic etal., 2005; Plailly etal.,
2007). Ripp etal. suggested that the increased connectivity of the le
MOG, a visual processing region, and le IFG allows the matching of
visual information with odour semantic information. ey further
propose that retrieved odour memory information, held in the
phonological store, along with visual information and semantic
information from the le MOG and IFG are passed via the inferior
fronto-occipital fasciculus, a large white matter tract connecting the
frontal, temporal and occipital lobes, to the temporal association
cortex where this information is fused into a multisensory percept.
Four fMRI studies involved connectivity-based analyses, with two
employing Dynamic Causal Modelling (DCM) (Novak etal., 2015;
Stickel etal., 2019), one employing psychophysical interaction analysis
(Sijben etal., 2018) and one performing graph theoretical network
analysis (Ripp etal., 2018). As with olfaction, this reects a trend
towards investigation of network-based interactions underlying
multisensory integration. As multisensory integration and crossmodal
correspondences require the integration of information from multiple
networks, including sensory specic processing networks and
memory networks, Sijben etal. (2018) argue that connectivity-based
analyses are a better tool to characterise these processes than
subtractive analysis models. Similar to Amsellem etal. (2018) and
Sijben etal. (2018) included a semi-congruent condition to investigate
the impact of semantic congruence on olfactory-visual integration.
eir results indicated a dierential connectivity of parcellations of
the IFG with seed regions from dierent networks involved in sensory
and multisensory processing depending on the degree of congruence
between the stimuli. is highlights the crucial role of the IFG in
multisensory processing, potentially functioning as a hub for
determining the degree of congruence between the stimuli. is
supports Ripp et al. (2018) suggestion that IFG supplies odour
working memory and semantic information for integration of visual-
olfactory information. Increased connectivity with the putamen
during congruent and semi-congruent multisensory processing also
reects previous ndings of putamen involvement in multisensory
integration. Using a connectivity approach, Sijben etal. (2018) were
able to go beyond identifying regions involved with visual-olfactory
integration, and instead were able to begin to describe the mechanisms
of action within these regions.
Rather than using an image to provide visual stimulation,
Österbauer et al. (2005) used colours. Odours and colours were
presented in unimodal, bimodal congruent or bimodal incongruent
form with participants responding as to how well the odour and
colour “t.” Unimodal odour presentation was associated with activity
in primary and secondary olfactory regions: bilateral piriformis and
amygdalae, putamen, right OFC and le insula. Using colour-odour
congruency as an additional parametric modulator, Österbauer etal.
identied a network of brain areas exhibiting increasing activity with
higher perceived congruence. is network was entirely le lateralised
and included OFC, IFG, gyrus rectus and anterior insula. Österbauer
FIGURE7
A schematic representation of commonly cited regions associated with olfactory imagery as identified in this review.
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etal. also performed an additional contrast to identify regions which
express superadditive responses to bimodal congruent stimuli, as
described by Calvert etal. (2000, 2001), and Calvert (2001). Regions
which express a greater response to bimodal stimulation than the
addition of responses to unimodal stimulation conditions (i.e.,
olfactory-visual > olfaction + visual) are said to express linear
superadditivity. Österbauer etal. also included behavioural ratings of
colour-odour congruence within their superadditivity model such that
BOLD response to colour-odour pairings which were rated as a “very
good t” were modelled as larger than the response to pairings which
a “very bad t.” ey identied superadditivity eects of colour-odour
stimulation within the SFG, ACC and OFC. Österbauer et al.
observation of right OFC involvement in unimodal olfactory
processing versus le OFC correlation with colour-odour congruence
further supports Broman etal. (2001) theory that the right hemisphere
is involved in low-level perceptually-based odour processing, and the
le hemisphere associated with higher-level cognitive-based odour
recognition and semantic interpretation.
Similarly, Yamashita et al. (2022) investigated colour-odour
correspondences. eir use of fNIRS allowed investigation of colour-
odour correspondences in a novel immersive paradigm; whilst
previous research has typically employed presentation of small-eld
colour patches or display stimuli, using fNIRS allowed Yamashita etal.
to sit participants within a booth illuminated in one of dierent
colours. fNIRS monitoring was performed with two channels covering
the le and the right OFC; the study compares the balance of le
versus right OFC involvement in each of the stimulation conditions.
