Abstract
Each olfactory cortical hemisphere receives ipsilateral odor information directly from the olfactory bulb and contralateral information indirectly from the other cortical hemisphere. Since neural projections to the olfactory cortex (OC) are disordered and nontopographic, spatial information cannot be used to align projections from the two sides like in the visual cortex. Therefore, how bilateral information is integrated in individual cortical neurons is unknown. We have found, in mice, that the odor responses of individual neurons to selective stimulation of each of the two nostrils are significantly correlated, such that odor identity decoding optimized with information arriving from one nostril transfers very well to the other side. Nevertheless, these aligned responses are asymmetric enough to allow decoding of stimulus laterality. Computational analysis shows that such matched odor tuning is incompatible with purely random connections but is explained readily by Hebbian plasticity structuring bilateral connectivity. Our data reveal that despite the distributed and fragmented sensory representation in the OC, odor information across the two hemispheres is highly coordinated.
- computational model
- correlated receptive fields
- Hebbian plasticity
- interhemispheric coordination
- olfactory cortex
Significance Statement
Like other sense organs, animals typically have two nostrils, but how odor information from the two sides is combined to build bilateral olfactory representations remains largely unknown. Grimaud et al. found that the responses of neurons in the olfactory cortex in awake mice to odors presented separately to the ipsilateral or contralateral nostril are significantly correlated, beyond chance. Such aligned responses could arise from Hebbian plasticity in interhemispheric connections that relies on common odor experiences across the two nostrils. While responses are correlated, the remaining asymmetries in responses to the two nostrils allowed decoding of stimulus laterality. This study points to unexpected order in an olfactory circuit and prompts future work on how olfactory experience can shape interhemispheric information integration.
Introduction
In vision and audition, information from paired sensors (eyes or ears) is combined in the brain to perform specific computations that are behaviorally relevant—for example, estimating distance to or the direction of the source of the stimuli (Cumming and DeAngelis, 2001; Grothe and Pecka, 2014). The existence of two sensors for olfaction has prompted hypotheses about their function (Mainland et al., 2002; Porter et al., 2005; Rajan et al., 2006; Catania, 2013; Rabell et al., 2017). Information from the two nostrils can be differentiated, or integrated, depending on the goal of such computation for the animal—for example, odor source localization when smooth gradients exist (Catania, 2013; Gire et al., 2016; Rabell et al., 2017; Álvarez-Salvado et al., 2018; Baker et al., 2018) or identification of relevant smells independent of the odor source location or nostril stimulated (Bojsen-Moller and Fahrenkrug, 1971; Kucharski and Hall, 1987, 1988; Eccles, 2000; Mainland et al., 2002; Kikuta et al., 2008; Dalal et al., 2020). These computations will dictate the nature of the interhemispheric communication and the mechanisms of integration of bilateral olfactory cues.
Odors are detected by olfactory sensory neurons in the nose, which project to the ipsilateral olfactory bulb (OB). The OB sends information to multiple ipsilateral brain regions, including the anterior olfactory nucleus (AON) and the anterior (APC) and posterior piriform cortices (Mori et al., 1999; Wilson and Sullivan, 2011; Giessel and Datta, 2014; Nagayama et al., 2014; Fig. 1A) In many species, including rodents, the two nares are separated by a septum that minimizes transfer of inhaled odors between nostrils (Wilson and Sullivan, 1999; Eccles, 2000; Kikuta et al., 2008). Therefore, information transfer across hemispheres is largely neural and originates in the cortex (Haberly and Price, 1978; Scott et al., 1980; Kikuta et al., 2008; Wilson and Sullivan, 2011; Boyd et al., 2012; Bekkers and Suzuki, 2013; Fig. 1A).
Figure 1-1
A New Method for Unilateral Odor Delivery. Related to Figure 1. (A) Odorants used in this study. (B) Control of the symmetry of the olfactometer. A PID measured the changes of odor concentration over time at the end of the right or the left end of the facial mask. Each measurement was repeated three times; therefore, each graph shows six over-imposed traces, three with the PID on the right, three on the left. Note the similarity between the traces obtained on the left and right of the mask, and the absence of odor detected on the contralateral side. Grey: odor presentation (here: isopropyl tiglate). All 15 odors were tested and showed similar results. (C) Diagram of the setup used for assessing the reliability of the face mask for respiration monitoring. The breathing of awake mice was monitored simultaneously with the mask and a pressure sensor connected to a chronic intranasal cannula (n = 3 animals, 15 min of continuous recording per mouse). (D) Exemplar breathing traces, simultaneously recorded from the intranasal cannula (light blue) and the face mask (purple). Note the synchrony of both signals and the apparent absence of phase shift. (E) Histogram of instantaneous breathing rates, calculated with the exhalation (top panel) or inhalation (bottom panel) onsets. The data shown here comes from the full monitoring session of one exemplar mouse. The distributions obtained from the intranasal cannula (light bars) or the face mask (dark bars) are almost identical. (F) Delay from cannula to mask, calculated with the exhalation (green) or inhalation (orange) onsets. The data from the three mice were combined here. Values: median ± SD. The phase shift between the intranasal cannula and the face mask is small and reliable. (G) Example of tetrode recording. The four electrodes belong to the same tetrode. Grey: odor delivery. Green: unsorted electrode traces. Blue and red: two single units. Purple: sniffing acquired simultaneously (up: inhalation). These exemplar traces were recorded in mouse APC1. (H) Example of single unit clustering. The red and blue units are the same as in (G). (I) Example of clustered single units. The red and blue units are the same as in (G). Lighter traces: all the peaks from the recording session were over-imposed. Darker traces: average peaks. (J) Spike count versus inter-spike interval (ISI) for the two units shown in (G) (blue and red). Note the presence of a refractory period. Download Figure 1-1, TIF file.
Figure 1-2
Instructions to Build an Odor Delivery Mask. Related to Figure 1. Below we explain how to build a mask, including all necessary measurements and materials. (A) Cut a piece of PVC tubing (outer diameter: 18 mm; inner diameter: 13 mm) as indicated on the diagram. PVC allows for a gentle yet firm seal around the mouse's face. (B) Using injection needles of gradually increasing diameters, puncture the piece of tubing at the positions indicated by the red marks on the diagram. When puncturing, insert the needle tangentially to the piece of tubing. Each hole should be less than 0.09in (2.2 mm) in diameter, to ensure that the tube fitting fits snugly. (C) Insert the nylon tube fittings as indicated on the diagram. For the odors and vacuums, use tube fittings ref. 51525K211 (McMaster-Carr, USA). For the breathing, use a tube fitting ref. 5463K439 (McMaster-Carr, USA), and cover the threads with teflon tape before insertion. Secure all tube fittings in position by applying a few drops of epoxy glue around each of them, on the outside of the mask. Let the mask rest for at least a week in a well-ventilated area, so as to eliminate any remaining epoxy fumes. Download Figure 1-2, TIF file.
Figure 1-3
Glomerular Activity at Various Odor Concentrations. Related to Figure 1. (A) Activity of one exemplary glomerulus to unilateral presentations of ethyl tiglate at various concentrations. Purple: odor delivery. Each trace is one odor delivery (3 repeats per side, per concentration). (B) Glomerular activity. Each line represents the activity of one glomerulus to one odor, averaged over three repeats on each side (N = 52 glomeruli from 3 mice). Four odors were tested in total (ethyl tiglate, isopropyl tiglate, hexanal, allyl butyrate), each odor was tested at three different concentrations (5%, 0.5%, and 0.05% volume/volume in diethyl phthalate), with three repeats for each condition. All recordings were performed on awake mice. Responses are shown as “delta F” (mean fluorescence during the 2 s of odor presentation minus mean fluorescence over the two seconds before) rather than “delta F over F0” as some glomeruli showed no detectable activity before odor presentations. The distributions of glomerular responses are also shown as box plots (center dot: median; thick bar: quartiles; thin bar: 95% confidence interval). (C) Left: ipsi- versus contralateral responses at 5% concentration. Right: ipsilateral responses at 5% concentration versus ipsilateral responses at 0.5% concentration. Dotted line: linear regression (response on the y-axis = slope x response on the x-axis). The regression slope is given on each graph. Download Figure 1-3, TIF file.
Figure 1-4
Glomerular Activity After Naris Occlusion. Related to Figure 1. (A) Activity of one exemplary glomerulus to unilateral presentations of ethyl tiglate, after the mouse's contralateral nostril has been occluded. Purple: odor delivery. Each trace is one odor delivery (3 repeats per side). (B) Glomerular activity after naris occlusion. Each line represents the activity of one glomerulus to one odor, averaged over three repeats on each side (N = 42 glomeruli from 4 imaging sessions - two mice were imaged once with their right nostril occluded, while a third mouse was imaged on two separate sessions, once with its right naris occluded and once with its left naris). While we blindly looked for glomeruli on both OB, only 4 glomeruli out of 42 were located on the OB ipsilateral to the occlusion. Four odors were tested in total, three repeats each (ethyl tiglate, isopropyl tiglate, hexanal, allyl butyrate; 5% volume/volume dilution in diethyl phtalate). All recordings were performed on awake mice, the day following the naris stitching. Responses are shown as “delta F” (mean fluorescence during the 2 s of odor presentation minus mean fluorescence over the two seconds before) rather than “delta F over F0” as some glomeruli showed no detectable activity before odor presentations. The distribution of glomerular responses to odors delivered to the not occluded and occluded nostrils are also shown as box plots (center dot: median; thick bar: quartiles; thin bar: 95% confidence interval). The median response to the non occluded nostril is 20.7 times greater than to the occluded nostril. (C) Same as (B), except we show the glomeruli from the OB contra- and ipsilateral to the naris occlusion separately. On the OB contralateral to the occlusion, the median response to the non occluded nostril is 15.4 times greater than to the occluded nostril. Download Figure 1-4, TIF file.
