Behavioral Status Influences the Dependence of Odorant-Induced Change in Firing on Prestimulus Firing Rate

Abstract

The firing rate of the mitral/tufted cells in the olfactory bulb is known to undergo significant trial-to-trial variability and is affected by anesthesia. Here we ask whether odorant-elicited changes in firing rate depend on the rate before application of the stimulus in the awake and anesthetized mouse. We find that prestimulus firing rate varies widely on a trial-to-trial basis and that the stimulus-induced change in firing rate decreases with increasing prestimulus firing rate. Interestingly, this prestimulus firing rate dependence was different when the behavioral task did not involve detecting the valence of the stimulus. Finally, when the animal was learning to associate the odor with reward, the prestimulus firing rate was smaller for false alarms compared with correct rejections, suggesting that intrinsic activity reflects the anticipatory status of the animal. Thus, in this sensory modality, changes in behavioral status alter the intrinsic prestimulus activity, leading to a change in the responsiveness of the second-order neurons. We speculate that this trial-to-trial variability in odorant responses reflects sampling of the massive parallel input by subsets of mitral cells.SIGNIFICANCE STATEMENT The olfactory bulb must deal with processing massive parallel input from ∼1200 distinct olfactory receptors. In contrast, the visual system receives input from a small number of photoreceptors and achieves recognition of complex stimuli by allocating processing for distinct spatial locations to different brain areas. Here we find that the change in firing rate elicited by the odorant in second-order mitral cells depends on the intrinsic activity leading to a change of magnitude in the responsiveness of these neurons relative to this prestimulus activity. Further, we find that prestimulus firing rate is influenced by behavioral status. This suggests that there is top-down modulation allowing downstream brain processing areas to perform dynamic readout of olfactory information.

Keywords: anticipatory; associative learning; olfaction; reward; sensory; top-down.

Figure 1.

Tetrode data acquisition during awake behaving recording. A, Example of raw data (spikes, 300–5000 Hz) recorded during 9 s for one trial (sample rate 24 kHz). Red vertical line indicates onset of odorant stimulation. B–D, Example of spike detection and sorting using wavelet analysis with superparamagnetic clustering of extracellular voltage recorded in tetrodes resulting in separation of 1 SU and 2 MUs (Li et al., 2014, 2015). B, Separation of tetrode spikes shown under C into three different units. The two coefficients were wavelets for spike shape in two of the electrodes in the tetrode. D, Temperature chosen under paramagnetic clustering to obtain three different units (the meaning of temperature under paramagnetic clustering is explained by Li et al., 2014, 2015). E, ISI histograms for the three units. Two are MUs and one is an SU. F, Example of the stability of the spike waveform throughout a go-no go session. F1, Trial-by-trial variability for pre-FR for this SU. F2, Spike waveforms remain stable throughout the session. Solid line indicates mean field potential. Light lines indicate mean ± SD (n = 30 trials). Bottom, First 30 trials. Top, Last 30 trials in the session. D, F2, The spike width is 1 ms.

Figure 2.

Classification of units to SUs based on the criterion that <0.75% of the ISIs are <1 ms results in unimodal pre-FR distributions regardless of whether recordings were with multielectrode arrays or tetrodes. A1–A6, Examples of six ISIs distributions for spikes recorded in entire sessions. A1–A3, Units for ISIs were recorded with tetrodes. A4–A6, Units for ISIs were recorded with multielectrode arrays. Vertical red lines are placed at 1 ms. B–D, Pre-FR histograms for units classified as single or MUs using different percent of ISIs <1 ms. B, Pre-FR histograms for units recorded using tetrodes (595 units) (Li et al., 2015). C, pre-FR histograms for units recorded using multielectrode arrays (1163 units) (Doucette and Restrepo, 2008). D, pre-FR histograms for units recorded using either tetrodes or multielectrode arrays (1758 units) (data from both Doucette and Restrepo, 2008 and Li et al., 2015). SUs were classified based on the following percent of ISIs falling within 0–1 ms: 0% (B1,C1,D1), 0.75% (B2,C2,D2), or 2.5% (B3,C3,D3). Using 0% results in a bimodal pre-FR distribution for units classified as MUs, whereas 2.5% results in a bimodal pre-FR distribution for units classified as SUs. As expected for single sharp electrodes (Gray et al., 1995), multielectrode array recordings include a large fraction of MUs. C2, Red line indicates information on the number of SUs making up each MU in the pre-FR distribution. This line was generated by fitting the MU pre-FR with the SU pre-FR with varying fractions of MUs made up by different numbers of SUs. The best fit was with the following fractions of MUs made up by different numbers of SUs: 0.12 for 2 units, 0.29/3, 0.23/4, 0.2/5, 0.11/6,0.06/7.

