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Functional transformations of odor inputs in the mouse olfactory bulb.

Adam Y, Livneh Y, Miyamichi K, Groysman M, Luo L, Mizrahi A - Front Neural Circuits (2014)

Bottom Line: Mitral cells population activity was heterogeneous and only mildly correlated with the olfactory receptor neuron (ORN) inputs, supporting the view that discrete input maps undergo significant transformations at the output level of the OB.In contrast, both MCs and GL-INs showed diverse temporal response patterns, suggesting that GL-INs could contribute to the transformations MCs undergo at slow time scales.Our data suggest that sensory odor maps are transformed by TCs and MCs in different ways forming two distinct and parallel information streams.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurobiology, Institute of Life Sciences, The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem Jerusalem, Israel.

ABSTRACT
Sensory inputs from the nasal epithelium to the olfactory bulb (OB) are organized as a discrete map in the glomerular layer (GL). This map is then modulated by distinct types of local neurons and transmitted to higher brain areas via mitral and tufted cells. Little is known about the functional organization of the circuits downstream of glomeruli. We used in vivo two-photon calcium imaging for large scale functional mapping of distinct neuronal populations in the mouse OB, at single cell resolution. Specifically, we imaged odor responses of mitral cells (MCs), tufted cells (TCs) and glomerular interneurons (GL-INs). Mitral cells population activity was heterogeneous and only mildly correlated with the olfactory receptor neuron (ORN) inputs, supporting the view that discrete input maps undergo significant transformations at the output level of the OB. In contrast, population activity profiles of TCs were dense, and highly correlated with the odor inputs in both space and time. Glomerular interneurons were also highly correlated with the ORN inputs, but showed higher activation thresholds suggesting that these neurons are driven by strongly activated glomeruli. Temporally, upon persistent odor exposure, TCs quickly adapted. In contrast, both MCs and GL-INs showed diverse temporal response patterns, suggesting that GL-INs could contribute to the transformations MCs undergo at slow time scales. Our data suggest that sensory odor maps are transformed by TCs and MCs in different ways forming two distinct and parallel information streams.

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Input-output relationships of TCs, GL-INs and MC ensembles. (A) Representative intrinsic signal imaging maps in response to three different odors from one OB. Maps were filtered to represent ORNs input (see Section Methods). r = Pearson correlation between the intrinsic signal maps across the data (mean ± S.E.M, n = 36 OBs; n = 252 maps). (B) Ensemble calcium activity for three odors in the three neuronal populations. Only responsive cells are shown (TCs, n = 291 cells, GL-INs, n = 179 cells, MCs, n = 114 cells). (C) Max ΔF/F for M-Prop as a function of max ΔF/F for Prop (left column) or E-tig (right column). r = correlation between the ensemble responses to the odor pairs. (D) Correlation of all odor pairs at the ORN level, calculated as in (A) from the intrinsic signal maps. Dendrogram shows hierarchal clustering of the correlation values. The three clusters are represented by color in the odor names (blue, red, and green). ISI—Intrinsic signal imaging. (E) Ensemble correlation for all odor pairs in the three neuronal populations. Dendrograms show hierarchical clustering of the correlation values. Odors as in (D). The intrinsic signal clusters are preserved in the TCs but not in the MCs. (F) Ensemble correlation of each population at 50 ppm concentration (as in C and E) were plotted as a function of the correlations between the ORNs input (as in A and D). r = Pearson correlations between both vectors. **p < 0.01, *p < 0.05. ISI—Intrinsic signal imaging. (G) Correlation between the intrinsic signal similarity and the ensemble correlation (as presented in panel F) as a function of the odor concentration (n.s–non-significant correlation values).
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Figure 6: Input-output relationships of TCs, GL-INs and MC ensembles. (A) Representative intrinsic signal imaging maps in response to three different odors from one OB. Maps were filtered to represent ORNs input (see Section Methods). r = Pearson correlation between the intrinsic signal maps across the data (mean ± S.E.M, n = 36 OBs; n = 252 maps). (B) Ensemble calcium activity for three odors in the three neuronal populations. Only responsive cells are shown (TCs, n = 291 cells, GL-INs, n = 179 cells, MCs, n = 114 cells). (C) Max ΔF/F for M-Prop as a function of max ΔF/F for Prop (left column) or E-tig (right column). r = correlation between the ensemble responses to the odor pairs. (D) Correlation of all odor pairs at the ORN level, calculated as in (A) from the intrinsic signal maps. Dendrogram shows hierarchal clustering of the correlation values. The three clusters are represented by color in the odor names (blue, red, and green). ISI—Intrinsic signal imaging. (E) Ensemble correlation for all odor pairs in the three neuronal populations. Dendrograms show hierarchical clustering of the correlation values. Odors as in (D). The intrinsic signal clusters are preserved in the TCs but not in the MCs. (F) Ensemble correlation of each population at 50 ppm concentration (as in C and E) were plotted as a function of the correlations between the ORNs input (as in A and D). r = Pearson correlations between both vectors. **p < 0.01, *p < 0.05. ISI—Intrinsic signal imaging. (G) Correlation between the intrinsic signal similarity and the ensemble correlation (as presented in panel F) as a function of the odor concentration (n.s–non-significant correlation values).

