A measure of smell enables the creation of olfactory metamers.

Wavelength is a physical measure of light, and the intricate understanding of its link to perceived colour enables the creation of perceptual entities such as metamers-non-overlapping spectral compositions that generate identical colour percepts1. By contrast, scientists have been unable to develop a physical measure linked to perceived smell, even one that merely reflects the extent of perceptual similarity between odorants2. Here, to generate such a measure, we collected perceptual similarity estimates of 49,788 pairwise odorants from 199 participants who smelled 242 different multicomponent odorants and used these data to refine a predictive model that links odorant structure to odorant perception3. The resulting measure combines 21 physicochemical features of the odorants into a single number-expressed in radians-that accurately predicts the extent of perceptual similarity between multicomponent odorant pairs. To assess the usefulness of this measure, we investigated whether we could use it to create olfactory metamers. To this end, we first identified a cut-off in the measure: pairs of multicomponent odorants that were within 0.05 radians of each other or less were very difficult to discriminate. Using this cut-off, we were able to design olfactory metamers-pairs of non-overlapping molecular compositions that generated identical odour percepts. The accurate predictions of perceptual similarity, and the ensuing creation of olfactory metamers, suggest that we have obtained a valid olfactory measure, one that may enable the digitization of smell.

An electronic nose can identify humans by the smell of their ear.

Terrestrial mammals identify conspecifics by body odor. Dogs can also identify humans by body odor, and in some instances, humans can identify other humans by body odor as well. Despite the potential for a powerful biometric tool, smell has not been systematically used for this purpose. A question arising in the application of smell to biometrics is which bodily odor source should we measure. Breath is an obvious candidate, but the associated humidity can challenge many sensing devices. The armpit is also a candidate source, but it is often doused in cosmetics. Here, we test the hypothesis that the ear may provide an effective source for odor-based biometrics. The inside of the ear has relatively constant humidity, cosmetics are not typically applied inside the ear, and critically, ears contain cerumen, a potent source of volatiles. We used an electronic nose to identify 12 individuals within and across days, using samples from the armpit, lower back, and ear. In an identification setting where chance was 8.33% (1 of 12), we found that we could identify a person by the smell of their ear within a day at up to ~87% accuracy (~10 of 12, binomial P < 10-5), and across days at up to ~22% accuracy (~3 of 12, binomial P < 0.012). We conclude that humans can indeed be identified from the smell of their ear, but the results did not imply a consistent advantage over other bodily odor sources.

Relationship between odor intensity estimates and COVID-19 prevalence prediction in a Swedish population.

In response to the COVID-19 pandemic, countries have implemented various strategies to reduce and slow the spread of the disease in the general population. For countries that have implemented restrictions on its population in a step-wise manner, monitoring of COVID-19 prevalence is of importance to guide decision on when to impose new, or when to abolish old, restrictions. We are here determining whether measures of odor intensity in a large sample can serve as one such measure. Online measures of how intense common household odors are perceived and symptoms of COVID-19 were collected from 2440 Swedes. Average odor intensity ratings were then compared to predicted COVID-19 population prevalence over time in the Swedish population and were found to closely track each other (r=-0.83). Moreover, we found that there was a large difference in rated intensity between individuals with and without COVID-19 symptoms and number of symptoms was related to odor intensity ratings. Finally, we found that individuals progressing from reporting no symptoms to subsequently reporting COVID-19 symptoms demonstrated a large drop in olfactory performance. These data suggest that measures of odor intensity, if obtained in a large and representative sample, can be used as an indicator of COVID-19 disease in the general population. Importantly, this simple measure could easily be implemented in countries without widespread access to COVID-19 testing or implemented as a fast early response before wide-spread testing can be facilitated.

SmellSpace: An Odor-Based Social Network as a Platform for Collecting Olfactory Perceptual Data.

