# Multimodal Integration of Olfactory and Visual Processing through DCM
analysis: Contextual Modulation of Facial Perception

Gianluca Rho1,2,∗, Alejandro Luis Callara1,2, Francesco Bossi1, Dimitri
Ognibene3,4, Cinzia Cecchetto5, Tommaso Lomonaco6, Enzo Pasquale
Scilingo1,2,†, and Alberto Greco1,2,† 1Dipartimento di Ingegneria
dell’Informazione, University of Pisa, Pisa, Italy  2Research Center “E.
Piaggio”, School of Engineering, University of Pisa, Pisa, Italy  3Università
Milano-Bicocca, Milan, Italy  4University of Essex, Colchester, UK
5Department of General Psychology, University of Padova, Italy  6Department of
Chemistry and Industrial Chemistry, University of Pisa, Italy  †These authors
contributed equally to the work<EMAIL_ADDRESS>
###### Abstract

This study examines the modulatory effect of contextual hedonic olfactory
stimuli on the visual processing of neutral faces using event-related
potentials (ERPs) and effective connectivity analysis. The aim is to
investigate how odors’ valence influences the cortical connectivity underlying
face processing, and the role arousal enhanced by faces plays on such visual-
odor multimodal integration. To this goal, a novel methodological approach
combining electrodermal activity (EDA) and dynamic causal modeling (DCM) was
proposed to examine cortico-cortical interactions changes. The results
revealed that EDA sympathetic responses were associated with an increase of
the N170 amplitude, which may be suggested as a marker of heightened arousal
to faces. Hedonic odors had an impact on early visual ERP components, with
increased N1 amplitude during the administration of unpleasant odor and
decreased vertex positive potential (VPP) amplitude during the administration
of both unpleasant and neutral odors. On the connectivity side, unpleasant
odors strengthened the forward connection from the inferior temporal gyrus
(ITG) to the middle temporal gyrus (MTG), involved in processing changeable
facial features. Conversely, the occurrence of sympathetic responses was
correlated with an inhibition of the same connection, and with an enhancement
of the backward connection from ITG to the fusiform face gyrus. These findings
suggest that negative odors may enhance the interpretation of emotional
expressions and mental states, while faces capable of enhancing sympathetic
arousal prioritize the processing of identity. The proposed methodology
provides insights into the neural mechanisms underlying the integration of
visual and olfactory stimuli in face processing.

Keywords: face processing, olfactory stimuli, dynamic causal modeling,
sympathetic responses, brain connectivity, ERP

##  1 Introduction

The processing of facial expressions is a fundamental mechanism for perceiving
others’ intentions and emotions [1]. In real-life situations, this process is
not limited to the visual system alone but is influenced by contextual
information from other sensory channels [2]. Olfactory stimuli have been found
to play a crucial role in modulating the hedonic perception of faces [1, 3].
Previous studies have shown that the emotional valence conveyed by contextual
odors can affect the recognition of facial expressions and subjective ratings
of faces [4, 5, 6, 7, 8, 9].

These behavioral responses are accompanied by physiological changes, as
evidenced by electroencephalographic (EEG) event-related potential (ERP)
studies. These studies have reported an effect of the valence of the odors at
both early sensory stages (P1/N1, N170, and Vertex Positivity Potential (VPP))
[8, 10, 11, 12] and later cognitive stages (late positive potential) of face
processing [13, 14, 15]. However, ERP waveforms reflect the overall activation
of the face-perception system, an ensemble of interconnected regions which
categorize visual stimuli as faces by analyzing various factors such as
expression, gender, and identity [16]. The close association between olfactory
and visual areas has been established in previous research [17, 18].
Therefore, investigating the effect of emotional odors on the interactions
among the areas of the face-perception system could provide valuable insights
into the integration mechanism between olfaction and vision. Such
investigations can enhance our understanding of how sensory cues interact to
shape our perception of others’ facial expressions and emotional states.

The standard ERP analysis based on components’ amplitude and latency can not
provide a sufficient level of detail to investigate brain dynamics at the
network level. Nevertheless, ERPs can be combined with more sophisticated
techniques to provide a window on the cortical sources’ dynamics underlying
face processing. In this context, EEG connectivity analysis allows to
investigate the interaction among neuronal assemblies [19]. Particularly,
effective connectivity estimates the direct and directional (i.e., causal)
influence that a source exerts over another [19]. Among the various effective
connectivity approaches [20, 19], Dynamic Causal Modeling (DCM) is a powerful
model-based technique designed to test for the effects of experimental factors
on the interactions among regions of a specified brain network, starting from
observed electrophysiological or functional imaging responses [21, 22].
Several studies applied DCM to reveal the dynamics among sources of the face-
perception network either through fMRI [23, 24, 25, 26, 27], MEG [28, 29], or
intracranial EEG [30] recordings. In this light, DCM can be combined with the
information provided by visual ERPs to investigate the modulatory effect of
hedonic contextual odors on the cortico-cortical interactions among face-
processing areas.

A potential factor of interest in the analysis of the face-odor multimodal
integration concerns the arousal elicited by faces. Specifically, within an
event-related paradigm, several repetitions of the stimulus are presented over
time, and it could be hypothesized that not all the faces are able to elicit
the same affective response due to the subjective saliency of facial features
[31, 32, 33]. This may affect the signal-to-noise ratio (SNR) of the observed
ERPs, and potentially reduce or even obscure the modulatory effect of
contextual odors on the connectivity. However, although arousal has already
been shown to influence face-evoked ERPs [34, 35], its effects when faces are
presented with concomitant olfactory stimuli are still poorly investigated.
Arousal has a crucial effect on the processing of external inputs.
Motivationally relevant stimuli, such as the perception of a face with
specific intrinsic features, are able to generate a transitory and automatic
enhancement of arousal [36, 37], that entails a prioritized processing of the
stimulus in the visual stream [35]. This mechanism is associated with a series
of physiological changes, including a top-down (i.e., endogenous) affective
influence on sensory gain control [35], an increase in the amplitude of
frequency-specific brain oscillations [38, 32] and in the magnitude of ERP
components [34, 35]. In this light, testing for the actual occurrence of
enhanced arousal is crucial to ensure that a face has been successfully
perceived, thus maximizing the SNR of ERP responses and the consequent effect
size of odors’ modulation.

