Editor's evaluation: Mechanisms and functions of respiration-driven gamma oscillations in the primary olfactory cortex

Abstract

Article Figures and data Abstract Editor's evaluation eLife digest Introduction Results Discussion Materials and methods Appendix 1 Data availability References Decision letter Author response Article and author information Metrics Abstract Gamma oscillations are believed to underlie cognitive processes by shaping the formation of transient neuronal partnerships on a millisecond scale. These oscillations are coupled to the phase of breathing cycles in several brain areas, possibly reflecting local computations driven by sensory inputs sampled at each breath. Here, we investigated the mechanisms and functions of gamma oscillations in the piriform (olfactory) cortex of awake mice to understand their dependence on breathing and how they relate to local spiking activity. Mechanistically, we find that respiration drives gamma oscillations in the piriform cortex, which correlate with local feedback inhibition and result from recurrent connections between local excitatory and inhibitory neuronal populations. Moreover, respiration-driven gamma oscillations are triggered by the activation of mitral/tufted cells in the olfactory bulb and are abolished during ketamine/xylazine anesthesia. Functionally, we demonstrate that they locally segregate neuronal assemblies through a winner-take-all computation leading to sparse odor coding during each breathing cycle. Our results shed new light on the mechanisms of gamma oscillations, bridging computation, cognition, and physiology. Editor's evaluation This fundamental study employs a publicly available dataset to examine the role of γ oscillations in the coding of olfactory information in the mouse piriform cortex. The authors convincingly show that γ originates in the piriform cortex, is driven by feedback inhibition, and that the time course of odour decoding is most accurate when γ oscillations are strongest. This work is relevant to a wide audience interested in the mechanisms and role of oscillations in the brain, and nicely demonstrates the benefits of well-curated, publicly available datasets. https://doi.org/10.7554/eLife.83044.sa0 Decision letter Reviews on Sciety eLife's review process eLife digest The cerebral cortex is the most recently evolved region of the mammalian brain. There, millions of neurons can synchronize their activity to create brain waves, a series of electric rhythms associated with various cognitive functions. Gamma waves, for example, are thought to be linked to brain processes which require distributed networks of neurons to communicate and integrate information. These waves were first discovered in the 1940s by researchers investigating brain areas involved in olfaction, and they are thought to be important for detecting and recognizing smells. Yet, scientists still do not understand how these waves are generated or what role they play in sensing odors. To investigate these questions, González et al. used a battery of computational approaches to analyze a large dataset of brain activity from awake mice. This revealed that, in the cortical region dedicated to olfaction, gamma waves arose each time the animals completed a breathing cycle – that is, after they had sampled the air by breathing in. Each breath was followed by certain neurons relaying olfactory information to the cortex to activate complex cell networks; this included circuits of cells known as feedback interneurons, which can switch off weakly activated neurons, including ones that participated in activating them in the first place. The respiration-driven gamma waves derived from this ‘feedback inhibition’ mechanism. Further work then examined the role of the waves in olfaction. Smell identification relies on each odor activating a unique set of cortical neurons. The analyses showed that gamma waves acted to select and amplify the best set of neurons for representing the odor sensed during a sniff, and to quieten less relevant neurons. Loss of smell is associated with many conditions which affect the brain, such as Alzheimer’s disease or COVID-19. By shedding light on the neuronal mechanisms that underpin olfaction, the work by González et al. could help to better understand how these impairments emerge, and how the brain processes other types of complex information. Introduction Since the pioneer works of Adrian, 1942 and Bressler and Freeman, 1980, gamma oscillations have been one of the most studied brain rhythms (Bastos et al., 2020; Bastos et al., 2015; Bragin et al., 1995; Buzsáki and Wang, 2012; Csicsvari et al., 2003; Fries et al., 2007; Gray et al., 1989; Sirota et al., 2008; Vinck et al., 2010; Womelsdorf et al., 2012; Womelsdorf et al., 2006). Gamma is believed to be critical for a variety of cognitive functions such as sensory processing (Fries et al., 2001), memory (Fernández-Ruiz et al., 2021), navigation (Colgin et al., 