Table 1-1
Mice Recorded in this Study. All tetrode recordings were performed in awake, head-restrained mice. Download Table 1-1, DOCX file.
Table 1-2
Statistical Tests Performed in this Study. The statistical tests showing significant differences are in red (critical value: 5%). For post-hoc tests, the p-values reported in the table have been corrected for multiple testing problem (Bonferroni method). Download Table 1-2, DOCX file.
Bilateral cortical interactions have been investigated in other senses including vision, where the relevant brain areas have ordered representations of the world using topographic maps, which allow neural matching through precise point-to-point mapping (Luo and Flanagan, 2007; Cang and Feldheim, 2013; Gu and Cang, 2016). However, the olfactory cortex (OC) contains no recognizable topography (Illig and Haberly, 2003; Stettler and Axel, 2009; Ghosh et al., 2011; Miyamichi et al., 2011; Sosulski et al., 2011; Wilson and Sullivan, 2011; Bekkers and Suzuki, 2013; Pashkovski et al., 2020), making point-to-point matching of bilateral information through continuous maps implausible. It is possible that odor representations in the two primary OC hemispheres are essentially independent, and downstream areas align them for coherent perception, as implicitly proposed by Schaffer et al. (2018).
Framing hypotheses about bilateral olfactory processing requires basic information about how single cortical neurons respond to the same odor presented to the two nostrils. Such information is fundamental for uncovering the computations performed, just as details about binocular matching and disparity in cortical responses were for vision (Blake and Wilson, 2011). Individual neurons could respond to ipsi- and contralateral odor stimuli in a congruent or disparate manner. There is some evidence for responses in olfactory cortical areas to contralateral odor stimulation, but there is little information on the alignment of ipsi- and contralateral responses (Wilson, 1997; Kikuta et al., 2008, 2010). Recent evidence suggests that mitral cells in the OB may have matched responses through organized feedback projections from the AON pars externa (Yan et al., 2008; Grobman et al., 2018). A cortical neuron may then be able to inherit the matched response from mitral cells (Dalal et al., 2020), but a direct evaluation of cortical matching is lacking. As this work was being prepared, Dikeçligil et al. (2023) reported that the piriform cortex responses to stimuli in the contralateral nostril were time delayed but similar to those evoked from ipsilateral stimulation, suggesting that OC neurons may inherit such matched responses.
To investigate how the OC combines information from the two nostrils, we recorded odor-evoked spiking activity of individual neurons in the OC of awake mice. We observed neuron responses to selective odor stimulation through the contralateral nostril in all cortical regions recorded. A significant fraction of OC neurons showed a strong match of ipsi- and contralateral response profiles. Population activity in OC could be used to accurately decode odor identity, as well as side identity. A computational model of the olfactory system suggested that the bilateral overlap we observed in mice is only possible with nonrandom bilateral projections. Our results provide fundamental and novel insight that defines how ipsi- and contralateral information streams converge and ultimately enhance the processing capabilities of the brain.
Materials and Methods
Experimental model and subject details
All tetrode recordings were obtained from male C57Bl/6J mice. In addition, we used male OMP-GCaMP3 mice (Isogai et al., 2011; Fig. 1), as well as male and female OMP-Cre crossed to floxed jGCaMP8s mice (The Jackson Laboratory, strain 037719; Extended Data Figs. 1-3, 1-4) for the glomerular imaging experiments. All the mice were 3–8 months old at the time of the experiments. Mice were singly housed after chronic tetrode implantation. A summary of the mice used for tetrode recording can be found in Extended Data Table 1-1. All experiments were performed in accordance with the guidelines set by the National Institutes of Health and approved by the Institutional Animal Care and Use Committee at Harvard University.
Bilateral odor stimulation
The odor panel was composed of 15 monomolecular odors, purchased from Millipore Sigma: isopropyl tiglate, ethyl tiglate, propyl acetate, isoamyl acetate, ethyl valerate, hexanal, heptanal, allyl butyrate, citronellal, hexyl tiglate, 4-allyl anisole, isobutyl propionate, 2-heptanone, ethyl propionate, and eucalyptol (Extended Data Fig. 1-1A). The odors were diluted in diethyl phthalate, also purchased from Millipore Sigma (final odor dilution, 5% volume/volume). In addition to these 15 odors, we also tested the response to diethyl phthalate alone, referred to as the “blank” trials (see below, Neuronal recording). The olfactometer was controlled by a custom LabVIEW script (National Instruments).
Odors were delivered through a custom-built facial mask (Fig. 1B; Extended Data Fig. 1-2). The mask was made of a large piece of tubing surrounding the mouse's muzzle. On each side of the mask, close to each nostril, a piece of tubing delivered odors at a small flow rate (50 SCCM). Also on the mask, right below each odor delivery tube, a vacuum (50 SCCM, similar to the odor line) ensured proper air cleaning. We used a flowmeter to inspect and regulate both the odor and vacuum lines daily. In addition, before each recording session, we ensured that the odor and vacuum lines were at a similar flow rate by visually inspecting that the breathing signal, which we monitored from a dedicated airflow sensor, was centered around zero, once the mask was pressed against the mouse's head (see below, Breathing monitoring). We confirmed that the mask could deliver similar amounts of odor on each side of the mask with a photoionization detector (PID) (miniPID 200B, Aurora Scientific; Extended Data Fig. 1-1B). We confirmed the absence of significant contralateral stimulations through calcium imaging at the surface of the OB and mitral/tufted (M/T) cell recordings (Fig. 1).
Odors were delivered for 2 s for each trial. To avoid odor habituation, a period of 15 s separated consecutive trials (i.e., 15 s from the end of a trial to the beginning of the next trial). Each odor was delivered in two configurations: ipsilateral and contralateral to the implantation. Each odor delivery was repeated seven times. Therefore, each recording session contained
Breathing monitoring
The face mask was connected to an airflow sensor (Honeywell AWM3300V) to monitor the animal's respiration during the experiments (Fig. 1B) and later align neuronal activity with the sniffs (Fig. 2C; Bolding and Franks, 2017). The accuracy of the face mask in monitoring respiration was tested against intranasal pressure transients (Extended Data Fig. 1-1C–F) as described previously (Reisert et al., 2014).
Figure 2-1
Post-Mortem Confirmation of Tetrode Placement in the Olfactory Cortex. Related to Figure 2. Sagittal sections of two exemplar animals, implanted in the AON (left) and APC (right). The round shape on the ventral end of the tetrode scar in the left panel was caused by post-mortem electrolesion. For animals recorded in the APC, the tetrodes went all the way through the brain, until they reached the ventral part of the skull. As a consequence, it was not possible to save the most anterior part of the section in the right panel. A: anterior. P: posterior. D: dorsal. V: ventral. CC: corpus callosum. LV: lateral ventricle. LOT: lateral olfactory tract. Scale bar: 0.25 mm (left) or 1 mm (center, right). DAPI staining. We did not trace the border between striatum and amygdala as we could not confidently determine it. Download Figure 2-1, TIF file.
Chronic tetrode implantation
Mice were anesthetized with an intraperitoneal injection of ketamine and xylazine (100 mg/kg and 10 mg/kg, respectively) and then placed in a stereotaxic apparatus. After the skull was cleaned and gently scratched, a custom-made titanium head bar was glued to it. A small craniotomy was performed above the implantation site, before eight custom-built tetrodes (Chang et al., 2013) were lowered together into the brain (coordinates for the AON pars principalis, anteroposterior 3 mm, mediolateral 1.5 mm, dorsoventral 2.3 mm; APC, anteroposterior 1.6 mm, mediolateral −2.8 mm, dorsoventral 3.4 mm; OB, anteroposterior 1.2 mm, mediolateral 1.1 mm, dorsoventral 0.3 mm; all anteroposterior and mediolateral coordinates are given relative to the bregma, except for the OB, for which they are given relative to the junction of the inferior cerebral vein and superior sagittal sinus; all dorsoventral coordinates are given relative to the brain surface). A reference electrode was implanted on the occipital crest. All the mice were implanted in the right hemisphere, except mouse APC2, which was implanted in the left hemisphere. We did not find any difference between APC2 and the other mice. The whole system was stabilized with dental cement. Mice were given a week of recovery before any new manipulation.
Neuronal recording
A week after chronic tetrode implantation, mice were progressively habituated to stay calm while head-fixed on the recording setup. The habituation process typically took a week. Brain activity was then recorded once a day, always at the same period of the day, while delivering odors to the ipsilateral or contralateral nostril. The mice were awake, head-restrained, and ad libitum breathing during all the recordings.