Modeling the effects of arousal at both the ERP and DCM connectivity levels
requires knowing in advance whether a stimulus has the property of eliciting
it. However, such a prior knowledge is far from being easily identified, since
particular features that could characterize a perceived stimulus as relevant
(e.g., facial identity, eye gaze, expression) may vary on a subjective basis.
Besides brain activity, arousal is known to influence autonomic nervous system
(ANS) dynamics. Particularly, states of high arousal are associated with an
increase of peripheral sympathetic activity [39]. In this light,
electrodermal activity (EDA) can be exploited as an objective means to
identify the occurrence of enhanced arousal to a given stimulus, possibly
related to intrinsic facial features or to emotional expressions [40, 41, 42,
43]. EDA is comprised of a slow-varying tonic component overimposed to a fast-
varying phasic component. Particularly, the latter is represented by a series
of stimulus-evoked skin conductance responses (SCRs), whose elicitation is
driven by peripheral sympathetic neural bursts: i.e., the sudomotor nerve
activity (SMNA) [39]. Accordingly, sympathetic responses to faces observed
from SMNA can be adopted as a reliable marker of enhanced arousal.

In this work, we investigate the effects of hedonic olfactory stimuli and
arousal enhancement on face perception, as measured by ERP components and
source effective connectivity. Specifically, we hypothesize that odors’
valence modulates the strength of specific pathways within the face-perception
network, and that arousal evoked by the saliency of faces could play a role on
such a multimodal sensory integration. To this aim, we acquired the EDA and
EEG signals from 22 healthy volunteers performing a passive visual stimulation
task with neutral faces and background pleasant, neutral and unpleasant odors.
We propose a novel methodological approach based on the convex-optimization-
based EDA (cvxEDA) framework [44] to identify face-evoked peripheral
sympathetic responses and characterize the stimuli according to their property
of eliciting arousal. The outcome of this classification procedure is then
adopted to model the effects of odors’ valence and arousal as between-trial
factors on the visual ERP components evoked by faces. We then exploit such
ERPs to carry out a DCM and Parametric Empirical Bayes (PEB) analysis of odors
and arousal on the connectivity among cortical sources found to be activated
by the task. Particularly, PEB allows to build a hierarchical analysis of
effective connectivity, where single-subject estimates about neuronal
parameters of interest (e.g., connections’ strength) are treated as stochastic
effects at the group level [45]. To validate our aforementioned hypothesis on
the role of enhanced arousal to faces, we aim to assess: (i) whether such
mechanism is effectively reflected by the observed ERPs, and (ii) the
modulation operated by odors on cortico-cortical interactions at different
levels of arousal.

##  2 Material and Methods

###  2.1 Participants

Twenty healthy volunteers (age 27 $\pm$ 3, 5 females) were enrolled in the
study. Volunteers did not report any history of neurological and
cardiovascular diseases, anosmia or being tested positive to COVID-19 over the
past 6 months. Volunteers were not allowed to have any food nor drink in the
30 minutes preceding the experiment. The study was conducted according with
the guidelines of the Declaration of Helsinki, and approved by the Bioethics
Committee of the University of Pisa Review No. 14/2019, May 3rd, 2019.

###  2.2 Olfactory stimuli

We selected three different odorants: i.e., banana (isoamyl acetate;
$CH_{3}COOCH_{2}CH_{2}CH(CH_{3})_{2}$), isovaleric acid
($(CH_{3})_{2}CHCH_{2}COOH$), and n-butanol ($CH_{3}CH_{2}CH_{2}CH_{2}OH$). We
chose such odorants to convey positive (banana), negative (isovaleric acid),
and neutral (n-butanol) valence according to previous literature results[46,
47]. For each subject, we prepared $1ml$ isointense solutions diluting pure
odorant substances in distilled water according to the ratios of 1/20 (banana,
n-butanol) and 1/10 (isovaleric acid) respectively. Odorants were delivered
through an in-house built computer-controlled olfactometer at a flow rate of
$60ml/min$.

###  2.3 Olfactometer device

The 4-channels computer-controlled olfactometer used herein is composed by i)
a pressure regulator to set air pressure at 7Bar, ii) four stainless-steel
containers (50mL) equipped with o-rings, stainless steel caps and clamping
rings to ensure a gas-tight closure, iii) 10 low dead-volume three-way
solenoid valves (Parker Hannifin, Italy), iv) a digital flow meter (Honeywell,
Italy), and v) a disposable nasal cannula. An Arduino in-house code controlled
the solenoid valves allowing them being opened and closed through a well-
defined sequence of actions. The software allows the delivery of pure clean
air (i.e., all valves are opened) or odors kept at room temperature within the
containers. Components were connected to each other using
polytetrafluoroethylene (PTFE) fittings and tubings (internal diameter of
0.3mm) to reduce the olfactometer dead-volume up to 1mL. The nasal cannula is
connected to the olfactometer though a 3 meter long PTFE line. The
olfactometer showed a negligible memory effect and a low background emissions
of chemicals in the air/odors mainstream as determined by thermal desorption
coupled to gas-chromatography and mass spectrometry protocol [48], an overall
air flow delivery variability less than 1$\%$, and a rise time (10-90$\%$ of
the final value) close to 300 ms.

###  2.4 Visual stimuli

For the visual stimuli of neutral faces, we used the Chicago Face Database
[49]. We chose 128 different actors showing a neutral expressions.
Particularly, we selected a balanced number of actors across gender, age, and
ethnicity, to mitigate potential confounding effects related to the intrinsic
characteristics of the actors. The pictures were presented in a completely
randomized order with respect to the olfactory condition. The visual stimuli
were shown on a 15” laptop screen with a refresh rate of 60Hz and a resolution
of 1920x1080. The pictures’ size was 15cm in width and 10cm in height, and
were displayed at about 50cm from the eyes of the participants, resulting in a
visual angle of about 17°.

###  2.5 Experimental protocol

The experimental protocol was divided into two parts. First, we presented the
three odorants in a randomized order for a duration of about 10s, and we asked
the participants to evaluate the perceived hedonic content in terms of valence
(from -2 to +2) and arousal (from 1 to 5) according to the Self-Assessment
Manikin (SAM) test [50]. Second, we designed an experimental protocol
comprised of 128 trials. As schematically reported in Fig.1, each trial
consisted of: (1) 3s of dark gray background; (2) 3s of a dark gray background
with a white fixation cross; (3) 1.5s of neutral face image presentation; (4)
6s of inter-trial rest. Within each inter-trial rest, subjects were asked to
evaluate the facial expression in terms of valence and arousal according with
the SAM test, through an interactive interface. The facial images were
presented in combination with clean air or one of the three different odorants
scored in the first part of the protocol, for a total of 128/4=32 trials for
each olfactory condition. Each odorant was delivered starting from the onset
of the dark gray background to the end of the visual stimulus. A wash-out with
clean air was performed during the inter-trial rest. We presented both visual
and olfactory stimuli in a randomized order through the PsychoPy software [51]

Figure 1: Schematic illustration of the experimental protocol. Each trial
consisted of: (1) 3s of dark gray background; (2) 3s of fixation cross; (3)
1.5s of neutral face image presentation; (4) 6s of inter-trial rest where
subjects rated the valence and arousal of neutral faces according to the self-
assessment manikin (SAM) test. A random odor among banana, n-butanol,
isovaleric acid and clean air was delivered starting from (1) to the end of
(3). A wash-out with clean air was performed during (4).