Perception of pleasant and unpleasant fragrances, and crossmodal
colour-odour stimulation were all associated with greater
oxyhaemoglobin (HbO) change in the le OFC than right OFC,
FIGURE8
(A) A table of modality general regions identified by McNorgan (2012). (B) The general imagery network (cool colours) identified using ALE analysis.
Conjunction analysis of studies comparing complex and resting-state baseline conditions identified nine clusters (hot colours) that were active across
all imagery conditions, regardless of baseline task. L, left; R, right; SMA, supplementary motor area; Med, medial; BA, Brodmann area. Retrieved from
McNorgan (2012).
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TABLE4 Summarising key regions associated with crossmodal visual-olfactory integration.
Subcortical Frontal Parietal Temporal Occipital
References Cognitive
process of
interest
PC Insula Hippo Amyg Put OFC MFG/
DLPFC
IFG/
VLPFC
SMA/
preSMA
PrCG PostCG PrC IPL SMG STG/STS Occ Fus
Gottfried and
Dolan (2003)
Crossmodal visual
facilitation or olfactory
perception
x x x x x x
Novak etal.
(2015)
Subthreshold negative
emotion perception
from olfactory-visual
integration
x x x x x
Österbauer etal.
(2005)
Odour responses in
human brain with
co-occurring colour
stimuli
x x x x x x
Ripp etal. (2018) Multisensory olfactory-
visual integration
x x x x x x x x x x
Schicker etal.
(2022)
Removing a modality
during visual-olfactory
stimulation
x x x x x
Sijben etal.
(2018)
Semantic congruence
in olfactory-visual
perception
x x x x x x
Stickel etal.
(2019)
Audio-visual and
olfactory-visual
integration in autistic
vs healthy controls
x x x x x x x x x x
Cortical areas which may be suitable for monitoring using fNIRS are shaded grey (see Discussion). PC, piriform cortex; Hippo, hippocampus; Amyg, amygdala; Put, putamen; OFC, orbitofrontal cortex; MFG, middle frontal gyrus; DLPFC, dorsolateral prefrontal
cortex; IFG, Inferior frontal gyrus; VLPFC, ventrolateral prefrontal cortex; SMA, supplementary motor area; pre-SMA, pre-supplementary motor area; PrCG, precentral gyrus; PostCG, postcentral gyrus; PrC, precuneus; IPL, Inferior parietal lobule; SMG,
supramarginal gyrus; STG, superior temporal gyrus; STS, Superior temporal sulcus; Occ, occipital regions; Fus, fusiform.
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agreeing with previous ndings of greater le hemisphere involvement
in higher order cognitive olfactory processes (Broman etal., 2001;
Hudry etal., 2014). Crossmodal presentation of odours resulted in
greater OFC signal change than crossmodal presentation of odour
names. As OFC has been shown to demonstrate superadditivity eects
during crossmodal olfactory-visual stimulation (Österbauer etal.,
2005), these results indicate that synaesthetically driven crossmodal
correspondences are more harmonious than semantically driven
correspondences. However, care must betaken when interpreting
these results as indications of neural activity. HbO signals are more
vulnerable to systemic artefacts which may articially amplify signal
changes and aect interpretation of results (Kirilina etal., 2012;
Tachtsidis and Scholkmann, 2016; Dravida etal., 2018). To draw any
rm conclusions, the study should berepeated with analysis of both
oxy- and deoxyhaemoglobin (HbR) signals to ensure the signal
changes are arising from neuronal activity rather than scalp level
haemodynamics or other systemic artefacts.
e most common paradigm to investigate crossmodal
interactions was a unimodal vs bimodal stimulation task. As expected,
unimodal olfactory stimulation was associated with activity within
primary and secondary olfactory regions. Bimodal olfactory and
visual stimulation was associated with activity in a largely le
lateralised network including OFC, IFG, gyrus rectus and insula.
ese ndings further support the theory of lateralised olfactory
processing proposed by Broman etal. (2001) that the right hemisphere
mediates low level olfactory perceptual processes and the right
hemisphere is mediating the higher level cognitive olfactory processes.