Since the tetrode headstage allowed for the tetrodes to be finely adjusted up or down, the tetrodes were slightly lowered in the brain after each recording session (∼40 µm deep), ensuring that different neurons were recorded each day.
Electrical activity was amplified, filtered (0.3–6 kHz), and digitized at 30 kHz (Intan Technologies, RHD2132, connected to an Open Ephys board). Single units were sorted off-line manually using the MClust script (Schmitzer-Torbert et al., 2005) written for MATLAB (MathWorks). Units with ∼1% of their interspike intervals below 2 ms refractory period were discarded. Units displaying large changes of amplitude or waveform during the recording were also discarded (Rey et al., 2015; Extended Data Fig. 1-1G–I). The position of the tetrodes in the brain was confirmed postmortem through electrolesion (200 μA for 4 s per channel; Extended Data Fig. 2-1).
The average response R to an odor o (in Hz) over the seven trials t was calculated as follows:
To determine the significance of a response to an odor o over the seven trials t, we compared the activity evoked by the odor
Finally, we investigated whether a neuron that responds to a particular odor with an increase in the firing rate tends to exhibit increases in firing rates for any other odor it responds to and, similarly, whether a neuron having an inhibitory response to an odor is also inhibited by any other odor it responds to. To do so, we calculated a “positive–negative balance” metric. For each neuron responding significantly to at least one odor, the positive–negative balance corresponds to the fraction of significant responses that are positive. For example, if a neuron has a positive–negative balance of 0.5, then we know that, among the odors eliciting a significant response, half of them elicit an increase of activity, while the other half elicit a decrease. Similarly, a balance of 0 means that all the significant odor responses are negative, and a balance of 1 indicates that all the significant responses are positive.
Calcium imaging
Mice were anesthetized with an intraperitoneal injection of ketamine/xylazine mixture (100 mg/kg and 10 mg/kg, respectively). After the skull was cleaned and gently scratched, a custom-made titanium head bar was glued to it. A large craniotomy was performed above the two OBs and covered with 1% low melting point agarose (Thermo Fisher Scientific). While the mouse was still anesthetized, calcium activity was imaged above the craniotomies with a custom-made epifluorescence microscope (10× objective, Olympus, NA 0.3; blue excitation LED, Thorlabs; emission filter, 500–550 nm, Thorlabs; CMOS camera, The Imaging Source) at 4 frames/s while presenting odors with the custom-built facial mask (Fig. 1A,B).
Alternatively, we also implanted glass coverslips over the craniotomy using Vetbond (3M). Briefly, two 3 mm No. 1 glass coverslips (Warner Instruments) were glued together with an optical adhesive (Norland Optical Adhesive 61). Mice postsurgery were treated with carprofen (6 mg/kg) and extended-release buprenorphine (3.25 mg/kg). Animals were allowed to recover for at least five days prior to imaging. For data from Extended Data Figures 1-3 and 1-4, images were acquired at 10 frames/s.
The acquired sequences of images were converted to ΔF/F images (change in fluorescence over resting fluorescence). Then the maximal response at each pixel was calculated for each of the 15 odors, for each stimulus configuration (right or left nostrils), and these maximum response maps were used to manually identify glomeruli for further analysis. Finally, for each odor, for each stimulus configuration, the response at each glomerulus was quantified by integrating the ΔF/F signal 4 s after the odor onset (which corresponds to the 2 s of odor presentation and the 2 s immediately following).
Nostril occlusion
We occluded the nostril of OMP-Cre; jGCaMP8s animals using 7–0 gauge MicroSutures (S-N718SP13). Mice were given lidocaine on the nostril and were anesthetized using an intraperitoneal injection of ketamine/xylazine mixture (100 mg/kg and 10 mg/kg, respectively). The suture was pulled through the upper lip of the nostril, and the nostril was closed completely. The closure of the nostril was confirmed using a drop of water placed on the nostril. Mice were administered extended-release buprenorphine (3.25 mg/kg). The occlusion was tested using the same method before and after the imaging sessions.
Decoding task
Neuronal decoding
For each neuron we created a feature vector by calculating the summed spike count in a 2 s window after the mouse first sniffs the odor and subtracting from it the same sum from the “blank” odor averaged across seven trials.
Odor identity decoding ability was assessed using linear classifiers and leave-out cross–validation on a pseudopopulation made up of all neurons recorded in a particular cortical area—AON or APC (Bolding and Franks, 2017). At each instance, we chose a particular dataset (either ipsilateral or contralateral odor presentation) and formed from it a training set by randomly removing one trial per odor. We calculated mean responses for each odor by averaging across the training set. We then chose our test set in two related ways. If we were testing on the same side as we trained (e.g., train ipsilaterally, test ipsilaterally), then the 15 trials (one per odor) outside the training set were used as the test set. If we were testing on the opposite side (e.g., train ipsilaterally, test contralaterally), then one trial per odor from the opposite side was randomly chosen to form the test set. Classification accuracy was measured by assigning each test trial to the closest Euclidean mean odor response and calculating the percentage of trials correctly assigned (Bolding and Franks, 2017). This process was repeated 200 times using different random test and train trial choices.
To examine the effect of changing number of neurons on classification accuracy, we randomly selected neurons from the pseudopopulation and performed classification using only the selected cells. This process was repeated 200 times. We then reported the average accuracy and standard deviation (SD) over those 200 selections of neurons and, for each set of neurons, 200 sets of train/test trials in Figure 4 and Extended Data Figure 4-1.
A very similar process was employed for side decoding. One trial per side was removed; the remaining trials were averaged to form a mean side response for both ipsilateral and contralateral presentations. The removed trials were then classified to the nearer mean side response, and accuracy was the percentage correctly assigned. This was repeated 200 times for different random removed trial choices.
Comparisons to the theoretical model
Our model predicts the trial-averaged neural responses. To make comparisons to the neuronal data, we designed a new classification task. For simulations and trial-averaged neuronal data, we performed odor decoding as follows: we assigned each of the 15 odor centroids from one side (contralateral or ipsilateral) to their closest neighbor on the other side. This classification was correct if an odor from one side was classified to the same odor on the other side. The accuracy was then the proportion of odors correctly assigned. Again, average accuracy and SD over 200 randomly chosen neurons were reported in Figure 4 and Extended Data Figure 4-1. One exception to the procedure above was Extended Data Figure 6-1D, right panel, where we classified centroids to their nearest angular neighbor.
For side classification comparisons, the two centroids corresponding to a given odor presented on both sides were removed. From the remaining 28 centroids, two points representing the ipsilateral and contralateral cortex were made by averaging over all remaining centroids from a particular side. The two points we removed were then classified as ipsilateral or contralateral depending on which mean they were nearest to. This procedure was repeated 15 times, removing a different odor each time. We reported the average accuracy and SD over 200 randomly chosen pseudopopulations in Figure 4 and Extended Data Figure 4-1.
Computational modeling
We built a feed-forward neural network model to explore the implications of a random or structured connection between the OC in each hemisphere. We effectively modeled one cortical region in each hemisphere, based on the AON.
To begin, we considered the cortical response to odors presented ipsilaterally; later we will link these to explore interhemispheric effects. Each cortical neuron's activity to an ipsilateral odor o is a weighted sum of glomerular activities passed through an activation function as follows:
The zero point of these neurons is set to be the basal firing rate plus response to a blank odor. Therefore, we modeled only odor-related perturbations from the blank and base to match all comparisons to the blank and base subtracted data.
The nonlinearity,
f, was chosen to recreate features of the electrophysiological recordings. First, it was found that the odor-related perturbations are equally likely to be positive or negative (of all the significant neuron–odor pairs, 46% are positive in the AON and 48% in the APC); therefore, we chose a nonlinearity that was symmetric around zero. Negative activity does not mean the neurons have a negative firing rate but that their firing rate is inhibited relative to their response to base plus blank. Second, consistent with previous findings (Poo and Isaacson, 2009; Bolding and Franks, 2017; Iurilli and Datta, 2017), the cortical response to odors was relatively sparse. Therefore, we chose the shrink function as follows:
Based on previous work, we then modeled the connectivity matrix between the bulb and the cortex to be completely random (Schaffer et al., 2018) with elements either equal to 0, with probability
Since input to each cortical neuron is statistically independent, to fully specify its distribution, we must simply set the odor–odor covariance matrix of the Gaussian. Under the assumption that the size of the OB output response is identical for each odor (
The threshold controls sparseness of cortical response; we match this to the AON recordings. The sparseness of the recordings is measured as the proportion of neuron–odor pairs for which the Wilcoxon signed-rank test p value comparing the set of seven trials to mean blank odor response is below 5% (22.4% ipsilateral, 25.2% contralateral). Labeling the sparseness as S, because of the assumed Gaussianity of cortical inputs, we get the following:
To compare ipsilateral to contralateral responses, we created two ipsilateral responses according to the scheme above. One of these was then mapped through a cross-cortical connectivity matrix to create a contralateral response. We used this contralateral response and the second ipsilateral creation for our comparisons to mice.