###  2.6 EEG and EDA acquisition

EEG signal was acquired using a high-density 128-channel geodesic EEG System
300 from Electrical Geodesic, Inc. (EGI). Electrodes were grounded through two
additional channels placed between Cz and Pz and referenced through Cz. We
always kept electrode impedances below 20$k\Omega$ during the acquisitions.
EEG was acquired at the sampling frequency of 500Hz.

EDA was acquired using a Shimmer3 GSR+ unit (Shimmer, USA) at the sampling
frequency of 250Hz. We recorded EDA through a pair of Ag/AgCl electrodes
placed on the proximal phalanx of the first and second fingers of the non-
dominant hand, respectively.

###  2.7 EDA-driven sympathetic activity estimation

We implemented a procedure based on the analysis of EDA signal to estimate the
occurrence of enhanced sympathetic responses associated with the visual
presentation of faces. A well-known problem in EDA analysis concerns the
temporal overlapping of consecutive SCRs, which may hamper the association
between a given response and its potential triggering stimulus [39, 44, 52].
To address this issue, we adopted the cvxEDA [44] model to recover an estimate
of the SMNA from the observed EDA responses. Specifically, cvxEDA considers
that each SCR is preceded in time by sparse and discrete bursts of SMNA. These
bursts are characterized by a higher temporal resolution compared to phasic
activity, and can thus be exploited to identify the time instants at which
peripheral sympathetic responses evoked by faces occur [44]. Accordingly, we
assumed that visual stimuli eliciting an enhanced peripheral sympathetic
response could be followed by the occurrence of an SCR and, thus, a non-zero
SMNA neural burst.

Operationally, for each subject and for each odor condition (i.e., pleasant,
unpleasant, neutral, air), we undersampled the EDA to the sampling frequency
of 50Hz, and we performed a Z-scoring on the data [44]. Then, we applied
cvxEDA to obtain an estimate of the SMNA. We identified discrete events of
enhanced sympathetic arousal associated with the presentation of visual
stimuli as those epochs having non-zero SMNA bursts occurring in the (1-5)s
after stimulus onset. This choice is supported by several studies indicating
that a stimulus-evoked SCR is observed to occur within that range of latency
after stimulus’ onset [39, 53]. A schematic illustration of the procedure is
reported in Fig.2.
For each odor condition, we then extracted: (1) the number of epochs
with/without a stimulus-related sympathetic response (nSymp), (2) the average
latency of sympathetic responses, (3) the average amplitude of the SMNA,
computed as the average amplitude across epochs and then over time, and (4)
the subject-average SCR generated by the SMNA bursts.

Figure 2: Schematic illustration of the proposed procedure to assess stimuli-
related peripheral sympathetic responses from the EDA signal. The raw EDA was
preprocessed by downsampling at the frequency of 50Hz and applying a Z-score
transformation in order to be given as input of the cvxEDA algorithm. SMNA
estimates are then epoched in the (0-8)s interval with respect to the onset of
each stimulus (i.e., 0s). Significant sympathetic responses to the stimuli are
identified as non-zero SMNA bursts occurring in the (1-5)s interval after
stimulus onset

###  2.8 EEG preprocessing

We preprocessed the EEG signal using EEGLAB [54]. First, we filtered the data
with a zero-phase low-pass antialiasing filter and then undersampled it to the
sampling frequency of 100Hz. Afterwards, we applied a zero-phase high-pass
filter at the cutoff frequency of 0.1Hz to improve data stationarity. We
removed flat and poorly correlated channels by exploiting the method presented
in [55]. Specifically, each channel was compared with its reconstructed
version obtained from the spherical interpolation of its neighbors and was
removed if the correlation coefficient was less than a user-defined threshold.
Here, we used a correlation threshold of 0.8. After visual inspection, we
recovered the removed channels through spherical interpolation, and we re-
referenced the data to its average. For each subject, we epoched the EEG
signal from -200ms to 1000ms with respect to the onset of visual stimuli
(i.e., 0ms), and we visually inspected the data to remove epochs contaminated
by artifact activity. A number of 125 $\pm$ 7 (mean $\pm$ standard deviation)
epochs was retained over the subjects. Finally, we decomposed EEG data through
ICA [56], and we removed ICs resembling artifact activity (e.g., muscular,
ocular and other sources of noise) through visual inspection of their
associated time course, scalp map, and power spectrum. Clean EEG epochs were
corrected for their baseline (i.e., from -200ms to 0ms) and low-pass filtered
at the cutoff frequency of 30Hz using default settings in EEGLAB. We grouped
the epochs according to the odor condition, and we further distinguished them
based on the presence or absence of a peripheral sympathetic activation
(hereinafter referred to as Symp condition).  Accordingly, we obtained
subject-average ERPs for a total of 8 conditions: clean air, n-butanol,
banana, and isovaleric acid, with/without the presence of a sympathetic
response.

We focused on relevant ERP components associated with the visual processing of
faces, as well as their typical regions of interest (ROIs) on the scalp, based
on the previous literature [8, 11, 57, 58, 16, 15, 10] (see Table 1 for
details).

Table 1: Channels region of interest (ROI) and time interval (ms) for each of the investigated ERP components. Channels’ name is reported according to the geodesic EGI 128-channels cap. | Channels | Time interval
---|---|---
P1 | E59 E65 E66 E70 E71 E76 E83 E84 E90 E91 | 120-160 ms
N1 |  | E6 E7 E13 E30 E31 E37 E54 E55 E79 E80 E87
---
E105 E106 E112 E129
120-160 ms
rN170 | E96 E97 E101 E102 E108 | 160-200 ms
lN170 | E45 E46 E50 E51 E58 | 160-200 ms
VPP |  | E7 E13 E30 E31 E37 E54 E55 E79 E80 E87
---
E105 E106 E112 E129
160-200 ms
P2 | E59 E65 E66 E70 E71 E76 E83 E84 E90 E91 | 220-300 ms
LPP | E52 E53 E60 E61 E62 E67 E77 E78 E85 E86 E92 | 300-500 ms

Specifically, we extracted: (1) the P1 component in the 120-160ms interval
after stimulus onset in the occipital region around O1 and O2, (2) the N1
component at the same latency as P1, in the central region around Cz, (3) the
right/left N170 component in the 160-200ms interval after stimulus onset in
the parieto-temporal regions near P7 and P8, respectively, and (4) the Vertex
Positive Potential (VPP) in the 160-200ms interval after stimulus onset in the
same ROI of the N1 component. Moreover, for a comparison with the previous
literature, we also identified: (5) the P2 component at 220-300ms after
stimulus onset in the same ROI of P1, and (6) the Late Positive Potential
(LPP), a sustained positivity at 300-500ms after stimulus onset around Pz. For
each subject and for each of these components, we extracted the mean
amplitudes across the respective time intervals and ROIs.