Mentions: To deliver odorants we used a custom-made 7-channel olfactometer. To avoid cross-contamination between odorants we used separate tubing for each channel (see Figure 2C). For odor delivery, we switched a N2 flow into one of the odor vials for the desired duration, while keeping the overall flow constant. We used a panel of seven odorants that activate different and partially overlapping areas in the dorsal OB (ethyl-acetate, butanal, pentanal, ethyl-tiglate, propanal, methyl-propionate and ethyl-butyrate; from Sigma-Aldrich; Figures 5, 6). We diluted the odorants in mineral oil according to their individual vapor pressures to give a nominal headspace concentration of 1000 ppm. We further diluted the odorants by N2 flow of 10–100 ml/min, mixed with N2 flow of 900–990 ml/min, and O2 flow of 1000 ml/min. This procedure achieves final concentration of 5–50 ppm. The flow of the two N2 channels was controlled using mass flow controllers (M100B, MKS Instruments, Andover, MA, USA). Odorants were presented for 1 s at 5, 10, 25 and 50 ppm (22 s inter-stimulus interval). Each protocol included 28 stimuli in pseudo-random order of odors and concentrations, and was repeated four times in each imaging field. Additionally, in each field we delivered a protocol of the seven odorants for 15 s at 50 ppm (36 s inter-stimulus interval, four repetitions). Normally we used for analysis all four repetitions, in rare cases we had to discard one trial. Two-photon excitation and image collection were triggered 3–5 s before the stimulus onset and lasted ~17 s or ~28 s (1 or 15 s respectively).


Functional transformations of odor inputs in the mouse olfactory bulb.

Adam Y, Livneh Y, Miyamichi K, Groysman M, Luo L, Mizrahi A - Front Neural Circuits (2014)

