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Supplemental Material Haddad Supp pp 1 Supplementary materials for: Global features of neural activity in the olfactory system form a parallel code that predicts olfactory behavior and perception Rafi Haddad1,2#, Tali Weiss1, Rehan Khan1σ, Boaz Nadler2, Nathalie Mandairon3, 3 1* 1*# Moustafa Bensafi , Elad Schneidman and Noam Sobel . Section 1: PCA analysis We provide a step by step example of how we conducted the PCA analysis. Assume we have 8 odors, each located on the vertexes of a 3 dimensional unit cube. Each odor can thus be represented by the exact binary code of the numbers 0 to 7. We can represent these odors in the following binary code matrix: Odor ID Pattern code: 1 0 0 0 2 0 0 1 3 0 1 0 4 0 1 1 5 1 0 0 6 1 0 1 7 1 1 0 8 1 1 1 1 Haddad Supp pp 2 Using any available mathematical tool (we used Matlab 'princomp' method) we can calculate the principle components scores of this matrix. In this case the values of the PC1 scores are: [1 0 0]. The PC1 projection value of each row is the projection of each row by the PC1 weight vector. For example the PC1 value of the first row is [0,0,0]X[1,0,0] = 0X1 + 0X0 + 0X0 = 0. (this is a vector multiplication). Thus the PC1 value of each row of this matrix is: 0,0,0,0,1,1,1,1. Note that usually the PC1 is calculated on a centered matrix (the columns of the matrix have zero mean) and thus the PC1 value might be shifted by some value. The same calculations are applied for the higher PCs. Section 2: Neural correlates of PC2 PC2 is orthogonal to PC1, that is <PC1, PC2> = 0 where PC1 and PC2 are vectors and <,> is the standard inner product. If the PC1 weights all have nearly the same sign (as is the case in several datasets) then <PC1, PC2> = 0 implies that PC2 values are half positive and half negative (assuming there is no prominent unit that gets a very high positive or negative value). Thus we get that a good estimation for PC2 is the total neural response of half the neurons subtracted by the total neural sum of the other half (the first group contains the neurons that has a positive PC2 weight and the second group contains the units that have a negative PC2 weight). Section 3: Global neural computation is less susceptible to lesions and gene ablations According to the suggested PCA based computation of odor preference each odors' neural response is projected on the first PC value. Thus, the score of an odor is a weighted sum of the neural responses. Consider two odors A and B, that each generated a different neural pattern. According to our computation the odor index of 2 Haddad Supp pp 3 A is A*PC1 and the odor index of B is B*PC1. Odor A is considered more attractive if A*PC1 > B*PC1 or if (A-B)*PC1 > 0. In case of random gene ablation of bulb lesion the resulted neural pattern of both odors A and B can be marked A' and B'. However we still get that (A'-B')*PC1 > 0 and thus odor A will still be more attractive than odor B Supplementary References Kreher SA, Mathew D, Kim J, Carlson JR (2008) Translation of sensory input into behavioral output via an olfactory system. Neuron 59:110-124. 3 Haddad Supp pp 4 Supplementary Figure 1: Correlation between PC1 and total neural response. The response matrix of dataset #2 reordered to reveal two groups of odors and neurons. This structure may explain the bifurcation seen in Fig. 1B, H and I. Supplementary Figure 2. Constructing human odor perceptual space Human odor perception can be described by representing each odor by the average ranking assigned to it using a large set of verbal odor descriptors. For example, Hexanal is very pungent, medical and heavy whereas Carvone is minty and flowery. This high dimensional representation of human odor perception can be reduced to a lower dimension using PCA. Thus each odor can be represented by its PC1 and PC2 values. 