###  2.9 Statistical analysis on self-report, EDA and ERP data

We investigated for possible differences in the valence and arousal subjective
ratings of olfactory stimuli through multiple Wilcoxon sign-rank tests, with a
significance level of $\alpha=0.05$. P-values were adjusted for multiple
comparisons testing with Bonferroni correction. Analogously, we tested for an
effect of olfactory stimuli on the valence and arousal subjective ratings of
faces through pairwise Wilcoxon tests ($\alpha=0.05$).

Concerning the physiological data, we tested for significant differences in
the amplitude and latency of SMNA responses across odor conditions through
separate 1x4 within-subject ANOVAs ($\alpha=0.05$). Multiple comparison
testing was controlled with false-discovery rate (FDR) for multiple testing
under dependency [59]. We further tested for an interaction between the
occurrence of sympathetic responses and the olfactory stimuli through a
within-subject two-way ANOVA on the number of EDA epochs grouped by odor
condition (i.e., clean-air, n-butanol, banana, isovaleric acid) and the
presence/absence of an SMNA response, respectively ($\alpha=0.05$).

Finally, we tested for a significant effect of contextual odors and
sympathetic responses on the amplitude of each ERP component described in
Section 2.8 through separate two-way ANOVAs, with the odor condition (i.e.,
clean-air, n-butanol, banana, isovaleric acid) and the presence/absence of an
EDA-driven sympathetic response as within-subject factors (p-values corrected
with FDR, $\alpha=0.05$). Post-hoc comparisons were conducted with a t-test,
and the resulting p-values were adjusted with the Bonferroni correction
($\alpha=0.05$).

###  2.10 Effective connectivity analysis with DCM

The analysis of effective connectivity was carried out using SPM12 [60].

We used DCM for ERPs [61, 22] to investigate the modulatory effect of odors
and sympathetic responses on the effective connectivity. Specifically, DCM
models the observed ERPs by combining a physiologically plausible neuronal
model of interacting cortical regions, and a spatial forward model that maps
the cortical activity to observed EEG data. Each region is described by three
interconnected neuronal subpopulations: interneurons, spiny stellate cells,
and pyramidal neurons. Regions are coupled to each other through extrinsic
connections, that are distinguished into forward, backward, and lateral
according to the hierarchical organization of the cortex [62]. The effect of
administered sensory stimuli on neuronal dynamics is accounted for through
specific input connections modeling the afferent activity relayed by
subcortical structures to the spiny stellate layer of target cortical regions
[61, 22]. Notably, such inputs are the same for each experimental condition.
Accordingly, differences among ERPs due to either contextual or stimulus
attributes are explained by modulatory coupling gains on the connection
strengths [61, 22]. The activity of pyramidal neurons from each region is then
projected to the EEG channels through an electromagnetic forward model which
accounts for the field spread effects in the head.

####  2.10.1 Network specification

The DCM for ERP framework explains ERP dynamics as arising from a small number
of brain regions [63]. The selection of which brain regions to include in the
network for DCM analysis can be made using either prior knowledge from the
literature or source reconstruction techniques. Here, we adopted a group
source reconstruction approach based on Multiple Sparse Priors (MSP)
implemented in SPM12 [64]. Particularly, while MSP has been shown to
potentially reduce localization error with high-density EEG systems [64],
group-inversion yields better results compared to individual inversions by
introducing additional constraints on the set of sources explaining subjects’
data [65]. Operationally, we used channels’ position co-registered to the MNI
standard head template as provided by SPM. Then, we inverted ERPs activity on
a cortical mesh comprising 8196 dipoles in the time range from -200 to 400 ms.
For each subject, we then created contrasts of log power differences in the
0-400 ms time range, collapsed over the experimental conditions, against the
prestimulus window (i.e., -200 to 0 ms). These contrast images were smoothed
with an 8mm Gaussian kernel to create a 3D volumetric NIFTI image. Images were
entered into a one-sample t-test design in SPM12 and we tested for significant
changes in power with respect to the prestimulus through an F-contrast.
Significant regions (p$<$0.05; Family-Wise Error Rate corrected at the cluster
level) were labeled according with the Automated Anatomical Labeling (AAL)
atlas [66].

####  2.10.2 DCM subject-level connectivity analysis

We performed DCM analysis on the subject-average ERPs relative to the odor
conditions, and with and without a sympathetic response. Accordingly, for each
subject, we specified a factor analysis on the single-subject connectivity,
with Odor and Symp as between-trial factors. We reduced the data to the first
four principal components (PC) or modes of EEG channels’ mixture. Such a
choice is a trade-off between the computational cost of DCM model inversion
and the percentage of variance explained by the data [67, 68, 69]. Notably,
reducing ERP data to their first four PC is indicated as sufficient to capture
the components of interest [61]. We focused model inversion on the 0-400 ms
time window with respect to the stimulus onset, through a Hanning window.
Finally, we adopted the ERP neural mass model (NMM) to model temporal dynamics
within and between the network sources.

Concerning the forward model, we modeled the spatial activity of brain sources
as equivalent current dipoles (ECD option in SPM12) on the cortex. The passive
volume conduction effects on the dipoles’ electric field were modeled through
a BEM model of the head made of three layers, i.e., cortex, skull and brain,
whose conductances were set to 0.33, 0.0042 and 0.33 S/m, respectively.
We allowed the effect of both Odor and Symp to modulate all the connections of
the network, including self-inhibitory effects on each node.