Figure 3.

Statistical test of linearity and difference in the slope from 1 for simulation of different dependences of odor FR on pre-FR. The pre-FR dependence for 1451 SU-odor pairs recorded from in the go-no go task (examples shown in Fig. 4D1–F1) were resampled to yield different odor FR versus pre-FR dependences. A–D, Examples of odor FR versus pre-FR (A1,B1,C1,D1) or ΔFR versus pre-FR (A2,B2,C2,D2) for the different dependences generated by resampling the pre-FR for the SU shown in Figure 4F1. Solid line indicates a best fit of the data. Dashed line indicates odor FR = pre-FR or ΔFR = 0. Thin lines above and below the solid line indicate 5th and 95th percentile estimates. A, Odor FR and pre-FR are independently resampled from the pre-FR distribution. B, Odor FR = pre-FR + random normal noise with an SD equal to one-fourth of the SD of the pre-FR distribution. C, Odor FR = 0 + random normal noise with a SD equal to one-fourth of the SD of the pre-FR distribution. D, Odor FR = 95 percentile pre-FR + random normal noise with a SD equal to one-fourth of the SD of the pre-FR distribution. Best-fit slope and intercept (Hz): A1, 0.08, 14.4; A2, −0.92, 14.3; B1, 1.03, −0.62; B2, 0.03, −0.62; C1, 0.018, −0.17; C2, −0.98, −0.17; D1, 0.02, 20.9; D2, −0.98, 20.9. E, F, The dependences shown in A–E were simulated for the 1451 SU-odorant pairs recorded in the go-no go experiments (Fig. 4) and were then tested for difference of the slope from 1 (E) or linearity (F) for odor FR versus pre-FR with correction for multiple comparisons as explained in Materials and Methods. E, Percent of slopes different from 1. F, Percent linear. Gray represents percent significant for an F test for linear best fit of odor FR versus pre-FR (0 degrees). Dashed lines indicate F test for linear best fit of odor FR versus pre-FR performed after the axes were rotated by −45 degrees. Black represents percent significant in either the 0 or −45 degrees tests.

Figure 4.