Our final task, therefore, was to design a cross-cortical connectivity matrix. In the body of the paper, we used a simple Hebbian structured matching of each odor between hemispheres. Here we include two additional connectivity schemes that are low-dimensional approximations to
The random component has elements drawn from a zero mean normal distribution with Variance 1 (this choice of variance is unimportant as the matrix will be rescaled in future) as follows:
The structured Hebbian component maps from contralateral to ipsilateral odor responses using the covariance rule formulation that arises from Hebbian synaptic plasticity (Dayan and Abbott, 2001) as follows:
These two matrices currently have outputs that are of arbitrary magnitudes. To ensure each contribution has a roughly similar sized output, the structured section is rescaled by a factor as follows:
The magnitude of the elements of this matrix is then scaled again such that the resulting contralateral representation has the desired sparseness when the same piriform threshold is applied as was used to create the ipsilateral sparseness. Finally, the conversion to hertz, γ, is used to compare with data.
We varied the α and looked at how four measures of cross-cortical alignment changed (Fig. 6E–J; see main text for details) and how simulations compared with our actual AON data. We chose the optimal α to be the point with the minimum z2-score across the four—the smallest squared sum of sigmas (error bars) between the point and the AON data line in all four plots.
We modeled our AON with 50,000 neurons, this is a rough underestimate based on other paleocortical neuronal densities (Srinivasan and Stevens, 2017) and measurements of AON size from the Allen Brain Atlas. Our model, however, was robust to changes in number of neurons, for example, we tried a 25,000-neuron version (Extended Data Fig. 6-1E–H), and the optimal α did not change.
Our first low-rank approximation uses singular value decomposition to factorize
Quantification and statistical analysis
All quantifications and statistical analyses were performed using custom scripts in MATLAB (MathWorks), except (1) the circular statistical analyses, which were performed with the CircStat toolbox written by Philipp Berens (Berens, 2009), and (2) Hartigan's dip tests, which were performed with the code written by Ferenc Mechler (Mechler and Ringach, 2002). The linear regressions were obtained with the least square method. The data are given as median ± SD, unless specified otherwise. For all statistical tests, the critical value was set to 5%. All statistical tests were two-sided, with the exception of the bootstrap strategies (where we asked whether the measurements were significantly higher than chance).
Additionally, the significance of the linear regressions was tested using either an F test (Fig. 3; Extended Data Figs. 3-1, 3-2). The significance of the percentage of bilaterally correlated neurons (BCNs) was tested using a bootstrap strategy (see figure caption for details). Extended Data Table 1-2 summarizes all the statistical tests performed in this study.
Figure 3-1
Bilaterally-Correlated Neurons. Related to Figure 3. (A) to (D) Percentage of bilaterally-correlated neurons per mouse. (A) Identical to Figure 3B. (B) Same as Figure 3B, except we kept only the neurons responding to at least one odor on one side for this analysis. (C) Same as Figure 3B, except we kept only the neurons responding to at least one odor on each side for this analysis (note that the odors eliciting responses can be different on each side). (D) Correlation across artificially-generated trials. For each mouse, each side, each neuron, each odor, two sets of 7 artificial trials were generated using a Poisson process. Then for each side, an analysis similar to Figure 3B was performed, except we looked for correlations between the first and second sets of 7 artificial trials, instead of correlations between ipsi and contralateral trials. A full circle means the percentage is higher than chance (significance is determined with a bootstrapping method similar to Figure 3C). Panel (D) allows us to estimate the apparent percentage of bilaterally-correlated neurons one would expect if all neurons were bilaterally-correlated. (E) Upper estimates of bilateral correlations in ideal conditions. For each mouse, each side, each neuron, we randomly split the 7 trials into two groups of 3 and 4, to calculate the correlation between the two groups of trials. We then computed the percentage of bilateral correlations among all neurons. We repeated this process for all the possibilities of trial splits. The + signs to the right of each distribution show the actual values from Figure 3B for comparison. The goal of this test is to estimate an upper bound for the percentage of bilateral correlations one could expect in each mouse. (F) Spike traces from mouse PPC1. The average spike of all the neurons recorded from APC1 are shown. The spike amplitudes were normalized for display. Note the apparent bimodal distribution of spike widths. (G) Histograms of the spike widths at half-maximum amplitude, per OC region. For each histogram, a Hartigan’s dip test was performed to confirm that the distribution of spike widths was not unimodal (AON p = 0.020, APC p = 0.016). The corresponding p-values are reported next to each graph. We also used a maximum likelihood estimate approach to fit a mixture of two Gaussian distributions to each distributions (AON: weight of the 1st Gaussian relative to the mixture w = 0.07, μ1 = 85.6, σ1 = 4.5, μ2 = 130.1, σ2 = 22.9; APC: w = 0.11, μ1 = 94.2, σ1 = 8.1, μ2 = 146.3, σ2 = 26.2). For each distribution, the vertical bar indicates spike width at which the Gaussian mixture reaches its minimum between the two peaks: we chose this value as the definition for “narrow” and “broad” spikes (AON 95μs; APC 110μs). Using this criterion, we found 85% of broad spikes in the AON and 89% in the APC. (H) Basal firing rate of the neurons with narrow versus broad spikes. Based on the histograms in (G), spikes were categorized as narrow or broad. White circle and value next to it: median. Thick bar: quartiles. Thin bar: range of values. The neurons with narrow spikes showed a significantly higher basal activity than the broad ones. This finding, along with the presence of a bimodal spike width distribution, suggests that the neurons with narrow spikes are mostly inhibitory, while the neurons with larger spikes are mostly excitatory. The purple bar shows the same analysis performed on the bilaterally-correlated (noted “cor”) neurons of the AON and APC (Kruskal-Wallis test across the “narrow”, “broad’, and “cor” categories: p = 3.2e-6; post-hoc Wilcoxon rank-sum test, “narrow” versus “broad” p < 0.0001, “narrow” versus “cor” p = 0.0008, “broad” versus “cor” p = 0.60). (I) Comparison of the spike width distribution of all AON and APC neurons versus bilaterally-correlated neurons. Unlike the overall distribution in the AON and APC, the spike width distribution for the bilaterally-correlated neurons is not significantly different from a unimodal distribution (Hartigan’s dip test, p = 0.06) Almost all the bilaterally-correlated neurons have a broad spike (97% neurons have a broad spike), suggesting they are putative excitatory neurons. (J) PID signal amplitude at different odor dilutions. All amplitudes are reported relative to the amplitude of the dilution used for the rest of this study (5%). Each grey dot is one PID measurement (10 repeats per condition), the black dots are the average amplitudes. For each odor, the red dot indicates the dilutions at which the PID signal is about 10 times weaker than the initial 5% dilution. (K) Response magnitude of the initial versus weaker dilutions. Each graph is one side of the mask. Each dot is one neuron-odor pair (n = 2 mice recorded in the AON, 120 neurons). We only tested the 6 odors shown in panel I (initial dilution: 5%; new dilution: see red dot in I). Only the neuron-odor pairs significantly responding to the initial 5% concentration are shown. Red: average for each side. When we stimulated the same side of the mask with 10 times less odorant, we obtained neuronal responses about 6 to 7 times weaker than with our initial 5% concentration. Therefore, cross-contamination in the mask, if any, cannot explain the presence of side-invariant odor responses. Download Figure 3-1, TIF file.
Figure 3-2
Correlation of Population Odor Representations. Related to Figure 3. (A) and (B) Ipsi- versus contralateral responses per odor in both OC regions tested. (A) AON. (B) APC. Red line: linear regression (contra = a x ipsi + b). Solid lines show significant regressions (F-test, p < 5%). Odor # is the same as Figure 1-1A. Each dot is one neuron. The top left graph for each region is also shown in Figure 3G. (C) Same as panel (A), odor 1, except neuron identity has been randomly shuffled on each side. Note the absence of significant correlation. (D) Significance of the regression slope for panel (A), odor 1. We built a chance distribution by repeating the shuffling shown in panel (C) 1000 times. We then compared the actual value of the slope to this distribution to determine its significance. Here we show the distribution of chance slopes for the AON, odor 1. The results of our bootstrap strategy matched the results of the F-test mentioned above. (E) We performed the same analysis as panels (A) and (B), but we looked at each mouse separately. Here we report the R2 value of each regression, as well as whether the correlation was significant (F-test). N.s. means that the correlation was not statistically significant (p ≥ 5%). Download Figure 3-2, TIF file.
Figure 3-3
Variability Across Mice. Related to Figures 3 and 4. In this figure we point at factors that may explain the variability we observed across mice in terms of percentage of bilaterally-correlated neurons, as well as odor decoding accuracy. (A) Mean number of odors eliciting response (related to Figure 2E). (B) Mean response magnitude (related to Figure 2F). (C) Percentage of bilaterally-correlated neurons (related to Figure 3B). Solid dot: significant percentage. (D) Mean accuracy of the odor decoding process with model trained on responses to ipsilateral presentations (related to Figure 4-1B). (E) Similar to (D) with training on contralateral presentations. Download Figure 3-3, TIF file.
Data and code availability
All data and code used to compute quantities presented in this study is available on the Murthy lab GitHub page: https://github.com/VNMurthyLab/IpsiContra_v2.
Results
A new method for unilateral odor delivery
A major goal of this study was to compare the bilateral odor receptive fields of OC neurons of awake mice. To do so, we needed a way to reliably deliver odors to one nostril at a time. Previous studies have used two different techniques to achieve such unilateral odor stimulations. Either a piece of tubing was inserted in each nostril (Wilson, 1997) or the two nostrils were separated by a plastic septum (Kikuta et al., 2008, 2010). Since mice have very sensitive muzzles, neither of these approaches is applicable to awake animals.