2009), and conscious awareness (Rodriguez et al., 1999). At the cellular scale, gamma rhythms modulate spiking activity, shaping the formation of transient neuronal partnerships, the so-called cell assemblies (Buzsáki, 2010). Computational models show that gamma depends on local inhibitory-inhibitory or excitatory-inhibitory interactions (Tort et al., 2007; Wang and Rinzel, 1992) and ultimately emerges from synchronous inhibitory postsynaptic potentials (Buzsáki and Wang, 2012). However, despite the initial evidence for their underlying principles being general, gamma oscillations are not a monolithic entity but actually encompass a diversity of rhythms observed experimentally (Lopes-Dos-Santos et al., 2018; Scheffer-Teixeira et al., 2012; Schomburg et al., 2014; Zhong et al., 2017). This warrants studying the mechanisms and functions of these oscillations in specific brain regions in vivo. Gamma oscillations usually appear nested within slower rhythms, a phenomenon known as cross-frequency coupling, in which the amplitude of gamma waxes and wanes depending on the phase of a slow oscillation (Canolty and Knight, 2010; Lisman and Jensen, 2013). Important examples are the coupling of specific gamma sub-bands to the hippocampal theta rhythm (Cavelli et al., 2020; Sirota et al., 2008; Tort et al., 2009; Tort et al., 2008) or to the phase of breathing cycles (Cavelli et al., 2018; Ito et al., 2014; Zhong et al., 2017). Regarding the latter, it is worth noting that respiration-entrained brain rhythms depend on nasal airflow and are not a consequence of the respiratory pattern generation in the brainstem (Lockmann et al., 2016; Moberly et al., 2018; Yanovsky et al., 2014). Therefore, it seems likely that respiration-entrained gamma activity arises from local computations driven by sensory inputs sampled at each breath and thus plays a major role in cognition. A promising area to study this hypothesis is the piriform cortex (PCx), which constitutes the primary olfactory area in the rodent brain (Bolding and Franks, 2018a; Stettler and Axel, 2009) and exhibits prominent gamma oscillations (Bressler and Freeman, 1980; Courtiol et al., 2019; Freeman, 1960; Freeman and Skarda, 1985; Kay et al., 2009; Kay and Freeman, 1998; Litaudon et al., 2008; Mori et al., 2013; Vanderwolf, 2000). Most of our understanding of piriform oscillations comes from the studies of Walter Freeman in the 20th century (Barrie et al., 1996; Eeckman and Freeman, 1990; Freeman, 1968; Freeman, 1960; Freeman, 1959). Freeman characterized gamma activity in terms of its topography, frequency range, and relationship to unitary activity and behavior. These observations led him to hypothesize that these oscillations constitute a fundamental sensory processing component that emerges from an excitatory-inhibitory feedback loop. However, despite his influential insights, Freeman’s conjectures could not be conclusively tested due to the technological limitations of his time. Thus, we still lack compelling experimental demonstrations of how gamma generation depends on the interactions between the different piriform neuronal subpopulations or how gamma relates to odor representations encoded as cell assemblies. Therefore, the mechanisms of gamma oscillations in the PCx and their functional role in olfaction need to be studied under the lens of modern experimental and analytical tools (Courtiol et al., 2019; Kay et al., 2009; Mori et al., 2013). In this report, we study gamma oscillations in the PCx of the awake mouse. We took advantage of modern genetic tools, which accurately identify neuronal populations and precisely modify the local connectivity of the PCx, thus enabling an unprecedented study of the mechanisms and functions of its oscillatory activity. We found that respiration drives gamma oscillations in this region, which derive from feedback inhibition and depend on recurrent connections between local excitatory and feedback inhibitory populations. This loop is triggered by the projection of mitral/tufted cells in the olfactory bulb onto the principal cells of the PCx. As functional consequences, we show that respiration-driven gamma oscillations determine odor-assembly representations through a winner-take-all computation taking place within breathing cycles. Results Respiration drives gamma oscillations in the piriform cortex To understand the mechanisms and functions of gamma oscillations and their relationship with respiration in the mouse brain, we analyzed local field potentials (LFP) from the PCx recorded simultaneously with the respiration signal (Figure 1A) during odorless cycles, hereafter referred to as ‘spontaneous’ activity. The dataset was collected by Bolding and Franks, 2018a and generously made available through CRCNS (http://crcns.org, pcx-1 dataset). Figure 1B depicts the LFP and respiration signal from a representative animal; notice that low-gamma oscillations (30–60 Hz) emerge following inhalation start. These low-gamma