####  2.10.3 PEB group-level connectivity analysis

We inferred the significant modulatory effects of Odor and Symp on the group-
level connectivity through the PEB framework [45, 70]. Such a framework allows
to build statistical hierarchical models where single-subject DCM parameters
of interest can be treated as random effects on the group-level connectivity:

$\displaystyle Y_{i}$
$\displaystyle=\Gamma(\theta_{i}^{(1)})+\epsilon_{i}^{(1)}$ (1)
$\displaystyle\theta^{(1)}$ $\displaystyle=(X_{b}\otimes
X_{w})\theta^{(2)}+\epsilon^{(2)}$ (2)

More specifically, single-subject ERPs $Y_{i}$ are explained by a DCM
$\Gamma(.)$ with unknown parameters $\theta_{i}^{(1)}$, plus a zero-mean white
Gaussian noise residual $\epsilon^{(1)}$. Parameter estimates of interest are
then grouped together across subjects (i.e., $\theta_{i}^{(1)}$) and modeled
at the group-level (2) through a General Linear Model (GLM) having design
matrix $X=(X_{b}\otimes X_{w})$ and group parameters $\theta^{(2)}$. The
$X_{b}$ matrix models the between-subject effects, whereas the $X_{w}$ matrix
models which single-subject parameters are influenced by such effects. The
$\otimes$ symbol denotes the Kronecker product. Any unexplained between-
subject difference (e.g., non-modeled sources of variations, random effects)
is modeled by the zero-mean white Gaussian residuals $\epsilon^{(2)}$.

We grouped together the single-subject estimates associated with the
modulatory effect of Odor and Symp such that
$\theta^{(1)}=[\theta_{Odor}^{(1)},\,\theta_{Symp}^{(1)}]^{T}$, and we fitted
a PEB model with between-subject design matrix $X_{b}=1^{T}$ and within-
subject design matrix $X_{w}=I$ to estimate the average effect of such
conditions on each connection of the group connectivity.

We then applied a greedy search to infer the best combination of group-level
parameters $\theta^{(2)}$ describing the average effects of Odor and Symp.
Specifically, we iteratively applied Bayesian Model Reduction (BMR) to obtain
an estimate of nested PEB models with/without a particular set of connections,
as well as their Posterior Probability (Pp) of being the best model describing
the observed data [71]. We then computed a Bayesian Model Average (BMA) over
the models resulted from the last iteration of greedy-search procedure. BMA
performs an average of the parameter posterior densities across models,
weighted for their Pp. Accordingly, we obtained a set of group-level
parameters whose value is no longer conditional on the particular model
assumed. Finally, we thresholded the BMA results pruning away those parameters
having a Pp of being modulated by either Odor or Symp lower than 0.95. We
based such thresholding on the free-energy of the models estimated during the
BMR procedure (see Appendix 3 of [45]).

##  3 Results

###  3.1 SAM statistical analysis results

In Fig.3 we report the statistical analysis results of the SAM on the
administered odorants (Mean $\pm$ Standard Error (SE)). Subjects rated
isovaleric acid as significantly more unpleasant (valence = $-0.62\pm 0.18$;
Fig.3a) and more arousing (arousal = $2.81\pm 0.22$; Fig.3b) compared to
banana and n-butanol. We did not find any significant differences between the
valence and arousal ratings of banana (valence = $0.38\pm 0.20$; arousal =
$2.33\pm 0.21$) and n-butanol (valence = $0.10\pm 0.18$; arousal = $1.86\pm
0.17$).

Figure 3: SAM statistical analysis results for the a) valence and b) arousal
ratings (Mean $\pm$ Standard Error (SE)) of the administered odorants (i.e.,
Banana, Butanol, Isovaleric Acid). Subjects perceived isovaleric acid as
significantly more unpleasant ($-0.62\pm 0.18$) and more arousing ($2.81\pm
0.22$) than the other odorants.

On the other hand, we did not find any significant difference for both the
valence (clean air = $-0.05\pm 0.05$; banana = $-0.14\pm 0.0.06$; n-butanol =
$-0.03\pm 0.05$; isovaleric acid = $-0.09\pm 0.07$) and arousal (clean air =
$2.17\pm 0.16$; banana = $2.23\pm 0.17$; n-butanol = $2.18\pm 0.16$;
isovaleric acid = $2.15\pm 0.17$) ratings of faces among different odor
conditions.

###  3.2 EDA statistical analysis results

We did not find any significant differences among odor conditions neither for
the average amplitude nor the latency of SMNA responses. Conversely, we found
a significant effect of nSymp following the 2x4 ANOVA
($F_{6,64}=5.76,p<10^{-5}$; FDR-corrected). More specifically, we found a
lower number of stimuli associated with a sympathetic response (11.76 $\pm$
3.06), compared to the stimuli without the manifestation of a sympathetic
response (19.61 $\pm$ 2.63), irrespective of the odor condition.

###  3.3 ERP statistical analysis results

We found a significant effect for the odor condition on the N1
($F_{6,64}=6.65,p<10^{-4}$; FDR-corrected) and the VPP
($F_{6,64}=3.63,p<10^{-4}$; FDR-corrected) components at the central ROI
around the Cz channel. In Fig.4, we report the grand-average ERP for each of
the odor conditions, together with the N1 and VPP significant intervals
highlighted in blue and green, respectively, and a schematic representation of
the ROI on the scalp map. Particularly, post-hoc analysis highlighted a
greater N1 amplitude during the administration of isovaleric acid, with
respect to clean air ($p<10^{-3}$). Moreover, we observed a lower VPP
amplitude during the administration of both isovaleric acid ($p<10^{-3}$) and
n-butanol ($p<10^{-3}$), with respect to clean air.

Figure 4: ERP grand-averages for the Clean Air (blue), Banana (red), N-Butanol
(yellow), and Isovaleric Acid (purple) conditions evaluated at the central
ROI, in the time range from -200ms to 800ms with respect to the stimulus onset
(i.e., 0ms). ERPs were averaged across 15 electrodes around Cz, schematically
represented by the red dots on the scalp map on the top-left of the figure.
The blue area highlights a significant effect for the odors on the average N1
amplitude, computed over the 120-160 ms interval ($p<0.05$, FDR-corrected). On
the other hand, the green area highlights a significant effect for the odors
on the average VPP amplitude, computed over the 160-200 ms interval ($p<0.05$,
FDR-corrected). Post-hoc analysis showed a greater N1 amplitude during
Isovaleric Acid, with respect to Clean Air ($p<0.05$, corrected with
Bonferroni). Moreover, the VPP was significantly lower for both the Isovaleric
Acid and the N-Butanol, with respect to Clean Air ($p<0.05$, corrected with
Bonferroni).

Moreover, we observed a significant effect for the Symp condition on the
amplitude of the left N170 component ($F_{1,17}=5.10,p=0.03$). Particularly,
as reported in Fig.5, the presence of a sympathetic response resulted in the
increase of the N170 amplitude. We did not find a significant interaction
between Odor and Symp for any of the ERP components investigated.

