supplementary figures - cell figures ... result of ambiguity in assigning responsive cells....

Axel_manuscript_supplement_090901 1

Supplementary Figures

Figure S1. Method for distinguishing somatal from neuropil response. A1. Image of Oregon Green 488 BAPTA-1 AM loaded cells. Scale = 50 µm. B1. Graded map depicting cross-trial average ∆F/F for the 2s following delivery of octanal calculated pixel-by-pixel with no filtering. C1. Same map for delivery of air. B2 and C2. Pixels falling within a significance limit (p<.04 rank-sum test performed on baseline vs post-stimulus pixel values) and exceeding a minimum number of contiguous positive pixels (8) are called positive (white). A2. Pixel-based octanal responsive map, in red, superimposed upon a high-pass filtered image of cell bodies from A1. B3. Graded map depicting cross-trial average ∆F/F for the 2s following delivery of octanal calculated for the average pixel values within automatically-defined cell bodies (shown in A3). C3 Same map for delivery of air. B4 and C4. Cells exceeding a significance limit (baseline deviation normalized 2s ∆F/F average larger than the baseline deviation normalized population response average + 2.58 standard deviations for air) are called positive (white). A4. Cell-based octanal responsive map, in red, superimposed upon a high-pass filtered image of the cell bodies from A1. These data show that a pixel-based analysis making no attempt to disregard neuropil responses yields a set of clearly-distinguished active regions that overlap with cell bodies. They additionally show that a second method of analysis based upon first identifying cell bodies yields highly similar responsive cells to the pixel-based method. The cell that is labeled as responsive to air by both methods seems to be a clear instance of contamination in this experiment rather than a result of ambiguity in assigning responsive cells.


Figure S2. Movement in the imaging protocol and results of alignment. A. A single image from a t-series. Red dots indicate the centers of four cells in each of the 35 images in the entire t-series. Two types of brain movement – 5-7 Hz small-amplitude movement associated with the pulse and 2-4 Hz larger-amplitude movement associated with breathing – can lead to noise in the data. In this typical example, the within-trial movement is confined to a 1.8 pixel (2.5 µm) range of vibration about an average position. The average width of a cell body is 7.8 pixels (10.6 µm). Because the movement is not homogenous across the field (the brain “jiggles”), image alignment is not carried out in such situations. Scale = 50 µm. B1-2. Close-up of A and the regions in the image automatically designated as cells. The pixels within the automatically-identified cells fall within the central regions of visually-identified cells and generally exclude the border regions likely to fall outside of the visually-identified cells with movement. C. Relative pixel averages from the region falling within the blue circle in A. The variability is largely driven by breathing-associated movement and is highly unlikely to cause an artifactual increase in fluorescence time-locked to odorant delivery consistently across 15 trials that might be interpreted as a response. The imaging frequency was 6.2 Hz. D1. The grayscale image depicts an average of the 35 images within a single t-series. Red dots indicate the centers of four cells in each of the 15 trials in the entire imaging session without alignment. This session contained an unusually high degree of movement across trials. In this case, the movement consisted of drift and a small jump averaging 6.5 pixels. This degree of mis-registration occurs in roughly one third of experiments, requiring alignment. D2. The grayscale image depicts a cross-trial average of the 15 trial averages showing the blurriness resulting from lack of registration across the trials. E1. Same background image as in D1. Red dots indicate the centers of four cells in each of the 15 trials in the imaging session after alignment. Post-alignment, the average error is 1.4 pixels. E2. The cross-trial average of the aligned data is crisp.


Figure S3. Raw images of cells responding to ethyl methyl sulfide (EMS). A1-5 depict square regions of interest slightly larger than the size of a cell body (15.4 µm across). Each set is data from one cell. Within each set, the rows depict consecutive t-series. Images were acquired at a rate of approximately 6.2 Hz. EMS was delivered 2 s into each series, indicated by black bar at top. The images have not been filtered, but the pixel values of each t-series have been scaled to the maximum value within that series. Percentages in the lower left are the average ∆F/F during the 2 s after EMS delivery. These data illustrate the high consistency of cell responses across trials. B. A collection of images from a larger region of interest (60 µm across) surrounding the cell depicted in A5 (indicated by arrows). Consecutive t-series are once again arranged in rows. Only the images immediately before and after the delivery of EMS, between the third and fourth image, are presented.


igure S4. Glutamate blockade diminishes odorant-evoked responses. The top row shows the

Fgraded ∆F/F responses of a field of cells to three odorants before delivery of NBQX and APV. The middle row shows the responses of those cells after the drugs were applied to the surface of the piriform cortex. The pre- and post-drug response traces of two cells, indicated by arrows, are shown on the bottom row. ∆F/F values were calculated on a pixel-by-pixel basis without first automatically identifying cells. Scale = 50 µm.