e involvement of the le IFG reects previous ndings that the le
IFG is associated with semantic recognition of odours and hedonic
judgements. Superadditive eects were noted in the SFG, ACC and
OFC. e involvement of the SFG and ACC provides further support
for the role of memory networks within the creation of crossmodal
percepts. Additionally, the dierential involvement of right OFC in
unimodal olfactory and le OFC in bimodal stimulation provides
further support for the laterality of olfactory processing proposed by
Broman etal. (2001). Functional connectivity analyses highlight the
involvement of parietal regions including the IPS and PrC exerting a
top-down control over primary sensory regions.
Discussion
is review analysed current literature to provide an overview of
brain regions associated with olfaction, olfactive imagery and
crossmodal visual-olfactive correspondences, and the common
protocols and methodologies used to research these topics. Wenow
focus our discussion to determine whether fNIRS would bea suitable
tool for further research within this eld. It is important to note, this
review summarises brain regions cited within the reviewed literature
for their involvement in olfaction, olfactory imagery and crossmodal
visual-olfactory integration, and highlights the potential accessibility
of these regions for monitoring via fNIRS technology. Many of the
cited regions are large regions with supercial aspects, but also extend
deeper within the brain. Where this review hypothesises on the
possibility of recording from these regions, this pertains to the
supercial aspects of these regions which are within the maximum
FIGURE9
A schematic representation of commonly cited regions associated with crossmodal visual-olfactory integration identified in this review.
FIGURE10
Eective connectivity model for olfactory-visual stimulation
identified using DCM by Stickel etal. (2019). Amy, amygdala; C,
cuneus; IPS, inferior parietal sulcus. Retrieved from Stickel etal.
(2019).
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recording depth of ~1.5 cm from the scalp surface. However, some of
the reviewed studies report peak activity associated with the cognitive
processes of interest within the deeper aspects of the regions.
is is particularly pertinent in the case of the orbitofrontal cortex
(OFC). OFC involvement in olfaction and olfactory imagery
interrogated with fMRI usually report peak voxels within the ventral
and medial aspects of the OFC; fNIRS technology, however, is only able
to record signals from the ventral and lateral aspects of the OFC. Whilst
the peak of activity may bebeyond the depth of fNIRS monitoring
abilities, task-based changes in regional cerebral blood ow may occur
across a larger area within the cited region which may bedetectable
with fNIRS within the supercial aspects of these cortical regions. As
summarised in Gunasekara etal. (2022), have used fNIRS to monitor
the OFC during olfactory stimulation. Twelve of these studies reported
fNIRS signal changes within the interrogated regions of the
OFC. However, it must benoted that ten of these studies only report
on HbO changes. Due to the susceptibility of this signal to confounding
noise, these activations cannot bereliably interpreted as neuronal
signal (see Tachtsidis and Scholkmann, 2016 for further information).
As such, further investigation would beneeded to validate that fNIRS
signals recorded in OFC during olfaction are from olfactory-related
neurovascular coupling, rather than systemic blood ow changes.
Whilst most primary and secondary olfactory processing regions
are subcortical structures, and hence inaccessible with fNIRS
technology, a number of supercial tertiary olfactive areas are
highlighted within the literature as being involved in olfactory
perception and higher-level olfactory processes. Whilst these
functional centres are not unique to olfactive processes, many report
reliable activation within olfactory tasks. Olfactory perception tasks
reliably activated the piriform cortex, as well as the insula and
cingulate gyrus (see Table 2); all commonly cited primary and
secondary sub-cortical olfactory regions. Additionally, all olfactory
studies found activation in at least one cortical region accessible to
fNIRS. e most commonly identied region was the OFC, a
secondary olfactory processing region, cited in ten papers. Other
commonly cited regions included IFG, PrC, SMA, PrCG, and
DLPFC. Using a well-established task which is known to involve
olfactory processing, olfaction can be studied using fNIRS, with
regions of interest accessible in the frontal and parietal lobes. As
summarised in Gunasekara etal. (2022), multiple studies have already
used fNIRS to study olfaction. ese studies predominantly
considered prefrontal regions of interest.