Figure 5: ERP grand-averages for the Clean Air Symp-absent (blue), Clean Air
Symp-present (red), Banana Symp-absent (yellow), Banana Symp-present (purple),
N-Butanol Symp-absent (green), N-Butanol Symp-present (light blue), Isovaleric
Acid Symp-absent (brown), and Isovaleric Acid Symp-present (dark blue)
conditions, evaluated at the left ROI around P7, in the time range from -200ms
to 800ms with respect to the stimulus onset (i.e., 0ms). ERPs were averaged
across 5 electrodes around P7, schematically represented by the red dots on
the scalp map on the top-left of the figure. The gray area highlights a
significant effect for the Symp condition on the average N170 amplitude,
computed over the 160-200ms interval ($p<0.05$, corrected with Bonferroni).
Specifically, the N170 deflection was greater during sympathetic responses
(i.e., Symp-present), irrespective of the odor condition.

###  3.4 Network identification results

We found activation of the right Inferior Occipital Gyrus (rIOG; MNI
coordinates: 48 -76 -2), right Fusiform Gyrus (rFFG; MNI coordinates: 38 -18
-32), right and left Inferior Temporal Gyrus (rITG; MNI coordinates: 52 -38
-20; lITG; MNI coordinates: -50 -38 -22), right and left Medial Temporal Gyrus
(rMTG; MNI coordinates: 52 -62 14; lMTG; MNI coordinates: -54 -64 10), and
right Secondary Visual Cortex (rVII; MNI coordinates: -10 -98 -10) (p$<$0.05;
Family-Wise Error Rate corrected at the cluster level).

We specified the network for DCM analysis focusing on the IOG, FFG, ITG, and
MTG for their central role in the processing of faces and their role in the
integration of visual and olfactory stimuli [25, 26, 72, 73, 74, 75].
Furthermore, in line with the previous literature, we focused on the right
visual stream of face processing [25, 76, 72, 24, 77]. We specified
rIOG$\rightarrow$rFFG, rFFG$\rightarrow$rITG and rITG$\rightarrow$rMTG as
forward-connections, and rFFG$\rightarrow$rIOG, rITG$\rightarrow$rFFG and
rMTG$\rightarrow$rITG as backward connections according with previously
reported evidence on the nodes’ hierarchical structure [25, 78, 79, 80, 24]
(Fig.6). We hypothesized IOG as the first stage responsible for face
processing in our network [81, 26, 82, 24]. Accordingly, we modeled the effect
of the thalamic sensory input relay of ”faces” directly entering the IOG
(i.e., the network input).

Figure 6: Axial (a), coronal (b) and sagittal (c) views of the network to be
modeled with DCM. The network includes the Inferior Occipital Gyrus (IOG),
Fusiform Face Gyrus (FFG), Inferior Temporal Gyrus (ITG), and Medial Temporal
Gyrus (MTG). These nodes were found through a group-inversion based on the MSP
approach in SPM12. Nodes were labeled according with the Automated Anatomical
Labeling (AAL) atlas. We specified rIOG$\rightarrow$rFFG,
rFFG$\rightarrow$rITG and rITG$\rightarrow$rMTG as forward-connections, and
rFFG$\rightarrow$rIOG, rITG$\rightarrow$rFFG and rMTG$\rightarrow$rITG as
backward connections.

###  3.5 DCM subject-level connectivity analysis

Following the results of SAM and ERP analysis, we decided to focus our
connectivity study on the effects of isovaleric acid and arousal enhancement
on the visual processing of faces. Accordingly, we built a 2x2 factorial DCM
analysis with factors Odor (i.e., clean air vs. isovaleric acid) and Symp
(i.e., sympathetic vs. no sympathetic response). For each subject, the DCM
model was successfully fitted to the observed data without observing any
problem of early convergence. The models showed a good representation of the
data, with an average explained variance of $90.77\%$.

In Fig.7 we report the result of the ERP reduction to the first four principal
modes of EEG channels’ mixture, together with the modes predicted by the
model, for an exemplary subject. As depicted, PCA channels’ reduction yielded
a parsimonious yet efficient representation of the data. Specifically, the
first three EEG modes represented data variance associated with the P1, N170,
and P2 components, whereas the fourth mode represented data variance mainly
associated with the VPP. Such a procedure allowed us to focus parameters’
estimation on the main features of the observed ERPs, while dramatically
reducing noise and computational complexity.

Figure 7: First four principal modes of channels’ ERPs for the CleanAir
(blue), CleanAir Symp (red), Isovaleric Acid (green), Isovaleric Acid Symp
(pink) conditions for an exemplary subject. Dashed lines indicate the observed
ERPs, whereas solid lines indicate the ERPs generated by the DCM model. The
model was inverted in the 0-400 ms interval with respect to the stimulus
onset.

###  3.6 PEB group-level connectivity analysis

In Fig.8, we report the results of the greedy-search and BMA procedure on the
PEB parameters associated with the average modulatory effects of the Odor
(i.e., clean-air vs. isovaleric acid) and Symp (i.e., absence vs. presence of
a sympathetic response) conditions on the group connectivity. These BMA
parameters indicate the effect size of the average group-modulatory effect
associated with the experimental variables of interest in the connectivity. In
particular, we report only those parameters having a Pp$>$0.95 of being
different from zero. The parameters surviving such thresholding are
represented by a black bar showing the estimated posterior effect size, and a
pink error bar representing the corresponding 90$\%$ credibility interval.

Concerning the Odor condition, we observed an increase of the forward
connection strength from ITG to MTG (effect size: $0.24\pm 0.09$). This
indicates that the administration of isovaleric acid induced a significant
(i.e., Pp$>$0.95) excitatory effect on the strength of the ITG$\rightarrow$MTG
connection at the group level, with respect to the administration of clean-
air. On the other hand, we observed a decrease of the ITG$\rightarrow$MTG
connection strength (effect size: $-0.19\pm 0.07$) associated with the Symp
condition. This, instead, indicates that the presence of a peripheral
sympathetic response induced an inhibitory effect on the strength of such
connection at the group level. Finally, we also observed a strengthening
effect of the Symp condition on the backward connection from ITG to FFG
(effect size: $0.15\pm 0.07$), indicating that peripheral sympathetic
responses were associated with an excitatory effect on such connection.

Figure 8: Results of BMA on the group effective connectivity among the ITG,
FFG, ITG, and MTG. For each experimental condition (i.e., Odor, Symp), we
report the effect sizes associated with their average modulatory effects on
the connections’ strength. In particular, we report only those parameters
having a posterior probability (Pp) $>$ 0.95 of being different from zero. For
each significant parameter, the height of the black bar indicates the
estimated average posterior effect size, whereas the pink error bar indicates
the corresponding 90$\%$ credibility interval. The brain figures depict the
rendering of the network on the axial plane. For each condition, connections
reporting a significant positive modulation are depicted in purple, whereas
connections reporting a significant negative modulation are depicted in red.