Figure S5. Imaged regions of the piriform cortex from 13 animals. The imaged sites are shown in dark shading, superimposed upon a picture of the cortical hemisphere. The border of the piriform, determined by referring to the Paxinos atlas and the cell-body dense layer 2 in slices, is indicated in blue. The imaged region in each animal was located within that animal’s cortical hemisphere by matching a map of the surface vessels taken via the 2-photon microscope at the time of imaging with a widefield photo of the brain taken by stereoscope. The locations of imaged regions in different animals were compiled by aligning the widefield photos of the brain in each with a standard. 16 structurally diverse odorants were imaged in these thirteen animals including octanal, hexanal, acetophenone, a-pinene, eugenol, butyric acid, cadaverine, benzaldehyde, fufural, ethyl butyrate, mouse urine, TMT, a-ionone, ethyl butyrate, 2,5-dimethylpyrazine, ethyl methyl sulfide. Not every animal was stimulated with every odorant. Scale = 1 mm. Figure S6 (following). Octanal responsive cells across the piriform cortex in 13 animals. The superimposed locations of the imaged regions are presented in Figure A1. Imaging parameters differed among the 13 animals (16X vs 40X objective; different optical zooms) but all of the resulting data were processed in an identical manner. Some variability in response frequency is observed across animals resulting from differences in signal-to-noise due to surgical or imaging differences, but response frequency does not vary consistently with location in the piriform. Octanal-responsive cells are distributed homogeneously across the imaged region. Scale = 500 µm.


Figure S6. A-G


Figure S6. H-M.


Figure S7. α-pinene responsive cells across the piriform cortex in 7 animals.


Figure S8. Acetophenone responsive cells across the piriform cortex in 7 animals.


Figure S9. Monte Carlo simulations of responsive cell distributions. The spatial distribution of responsive cells (to octanal) found in a sample experiment is shown in A. This experiment was chosen as an example because it contains, as is sometimes observed, a small region with higher density (on right). For B1-13, the same number of responsive cells was distributed randomly among the locations of all cell bodies seen experimentally. These maps regularly include areas with low (e.g., B7) and/or high (B11) density. Scale = 200 µm.


igure S10. Auto- and cross- correlation analysis reveals no consistent fine-scale patterning in

Fodorant responses. A-C. Auto- and cross- correlograms computed with data from 3 animals. First, the cell bodies in black and white maps of responsive cells were replaced with circles of 10 µm diameter so that differences in cell size did not affect subsequent calculations. The resulting maps were filtered with a Gaussian filter with sigma = 25 µm and diameter = 200 µm. Filtering increased the chances of finding patterning on the scale of >50 µm in the correlograms and to some degree mimicked the distribution of pyramidal cell dendrites in layer 1 which determine the bottom-up input to the imaged cells. The images were then multiplied pixel-by-pixel either by themselves (autocorrelation) or by the processed map of another odorant (cross-correlation) at 1 pixel shifts in both the x and y dimensions across a range equaling double the maps’ dimensions. The sum of these products for a given x and y shift, constituting the correlation value at that location, was then plotted at that location in the correlogram. The bright spots in the autocorrelograms represent the maximum correlation value obtainable when the maps were multiplied by themselves with no shift. The modulation of pixel values away from the center is weak compared to the peak in individual autocorrelograms and reveal no consistent periodic patterning across experiments. The cross-correlograms reveal a similar lack of periodic patterning. Scale = 1 mm.


Figure S11. Highly responsive cells to an odorant exhibit the same spatial organization as the full population of responsive cells. A-C Octanal-responsive cells in three animals. All cells surpassing the standard threshold for designation as responsive (see Methods) are presented in red. Cells with a ∆F/F greater than 5% (which constitute 27% of the population across animals; s.e.m. = 4.0, n = 7) are presented in yellow. Scale = 500 µm.


Figure S12. A model of piriform responses based upon random connectivity between the bulb and piriform can generate the observed odorant representations. A. Schematic of the model. Each piriform cell (triangles) is randomly connected to mitral cells (columns at top) that can either excite (+) or inhibit (-) the piriform cells. Inhibition is assumed to result from intervening inhibitory neurons in the piriform. The activity of mitral cells is transmitted to connected piriform cells and summed linearly (columns at bottom). Net excitations exceeding a threshold (in orange) elicit responses in the piriform cells. B. Comparison of the observed imaging results with the output of the model. The solid red triangles are ranked-ordered responses to octanal observed experimentally, and the open red triangles are the experimental responses of the same cells to a mix of octanal plus α-pinene. The data is taken from Fig. 5C. The solid blue triangles are responses of piriform cells to an odorant simulated by the model, and the open blue triangles are simulated responses of the same cells to a mix of odorants. For this simulation, excitatory convergence was 130, inhibitory convergence was 260, the relative magnitude of an average inhibitory input was 65% of the magnitude of an average excitatory input, and the threshold required to generate a response equaled 2 average excitatory inputs. C. The simulated frequency


of responsive piriform cells produced by different numbers of active glomeruli. When few glomeruli are active, the frequency of responsive piriform cells increases as more glomeruli become active. When greater numbers of glomeruli are active, the number of responsive piriform cells saturates due to increased inhibition. The pattern of responses upon increasing the number of active glomeruli produced by the model is consistent with the saturation observed upon increasing odor concentration and with the suppression observed with odor mixes.