Examples in the go-no go task of the linear dependence of the trial-to-trial variability of the odorant-induced change in FR on the pre-FR in a subset of M/T cells. A, Time course for each trial. Pre-FR is defined as the FR for 2.5 s before addition of the odorant (pre), and odor-FR is the FR for 2.5 s during exposure to the odorant (odor). B, Example of behavioral performance during a session of 200 trials in the go-no go task. Percent correct: percent of trials when the animal responded correctly in blocks of 10 rewarded/10 unrewarded odorant trials. C, Example of odorant responses for a SU. Top, Raster plots (bottom to top: first to last trial). Bottom, PSTH. Left to right, All, low and high pre-FR trials (high pre-FR ≥ mean pre-FR). D–F, Examples of the dependence of odor FR on pre-FR. D1–F1, Histogram of mean per-trial pre-FRs for the entire go-no go session. D2–F2, Histogram of mean per-trial odor-FRs for the entire go-no go session. D3–F3, Trial-per-trial variability for pre-FR through the session. D4–F4, Dependence of odor FR on pre-FR. Dashed line indicates odor FR = pre-FR. The slope of the best-fit line was statistically different from 1 for D4 and E4, but not for F4. The difference of the slope from 1 was tested using significance for best linear fit corrected for multiple comparisons by calculating the critical significance level of the false discovery rate (pFDR) (Curran-Everett, 2000) as explained in Materials and Methods (D3,E3: p ≤ pFDR = 0.0055, n = 1451 SU-odor pairs). A statistical test of linearity corrected for multiple comparisons indicated that D4 and E4, but not F4, were linear (significant p values < pFDR, pFDR_{0 degrees} = 0.028, pFDR_{−45 degrees} = 0.055, n = 1451 SU-odor pairs; for details on statistical test for linear dependence, see Materials and Methods and Fig. 3). D4, E4, Unlabeled arrow indicates the point where the best-fit line and odor FR = pre-FR cross. ΔFR is the odor-induced change in FR, which is shown at a specific pre-FR in D4 and E4. Best-fit slope and intercept (Hz): D4, 0.034, 87.1; E4, 0.09, 12.3; F4, 0.043, 15.6. D5–F5, Dependence of ΔFR on pre-FR. Solid line indicates a best fit of the data. Dashed line indicates ΔFR = 0. Thin lines above and below the solid line are 5th and 95th percentile estimates. Best-fit slope and intercept (Hz): D5, −0.97, 87.1; E5, −0.91, 12.3; F5, −0.96, 15.6.

Figure 5.

Slope and pre-FR for the dependence of the FR in the presence of the odorant on the pre-FR for the go-no go sessions. A, Cumulative histogram for the slope of odor-FR versus pre-FR for SU-odor pairs that displayed a linear relationship with a slope significantly different from 1 (n = 159). B, The pre-FR did not differ when it was calculated for different intervals before the addition of the odorant. The pre-FR is shown calculated for intervals of 2.5 or 1 s before odor addition, or for calculation of pre-FR during the time from odor port entry to odor addition that varies randomly from 1 to 1.5 s in each trial (t-start). B1, Cumulative histogram. Pairwise threefold comparison with Kolmogorov–Smirnov, p > 0.99, pFDR = 0.05, n = 735 SUs. B2, Box-and-whisker plot with MWT, p > 0.7, pFDR = 0.05, n = 735. C, Dependence of pre-FR on whether the animal was or was not rewarded in the previous trial. C1, Cumulative histogram. Kolmogorov–Smirnov, p < 0.0001, n = 735 SUs. C2, Box-and-whisker plot. MWT, p = 0.002, n = 735.

Figure 6.

The pre-FR differs between correct rejections and FAs when the animal is learning to differentiate between the two odorants. A, Example of behavioral performance during a session of 200 trials in the go-no go task. Percent correct: percent of trials when the animal responded correctly in blocks of 10 rewarded/10 unrewarded odorant trials. Learning is the segment for percent correct ≥45% and ≤65%. Retrieval is the segment for percent correct ≥80%. B, Example for an SU-odor pair of the dependence of odor FR on pre-FR for the unrewarded odorant during the learning segment. Green represents CR. Light blue represents FA. This dependence tested significant for linearity and with a slope different from 1. Solid line indicates the best fit. Dashed line indicates odor FR = pre-FR. Thin solid lines above and below the best-fit line are 5th and 95th percentiles. Unlabeled arrow indicates the point where the best-fit line and odor FR = pre-FR cross. Best-fit line slope and intercept: 0.2, 12.3 Hz. C–F, Cumulative probability for the distance along the pre-FR axis between a point and the intercept between the best-fit line and odor FR = pre-FR. Dividing by the SD of pre-FR normalized this distance. C, Hits versus Miss for the learning segment (Kolmogorov–Smirnov, p = 0.82, number of trials: 577 hits, 186 misses, pFDR = 0.04, 28 pairwise comparisons). D, Hits versus Miss for the retrieval segment (Kolmogorov–Smirnov, p = 0.99, number of trials: 190 hits, 34 misses, pFDR = 0.04). E, CR versus FA for the learning segment (Kolmogorov–Smirnov, p < 0.0001, number of trials: 233 CRs, 667 FAs, pFDR = 0.04). F, CR versus FA for the retrieval segment (Kolmogorov–Smirnov, p = 0.63, number of trials: 398 CRs, 86 FAs, pFDR = 0.04).