A common goal in olfaction research is modeling the link between odorant structure and odor perception. Such modeling efforts require large data sets on olfactory perception, yet only a few of these are publicly and freely available. Given that individual odor perception may be informative on personal makeup and interpersonal relationships, we hypothesized that people would gladly provide olfactory perceptual estimates in the context of an odor-based social network. We developed a web-based infrastructure for such a network we called SmellSpace and distributed 10 000 scratch-and-sniff registration booklets each containing a subset of 12 out of 35 microencapsulated odorants. Within ~100 days, we obtained data from ~1000 participants who rated the odorants along 13 verbal descriptors. To verify that these estimates are comparable to lab-collected estimates we tested 26 participants in a controlled lab setting using the same odorants and descriptors. We observed remarkably high overall group correlations between lab and SmellSpace data, implying that this method provides for credible group-representations of odorants. We further estimated the usability of the data by applying to it two previously published models that used odorant structure alone to predict either odorant pleasantness or pairwise odorant perceptual similarity. We observed statistically significant predictions in both cases, thus further implying that the current data may be helpful toward future efforts of modeling olfactory perception from structure. We conclude that an odor-based social network is a potentially useful instrument for collecting extensive data on olfactory perception and here post the complete raw data set from the first ~1000 participants.

Individual olfactory perception reveals meaningful nonolfactory genetic information.

Each person expresses a potentially unique subset of ∼ 400 different olfactory receptor subtypes. Given that the receptors we express partially determine the odors we smell, it follows that each person may have a unique nose; to capture this, we devised a sensitive test of olfactory perception we termed the "olfactory fingerprint." Olfactory fingerprints relied on matrices of perceived odorant similarity derived from descriptors applied to the odorants. We initially fingerprinted 89 individuals using 28 odors and 54 descriptors. We found that each person had a unique olfactory fingerprint (P < 10(-10)), which was odor specific but descriptor independent. We could identify individuals from this pool using randomly selected sets of 7 odors and 11 descriptors alone. Extrapolating from this data, we determined that using 34 odors and 35 descriptors we could individually identify each of the 7 billion people on earth. Olfactory perception, however, fluctuates over time, calling into question our proposed perceptual readout of presumably stable genetic makeup. To test whether fingerprints remain informative despite this temporal fluctuation, building on the linkage between olfactory receptors and HLA, we hypothesized that olfactory perception may relate to HLA. We obtained olfactory fingerprints and HLA typing for 130 individuals, and found that olfactory fingerprint matching using only four odorants was significantly related to HLA matching (P < 10(-4)), such that olfactory fingerprints can save 32% of HLA tests in a population screen (P < 10(-6)). In conclusion, a precise measure of olfactory perception reveals meaningful nonolfactory genetic information.

Perceptual convergence of multi-component mixtures in olfaction implies an olfactory white.

In vision, two mixtures, each containing an independent set of many different wavelengths, may produce a common color percept termed "white." In audition, two mixtures, each containing an independent set of many different frequencies, may produce a common perceptual hum termed "white noise." Visual and auditory whites emerge upon two conditions: when the mixture components span stimulus space, and when they are of equal intensity. We hypothesized that if we apply these same conditions to odorant mixtures, "whiteness" may emerge in olfaction as well. We selected 86 molecules that span olfactory stimulus space and individually diluted them to a point of about equal intensity. We then prepared various odorant mixtures, each containing various numbers of molecular components, and asked human participants to rate the perceptual similarity of such mixture pairs. We found that as we increased the number of nonoverlapping, equal-intensity components in odorant mixtures, the mixtures became more similar to each other, despite not having a single component in common. With ~30 components, most mixtures smelled alike. After participants were acquainted with a novel, arbitrarily named mixture of ~30 equal-intensity components, they later applied this name more readily to other novel mixtures of ~30 equal-intensity components spanning stimulus space, but not to mixtures containing fewer components or to mixtures that did not span stimulus space. We conclude that a common olfactory percept, "olfactory white," is associated with mixtures of ~30 or more equal-intensity components that span stimulus space, implying that olfactory representations are of features of molecules rather than of molecular identity.

Predicting odor perceptual similarity from odor structure.