##  4 Discussion

In this study, we investigated the modulatory effect of contextual hedonic
olfactory stimuli on the visual processing of neutral faces through the
analysis of ERP components and effective connectivity. Particularly, we
assumed that enhanced arousal to the perception of faces could play a role on
their integration with olfactory stimuli, improving the SNR of the observed
responses and the consequent effect size of the odors [40, 83]. To this aim,
we applied a novel methodological approach exploiting EDA-driven SMNA to
classify visual stimuli into two cases: i.e., ”eliciting” or ”not-eliciting” a
stimulus-evoked peripheral sympathetic response. We included the outcome of
this procedure as an additional experimental factor, together with the valence
of contextual odors, into a standard analysis of ERP components. We further
modeled observed ERPs through DCM and PEB to test for the presence of specific
cortical connections being concomitantly modulated by enhanced arousal and
hedonic olfactory stimuli. Our results highlight the role arousal plays on the
visual processing of human faces and its multimodal integration with olfactory
stimuli.

The ERP analysis revealed an effect of EDA-driven arousal on the left N170
component, irrespective of the odor condition. More specifically, the
occurrence of a sympathetic response was associated with an enhanced N170
amplitude. Previous studies have found a significant correlation between the
N170 amplitude and the perceived level of arousal associated with faces,
irrespective of their valence, such that arousing stimuli were associated with
increased ERP responses [34]. The N170 is considered to be the earliest marker
of higher-order visual processing, marking the structural encoding of a
stimulus as a face. In particular, during the N170 time window, it is
hypothesized that multiple cortical processes underlie the fine-grained
categorization of faces based on configural processing of their features,
e.g., eyes, nose, mouth, and the spatial relationship among them [16, 84]. In
this light, we suggest that the higher N170 amplitude observed during
sympathetic responses could represent a marker of enhanced arousal elicited by
salient features of faces.

In addition to the effect of arousal, we observed a greater N1 amplitude
during the administration of isovaleric acid, with respect to clean air.
Furthermore, we observed a lower VPP amplitude during the administration of
both isovaleric acid and n-butanol, again with respect to clean air. These
findings are in line with previous studies reporting an effect of hedonic
odors on early visual ERP components [11, 12, 8]. The VPP, together with its
inverse N170, is known for being particularly sensitive to faces [85, 3, 58],
and it has also been implicated in contextual odor effects [11]. Hence, our
findings on the VPP modulation by both the unpleasant (i.e., isovaleric acid)
and neutral (i.e., n-butanol) odors, are in agreement with previous studies
reporting an overall effect of olfactory stimuli irrespective of their valence
[11]. On the other hand, greater amplitudes of the N1/P1 component have been
associated with the enhanced allocation of attention towards faces [10, 3].
Overall, given the ERPs results, we can state that hedonic odors generate a
contextual effect and thus a top-down influence on face processing in
different stages. In this view, we may suggest that the administration of
isovaleric acid had a greater arousing effect on the early processing of
visual stimuli, compared to the other odorants. This is further corroborated
by our findings on the subjective ratings of odors, showing isovaleric acid as
being perceived as significantly more arousing and unpleasant compared to
banana and n-butanol. Accordingly, we decided to carry out the remainder of
our study focusing on the contrast between isovaleric acid and clean air.

Using DCM for ERP and PEB, we investigated the concomitant effect of Odor and
Symp on the brain connectivity at the group level. The study revealed
interesting results. On the one hand, we found that isovaleric acid, compared
to clean air, strengthened the forward connection from the ITG to the MTG. In
classical models of face processing [78, 25, 79], the MTG and the superior
temporal sulcus (STS) are cortical areas specialized in processing changeable
facial features (e.g., emotional expressions, gaze, lip movements). The
strengthening of the connection towards these areas due to isovaleric acid may
represent a crucial aspect of our study. Variable characteristics of the face
are fundamental features for intra-species communication and social
interaction [86]. For this reason, an interpretation of this result may
consist of the fact that a negative odor can influence face processing mostly
for interaction purposes. A repulsive smell would enhance the ability to
interpret intentions, expressions and mental states of a possible other person
(or threatening agent) in a more efficient way, bringing therefore an
evolutionary advantage. On the other hand, the same connection suffered from
an inhibitory effect when a simultaneous peripheral sympathetic response
occurred, irrespective of the odor condition. Moreover, the occurrence of such
sympathetic responses caused an excitatory effect on the backward connection
from ITG to FFG. Face processing network models [78, 79] highlight the
functional dissociation between the fusiform face area (in the FFG) and
temporal areas. As stated above, MTG and STS are designated to process
changeable facial aspects, while the FFA is crucial to process invariant
aspects of the face to access information related to the identity. According
to our results, the facial information processing appears to stop at the FFG
stage for faces that generate a sympathetic response, since forward
connections towards the MTG are inhibited while backward connections to the
FFG are enhanced. Therefore, we can speculate that the processing of faces
that evoke a sympathetic response may be focused on identity-related
information, to the disadvantage of emotional expressions and other changeable
features. In other words, some intrinsic facial features create a sympathetic
response which, in turn, may result in a top-down enhanced processing of
identity. Speculatively, if a face generates a sympathetic response it is
likely that this individual will feel in jeopardy, or more generally anxious.
In this situation, fast processing of the face identity may represent an
advantage in order to understand whether s/he is in actual danger and, in that
case, adopt the appropriate fight or flight strategies connected to higher
arousal. Moreover, these dynamics seem to suggest a crucial role ITG plays in
processing sympathetic arousal that can be associated with odor perception.
The ITG has been described as an associative area integrating information
regarding face features from the FFG and IOG to process the identity of faces
[87]. Furthermore, the ITG sends bidirectional projections to limbic areas
involved in emotions and memory processing, such as the amygdala, hippocampus,
entorhinal cortex, and to the frontal cortices [87, 88]. In this context,
previous studies have reported a role for the ITG in visual short-term memory
[89, 90, 91], as well as in hedonic olfactory tasks and in the perception of
faces with background odor cues [73, 92]. In this light, the ITG appears to be
a hub in which dissociation in different pathways for affective processing of
faces are handled.