Figure 7.

Differences in the dependence of odor FR on pre-FR between the go-no go and go-go tasks. A, Examples of behavioral performance during the go-no go and go-go tasks. Percent correct: percent of trials when the animal responded correctly in blocks of 10 rewarded/10 unrewarded odorant trials. B, Example of the dependence of odor FR on pre-FR for an odor-SU pair recorded during a go-go task where both odorants were rewarded. B1, Histogram of per-trial pre-FRs for the entire go-go session. B2, Trial-per-trial variability for pre-FR through the session. B3, Dependence of odor FR on pre-FR. Dashed line indicates odor FR = pre-FR. The slope of the best-fit line was statistically different from 1 (p value ≤ pFDR = 0.01, n = 300 SU-odor pairs), and a statistical test corrected for multiple comparisons indicated that the relationship was linear (significant p values < pFDR, pFDR_{0 degrees} = 0.035, pFDR_{−45 degrees} = 0.01, n = 300 SU-odor pairs; for details on statistical test for linear dependence, see Materials and Methods and Fig. 3). Best-fit slope and intercept: −0.074, 6.47 Hz. B4, Dependence of ΔFR on pre-FR. Solid line indicates a best fit of the data. Dashed line indicates ΔFR = 0. Thin lines above and below the solid line are 5th and 95th percentile estimates. Best-fit slope and intercept (Hz): −1.07, 6.47. C–G, Comparison between go-go and go-no go in SU data recorded from 4 mice that underwent both go-go and go-no go tasks. C, The mean pre-FR per session does not differ between the go-no go and the go-go tasks (n = 43 SUs in the go-no go task and 131 SUs in the go-go task). C1, Cumulative probability (Kolmogorov–Smirnov, p = 0.12). C2, Box-and-whisker plot for pre-FR (MWT, p = 0.1). D, The percent of SU-odor pairs displaying a linear dependence of odor FR versus pre-FR does not differ between go-no go and go-go tasks (χ², p > 0.05). E, The percent of SU-odor pairs displaying a linear dependence with a slope different from 1 does not differ between go-no go and go-go tasks (χ², p > 0.05). F, The distribution of the slopes of the odor FR versus pre-FR dependence does not differ between go-no go and go-go tasks. F1, Cumulative histogram (Kolmogorov–Smirnov, p = 0.62). F2, Box-and-whisker plot (MWT, p = 0.44). G, The change in FR elicited by the odorant at five percentile pre-FR (normalized by dividing by the SD of pre-FR) differs between go-no go and go-go. G1, Cumulative histogram (Kolmogorov-Smirnov, p = 0.0002). G2, Box-and-whisker plot (MWT, p = 0.005). *Statistically significant.

Figure 8.

Dependence of prestimulus FR on licking. A, Performance of a mouse in a go-no go experiment. B, Trial per trial time course for licks for the experiment whose behavioral performance is shown in A. Red represents tongue touches the lick tube. C, Example of the relationship between pre-FR and the percent of the time that the mouse licks in the preodor period (percent lick) for an SU recorded from in this session. Line indicates the best fit (slope = −0.027, intercept = 12.6). The correlation coefficient (−0.18) was not significant (p = 0.024 > pFDR = 0.017, n = 383 SUs for go-no go). Best-fit slope −0.02 Hz/%, intercept 12.7 Hz. D, Percent of SUs that display a statistically significant correlation between pre-FR and percent lick (pFDR is 0.014 for 150 SUs in the go-go sessions, χ², p value for difference in percent correlation > 0.05). E, Mean and SEM for the correlation coefficients for all SUs recorded from in the go-go and go-no go sessions (unpaired t test, p = 0.08). F, Cumulative distribution of correlation coefficients (Kolmogorov–Smirnov, p = 0.04). G, H, Percent of the variance of pre-FR that is accounted for by the dependence between pre-FR and percent lick (percent variance) determined for SUs that displayed significant correlation (129 SUs for go-no go and 43 SUs for go-go). G, Boxplot for percent variance (MWT, p = 0.04). H, Cumulative distributions for the percent variance (Kolmogorov–Smirnov, p = 0.15). *Statistically significant.