To understand the brain mechanisms of olfaction we must understand the rules that govern the link between odorant structure and odorant perception. Natural odors are in fact mixtures made of many molecules, and there is currently no method to look at the molecular structure of such odorant-mixtures and predict their smell. In three separate experiments, we asked 139 subjects to rate the pairwise perceptual similarity of 64 odorant-mixtures ranging in size from 4 to 43 mono-molecular components. We then tested alternative models to link odorant-mixture structure to odorant-mixture perceptual similarity. Whereas a model that considered each mono-molecular component of a mixture separately provided a poor prediction of mixture similarity, a model that represented the mixture as a single structural vector provided consistent correlations between predicted and actual perceptual similarity (r≥0.49, p<0.001). An optimized version of this model yielded a correlation of r = 0.85 (p<0.001) between predicted and actual mixture similarity. In other words, we developed an algorithm that can look at the molecular structure of two novel odorant-mixtures, and predict their ensuing perceptual similarity. That this goal was attained using a model that considers the mixtures as a single vector is consistent with a synthetic rather than analytical brain processing mechanism in olfaction.

There is chemistry in social chemistry.

Nonhuman terrestrial mammals sniff themselves and each other to decide who is friend or foe. Humans also sniff themselves and each other, but the function of this is unknown. Because humans seek friends who are similar to themselves, we hypothesized that humans may smell themselves and others to subconsciously estimate body odor similarity, which, in turn, may promote friendship. To test this, we recruited nonromantic same-sex friend dyads and harvested their body odor. We found that objective ratings obtained with an electronic nose, and subjective ratings obtained from independent human smellers converged to suggest that friends smell more similar to each other than random dyads. Last, we recruited complete strangers, smelled them with an electronic nose, and engaged them in nonverbal same-sex dyadic interactions. We observed that dyads who smelled more similar had more positive dyadic interactions. In other words, we could predict social bonding with an electronic nose. We conclude that there is indeed chemistry in social chemistry.

An olfactory self-test effectively screens for COVID-19.

Background: Key to curtailing the COVID-19 pandemic are wide-scale screening strategies. An ideal screen is one that would not rely on transporting, distributing, and collecting physical specimens. Given the olfactory impairment associated with COVID-19, we developed a perceptual measure of olfaction that relies on smelling household odorants and rating them online. Methods: Each participant was instructed to select 5 household items, and rate their perceived odor pleasantness and intensity using an online visual analogue scale. We used this data to assign an olfactory perceptual fingerprint, a value that reflects the perceived difference between odorants. We tested the performance of this real-time tool in a total of 13,484 participants (462 COVID-19 positive) from 134 countries who provided 178,820 perceptual ratings of 60 different household odorants. Results: We observe that olfactory ratings are indicative of COVID-19 status in a country, significantly correlating with national infection rates over time. More importantly, we observe indicative power at the individual level (79% sensitivity and 87% specificity). Critically, this olfactory screen remains effective in participants with COVID-19 but without symptoms, and in participants with symptoms but without COVID-19. Conclusions: The current odorant-based olfactory screen adds a component to online symptom-checkers, to potentially provide an added first line of defense that can help fight disease progression at the population level. The data derived from this tool may allow better understanding of the link between COVID-19 and olfaction.

Proof of concept for real-time detection of SARS CoV-2 infection with an electronic nose.

Rapid diagnosis is key to curtailing the Covid-19 pandemic. One path to such rapid diagnosis may rely on identifying volatile organic compounds (VOCs) emitted by the infected body, or in other words, identifying the smell of the infection. Consistent with this rationale, dogs can use their nose to identify Covid-19 patients. Given the scale of the pandemic, however, animal deployment is a challenging solution. In contrast, electronic noses (eNoses) are machines aimed at mimicking animal olfaction, and these can be deployed at scale. To test the hypothesis that SARS CoV-2 infection is associated with a body-odor detectable by an eNose, we placed a generic eNose in-line at a drive-through testing station. We applied a deep learning classifier to the eNose measurements, and achieved real-time detection of SARS CoV-2 infection at a level significantly better than chance, for both symptomatic and non-symptomatic participants. This proof of concept with a generic eNose implies that an optimized eNose may allow effective real-time diagnosis, which would provide for extensive relief in the Covid-19 pandemic.

Sniffing the human body volatile hexadecanal blocks aggression in men but triggers aggression in women.

In terrestrial mammals, body volatiles can effectively trigger or block conspecific aggression. Here, we tested whether hexadecanal (HEX), a human body volatile implicated as a mammalian-wide social chemosignal, affects human aggression. Using validated behavioral paradigms, we observed a marked dissociation: Sniffing HEX blocked aggression in men but triggered aggression in women. Next, using functional brain imaging, we uncovered a pattern of brain activity mirroring behavior: In both men and women, HEX increased activity in the left angular gyrus, an area implicated in perception of social cues. HEX then modulated functional connectivity between the angular gyrus and a brain network implicated in social appraisal (temporal pole) and aggressive execution (amygdala and orbitofrontal cortex) in a sex-dependent manner consistent with behavior: increasing connectivity in men but decreasing connectivity in women. These findings implicate sex-specific social chemosignaling at the mechanistic heart of human aggressive behavior.