We developed a new facial mask for unilateral odor delivery in awake mice (Fig. 1B). On each side of the mask, close to the nostrils, we used a computer-controlled olfactometer to deliver 15 monomolecular odors, one at the time, to the left or the right nostril (Extended Data Fig. 1-1A,B). The mask was connected to an airflow sensor for respiration monitoring (Extended Data Fig. 1-1C–F). We aligned all neuronal odor-evoked activity to the first sniff after odor onset (see Materials and Methods).
We tested the side selectivity of the mask through two methods. First, we simultaneously monitored the glomerular activity at the surface of the OBs of OMP-GCaMP3 mice (Isogai et al., 2011) through calcium imaging while delivering odors to either nostril (Fig. 1C; n = 155 glomeruli, six OBs, three mice). When odors were presented on the contralateral nostril, glomeruli showed little to no response (Fig. 1D–F).
We also performed extracellular recordings of M/T cells in the OB of awake mice using chronically implanted tetrodes (n = 42 single units isolated from four mice; Fig. 1G; Extended Data Fig. 1-1G–L; Extended Data Table 1-1). M/T cells hardly responded to contralateral odor presentations: out of the 42 isolated neurons, 32 responded to ipsilateral stimulations, while 10 showed significant contralateral responses (Fig. 1H; Extended Data Fig. 1-1M). Furthermore, while the ipsilateral M/T cell responses were largely positive, the few contralateral responses were always negative (Fig. 1H).
The ratio of contra- to ipsilateral response magnitude elicited by odors presented within the mask was 0.12 in our glomerular imaging data (ΔF/F magnitude; average ± SEM; ipsilateral, 7.9 ± 0.025%; contralateral, 0.93 ± 7.0 × 10−3%; Fig. 1I) and 0.10 in our M/T recordings (response magnitude; average ± SEM; ipsilateral, 1.6 ± 0.019 Hz; contralateral, 0.16 ± 2.1 × 10−3 Hz; Fig. 1J). Therefore, on average, odor responses were 8–10 times stronger on the ipsilateral side of the mask, compared with the contralateral side. In addition, by systematically lowering the concentration of odor stimuli presented to the two nostrils, we found that the glomerular responses to contralateral stimuli were comparable with presenting a 10-fold lower-intensity ipsilaterally (Extended Data Fig. 1-3). Finally, we further evaluated potential cross-contamination by imaging the glomerular activity of awake mice after naris occlusion while presenting odors through our mask (Extended Data Fig. 1-4). This experiment confirmed our other controls: when considering the odor-evoked activity of the OB contralateral to the occlusion (i.e., the OB on the unoccluded side), the glomerular responses were >10 times when stimulating the open nostril, compared with puffing odors on the occluded naris (Extended Data Fig. 1-4C).
Previous work has indicated that M/T cell responses to a given odor at a concentration of 0.1% were quite similar to the same odor at a concentration of 1% (Bolding and Franks, 2017, 2018). Since the M/T cell responses we observed are substantially smaller for contralateral presentations, the effective odor concentration in the ipsilateral nostril, when odors are presented contralaterally, is likely to be much <10% of the initial concentration from ipsilateral presentations.
Together, these results confirmed that odors, when delivered to one nostril through our custom-built mask, activate sensory neurons in the other nostril at least an order of magnitude less than ipsilateral activation.
Unilateral odor tuning of neurons in different regions of the OC
We next used our unilateral odor delivery system to characterize how the OC responds to unilateral odor presentations. To do so, we monitored the activity of the AON and APC through extracellular recordings in awake mice (n = 7 mice; 1,316 neurons total; AON, 3 mice, 385 neurons; APC, 4 mice, 931 neurons; Fig. 2A; Extended Data Fig. 2-1; Extended Data Table 1-1).
Overall, neurons in the OC had low spontaneous activity (Fig. 2B), in accordance with previous studies (Litaudon et al., 2003; Zhan and Luo, 2010; Bolding and Franks, 2017). Spontaneous activity in the AON was significantly higher than in the APC (average spontaneous activity ± SEM; AON, 3.9 ± 0.012 Hz; APC, 2.8 ± 3.6 × 10−3 Hz; rank-sum test; p < 0.0001).
We found a diversity of responses to ipsi- as well as contralateral odor stimulations in both the AON and APC, including neurons that responded to odors presented to either one or the other nostril or to both nostrils (Fig. 2C,D). Importantly, in both OC regions, a sizeable fraction of neurons responded to contralateral odor presentations (fraction of neurons significantly responding to at least one odor presented on the ipsilateral nostril; AON, 86%; APC, 81%; contralateral side, AON, 87%; APC, 83%), expanding earlier anesthetized observations to the awake OC (Wilson, 1997; Kikuta et al., 2008, 2010).
OC neurons typically responded to at most a few odors from our odor panel (Fig. 2E), as previously reported with bilateral odor stimulations (Bolding and Franks, 2017; Iurilli and Datta, 2017). We found a wide range of response magnitudes across the OC, and contralateral responses were as strong as ipsilateral ones in the APC and slightly weaker in the AON (Fig. 2F; Wilcoxon signed-rank test, ipsi- vs contralateral distribution; AON, p = 8.2 × 10−3; APC, p = 0.94).
An idiosyncratic feature of olfactory cortical responses is that a neuron that responds to a particular odor with an increase in the firing rate tends to exhibit increases in firing rates for any other odor it responds to; similarly, a neuron having inhibitory response to an odor is also inhibited by any other odor it responds to (Kikuta et al., 2008; Zhan and Luo, 2010; Otazu et al., 2015; Bolding and Franks, 2017; Hu et al., 2017). We find that this feature applies to both cortical regions we examined (Fig. 2G).
Overall, we found reliable odor responses in the OC to unilateral stimulation through both the ipsi- and contralateral nostrils.
Strong bilateral correlations in the OC
Our data indicate that the OC receives robust contralateral odor information, but it is unclear whether this information is similar to that arriving ipsilaterally. To examine this issue, we related the average odor-evoked responses to ipsilateral against contralateral presentations.
First, we plotted the ipsi- versus contralateral odor responses for each neuron individually. Given the prevalent view of OC connectivity as largely random, we expected OC neurons to show little to no match between their ipsi- and contralateral odor response profiles. To our surprise, we found many neurons with bilaterally correlated odor responses (Fig. 3A; F test; significance of the linear model, from left to right neuron; top row, p = 0.41; p = 0.84; p = 0.54; p = 0.59; bottom row, p = 5.3 × 10−6; p = 5.5 × 10−4; p = 6.6 × 10−3; p = 2.2 × 10−4; see also Fig. 2D, neurons with an asterisk). Overall, for each mouse recorded in the OC, we consistently found a sizeable fraction of bilaterally-correlated neurons (BCNs) (Fig. 3B).
We used a bootstrap strategy to determine that the fraction of BCNs in each mouse was significantly higher than what one would expect from chance. In brief, for each mouse, each neuron, we randomly shuffled odor identities on each nostril separately. We repeated this shuffling for all neurons and calculated the resulting percentage of BCNs. We reiterated this procedure 10,000 times in order to build a “chance” distribution. Finally, we compared this distribution with the actual fraction of BCNs. In all mice recorded in the OC, the fraction of BCNs was greater than one would expect from chance (Fig. 3B,C; Extended Data Fig. 3-1A–D).
While our initial estimates of the fraction of BCNs include all neurons recorded (Fig. 3B; Extended Data Fig. 3-1A), we verified that our conclusions remained true when selecting only the significantly responding (Extended Data Fig. 3-1B,C).
We performed additional analysis to determine the extent to which response variability masks response correlations. We generated individual trials from a Poisson process with rates determined from our recordings. We then enquired how correlated responses would be for two independent sets of seven trials generated with the same set of data. A large number of such sampling revealed that the fraction of (simulated) neurons that show significant response correlations was on the order of 40–80%. Therefore, even when excluding virtually all variability sources except for the neurons’ trial-to-trial variability, we still cannot reach a perfect correlation (100%). In other words, response variability probably lowers the apparent bilateral correlations we observe (Extended Data Fig. 3-1D). This analysis indicates that the fraction of significantly correlated neurons in the AON observed in our recordings may underestimate the real fraction of BCNs.
In addition, for each mouse, each side, and each neuron, we randomly split the seven trials into two groups of three and four, to calculate the correlation between the two groups of trials. We then computed the percentage of bilateral correlations among all neurons. We repeated this process for all the possibilities of trial splits. The goal of this test was to estimate an upper bound for the percentage of bilateral correlations one could expect in each mouse. The correlations obtained through this method were visually similar to the actual percentages. This suggests that the variability across mice in the actual data comes, at least partly, from the trial-to-trial response variability and/or overall weak odor responses, rather than a sizable difference of fraction of BCNs across mice. The analysis also suggests that the percentages of BCNs that were obtained in each mouse are rather close to the maximum correlation one could expect (Extended Data Fig. 3-1E).