Figure 9.

M/T unit pairs show correlated changes in pre-FR for both SUs and MUs. A–D, Examples showing positive (A,B) or negative (C,D) correlation between SU pair pre-FRs. A, C, Dependence of the per-trial pre-FRs between these unit pairs. Both units pairs displayed correlated pre-FR (p value for the correlation coefficient, ρ/pFDR = <0.001/[0.02 SU×SU, 0.02, SU×MU, 0.04 MU×MU], n = 2972 SU×SU, 4489 SU×MU, and 4719 MU×MU). Best-fit slope and intercept (Hz): A, 0.49, −0.1; C, −0.59, 75. B, D, pre-FR as a function of time within the session for SUs in A, C. E, F, Pseudocolor plot for ρ and the significance p value for ρ determined for all unit pairs recorded in two different sessions. Gray represents nonsignificant p values for ρ (> pFDR calculated for all windows within the session). G, Time course of the correlation coefficient, evaluated in a sliding window of 40 trials, for a subset of unit pairs for the session shown in E. ρ is significant for the points in red (p < pFDR). H, Percent of unit pairs that have significant correlation coefficients for pre-FRs. The number of unit pairs are 2972 (SU×SU), 4489 (SU×MU), and 4719 (MU×MU). Red represents a significant correlation. I, Cumulative probability histograms for the pre-FR correlation coefficients for all unit pairs showing more significant correlations in pre-FR between original versus resampled data. S×S, M×S, M×M: original data for SU/MU unit pairs; S×S res, M×M Res: the pre-FR for unit 2 was calculated by random resampling the pre-FR histogram for 1 unit in the pair (M×S Res, yields the same curve; data not shown). A Kolmogorov–Smirnov indicated that S×S/S×M differ from M×M and S×Sres/M×Mres. SxM does not differ from S×S, and S×Sres does not differ from M×M res (p/pFDR: < 0.05/0.05, 15 pairwise tests).

Figure 10.

Mexican-hat dependence for unit pair pre-FR correlation dependence on the distance between electrodes/tetrodes. A, Distances for multielectrode arrays (top, blue) (Doucette et al., 2011) or tetrodes (bottom, red) (Li et al., 2015). B, Simplified diagram of OB circuitry illustrating the fact that the MTs spread in a cone under the glomerulus they innervate. GCs, Granule cells; OSN, olfactory sensory neuron. C, D, Distance dependence of the percent of unit pairs with correlated pre-FRs (C) and the correlation coefficient values (ρ) for all unit pairs (D, mean ± SEM). The number of unit pairs is 12,180 (ρ, p value < pFDR estimated at each distance bin).

Figure 11.

pre-FR is not correlated with the rate of sniff in the period before odorant addition. The period before odorant addition is defined as in Figure 4A. A, Example of the time course for nose pressure measured throughout a trial. Red line indicates application of odorant. B, Time course for the mean sniff frequency ± SD as a function of time (450 trials from 4 go-go sessions). C, D, Cumulative histogram for the correlation coefficient between per trial pre-FR and sniff frequency before odorant addition calculated for 36 odorant-unit pairs in 4 go-go sessions. C, Correlation coefficient (ρ) for pre-FR and sniff frequency. D, p value for the correlation coefficient (ρ). Blue represents not significant. Red represents significant (p value/pFDR: < 0.003/0.003, n = 36 SUs).

Figure 12.