As fNIRS devices are portable and wearable and do not require a
specialist shielded room or strong magnetic environment, as with
EEG and fMRI, fNIRS technology lends itself to multi-modal
monitoring. As demonstrated by Hucke etal. (2023), fNIRS can
becombined with EEG allowing insights into olfactory processing
beyond what would bepossible by a single monitoring modality alone.
fNIRS can also becombined with physiological measurements such
as cardiac and blood pressure monitoring, breathing monitoring,
electrodermal monitoring and plethysmography monitoring. As
hedonic odour processing, and the highly emotive nature of olfactive
memory are common themes within olfactory research, application
of fNIRS monitoring with accompanying physiological measurements
may beable to provide new insights within this eld. Royet etal. (2003)
investigated emotional responses to pleasant and unpleasant odours
using fMRI with accompanying electrodermal, plethysmography and
breathing monitoring to detect covert emotional responses. Whilst
multimodal monitoring in this way is possible with existing
neuroimaging techniques, this oen requires specialised and
expensive systems due to the restrictive environments required for
these imaging methods. e ease of including multimodal
physiological measurements alongside fNIRS may allow future studies
to similarly study the impact of covert emotional responses on
olfactory hedonic judgements, and olfactory memory encoding and
recall. In a similar vein, applying fNIRS with accompanying
multimodal physiological monitoring to the study of olfactory
imagery and crossmodal visual-olfactory interactions may allow for
novel insights into the role of emotional association in the recall and
imagination of odours, such as imagining personally nostalgic odours,
and the multisensory integration of emotionally charged odours with
congruent visual cues. Accompanying electrodermal and
plethysmography recordings may vary between fMRI and fNIRS due
to the dierent postures during monitoring, signal change ndings
should remain consistent between the two modalities.
Furthermore, applying fNIRS technology to a paradigm such as
described in Leclerc etal. (2019) may result in signicant ndings
between sham and tDCS conditions which were not found in their
present study. Leclerc etal. applied real or sham tDCS to the SMA
prior to performing an imagery and source memory task. ey
hypothesised tDCS would result in neuromodulatory eects to the
SMA which would alter source memory and imagery generation.
However, they found no signicant eects of tDCS on imagery or
source memory performance and concluded that the
neuromodulatory eects may have been lost to washout before
scanning could beperformed. As fNIRS technology can beused in
conjunction with tDCS (Muthalib etal., 2013; McKendrick etal.,
2015), repeating Leclerc etal. paradigm with fNIRS rather than fMRI
could allow neuromodulatory eects of tDCS to beinvestigated
without losing them to washout during preparation and set-up of
the scanner.
Olfactory imagery has been consistently demonstrated to recruit
olfactory regions including the piriform cortex, insula, hippocampus,
amygdala and OFC (Djordjevic etal., 2005; Bensa etal., 2007; Plailly
etal., 2011; Royet etal., 2013a,b; Flohr etal., 2014; Schienle etal.,
2017). All reviewed studies reported activation in at least one of these
regions during olfactory imagery. Multiple reviewed studies also cited
additional supercial cortical regions activated during olfactory
imagery. Leclerc et al. (2019) identied a mostly le-lateralised
network of cortical regions exhibiting greater activation during
olfactory imagery than during olfactory perception. Regions included
le DLPFC, IFG, IPS, angular gyrus and pre-SMA, and right frontal
pole and IFG. Each of these regions were cited in at least one other
reviewed article. e le DLPFC, IFG, IPS, angular gyrus and
pre-SMA are regions which have also been implicated in modality-
general imagery (McNorgan, 2012). McNorgan (2012) analysis
identied four le lateralised regions recruited exclusively by olfactive
imagery. Of these four regions, only the cluster in the le SPL would
beaccessible using fNIRS. Le DLPFC, IFG, IPS, angular gyrus and
pre-SMA can bemonitored using fNIRS to identify rCBF changes
within these regions during olfactive imagery, but care must betaken
to ensure the task is evoking olfactory mental imagery, and not
involuntary imagery across other, more dominant, sensory modalities
such as visual imagery. Using a task such as Han etal. (2022) which
used visual imagery generation as a control condition in an olfactory
imagery task could allow for the subtraction of activations associated
with involuntary visual imagery generation from olfactory imagery
activation. Alternatively, asking participants to self-report whether
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they experienced co-occurring imagery across other modalities when
generating an olfactive image could beused to ensure the task is
evoking olfactory imagery.