Overall, the bilateral correlations were strong (Fig. 3D), with mostly positive slopes (Fig. 3E), and a distribution of intercepts centered on zero (Fig. 3F). In other words, BCNs appear to respond to unilateral odor presentations in a mostly side-invariant way, with their ipsi- and contralateral odor responses similar in sign and amplitude. The distribution of slopes was similar between the bilateral correlations originating from the AON and APC (mean ± SEM; AON, 0.79 ± 0.025; APC, 0.69 ± 0.036; Wilcoxon rank-sum test; p = 0.25). In addition, we found minimal but significant differences between the distribution of coefficients of determination (mean ± SEM; AON, 0.60 ± 0.017; APC, 0.54 ± 0.016; Wilcoxon rank-sum test; p = 0.013), as well as intercepts (mean ± SEM; AON, −0.24 ± 0.11; APC, 0.37 ± 0.16; Wilcoxon rank-sum test; p = 3.9 × 10−3) between the two regions.
We wondered if the bilaterally matched responses occurred preferentially in a particular neuron type. We used a classic method for identifying putative excitatory and inhibitory neurons—the width of extracellular action potentials (Suzuki and Smith, 1985; Gur et al., 1999; Frank et al., 2001; Swadlow, 2003; Povysheva et al., 2006; Hassani et al., 2009; Weir et al., 2015). Most of the BCNs in the AON and APC had broader spikes and lower spontaneous activity (Extended Data Fig. 3-1F–I), suggesting they are excitatory.
When testing our mask for cross-contamination (Fig. 1), we estimated that at most 10% of odors puffed on one side of the mask could reach the contralateral nostril. Can this cross-contamination alone explain the side-invariant responses we observed in the OC? To test this hypothesis, we first determined how much each odor from our panel needed to be diluted in the solvent to elicit a PID signal 10 times weaker than our initial dilution (5% volume/volume; Extended Data Fig. 3-1J). We then recorded individual neurons in the AON of awake mice (n = 2 mice; 120 neurons) while delivering odors on either side of the mask, both at initial (5%) and new dilutions (i.e., dilutions resulting in PID signals 10 times weaker; Extended Data Fig. 3-1K). On average, for a given side of the mask, new dilutions elicited neuronal responses 6–7 times weaker than our initial dilutions (neuronal response; mean ± SEM; for ipsilateral presentations; initial dilution, 2.1 ± 0.023 Hz; final dilution, 0.34 ± 2.7 × 10−3 Hz; for contralateral presentations; initial dilution, 2.5 ± 0.031 Hz; final dilution, 0.37 ± 2.5 × 10−3 Hz). In other words, since weak ipsilateral presentation generates responses that are much weaker than the contralateral responses we measured, contralateral cross-contamination in the mask, if any, is not sufficient to explain the presence of side-invariant odor responses.
Finally, we compared the ipsilateral and contralateral population representation in the two OC regions recorded for each odor (Fig. 3G; Extended Data Fig. 3-2A–C). Consistent with our analysis of individual BCNs, we found that unilateral population representations were significantly and strongly correlated for all odors tested in the AON and APC (Extended Data Fig. 3-2A,B; see Extended Data Table 1-2 for detailed statistics). Analyzing population correlations for each odor individually ensures that the correlations we observe are not driven by few neurons responding strongly and similarly to all odors, which would otherwise create an artificially strong positive correlation. Furthermore, a bootstrap analysis similar to what we described above confirmed that the strong correlation we observed for each odor individually was unlikely to occur by chance (Extended Data Fig. 3-2C,D). When considering each mouse separately, our conclusions held true for most odors (Extended Data Fig. 3-2E).
Together, these data indicate that the odor information arriving contralaterally to the OC strongly matches that arriving ipsilaterally.
Accurate decoding of odor identity from contralateral activity
Odor selectivity in the OC allows a population representation of odor identity, and odor identity can be decoded from the activity of a sufficient number of neurons (Bolding and Franks, 2017). If odor responses are similar for ipsi- and contralateral presentations, decoding of identity should be transferrable. To formally test this, we asked a linear classifier to decode odor identity from OC responses to stimuli in one nostril, after we trained it with OC responses to stimuli in the opposite nostril.
In brief, at each instance we created a training set by randomly removing one trial per odor (Bolding and Franks, 2017). If we were testing on the same side as we trained (e.g., train ipsilaterally, test ipsilaterally), then we tested the classifier on the trials outside of the training set. If we were testing on the opposite side (e.g., train ipsilaterally, test contralaterally), then one trial per odor from the opposite side was randomly chosen to form the test set (Fig. 4A). We performed all our decoding analyses over an odor response window of 2 s, as it maximized performance of odor decoding on the same side (Extended Data Fig. 4-1A).
Figure 4-1
Ipsi- and Contralateral Odor Decoding. Related to Figure 4. (A) Decoding accuracy with varying odor response time windows. For each data point, response vectors were built by summing spikes from 0 s to the time indicated on the x-axis (responses aligned to the first sniff after odor onset, see Methods for details). This example shows the odor decoding for all neurons in the AON, train ipsi, test ipsi. Odor decoding accuracy is maximal at 2 s, therefore we decided to perform all our decoding analysis over an odor response window of 2 s. (B) Same as Figures 4B-4C, except the decoding process was applied to each mouse separately. Accuracy at n = 50 neurons is significantly higher than chance for all mice. The mice with poorer decoding accuracy were usually the ones in which neurons showed fewer / weaker responses on average, as well as fewer bilaterally-correlated neurons (see Figure 3-3). Download Figure 4-1, TIF file.
In both the AON and APC, the decoder performed with similarly high levels of accuracy when tested on data from the same or the opposite side (Fig. 4B,C). In other words, odor identity decoding optimized with responses to stimulation from one nostril transfers very well to stimulation of the other side.
Note that the seeming decrease of decoding performance from AON to APC is most probably due to variability across mice (Extended Data Fig. 4-1B). More precisely, the three mice with overall weaker and fewer odor-evoked responses (i.e., mice AON2, APC1, and APC2) showed lower odor decoding performances, no matter the side tested (Extended Data Fig. 3-3). In addition, AON2 is the mouse from which we isolated the fewest neurons (73 neurons, while all other OC recorded mice have over a hundred neurons each; Extended Data Table 1-1). Predictably, in these same three mice, the fractions of BCNs, while significantly higher than chance, were the lowest.
Rodents are able to determine the direction of the odor stimulation at short distances (Rajan et al., 2006; Rabell et al., 2017). We asked whether population activity in a single hemisphere differs sufficiently for stimulation of ipsi- and contralateral nostril to allow decoding of intensity differences between the two sides. Side identity, or laterality, could indeed be decoded with high accuracy (Fig. 4D), which confirms that odor representations from both sides, despite similarities, remain distinct and separable.
In addition, we asked whether the way we compute odor responses could influence the decoder's results. To do so, we replicated the decoding results from Figure 4B,D, using odor responses calculated with no blank trial subtraction (Extended Data Fig. 4-2). Overall, the decoding results obtained with and without blank subtraction are very similar, suggesting that the way we built our initial metric had little impact on the decoder's performance.
Figure 4-2
Ipsi- and Contralateral Odor Decoding with No Blank Subtraction. Related to Figure 4. We replicate the decoding results from Figure 4B-D, using a new metric to compute odor responses with no blank trial subtraction. More precisely, we performed decoding on odor responses R calculated using the following formula:
Finally, we tested how BCNs affected the decoding results, by performing the decoding analysis described in Figure 4 while varying the proportion of BCNs in the dataset fed to the decoder (Extended Data Fig. 4-3). Decoding performance degraded as we removed BCNs from the dataset, which indicates that BCNs are the main drivers of the high performances of our odor decoder. In addition, same-side decoding was also affected by the removal of BCNs. This suggests that BCNs carry a sizable fraction of the total odor information, presumably because of stronger responses. Lastly, side identity could be decoded with high accuracy, even in the absence of BCNs, which confirms that the odor response profiles from both sides remain distinct, even in the noncorrelated population.
Figure 4-3
Bilaterally-Correlated Neurons and Decoding Performance. Related to Figure 4. (A) Same as Figure 4B, except we removed all the bilaterally-correlated neurons before computing the accuracies. (B) For each decoding, we randomly took 135 neurons, including a certain fraction of bilaterally-correlated neurons (as indicated on the y-axis). For each region, each fraction of bilaterally-correlated neurons, each test side, we repeated this decoding 50 times, each time with a new set of randomly-picked neurons. The box plots show the median (white and black dot), 1st and 3rd quartiles (thick bar), 95% confidence interval (thin bar), and the outliers (black circles). All training data used for this panel are from the ipsilateral side. (C) Same as panel (A), for side decoding instead of odor decoding. (D) Same as panel (B), for side decoding instead of odor decoding. (E) Same as panel (A), except the analysis was performed on each individual mouse. (F) Same as panel (B), except the analysis was performed on each individual mouse. Here, for each decoding, instead of 135 neurons, we took N neurons, N being the total number of bilaterally-correlated found in each mouse. For all panels, the gray dotted line shows chance level. Download Figure 4-3, TIF file.
Together, our data indicate that odor decoding is transferrable across sides. Ipsi- and contralateral odor representations, while distinct, share a high degree of similarity.
A computational model of bilateral matching in the OC
How does one OC respond so similarly to inputs from two distinct routes/nostrils? So similarly, in fact, that a linear classifier trained on one set of representations can decode the other?