oscillations, already evident in raw recordings, are part of a larger gamma peak (30–100 Hz) in the LFP power spectrum (Figure 1C), reflecting a true oscillation (Yuval-Greenberg et al., 2008). Consistent with Figure 1B, only the low-gamma sub-band couples to the respiration cycle across animals, as its amplitude is modulated by both a 2–3 Hz LFP rhythm coherent to respiration (Figure 1D top panel; see also Figure 1—figure supplements 1–3) and by respiration itself (Figure 1D bottom panel). Thus, respiration entrains low-gamma oscillations in the PCx. Figure 1 with 5 supplements see all Download asset Open asset Respiration drives feedback inhibition-based gamma oscillations in the piriform cortex. (A) Experimental scheme, probe localization, and diagram of the local piriform circuit (modified from Bolding and Franks, 2018a). (B) Example of simultaneously recorded local field potentials (LFP) (top) and respiration (middle) signals, along with the LFP wavelet spectrogram (bottom). Notice prominent rhythmical appearance of gamma oscillations. (C) Average LFP power spectrum (± 2*SEM; n=13 recording sessions from 12 mice). The spectrum was whitened by multiplying each value by the associated frequency. Average LFP-Respiration coherence is superimposed in blue. (D) Average phase-amplitude comodulogram using either the LFP (left) or the respiration (Resp; middle) phase. Superimposed white lines show the LFP or Resp power spectrum (solid) and the LFP-Resp coherence (dashed). The right panel shows the normalized amplitude for LFP-filtered frequency components as a function of the Resp phase (average over n=13 recording sessions from 12 mice). (E) Directionality analyses between Resp and the gamma envelope (30–60 Hz). Shown are the average (± SEM, n=13 recording sessions from 12 mice) gamma envelope triggered by inhalation start (top), and the Granger causality for the Resp→gamma and gamma→Resp directions (bottom; boxplots show the median, 1st, 3rd quartiles, and the distribution range; each dot shows an individual mouse). (F) Average current source density for the gamma band (n=13 recording sessions from 12 mice). Superimposed black lines show the average gamma waveforms for each recording site. Bar plots depict statistical comparisons against a zero-current distribution (mean ± SEM; n=13 recording sessions from 12 mice). (G) Respiration-evoked LFP responses. Top: average inhalation-triggered whitened spectrogram (n=15 recording sessions from nine mice). Bottom: Normalized spike rate (mean ± SEM) of excitatory (EXC; VGAT-, 858 neurons), feedback inhibitory (FBI; VGAT+, 40 neurons), and feedforward inhibitory (FFI; VGAT+, 13 neurons) neuronal populations triggered by inhalation. Inset shows the normalized spike-triggered gamma amplitude envelope for each neuronal subpopulation (mean ± SEM). Normalization consisted of dividing the triggered gamma amplitude values by the mean amplitude 500 ms before each spike. To further characterize the interaction between respiration and low-gamma oscillations in the PCx, we performed directionality analyses (Figure 1E and Figure 1—figure supplement 4). The gamma amplitude envelope showed a peak ~200 ms following the inhalation start, coinciding with a large positive cross-correlation peak between these signals, which suggests that respiration causes gamma. Consistent with these results, time-domain Granger causality was significantly higher in the respiration→gamma direction than in the opposite one (t(12) = 9.82, p<10–6). Together, these results show that respiration drives low-gamma oscillations in the PCx. We analyzed the contribution of the different PCx neuronal populations to network low-gamma oscillations. First, we performed a current source density analysis (Figure 1F), which revealed that these oscillations are generated locally within the piriform circuit and show a phase reversal near layer 2 (where pyramidal cells are located). Next, we classified single units according to the expression of a light-sensitive channelrhodopsin coupled to the vesicular GABA transporter (VGAT). This allowed us to discriminate between VGAT- principal cells and VGAT+ inhibitory interneurons. Additionally, VGAT+ neurons were further classified into feedback inhibitory interneurons (FBI) and feedforward interneurons (FFI) according to their location relative to the principal cell layer (FFI are located at layer 1 while FBI tend to be located within the cell layer 2/3; Bolding and Franks, 2018a; Figure 1—figure supplement 5). Upon averaging the activity of each neuronal subpopulation, we found that the time course of FBI firing rate changes correlates with the amplitude of low-gamma oscillations in time (Figure 1G). In contrast, principal cells and FFI spike earlier within the respiratory cycle and return to baseline during the gamma amplitude peak (Figure 1G). Notice further that the gamma peak coincides with principal cell inhibition, as evidenced by their firing rate