Supplementary Discussion

The absence of stereotyped, patterned responses to odorants suggests the possibility that the

mitral cell inputs from glomeruli to piriform may be random. A model that invokes random,

convergent feedforward mitral cell inputs onto piriform cells can reproduce many features of the

imaging results. The intrinsic horizontal connections extended by pyramidal cells, which

could play a role in shaping piriform odorant responses, have been excluded from the

model for simplicity. In the model, each piriform cell receives inputs from different yet

overlapping sets of random glomeruli (Fig. S12A). The population of piriform cells, each

connected with an independent combination of glomeruli, thus samples possible combinations in

an unbiased manner. The number of mitral cells innervating a given cortical neuron defines the

excitatory convergence and these excitatory inputs are summed linearly. The mitral cell responses

to an odorant are distributed exponentially. Each pyramidal cell also receives a second,

inhibitory, set of inputs. The number of mitral cells exerting an inhibitory effect on a piriform

cell, its inhibitory convergence, can differ from its excitatory convergence. The magnitude of the

inhibitory effect of an average active mitral cell upon a piriform cell can also differ from its

excitatory effect. Inhibition exerted by mitral cells upon piriform cells is assumed to be mediated

by inhibitory neurons located in the piriform. These inhibitory inputs are also summed linearly,

and the difference between the sum of the excitatory and inhibitory inputs must exceed a

threshold of net excitation to produce a response. Significant parameters in the model are,

therefore, the degree of excitatory convergence, the degree of inhibitory convergence, the relative

magnitude of individual excitatory and inhibitory inputs, and the response threshold.

The model affords an explanation for the spatial distribution of odorant responses and also

fits the quantitative aspects of the data. It reproduces the observed frequency of cells responsive

to individual odorants, their response magnitude distributions and the suppression elicited by

mixes (Fig. S12) as well as the number of cells responsive to mixes and the degree of overlap


between populations responsive to different odorants (not shown). In the model, increasing the

number of active glomeruli initially results in increases in the number of responsive piriform

cells, but piriform response frequencies eventually saturate, a finding consistent with the

saturation observed either with mixes or upon increasing odorant concentrations (Fig. S12). The

model generates these results given a small number of assumptions. First, we assume that an

odorant activates twenty to one hundred glomeruli, values derived from imaging studies in the

bulb. Second, we assume that inhibitory convergence on a piriform cell is greater than excitatory

convergence, a feature of the model consistent with our observation that suppression exhibits far

less specificity than does excitation.

Imposing these constraints allows us to define scenarios at different levels of connectivity

between the bulb and piriform that can reproduce our data. For example, at low levels of

convergence, where the mitral cells of only 15 glomeruli provide excitatory input to each piriform

cell, we calculate that a response in a piriform cell would require a threshold of 1-3 net excitatory

inputs. In general, the threshold required to generate a response in a piriform cell increases as the

excitatory convergence increases, otherwise the frequency of responses to individual odorants

will mushroom. At an excitatory convergence of 150, therefore, the threshold must be increased

to 4-8 to fit our imaging results. The gross number of coincidently active excitatory inputs

required to produce a response can be determined for a given convergence and threshold. Unlike

convergence and threshold, the gross excitatory input necessary for a response is not a parameter

in the model but instead derived from the model’s performance. With an excitatory convergence

of 15, the average number of active excitatory inputs required to produce a response is 3 when

100 glomeruli in total are active. At an excitatory convergence of 150, with the same overall bulb

activity, the average number of excitatory inputs necessary for a response is 19.

The model reveals that the number of excitatory inputs necessary for a response increases linearly

as the number of active glomeruli increases, even if all the model’s parameters are fixed. The

average number of active excitatory inputs a responsive cell receives, for example, at an


excitatory convergence of 150, is 11 when 50 glomeruli are active, 19 when 100 glomeruli are

active, and 27 when 150 glomeruli are active. This increase in the number of active inputs

necessary to drive a response with increasing numbers of active glomeruli results from an

increase in inhibitory input to the piriform. This behavior underlies the subadditivity of piriform

cell responses observed in the model upon increasing the number of glomeruli activated (Fig.

S12) and is in accord with our imaging experiments with mixes of odorants. Thus, a random

model of bulb to piriform connectivity produces results that fit our data with plausible constraints

and makes predictions concerning the anatomical and physiological aspects of the piriform circuit

that could underlie our imaging observations.

supplementary figures - cell figures ... result of ambiguity in assigning responsive cells....

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