An appealingly simple resolution might suggest itself based on recent theoretical work. It has been shown that if two correlated representations are projected through the same random matrix, the output representations are also correlated (Babadi and Sompolinsky, 2014; Schaffer et al., 2018). Thus, one could hypothesize that any interhemispheric projection between two OCs could preserve the structure inherited from the underlying OB representations, hence explaining the similarity. However, this argument is insufficient, as illustrated in Figure 5. The correlations in glomerular neural representations of the 15 odors used in our study are shown in Figure 5B. These correlations are partially preserved when projected through two different random matrices (to represent the OB to OC projections of each hemisphere; Fig. 5C, top and bottom block diagonals). However, the two OC hemispheres connected with random projections will not yield correlated responses to the same odor presented to the left or right side (Fig. 5C). In the extended data (Extended Data Note 5-1) we show that, even in the limiting case where the response of ipsi- and contralateral cortices are identical, unstructured interhemispheric connectivity ensures that the responses of one cortex to odors presented in different nostrils will be uncorrelated. Hence, there must exist some structured connectivity that is aligning the cortical responses (Fig. 5D; note the much higher similarity values in the two off-diagonal blocks).
Figure 5-1
Visualization of the Relationship Between Phi and Gamma. Related to Figure 5. This figure shows a heatmap representation of the relationship between gamma, phi, and S, as shown in equation 7. In addition, we provide variance equivalents to the gamma values, for an exemplar data set with the following characteristics: 50 glomeruli, 15% chance that a given neuron-odor pair is significantly responding, magnitude of average odor response 1.6 Hz. Download Figure 5-1, TIF file.
Extended Data Note 5-1
Random Connections Produce Zero Correlations – a Derivation. Download Extended Data Note 5-1, DOCX file.
We then asked what type of structured interhemispheric connectivity is sufficient to achieve the observed tight alignment of responses from different nostrils. Through computational modeling, we tested a simple and plausible model for structured matching, a Hebbian connectivity matrix that links together the same-odor representations in each hemisphere. We combined this Hebbian matrix (GStruct) with a random matrix (GRand), which models unstructured elements of connectivity and whose elements are drawn independently from a Gaussian distribution. We vary the degree of structure using a parameter α. Explicitly,
Our modeling strategy began by creating a cortical representation of odors presented in the ipsilateral nostril, based on the AON. Random OB-to-OC connectivity indicates that each cortical olfactory neuron samples a random subset of glomeruli. This implies that the input to each cortical neuron is statistically independent and that the response of one cortical neuron to different odorants can be modeled as a thresholded multivariate Gaussian whose covariance matrix can be extracted from activities in the OB (Fig. 6A; see Materials and Methods). We validated this model of a single AON against neuronal recordings: our model replicated the population statistics remarkably well with minimal parameter adjustment (Fig. 6B–D; Extended Data Fig. 6-1A–C).
Figure 6-1
Modeling Olfactory Cross Cortical Connections. Related to Figure 6. (A) to (C) Same as Figures 6B-6D, except for we are comparing the distributions of mouse ipsilateral data and our simulations of ipsilateral responses with optimal alpha (0.42). Mouse data is identical to the contralateral lines from Figures 2E-2F, top panels. (D) Same as Figure 6I, except we decoded ipsilateral odors using the contralateral presentations. (Left) Using closest Euclidean neighbor decoding is markedly worse in simulations than mice, due mainly to the difference in magnitude of responses that results as you increase the structure parameter. (Right) Using closest angular neighbors instead recaptures better performance, and the best alpha overlaps with the AON performance. This suggests that, in the left panel, the decoder limited performance rather than lack of information. (E) to (H) Same as Figures 6F, 6G, 6I, and 6J respectively, except each OC layer contains 25,000 neurons (instead of 50,000 neurons in Figure 6, see Methods). These show that the optimal alpha level (0.42) is robust to changes in the number of cortical neurons simulated. Download Figure 6-1, TIF file.
Figure 6-2
Low-dimensional structured connectivity is sufficient to produce alignment. Related to Figure 6. (A) and (B) Odor decoding accuracy using 385 neurons as Q is varied, averaged over 200 iterations of decoding and 10 different creations of G_Struct, (A) approximates using SVD and (B) using PCA. (C) – (F) are the same as Figure 6F, G, I, and J but using an approximate G_Struct created with 7 singular values. (G) – (J) are the same but using 6 PCA components. Download Figure 6-2, TIF file.
We then linked two simulated AONs using the connectivity matrix G. The computational analog of the cortical representations of an odor presented contralaterally is the representation after mapping through the interhemispheric matrix, G. Thus, by comparing the simulated and measured alignment of ipsi- and contralaterally presented odors as we vary α, we can ask if this simple Hebbian structure is sufficient to recapture the degree of alignment observed.
We compared the alignment using four metrics: the overall correlation of odor population representation across sides (Fig. 6E,F; similar to Fig. 3G), the fraction of BCNs (Fig. 6G; similar to Fig. 3B), the accuracy of odor identity decoding (Fig. 6H,I and Extended Data Fig. 6-1D; similar to Fig. 4B,C), and the accuracy of side identity decoding (Fig. 6J; similar to Fig. 4D).
The four computed measurements best matched our actual AON neuronal recordings with a nonzero α (∼0.4). This result held true, even when we fitted each animal separately (AON1 α = 0.3; AON2 α = 0.1; AON3 α = 0.3; APC1 α = 0.1; APC2 α = 0.1; APC3: α = 0.25; APC4 α = 0.35). In summary, for all animals, α was greater than zero.
This result did not vary when we changed the size of the modeled OC population (Extended Data Fig. 6-1E–H). This suggests that a significantly Hebbian connectivity between cortices is sufficient to explain the observations.
We chose this particular structured connectivity, the 15 odor Hebbian matrix GStruct, for its appealing simplicity and biological plausibility. For example, early in life, the animal might have smelt these odors in both nostrils and associatively matched the responses between hemispheres. However, there are many other sufficient alignment schemes that do not rely on having experienced specifically these 15 odors; we implemented two such examples in the supplementary material. The first uses a set of Hebbian matching experiences that span the subspace in which the 15 odorants lie. Roughly six of these are needed to achieve similar levels of alignment (Extended Data Fig. 6-2G–J) showing that a smaller number of more general early learning experiences could also explain the alignment seen in the data. The second technique uses singular value decomposition to create a low-rank approximation of GStruct; again, a rank of about 7 is necessary to match the data (Extended Data Fig. 6-2C–F). Effectively, any connectivity that links same-odor representations in each hemisphere together will be able to match the alignment seen in the data.
Discussion
In many animals, odor information collected by the two nostrils remains separate until it reaches secondary brain areas (Bojsen-Moller and Fahrenkrug, 1971; Eccles, 2000; Kikuta et al., 2008). How the two streams of information are integrated, as well as the functional role of this integration, remain unclear.
Here, we reveal that a sizeable fraction of neurons in the OC are bilaterally correlated, meaning that they have matched ipsi- and contralateral odor tuning. Functional alignment of responses was also confirmed by the ability to decode odor identity from neural populations trained using only odor responses from the opposite nostril. Finally, our computational model suggests that such bilateral matching requires structured interhemispheric projections. Our findings shed light on how brains make sense of this bilateral experience in the olfactory system.
Importance of the integration of bilateral information
In many senses, information is collected through a pair of sensors and is integrated within the brain. Bilateral perception allows us to perform vital behavioral computations such as sound localization or depth perception. Since the two nostrils may sample independent pockets of air (Wilson and Sullivan, 1999; Eccles, 2000; Kikuta et al., 2008), can bilateral information be used to discover features of the odor stimulus not readily available to single nostrils? Animals may compare odor concentration across both nostrils to locate odor sources (Catania, 2013; Parthasarathy and Bhalla, 2013; Gire et al., 2016; Rabell et al., 2017), which requires bilateral integration and comparison of different signals. Two brain regions that seem to fulfil these criteria are the AON and APC (Wilson, 1997; Kikuta et al., 2008, 2010). Recent studies have indicated that the AON may be sensitive to the differential activation of left and right nostrils, therefore computing the direction of the stimulus in front of the animal (Rabell et al., 2017). Our own data show that side identity can be decoded from odor responses, which provides further evidence for differential processing (Fig. 4D).
In contrast, many behaviors require the animal to make inferences independent of the nostril stimulated. One simple example can be found in our everyday life: one nostril is usually more blocked than the other, and which nostril gets clogged changes several times a day (Eccles, 2000). Nevertheless, we can identify smells and have a unified olfactory experience. In the same fashion, animals need to be able to identify relevant smells like food or a predator, no matter where the odor source is relative to their nose. Indeed, there is evidence for nostril-independent recognition of odors: rats trained to associate odors with reward using only one naris can generalize to stimuli delivered through the other nostril (Kucharski and Hall, 1987, 1988; Yan et al., 2008). This behavior requires interhemispheric communication, suggesting a convergence of ipsilateral and contralateral representations in higher brain areas (Dalal et al., 2020).