decrease. Moreover, by computing spike-triggered gamma envelope averages, we confirmed that gamma oscillations closely follow FBI spiking (Figure 1G inset). Therefore, we conclude that respiration-driven low-gamma oscillations in the PCx arise from feedback inhibition. Respiration-driven gamma oscillations depend on recurrent connections within the piriform cortex We next studied the circuit mechanisms responsible for the respiration-driven gamma oscillations in the PCx. To that end, we analyzed spontaneous PCx LFPs following the selective expression of the tetanus toxin light chain in principal cells of a targeted hemisphere (TeLC ipsi; Figure 2A). Under this approach, TeLC expression blocks excitatory synaptic transmission without affecting cellular excitability (Bolding and Franks, 2018a), allowing us to study the local computations underlying the respiration-driven gamma. Figure 2B shows that TeLC ipsi LFPs had a large significant reduction in low-gamma oscillations with respect to either control mice (t(19) = 4.11, p<0.001) or the contralateral PCx (TeLC contra, not infected; t(12) = 2.99, p=0.0055). Moreover, TeLC ipsi LFPs also showed a significant decrease in respiration-gamma coupling compared to control (t(19) = 5.87, p<10–5) or TeLC contra LFPs (t(12) = 3.10, p=0.0045) (Figure 2C and D), despite the respiration signal still reaching the PCx (note the dotted white traces in Figure 2C showing LFP-Resp coherence). These results demonstrate that gamma oscillations and their coupling to respiration depend on local recurrent excitatory connections within the PCx. Noteworthy, respiration-driven low-gamma oscillations also depend on the cognitive state since ketamine/xylazine anesthesia abolishes them (Figure 2—figure supplement 1, Appendix 1). Figure 2 with 1 supplement see all Download asset Open asset Respiration-driven gamma oscillations depend on recurrent connections within the piriform cortex. (A) Schematic of circuit changes after TeLC expression in principal cells (PCs) of the piriform circuit (MTCs: mitral cells; FFIs: feedforward interneurons; FBIs: feedback interneurons). Recordings were made both ipsi- and contralaterally to the TeLC expression (modified from Bolding and Franks, 2018a). (B) Average (± 2*SEM) power spectra for control and TeLC-infected animals (Control, n=13 recording sessions from 12 mice; TeLC ipsi, n=8 recording sessions from eight mice; TeLC contra, n=6 recording sessions from 6 mice). Notice that local TeLC expression abolishes ipsilateral gamma oscillations in the PCx. (C) Average respiration-LFP comodulograms for control and TeLC-infected animals. Respiration power and LFP-respiration coherence are shown superimposed (same scale across plots). (D) Boxplots showing gamma power (top) and the Resp-low gamma modulation index (bottom) for control and TeLC-infected animals. Olfactory bulb mitral-cell projections trigger feedback inhibition-based gamma oscillations in the piriform cortex After confirming that respiration-driven gamma oscillations depend on recurrent connections formed by principal cells, we asked how PCx inputs affect gamma generation. We expected mitral/tufted cell activation in the olfactory bulb (OB) to trigger similar low-gamma oscillations since these projections convey the respiratory inputs to the PCx (Pashkovski et al., 2020). Consistently, optogenetic activation of the OB (Thy-Control) triggered piriform low-gamma oscillations, which matched the laser time course (Figure 3A, top panel). Interestingly, TeLC ipsi LFPs showed almost no gamma activity following laser onset (Figure 3A, middle panel), while TeLC contra LFPs still exhibited gamma activity upon stimulation of the OB (Figure 3A, bottom panel). Analyzing the group response, we found a significant reduction of low-gamma activity following the laser onset in TeLC ipsi LFPs compared to control (t(17) = 5.12, p<0.0001) or TeLC contra LFPs (t(19) = 3.38, p=0.0015) (Figure 3B and C), further confirming the importance of recurrent connections for gamma generation. Moreover, low-gamma power in control and TeLC contra LFPs increased with laser intensity, while it remained constant in TeLC ipsi LFPs (Figure 3D). Nevertheless, it should be noted that the contralateral TeLC hemisphere showed lower amplitude gamma oscillations following light stimulation than control recordings, though whether this gamma difference is related to an impaired network interplay between both hemispheres or to genetic differences between mouse lines remains to be determined. In any event, these results experimentally prove two critical facts about respiration-driven piriform gamma oscillations. First, that respiration drives PCx low-gamma oscillations mediated by OB projections from mitral/tufted cells. Second, feedforward interneurons do not generate low gamma, which necessarily requires the principal cells to excite local feedback interneurons. Figure 3 Download asset Open asset Piriform recurrent connections are