Human Olfaction without Apparent Olfactory Bulbs.

The olfactory bulbs (OBs) are the first site of odor representation in the mammalian brain, and their unique ultrastructure is considered a necessary substrate for spatiotemporal coding of smell. Given this, we were struck by the serendipitous observation at MRI of two otherwise healthy young left-handed women, yet with no apparent OBs. Standardized tests revealed normal odor awareness, detection, discrimination, identification, and representation. Functional MRI of these women's brains revealed that odorant-induced activity in piriform cortex, the primary OB target, was similar in its extent to that of intact controls. Finally, review of a public brain-MRI database with 1,113 participants (606 women) also tested for olfactory performance, uncovered olfaction without anatomically defined OBs in ∼0.6% of women and ∼4.25% of left-handed women. Thus, humans can perform the basic facets of olfaction without canonical OBs, implying extreme plasticity in the functional neuroanatomy of this sensory system.

A Cross Modal Performance-Based Measure of Sensory Stimuli Intricacy.

We define a new measure of sensory stimuli which has the following properties: It is cross modal, performance based, robust, and well defined. We interpret this measure as the intricacy or complexity of the stimuli, yet its validity is independent of its interpretation. We tested the validity and cross modality of our measure using three olfactory and one visual experiment. In order to test the link between our measure and cognitive performance we also conducted an additional visual experiment. We found that our measure is correlated with the results of the well-established Rapid Serial Visual Presentation masking experiment. Specifically, ranking stimuli according to our measure was correlated at r = 0.75 (p < 0.002) with masking effectiveness. Thus, our novel measure of sensory stimuli provides a new quantitative tool for the study of sensory processing.

A Mechanistic Link between Olfaction and Autism Spectrum Disorder.

Internal action models (IAMs) are brain templates for sensory-motor coordination underlying diverse behaviors. An emerging theory suggests that impaired IAMs are a common theme in autism spectrum disorder (ASD). However, whether impaired IAMs occur across sensory systems and how they relate to the major phenotype of ASD, namely impaired social communication, remains unclear. Olfaction relies on an IAM known as the sniff response, where sniff magnitude is automatically modulated to account for odor valence. To test the failed IAM theory in olfaction, we precisely measured the non-verbal non-task-dependent sniff response concurrent with pleasant and unpleasant odors in 36 children--18 with ASD and 18 matched typically developing (TD) controls. We found that whereas TD children generated a typical adult-like sniff response within 305 ms of odor onset, ASD children had a profoundly altered sniff response, sniffing equally regardless of odor valance. This difference persisted despite equal reported odor perception and allowed for 81% correct ASD classification based on the sniff response alone (binomial, p < 0.001). Moreover, increasingly aberrant sniffing was associated with increasingly severe ASD (r = -0.75, p < 0.001), specifically with social (r = -0.72, p < 0.001), but not motor (r < -0.38, p > 0.18), impairment. These results uncover a novel ASD marker implying a mechanistic link between the underpinnings of olfaction and ASD and directly linking an impaired IAM with impaired social abilities.

The perceptual logic of smell.

Mammals have ∼1000 different olfactory receptor subtypes, each responding to a number of different odorants, and each odorant activating a number of different receptor subtypes. These molecular and anatomical underpinnings of olfaction imply a perceptual structure of very high dimensionality that relies on combinatorial coding. In contrast to this expectation, the study of olfactory perception reveals a structure of much lower dimensionality. Moreover, a low-dimensionality approach to olfaction enabled derivation of perception-based structural metrics for smell. These metrics provided meaningful predictions of odorant-induced neural activity and perception from odorant structure alone. Based on this low functional dimensionality, we speculate that olfaction likely does not functionally rely on 1000 different receptor subtypes, and their persistence in evolution may imply that they have additional roles in non-olfactory functions such as in guidance of embryogenesis and development.