Input-output relationships of TCs, GL-INs and MC ensembles. (A) Representative intrinsic signal imaging maps in response to three different odors from one OB. Maps were filtered to represent ORNs input (see Section Methods). r = Pearson correlation between the intrinsic signal maps across the data (mean ± S.E.M, n = 36 OBs; n = 252 maps). (B) Ensemble calcium activity for three odors in the three neuronal populations. Only responsive cells are shown (TCs, n = 291 cells, GL-INs, n = 179 cells, MCs, n = 114 cells). (C) Max ΔF/F for M-Prop as a function of max ΔF/F for Prop (left column) or E-tig (right column). r = correlation between the ensemble responses to the odor pairs. (D) Correlation of all odor pairs at the ORN level, calculated as in (A) from the intrinsic signal maps. Dendrogram shows hierarchal clustering of the correlation values. The three clusters are represented by color in the odor names (blue, red, and green). ISI—Intrinsic signal imaging. (E) Ensemble correlation for all odor pairs in the three neuronal populations. Dendrograms show hierarchical clustering of the correlation values. Odors as in (D). The intrinsic signal clusters are preserved in the TCs but not in the MCs. (F) Ensemble correlation of each population at 50 ppm concentration (as in C and E) were plotted as a function of the correlations between the ORNs input (as in A and D). r = Pearson correlations between both vectors. **p < 0.01, *p < 0.05. ISI—Intrinsic signal imaging. (G) Correlation between the intrinsic signal similarity and the ensemble correlation (as presented in panel F) as a function of the odor concentration (n.s–non-significant correlation values).
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Figure 6: Input-output relationships of TCs, GL-INs and MC ensembles. (A) Representative intrinsic signal imaging maps in response to three different odors from one OB. Maps were filtered to represent ORNs input (see Section Methods). r = Pearson correlation between the intrinsic signal maps across the data (mean ± S.E.M, n = 36 OBs; n = 252 maps). (B) Ensemble calcium activity for three odors in the three neuronal populations. Only responsive cells are shown (TCs, n = 291 cells, GL-INs, n = 179 cells, MCs, n = 114 cells). (C) Max ΔF/F for M-Prop as a function of max ΔF/F for Prop (left column) or E-tig (right column). r = correlation between the ensemble responses to the odor pairs. (D) Correlation of all odor pairs at the ORN level, calculated as in (A) from the intrinsic signal maps. Dendrogram shows hierarchal clustering of the correlation values. The three clusters are represented by color in the odor names (blue, red, and green). ISI—Intrinsic signal imaging. (E) Ensemble correlation for all odor pairs in the three neuronal populations. Dendrograms show hierarchical clustering of the correlation values. Odors as in (D). The intrinsic signal clusters are preserved in the TCs but not in the MCs. (F) Ensemble correlation of each population at 50 ppm concentration (as in C and E) were plotted as a function of the correlations between the ORNs input (as in A and D). r = Pearson correlations between both vectors. **p < 0.01, *p < 0.05. ISI—Intrinsic signal imaging. (G) Correlation between the intrinsic signal similarity and the ensemble correlation (as presented in panel F) as a function of the odor concentration (n.s–non-significant correlation values).
Mentions: To deliver odorants we used a custom-made 7-channel olfactometer. To avoid cross-contamination between odorants we used separate tubing for each channel (see Figure 2C). For odor delivery, we switched a N2 flow into one of the odor vials for the desired duration, while keeping the overall flow constant. We used a panel of seven odorants that activate different and partially overlapping areas in the dorsal OB (ethyl-acetate, butanal, pentanal, ethyl-tiglate, propanal, methyl-propionate and ethyl-butyrate; from Sigma-Aldrich; Figures 5, 6). We diluted the odorants in mineral oil according to their individual vapor pressures to give a nominal headspace concentration of 1000 ppm. We further diluted the odorants by N2 flow of 10–100 ml/min, mixed with N2 flow of 900–990 ml/min, and O2 flow of 1000 ml/min. This procedure achieves final concentration of 5–50 ppm. The flow of the two N2 channels was controlled using mass flow controllers (M100B, MKS Instruments, Andover, MA, USA). Odorants were presented for 1 s at 5, 10, 25 and 50 ppm (22 s inter-stimulus interval). Each protocol included 28 stimuli in pseudo-random order of odors and concentrations, and was repeated four times in each imaging field. Additionally, in each field we delivered a protocol of the seven odorants for 15 s at 50 ppm (36 s inter-stimulus interval, four repetitions). Normally we used for analysis all four repetitions, in rare cases we had to discard one trial. Two-photon excitation and image collection were triggered 3–5 s before the stimulus onset and lasted ~17 s or ~28 s (1 or 15 s respectively).

Bottom Line: Mitral cells population activity was heterogeneous and only mildly correlated with the olfactory receptor neuron (ORN) inputs, supporting the view that discrete input maps undergo significant transformations at the output level of the OB.In contrast, both MCs and GL-INs showed diverse temporal response patterns, suggesting that GL-INs could contribute to the transformations MCs undergo at slow time scales.Our data suggest that sensory odor maps are transformed by TCs and MCs in different ways forming two distinct and parallel information streams.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurobiology, Institute of Life Sciences, The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem Jerusalem, Israel.

ABSTRACT
Sensory inputs from the nasal epithelium to the olfactory bulb (OB) are organized as a discrete map in the glomerular layer (GL). This map is then modulated by distinct types of local neurons and transmitted to higher brain areas via mitral and tufted cells. Little is known about the functional organization of the circuits downstream of glomeruli. We used in vivo two-photon calcium imaging for large scale functional mapping of distinct neuronal populations in the mouse OB, at single cell resolution. Specifically, we imaged odor responses of mitral cells (MCs), tufted cells (TCs) and glomerular interneurons (GL-INs). Mitral cells population activity was heterogeneous and only mildly correlated with the olfactory receptor neuron (ORN) inputs, supporting the view that discrete input maps undergo significant transformations at the output level of the OB. In contrast, population activity profiles of TCs were dense, and highly correlated with the odor inputs in both space and time. Glomerular interneurons were also highly correlated with the ORN inputs, but showed higher activation thresholds suggesting that these neurons are driven by strongly activated glomeruli. Temporally, upon persistent odor exposure, TCs quickly adapted. In contrast, both MCs and GL-INs showed diverse temporal response patterns, suggesting that GL-INs could contribute to the transformations MCs undergo at slow time scales. Our data suggest that sensory odor maps are transformed by TCs and MCs in different ways forming two distinct and parallel information streams.

Show MeSH
Related in: MedlinePlus