necessary for olfactory bulb (OB) mitral/tufted cells to trigger low-gamma oscillations. (A) Left: experimental conditions for each group. Right: Average piriform cortex (PCx) spectrograms during optogenetic stimulation of the olfactory bulb (OB). (B) Average gamma power during OB stimulation for the control (n=5 recording sessions from five mice), TeLC ipsi (n=14 recording sessions from eight mice) and contralateral recordings (n=7 recording sessions from five mice). Note that a logarithmic y-axis is employed here while subsequent plots use a linear scale. (C) Boxplots showing the gamma power difference between the laser and pre-laser periods. (D) Gamma power as a function of the laser intensity for each experimental condition (mean ± SEM). Odors induce long-lived gamma oscillations Having studied the mechanisms of spontaneous respiration-driven low-gamma oscillations in awake mice, we next analyzed their behavior during odor sampling (Figure 4). First, we noted that odors elicited large beta oscillations (10–30 Hz), which have been widely associated with olfactory processing (Lepousez and Lledo, 2013; Martin et al., 2006; Poo and Isaacson, 2009). Interestingly, the amplitude of gamma oscillations was not affected by odor delivery, but the duration of gamma activity increased notoriously (Figure 4B). Of note, the increase in gamma duration specifically depended on odor stimulation and not on longer breaths as previously suggested by Vanderwolf, 2000, since longer odorless cycles did not prolong the induced gamma activity (Figure 4—figure supplement 1). Figure 4 with 1 supplement see all Download asset Open asset Odor delivery evokes beta and induces longer lasting gamma oscillations. (A) Average whitened local field potentials (LFP) power spectrum for odor and odorless respiration cycles (± 2*SEM; n=13 recording sessions from 12 mice). (B) Top: Average beta (top) and gamma (bottom) amplitude for odorless respiratory cycles (blue) and for cycles with odor delivery (orange). (C) Filtered beta (10–20 Hz) and gamma (30–60 Hz) oscillations during odor delivery. (D) Top: Phase-resetting index for each oscillation. Middle: Normalized induced (green) and evoked (purple) beta (middle) and gamma (bottom) amplitude triggered by inhalation. The normalization consisted of removing the average amplitude across time. All results obtained during odor delivery. Traces show mean ± SEM. (E) Average beta (left) and gamma (right) amplitude during odor cycles. Top panels show the average amplitude for different odorants at the same concentration (0.3% v./v., n=13 recording sessions from 12 mice). Bottom panels show the response to increasing odor concentrations (amplitudes averaged for ethyl butyrate and hexanal odorants; n=5 recording sessions from fivemice). (F) Amplitude envelopes during odor cycles for beta (left) and gamma oscillations (right) in TeLC experiments. Shades represent the mean ± SEM. Notably, beta oscillations occurred 100 ms before gamma onset, and, moreover, showed a consistent phase resetting during each sniff cycle (Figure 4C and D top), resulting in a large amplitude envelope of the inhalation-trigged average of beta-filtered LFPs (Figure 4D middle), which is to say that beta was evoked at each cycle. The unfamiliar reader is referred to Tallon-Baudry and Bertrand, 1999 for a discussion about evoked vs. induced oscillations. In short, an oscillatory activity that shows up in the average filtered trace is said to be evoked since this requires phase consistency (or ‘resetting’) following each stimulus. On the other hand, oscillations that increase in amplitude following each stimulus (in our case, an inhalation), but that exhibit phase jitters from trial to trial, cannot be properly detected in the averaged trace due to peak-trough cancellations across trials. Such oscillations, referred to as ‘induced,’ can only be detected by inspecting the average across all individual trial amplitudes (or spectrogram; see Tallon-Baudry and Bertrand, 1999). This is the case of piriform low-gamma oscillations (Figure 4D bottom) since they increase following each inhalation but exhibit time jitters in peak activity from cycle to cycle and no phase resetting (Figure 4D top). That is, our results show that gamma oscillations are not evoked but induced at each sniff cycle, contrasting therefore, with the evoked beta oscillations. We also investigated how beta and gamma responses depended on odor identity and concentration (Figure 4E). Different odorants triggered similar beta and gamma responses (Figure 4E top). The amplitude of beta oscillations depended on concentration (F(3,12)=5.84, p=0.01), while gamma amplitude did not though its variability increased (F(3,12)=2.89, p=0.079; Figure 4E bottom). Importantly, when comparing TeLC-infected hemispheres with the contralateral ones during odor delivery, we found that only odor-induced gamma oscillations depended on the local piriform recurrent connections, while beta