fNIRS can also beused to investigate lateralisation of function
across hemispheres, a prominent topic of investigation across all three
of the reviewed research domains. e degree of lateralisation of
activity can beevaluated by calculating the laterality index, similar to
methods used by Zhou etal. (2019), where laterality is equal to le
hemispheric activity minus right hemispheric activity, divided by
combined le and right hemisphere activity (Ishikawa etal., 2014).
is results in a laterality index score between [−1 to 1] where negative
values represent greater right hemispheric lateralisation and positive
values represent greater le hemispheric lateralisation. In this manner,
laterality can beassessed on a whole hemisphere basis, on particular
regions, or on a single channel-wise basis. Applying fNIRS to any of
these research domains, laterality can easily bestudied in this way. For
example, Leclerc etal. (2019) identied that the olfactory imagery
network is largely le lateralised. Repeating Leclerc etal. paradigm
and using a lateralisation analysis should result in a positive laterality
index between the le and right hemispheres on a whole brain level,
and positive laterality indices between channels covering the le and
right DLPFC, IPS, angular gyri and pre-SMA. Additionally, comparing
the le and right IFG should result in a positive laterality index, but to
a lesser degree, and comparing the le and right frontal poles should
result in a negative laterality index during olfactory imagery.
fNIRS technology applied to a unimodal vs. bimodal paradigm
could also beused to evaluate linear superadditivity during bimodal
olfactory-colour stimulation as described in Österbauer etal. (2005).
As with fMRI, amplitude of signal change can beevaluated with fNIRS
to identify regions which express greater activation to bimodal
olfactory-colour stimulation than the sum of unimodal olfactory and
unimodal colour stimulation. Using a paradigm such as Österbauer
etal., unimodal odour stimulation should result in detectable signal
changes in the right OFC. Bimodal presentation of odour and colours
should result in detectable signal changes in le IFG, frontal operculum
and temporal pole. Additionally, these regions should exhibit increasing
activity with higher perceived congruence. Finally, superadditive signal
increases should bedetectable in the le SFG. However, as seen in
Yamashita etal. (2022), application of fNIRS technology could allow
for extension of Österbauer etal. paradigm beyond colour patches to
create a more immersive paradigm by placing participants in a
coloured booth, or allowing participants to freely move between
environments with dierent odour and colour combinations; this can
allow for investigation of superadditive eects of olfactory-colour
stimulation within more ecologically valid environments.
As fNIRS signals are extremely susceptible to contamination by
physiological noise, block designs are commonly employed to
maximise statistical power (Tie etal., 2009; see also Friston etal.,
1999; Brockway, 2000). Whilst event-related designs can beused
with fNIRS, they have less statistical power than blocked designs,
and as such, require a greater number of participants and repetitions
to increase this power (Tie etal., 2009). Presentation of olfactory
stimuli in a rapid, time-locked procession as is required for event
related designs required highly specialised equipment. Furthermore,
for use with fMRI and EEG, this equipment must bespecically
designed to meet the environmental requirements of these
modalities. Use of block designs in fNIRS removes the need for
rapid event-related stimulus presentation. However, care must still
betaken in the consideration of odour delivery methods to ensure
odour presentation can still betime-locked, odours can persist at
an even intensity across block length, and that odours do not persist
beyond the block length. As such, a specialised odour delivery tool
may still berequired. Alternatively, the portability of fNRIS could
allow for the creation of novel paradigms which could present
dierent odours to the participant by the use of dierently
fragranced rooms, for example. With the advancement of tools for
statistical analysis of fNIRS signals collected from naturalistic
paradigms, odours could bedelivered in an even more ecologically
valid method such as creating “odourscapes” in which the
participant could move freely.