The methodology proposed in this study could represent an effective means to
quantitatively study the effect of sympathetic arousal on both the ERPs and
effective connectivity. Indeed, in previous studies, we adopted subjective
ratings as a means to quantify arousal and investigate its central correlates
at the connectivity level [93, 69]. However, affective ratings may deviate
from physiological responses [69], which are supposed instead to represent an
objective window on emotional stimuli processing (see e.g. [94]). Other
studies instead investigated the coupling between EDA and EEG dynamics through
correlation [95, 96, 97, 98], phase and amplitude coupling [99], and coherence
measures [100]. Yet, to the best of our knowledge, a methodology to
investigate the effect of sympathetic arousal on the EEG effective
connectivity as estimated through EDA is missing. In this light, we proposed
an approach based on the identification of enhanced sympathetic arousal
through the analysis of EDA and the connectivity framework of DCM for ERP.
Particularly, we adopted cvxEDA as an efficient approach to recover an
estimate of the hidden SMNA, and identified the time instants at which
stimulus-evoked sympathetic responses have occurred. The DCM framework then
allowed to investigate such effects on the effective connectivity with the
highest level of physiological plausibility.

It is worth highlighting that the observed peripheral sympathetic responses
could be attributed either to the effect of background olfactory stimuli, the
presentation of visual faces, or the combination of both. With this regard,
our EDA analysis results did not show any significant effects of the
administered background olfactory stimuli on either the amplitude, the
latency, or the frequency of the sympathetic responses following the
presentation of human faces. Accordingly, it is reasonable to assume that the
observed peripheral sympathetic responses time-locked to the presentation of
faces could be associated with the intrinsic arousing properties of the faces
themselves. Thus, the proposed methodology allows accounting for two distinct,
yet concomitant, effects on the EEG cortical connectivity, i.e., the
modulation of contextual hedonic odors on the perception of human faces, and
the modulation of enhanced sympathetic arousal elicited by the visual
presentation of faces. Nevertheless, to the best of our knowledge, this is the
first investigation of EDA dynamics in the scenario of the multimodal
integration between visual faces and olfactory stimuli. Hence, further
investigations will be necessary to corroborate this hypothesis.

The choice of focusing our DCM analysis only on the right hemisphere was
supported by the literature[25, 76, 72, 24, 77, 101] and allowed us to limit
the number of parameters (i.e., nodes, connections) at the advantage of less
uncertainty on their estimates. However, this can be seen in contrast to the
ERP analysis that revealed an effect of sympathetic arousal on the left N170
component, while no effects were observed in the right hemisphere.
Nevertheless, it is worth noting that multiple sources underlie the processing
of faces in the N170 (160-200)ms interval. Hence, ERP components may reflect
the global activation of the face perception system, potentially leading to
small or null effects [16]. In this view, DCM offers a powerful means to
investigate the effect of sympathetic arousal to a deeper extent through a
physiologically plausible description of how observed dynamics are associated
with its underlying cortical underpinnings. Accordingly, future works should
increase statistical power by including more subjects and extend our network
to also include left-hemisphere ITG and MTG found during ERP inversion.

The framework proposed does not include the amplitude of the SMNA in the
model. This is a limitation of the model since we cannot exclude that a
potential relationship between central responses and the magnitude of the
sympathetic neural bursts could exist. Yet, modeling such an effect through
DCM would require to specify a single-trial within-subject modulatory input
where each administered stimulus is associated to its respective level of
sympathetic arousal (as quantified through the amplitude of SMNA). To the best
of our knowledge, the DCM for ERP framework is limited to model between-trials
effects (i.e., differences among grand-average ERPs), rather than single-trial
effects. Accordingly, we limited our methodology to the evaluation of
connectivity differences in the binary case of the presence/absence of an
evoked peripheral sympathetic response, without taking into account their
magnitude.

SMNA responses may be triggered by a number of factors different from the
perception of the designed administered stimulus (in this context, human
faces). Particularly, spontaneous fluctuations, i.e., sudomotor nerve
responses which stem from uncontrolled cognitive and emotional processes [39],
may lead to the erroneous detection of a face-evoked peripheral sympathetic
responses. While we attempted at mitigating such effects constraining
stimulus-evoked SMNA bursts in the 1-5s time window after stimulus onset [39],
we cannot exclude that errors in the binary classification of the stimuli may
still occur.

Another potential limitation of our methodology concerns the relationship
between EDA-driven peripheral sympathetic responses and EEG central responses.
Specifically, we could not explicitly include EDA dynamics into the analysis
of effective connectivity, as the DCM framework only allows to model
modulatory effects on the observed EEG data due to either contextual factors
or properties of the administered stimuli [22]. Accordingly, nothing can be
concluded about the causal relationship between EEG and EDA responses. To
overcome these limitations, future studies will aim develop a framework
including EDA dynamics as a representative node in the connectivity network.

##  5 Conclusion

In this work, we proposed a novel methodological approach based on the
analysis of EDA and EEG to investigate the effect of contextual hedonic odors
and sympathetic arousal on the visual processing of neutral faces. Our main
findings highlighted a higher N1 component during the unpleasant odor
condition, whereas enhanced arousal to faces increased the N170 component.
Moreover, both factors exerted a significant modulatory effect on the
effective connectivity among cortical areas involved in face processing.
Particularly, the unpleasant odor strengthened the forward connection from ITG
to MTG, whereas the same connection was inhibited by the simultaneous
occurrence of face-evoked sympathetic responses. These results may suggest
that, on the one hand, face processing in the context of an unpleasant odor
are more focused on changeable facial aspects related to social interaction.
On the other hand, increased arousal appears to enhance identity processing in
the FFG. Future works will include SMNA dynamics as a representative node in
the connectivity network, to investigate EDA-EEG causal interactions and
deepen the understanding of sympathetic arousal’s role on the visual-odor
multimodal integration.

## Conflict of interest statement

The authors declare no conflict of interest.

The research leading to these results has received partial funding from the
Italian Ministry of Education and Research (MIUR) in the framework of the
CrossLab project (Departments of Excellence). This research has received
partial funding from European Union Horizon 2020 Programme under grant
agreement n 824153 of the project “POTION: Promoting Social Interaction
through Emotional Body Odours”. Research partly funded by PNRR - M4C2 -
Investimento 1.3, Partenariato Esteso PE00000013 - “FAIR - Future Artificial
Intelligence Research” - Spoke 1 “Human-centered AI”, funded by the European
Commission under the NextGeneration EU programme. This publication was
produced with the co-funding European Union - Next Generation EU, in the
context of The National Recovery and Resilience Plan, Investment 1.5
Ecosystems of Innovation, Project Tuscany Health Ecosystem (THE), Spoke 3
“Advanced technologies, methods, materials and heath analytics” CUP:
I53C22000780001

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