oscillations were still present in infected animals (Figure 4F). These results suggest that beta oscillations do not relate to local piriform computations and are likely of OB postsynaptic origin. Respiration-driven gamma oscillations determine odor-assembly representations through a winner-take-all computation Next, we studied how gamma oscillations influenced piriform spiking patterns during odor processing. First, we compared the firing rate of each piriform neuronal subtype during odorless and odor cycles (Figure 5A). Notably, feedback interneurons showed the most pronounced changes during odor cycles, substantially increasing their firing rate and prolonging their spiking period above baseline, thus mirroring the increase in gamma duration. Nonetheless, we observed that the increased FBI spiking (~100 ms) preceded gamma amplitude increase (~200 ms; c.f. Figures 4B and 5A). This result may be related to the time required for different FBIs to synchronize their activity and generate gamma, as observed in computational models of gamma generation (Wang and Buzsáki, 1996). Figure 5 Download asset Open asset Respiration-driven gamma oscillations relate to single-cell spiking specificity to odors. (A) Neuronal firing rates (mean ± 0.5*SEM, n=858 EXC, 40 FBI, 13 FFI) during odorless (top) and odor cycles (bottom). (B) Principal cell spiking during each phase bin of the respiration cycle (bottom; 0 degree corresponds to the start of the inhalation); neurons are sorted according to the normalized firing rate in the first bin. Gamma power is shown on top (mean ± SEM, n=15 recording sessions from nine mice). (C) Top: preferred respiratory phase differences between the first and last thirds of the recording session (n=858 neurons). Middle: Spike-Resp coupling during odor and odorless cycles (n=858 neurons). Bottom: Spike-gamma coupling during odor and odorless cycles (n=858 neurons). (D) Z-scored firing rate at the gamma peak in response to different odors for a representative mouse. Columns show the firing rates of each principal cell. The bottom panel shows a zoom-in view of the differential spiking activity across odors. (E) Odor specificity index and normalized gamma amplitude following inhalation start (mean ± SEM, n=15 recording sessions from nine mice exposed to six different odorants at 0.3% v./v. concentration; gamma traces were rescaled to fit the plot). We then studied how gamma oscillations influence principal cell spiking during odor delivery, pooling together all recorded principal cells and analyzing their spiking as a function of the respiration phase. We found that the respiration phase modulated principal cell spiking within breathing cycles (Figure 5B), though the preferred spiking phase differed across neurons. Interestingly, while a large proportion of cells were inhibited during the same respiration phase as the maximal gamma amplitude, some cells increased their spiking coincidently with the gamma peak. The respiratory phase preference was stable throughout the recording session (Figure 5C, top panel; t(857) = –0.18, p=0.57), and respiratory modulation of principal cell spiking did not change between odor and odorless cycles (Figure 5C, middle panel; t(857) = –0.57, p=0.71). Remarkably, spike-gamma phase coupling increased during the processing of odors (Figure 5C, bottom panel; t(857) = 9.49, p<10–19), suggesting that these prolonged oscillations play a role in shaping odor coding. Consistently, we found that odor context determined which cells fired during the gamma oscillation (Figure 5D). We further confirmed this observation by measuring the spiking specificity to odors, which closely followed the gamma envelope (Figure 5E). Thus, these results demonstrate that gamma inhibition shapes single-cell responses during olfaction. The gamma spiking specificity supports the conjecture that respiration-driven gamma oscillations could mediate odor assembly representations. To further study this possibility, we analyzed cell assembly compositions for each odor by measuring the contribution of each principal cell to the first independent component (IC) of the population response (El-Gaby et al., 2021; Lopes-dos-Santos et al., 2013; Trouche et al., 2016). We found highly skewed distributions for each odor, where only a small fraction of neurons showed a strong positive contribution to the 1st IC, hereafter referred to as winner cells, while the vast majority showed low weights, referred to as loser cells (Figure 6A and B). Notably, the few winning neurons determining the 1st IC activity changed from odor to odor (Figure 6A); in other words, there was strong orthogonality in the 1st IC weight distribution across different odors. Consistent with this, the 1st IC weights were not significantly correlated between odors (corrected by multiple comparisons) (Figure 6C). We note that a similar odor separation was achieved when computing ICA on all odors together and analyzing the top six ind