The portability of fNIRS devices could also allow future novel
paradigms to bedeveloped which allow participants to explore
olfaction, odour imagery and crossmodal interactions whilst
moving freely in an immersive environment. Indeed, Yamashita
et al. (2022) paradigm reflects a move in this direction by
applying fNIRS technology to investigate crossmodal colour-
odour correspondences in an immersive lighting environment.
Future research could investigate the perception or imagination
of odours, or crossmodal visual-odour correspondences within
naturalistic environments with rich ecological validity.
Neuroimaging study design usually requires stringent time-
locked events. However, advanced analytic approaches, such as
Automatic IDentification of functional Events (AIDE) method,
can allow for a brain-first approach to identify event onsets from
real-world fNIRS neuroimaging data (Pinti etal., 2017). This can
allow for flexible self-paced paradigms without the need for
stringent time constraints, further increasing the ecological
validity of the study.
Whilst there are a number of neuroimaging and behavioural
paradigms which can beadapted for research using fNIRS, and scope
for the development of novel naturalistic paradigms, care but betaken
when designing these studies to ensure the reliability, validity and
reproducibility of any ndings. As fNIRS signals are recorded at the
scalp level, they are vulnerable to contamination from systemic noise
(see Tachtsidis and Scholkmann, 2016 for full review). Physiological
noise sources such as heart rate, breathing, mayer waves and scalp
haemodynamic changes can becharacterised using short-separation
channels and physiological monitoring, and these components can
beregressed from the fNIRS signal. Using additional physiological
monitoring of respiration characteristics is particularly pertinent in
olfactory and odour imagery research as both olfaction and odour
imagery are associated with modulations to breathing (Bensa etal.,
2005; Mainland and Sobel, 2005; Kleemann etal., 2008; Rinck etal.,
2008). Additionally, study designs should avoid stimulation
frequencies which overlap with systemic oscillations such as the
respiration rate (~0.3 Hz) and the mayer wave (~0.1 Hz) as these can
articially amplify the fNIRS signal. As described above, jittering rest
periods can also help to avoid synchronisation with systemic
uctuations. It is also crucial to investigate both oxy- and
deoxyhaemoglobin signals. During a haemodynamic response to
support neuronal activity, the concentrations of HbO increases and
HbR decreases due to the oversupply of blood ow to support
neuronal function. As such, the fNIRS signals for HbO and HbR
concentration should beanticorrelated within the active region.
Failure to investigate both parameters could lead to misinterpretation
of signal changes from systemic sources as evidence of neuronal
Boot et al. 10.3389/fnins.2024.1266664
Frontiers in Neuroscience 24 frontiersin.org
activity (for further information regarding the best practices for
fNIRS research and publications, see Yücel etal., 2021).
Conclusion
Olfaction, olfactive imagery and crossmodal visual-olfactory
integration are all associated with activation in widespread cortical
regions across frontal, parietal, temporal and occipital lobes. Many of
the regions functionally activated during these processes would
beaccessible for monitoring using fNRIS. Additionally, many of the
common paradigms and protocols would besuitable for conducting
research with fNRIS technology. Furthermore, fNIRS suitability for
use in naturalistic settings may allow for development of new research
paradigms in naturalistic settings with greater ecological validity than
previously available neuroimaging techniques.
Author contributions
EB: Writing – original dra, Writing – review & editing. AL:
Writing – review & editing. GG: Writing – review & editing. NG:
Writing – review & editing. EP: Writing – review & editing. EK:
Writing – review & editing. MJ: Writing – review & editing. IT:
Writing – review & editing.
Funding
e author(s) declare nancial support was received for the
research, authorship, and/or publication of this article. Metabolight
Ltd. was funded by Givaudan to conduct this review. e funder was
not involved in the study design, collection, analysis, interpretation of
data, the writing of this article, or the decision to submit it
for publication.
Conflict of interest
EB and AL were employed by Metabolight Ltd. IT was the CEO
of Metabolight Ltd. and received funding from Givaudan to conduct
this literature review. GG, EK, EU, and MJ were employed by the
Givaudan UK Limited. NG was studying at University College London
under an Engineering and Physical Sciences Research Council
Studentship funded in part by Givaudan. Metabolight Ltd. was
contracted by Givaudan to conduct this literature review.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their aliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
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