M/T cell responses to optogenetic activation of olfactory neuron axons, applied at random times, depend on pre-FR. A, Optogenetic activation performed at random times within the sniff cycle. A1, Example of extracellular potential (Raw) and voltage recorded by the nose pressure sensor (Sniff). Light pulses (Sti). A2, Light pulses are not synchronized with sniff. Top, Average pressure voltage (681 sniffs). Bottom, Light activation. A Rayleigh test indicated that there was no specific distribution (p < 0.05) (reproduced from Li et al., 2014). B, Time course per trial. C, D, Example SU from awake mouse. C1, C2, Raster plots. C1, Unsorted. Bottom-top, First-last. C2, Sorted. Bottom-top, Lowest-highest pre-FR. C3, PSTH. D, Dependence of Light FR on pre-FR in an example SU (same SU as C). D1, Histogram of the per-trial pre-FRs. D2, Trial-to-trial variability in pre-FR. D3, Dependence of light FR on pre-FR. Dashed line indicates light FR = pre-FR unity line. The slope of the best-fit line was linear and different from 1 (p_linear ≤ pFDR, pFDR_{0 degrees} = 0.0179, pFDR_{−45 degrees} = 0.0057, n = 70 SU-light pairs; p_{different from 1} ≤ pFDR = 0.0057, n = 70 SU-light pairs). Best-fit slope 0, intercept 47.6 Hz. D4, Dependence of ΔFR on pre-FR. Solid line indicates a best fit of the data. Dashed line indicates ΔFR = 0. Thin lines above and below the solid line indicate 5th and 95th percentile estimates. Best-fit slope and intercept (Hz): −0.99, 47.6. E, Box-and-whiskers plot showing that mean pre-FR for SU-light pairs does not differ between awake (n = 70) and anesthetized (n = 79) mice (MWT, p = 0.2869). Conventions for the box-and-whisker plot: box represents the interquartile range (first to third quartile) and the median value (line), and whiskers extend to the most extreme data point within 1.5 times the interquartile range. F, Box-and-whiskers plot for slopes of best fit light FR versus pre-FR line for SU-light pairs differ between awake (n = 70) and anesthetized (n = 79) mice (MWT, p < 0.01). G1, Percent of SU-light pairs with a linear dependence of light FR on pre-FR differs between awake and anesthetized mice (χ², p < 0.05). G2, Percent of SU-light pairs with a linear dependence with a slope distinct from 1 differs between awake and anesthetized mice (χ², p < 0.0001). *Statistically significant.

Figure 13.

M/T cell responses to optogenetic activation of olfactory neuron axons, applied at random times, depend on pre-FR. A, B, Dependence of light FR on pre-FR in two example SUs. A, Recorded from an SU in an awake animal. B, Recorded in an anesthetized animal. A1, B1, Histogram of the per-trial pre-FRs. A2, B2, Trial-to-trial variability in pre-FR. A3, B3, Dependence of light FR on pre-FR. Dashed line indicates light FR = pre-FR unity line. The slope of the best-fit line was linear and different than 1 (A3: p_linear ≤ pFDR, pFDR_{0 degrees} = 0.0179, pFDR_{−45 degrees} = 0.0057, p_{different from 1} ≤ pFDR = 0.0057, n = 70 SU-light pairs; B3: p_linear ≤ pFDR, pFDR_{0 degrees} = 0.0077, pFDR_{−45 degrees} = 0.0241, p_{different from 1} ≤ pFDR = 0.0241, n = 79 SU-light pairs). Best-fit slope and intercept (Hz): A3, −0.09, 74.9. B3, −0.04, 43.9. A4, B4, Dependence of ΔFR on pre-FR. Solid line indicates a best fit of the data. Dashed line indicates ΔFR = 0. Thin lines above and below the solid line indicate 5th and 95th percentile estimates. Best-fit slope and intercept (Hz): A4, −1.09, 74.9. B4, −1.04, 43.9. C, Cumulative probabilities for the slope of light FR versus pre-FR best-fit line for SUs recorded in awake and anesthetized animals. The distributions of the slopes differed between groups (Kolmogorov–Smirnov, p < 0.05, n = 70 awake SUs, n = 79 anesthetized SUs).