Robust contralateral odor responses in the OC
Here, we developed and tested a new method to deliver odor stimuli selectively to individual nostrils in awake, freely breathing mice (Fig. 1). We performed several experiments to confirm selective stimulation. First, PID recordings indicated that odors were delivered at the same concentration on either side and are not detectable on the side contralateral to the stimulus. Second, direct measurements of sensory input to the OB indicated that glomerular responses to contralateral stimulation were below detection threshold. It is possible that some low-level cross-contamination went undetected by the PID and glomerular recordings, but it is unlikely that such weak inputs could contribute to contralateral responses that are as strong as those to ipsilateral stimulation, as shown by our recordings in AON to 10-fold diluted odors (Extended Data Fig. 3-1K).
Third, recordings from putative M/T cells in the OB using tetrodes revealed few detectable responses to contralateral nares stimulation. These results seem at odds with a recent report suggesting that topographic projections from contralateral AONpE to M/T cells support bilaterally matched odor responses (Grobman et al., 2018). A possible source of the discrepancy is the much lower concentration of odor used in our mask. However our observations confirm former work from various teams regarding the near absence of contralateral M/T response in the OB (Wilson, 1997; Kikuta et al., 2008, 2010). Our results are also consistent with the known projections from the AON pars externa to the GABAergic granule cells of the contralateral OB (Schoenfeld and Macrides, 1984; Yan et al., 2008), since the infrequent significant responses we observed in M/T cells to contralateral nostril stimulation were inhibitory. The minimal responses in glomeruli and M/T cells also argue strongly against bilateral return of odors from the respiratory system during exhalation.
A recent study in humans (Dikeçligil et al., 2023), published after completion of our study, strongly corroborates our findings. Intracranial electrocorticogram recordings in the piriform cortex from subjects presented with odors separately in each nostril revealed significant contralateral responses, which were delayed compared with ipsilateral ones. Nevertheless, the signals also exhibited odor identity-related correlations. These results complement our findings that ipsilateral and contralateral responses are correlated but have enough differences to allow decoding laterality.
Bilateral integration in the OC is not random
Our first key finding is that responses to the stimulation of the contralateral nostril could readily be detected in the AON and APC (Fig. 2). This is not entirely surprising, since crossed projections already occur at the level of the AON and APC. Previous studies indicated that contralateral stimulations evoked fewer and weaker responses in the AON (Kikuta et al., 2008), and the responses from the two sides appeared to be significantly correlated. However, these studies reported a relatively small number of neurons in anesthetized animals, and the matching responses were characterized in terms of similarity of odor chemical categories, rather than firing rate responses. While we confirmed the presence of weaker and fewer responses to contralateral stimulations in the AON, the differences were smaller than previously reported. Another recent study implicated the AON in interhemispheric communication using a variety of methods, but the responses of neurons in this area were not measured (Yan et al., 2008). Finally, one previous study reported the presence of neurons responsive to contralateral naris stimulations in the APC of anesthetized rats, but did not test the correlation of the responses between the two nares (Wilson, 1997).
We found that the odor information arriving ipsilaterally to the OC strongly matches that arriving contralaterally (Fig. 3). Furthermore, our data offer strong hints that principal neurons are a major part of the ensemble of neurons that have matched responses to stimuli from both nostrils (Extended Data Fig. 3-1). This feature would allow downstream areas receiving olfactory cortical inputs to readily have access to side-stable odor responses. Indeed, we showed explicitly that odor decoding is transferable across sides (Fig. 4), as expected from the presence of matched responses in the OC.
Finally, we considered how the prevalent view of OC connectivity could explain our data (Fig. 5). Recent studies have shown that two representations projected through the same random matrix remain correlated (Babadi and Sompolinsky, 2014; Schaffer et al., 2018). In addition, another study reported that population response correlations between odors is shaped in the cortex, presumably by experience, such that some response similarities in the OB are enhanced and others are suppressed (Pashkovski et al., 2020). However, these studies do not address alignment due to projections between cortices. Here we implemented a computational model of the olfactory system to test matching of ipsilateral and contralateral odor tuning in the OC as a function of structure in bilateral projections. We showed that, in our model, purely random projections cannot explain our OC results. That is, even if the two hemispheres have the same ordered representations for similar odors, unstructured interhemispheric cortical projections will disrupt these correlations.
Limitations and potential future directions
A key feature of our experimental design is the selective stimulation of either nostril; therefore, it is important to consider the possibility of cross-contamination of odors to other nostril such that responses to the intended contralateral stimulation may arise through ipsilateral neural pathway. While our tests with miniPID recordings indicated virtually no contralateral odor contamination in the mask (Extended Data Fig. 1-1B), the control experiments we performed in the OB suggest that up to 10% of an odor delivered to one nostril may be sampled by the other nostril within the mask (Fig. 1). One could argue that the ipsi- and contralateral stimulations could activate similar neuron populations solely by the potential odor cross-contamination reported here. In other words, the contralateral odor stimulations might act as diluted ipsilateral stimuli and, as such, activate the same OC neurons as when odors are presented ipsilaterally. The diluted ipsilateral stimuli might then be potentially boosted by the normalization mechanisms proposed in recent studies (Bolding and Franks, 2018; Stern et al., 2018), thus explaining the side-invariant responses we observed. However, we consider this unlikely because individual neurons in the APC have complex and often nonmonotonic tuning to odor concentration (Chae et al., 2022; Zak et al., 2023), such that responses to the same odor at different concentrations may not be strongly correlated. Finally, our control experiments revealed that responses in AON to 10-fold more dilute odors are highly attenuated (Extended Data Fig. 3-1J,K). Collectively, these features argue against contamination playing a significant role in the aligned responses, but we acknowledge this possibility as a limitation. Future experiments in mice with lesion of the anterior commissure linking the two OC hemispheres could help resolve this issue.
We were unable to extract any differences in the latency of responses for stimuli from the two sides. A major reason for this is likely to be the variable spiking responses and the inadequate control of odor delivery in relation to timing of sniffs. We note, however, that the small size of mouse brains and the known conduction and dendritic integration speeds may lead to very little delays—for example, the latencies of odor responses in the entorhinal cortex are barely different from those in the piriform cortex (Bitzenhofer et al., 2022).
We also acknowledge the variability in the number of correlated neurons across animals. Such differences could be due to a sampling issue, in which an overall lower responsiveness to odors in the finite sample in each animal could mask underlying correlations. It is also possible that there is genuine biological variability related to individual experiences of the animals. Larger recording samples in future studies, combined with manipulation of experiences, might help overcome such statistical limitations and uncover any biological variability.
Finally, we note that our simplified model considers dense connectivity across the two hemispheres, but the actual underlying connectivity might be sparse (Hagiwara et al., 2012). The model is presented as a plausible explanation and not as a proof, and future work must investigate the effectiveness and adequacy of sparse interhemispheric connectivity.
Reconciling our results with known connectivity schemes
How can the responses of cortical neurons to selective stimulation of each of the two nostrils be matched? First, there may be topographically matched crossed projections somewhere in the olfactory system allowing bilateral response matching. The only known candidate for such matching is the projection from AON pars externa to OB (Schoenfeld and Macrides, 1984; Yan et al., 2008). This projection, however, should result in net inhibition of M/T cells since its major target are the GABAergic granule cells (Schoenfeld and Macrides, 1984; Yan et al., 2008; but see Grobman et al., 2018). Furthermore, previous studies (Wilson, 1997; Kikuta et al., 2008, 2010) and our own data (Fig. 1) suggest that this projection is too weak to support the widespread bilateral matching we observed in the OC. However, M/T cells that respond to contralateral odor presentations in a correlated way (Grobman et al., 2018) may report to ipsilateral OC neurons the same information for ipsi- and contralateral stimulation under some conditions, for example, high odor concentrations. Even so, unstructured interhemispheric cortical projections will disrupt this potential alignment in the cortex. Therefore, additional mechanisms, such as Hebbian plasticity, are needed to explain our observations.
A second way in which matched responses may arise is through fine-scale order in anatomical connectivity that somehow allows the same glomerular output channels from both OBs to converge on individual neurons. Such structure seems implausible and has no precedent but remains formally possible.
A third explanation that seems likely because of its simplicity and local nature is that synaptic connectivity in cortical regions is refined by Hebbian plasticity (Best and Wilson, 2003; Franks and Isaacson, 2005; Johenning et al., 2009) to allow matched responses from both sides. During their normal experience, mice will frequently inhale similar odors through both nostrils. This can allow associative plasticity in cortical areas to select for matched inputs from both hemispheres, similar to, for example, the well-studied map development in the visual system (Hubel and Wiesel, 1962; Bednar and Wilson, 2016; Gu and Cang, 2016). This hypothesis of experience-dependent matching of bilateral inputs to individual cortical neurons is open to experimental testing.
Footnotes
The authors declare no competing financial interests.
We thank Joseph Zak and Luis Boero for helping with the cranial windows. We thank Ed Soucy, Vikrant Kapoor and all members of the Murthy and Pehlevan labs for their helpful feedback. We also thank Massimo Vergassola and Gautam Reddy for their critical discussion. Finally, we thank Naoshige Uchida and Kenneth Blum for their valuable comments on a previous version of this manuscript.
Research reported in this paper was supported, in part, by grants from the National Institutes of Health (R01DC016289, R01DC011291), the Harvard Merit Fellowship, and the Herchel Smith Scholarship.
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