4 Haddad Supp pp 5 Supplementary Table 1: The odor response index for Drosophila larvae and adult. For Drosophila Larvae we took the data reported in (Kreher et al., 2008) (including odors reported in the supplementary information). For Drosophila we searched the literature for Drosophila behavioral data under similar conditions (1/100 dilution in mineral oil and T-maze attraction repulsion test limited to less than few minutes). We found data for nine additional odors. Among these nine odors, two odor scores were similar to the larvae's scores, two odors had different scores and five odors did not have any larvae scores. Supplementary Table 2: Human odor pleasantness ratings 20 odors and their average pleasantness ratings as rated by 18 subjects. Supplementary Table 3: Rat and mouse oral toxicity List of odors and the oral toxicity of rats and mice reported as LD50 in mg/kg. In cases of multiple sources we took the average value or the latest source. Values above 35000mg/kg are not LD50 values, but rather TDLO values (lowest published toxic dose) in mg/kg/3w, and were thus not included in the analysis. LD50 values listed as -1 or -2 denote no available value. Supplementary Table 4: List of perceptual PC2 values Supplementary Table 5: List of odors and their vapor pressure. The link column points to the source of information from which we obtaining the vapor pressure (usually the MSDS). 5 Haddad Supp pp 6 Supp Fig 1 6 Haddad Supp pp 7 Supp. Fig. 2 7 Haddad Supp. Fig. 1 Haddad Supp. Fig. 2 Color codes and comments The first experiment of kreher Second exp of kreher (supp material) not used in correlation between larvae odor preference Data is extrapolated from the second species Taken from external sources ID Name CID Larvae Drosophila Source 1 Propionic acid 1032 0.47 0.47 2 Geranyl acetate 7780 -0.4 -0.4 3 E2-Hexanal 5281168 0.73 0.73 4 Cyclohexanone 7967 0.3 0.3 5 2,3-butanedione 650 0.6 0.6 6 2-Heptanone 8051 0.7 0.7 7 (-) Fenchone 14525 -0.3 -0.3 8 Methyl salicylate 4133 -0.23 -0.23 9 Benzaldehyde 240 -0.21 -0.21 10 Acetophenone 7410 0.32 0.32 11 Anisole 7519 0.4 0.4 12 Methyl euganol 7127 0.22 0.22 13 2-Methylphenol 335 -0.3 -0.3 14 4-Methylphenol 2879 0.2 0.2 15 1-Butanol 263 0.28 0.28 16 1-Hexanol 8103 0.52 -0.4 17 1-Heptanol 8129 0.38 0.38 18 3-Octanol 11527 0.57 0.57 19 1-Octen-3-ol 18827 0.37 0.37 20 1-Nonanol 8914 -0.3 -0.3 21 Ethyl acetate 8857 0.55 0.5 22 Ethyl butyrate 7762 0.6 0.6 23 Propyl acetate 7997 0.72 0.72 24 Pentyl acetate 12348 0.54 0.54 25 Isoamyl acetate 31276 0.57 0.57 26 Octyl acetate 8164 0.1 0.1 27 CO2 280 -0.2 -0.2 28 1-pentanol 6276 -0.01 -0.01 29 2-hexanol 12297 0.53 0.53 30 3-methyl-3-pentanol 6493 0.45 0.45 31 butyl acetate 31272 0.79 0.79 32 hexyl acetate 8908 0.8 0.8 33 methyl butyrate 12180 0.72 0.72 34 2,3-hexanedione 19707 0.55 0.55 35 3-pentanone 7288 0.57 0.57 36 butyric acid 264 0.11 0.11 37 e2-pentenal 5364752 0.59 0.59 38 3-methylphenol 341 0.04 0.04 39 4-methylacetophenone 8500 0.18 0.18 40 2-methoxy-4-methylphenol 7144 0.02 0.02 41 2-phenylpropionaldehyde 7146 0.07 0.07 Acetoin 179 0.25 0.25 Hanson Butyl butyrate 7983 -0.2 -0.2 Hanson Phenylacetonitrile -1 0.5 0.5 Hanson 46 ethyl 3-hydroxybutyrate 62572 0.4 0.4 Hanson 47 ethyl hexanoate 31265 0.1 0.1 Hanson ID Pubchem Cid Cas Name Rated pleasantness (0-10) 1 323 91-64-5 cumarin 6.698344925 2 379 2.154906088 3 454 124-13-0 Octanal 2.090484955 4 957 111-87-5 octanol 2.658956623 5 1032 0.313252559 6 6184 66-25-1 hexanal 2.819237008 7 6276 71-41-0 pentanol 4.256721573 8 7410 98-86-2 acetophenone 3.728977383 9 7793 5.487098765 10 7895 107-87-9 2-pentanone 4.491632977 11 7991 109-52-4 pentanoic acid 0.140360904 12 8051 110-43-0 2-heptanone 5.042654586 13 8094 111-14-8 hepatnoic acid 1.305780652 14 8103 111-27-3 hexanol 5.354866545 15 8129 111-70-6 heptanol 5.365568714 16 8130 111-71-7 Heptanal 2.23846225 17 8158 1.595967916 18 8174 112-30-1 decanol 3.142415287 19 8892 142-62-1 hexanoic acid 0.600946385 20 8908 142-92-7 hexyl acetate 6.094352785 21 12756 4.547092946 22 159055 464-49-3 R camphor 5.234080925 23 439570 6485-40-1 R-carvone 9.734575009 24 444294 464-48-2 (+)camphor 5.292546624 25 637566 106-24-1 geraniol 6.624734496 26 1201521 4.729794253 CID Name CAS rat ld50 mouse ld50 8815 4 allyl anisole 140-67-0 1230.00 1250 61771 2-secbutyl Cyclohexanone 14765-30-1 2400.00 0 77698 Methoxy acetophenone 4079-52-1 0.00 0 20499 furfuryl disulfide 4437-20-1 0.00 0 11747 2,3 pentane dione 600-14-6 3000.00 0 11748 methyl pyruvate 600-22-6 0.00 0 5367762 propyl tiglate 61692-83-9 0.00 0 8205 Dodecyl acetate 70808-58-1 0.00 0 6568 2-butenol 78-92-2 6480.00 0 7335 carvyl acetate 97-42-7 0.00 0 240 benzaldehyde 100-52-7 1300 0 7654 Phenyl ethyl acetate 103-45-7 0.00 0 7749 ethyl propionate 105-37-3 3500.00 0 7762 ethyl butyrate 105-54-4 13050.00 0 7765 Acetal 105-57-7 4600.00 3500 7770 Propyl butyrate 105-66-8 15000 0 61527 3-acetyl 2,5 dimethyl Furan10599-70-9 0.00 0 7794 Citronellal 106-23-0 0.00 0 637566 geraniol 106-24-1 3600 3500 7797 ethyl heptanoate 106-30-9 0.00 0 7799 ethyl octanoate 106-32-1 25960.00 0 7802 3-Heptanone 106-35-4 2760 0 7820 DBE basic ester 106-65-0 0.00 0 246728 3-Octanone 106-68-3 5000 2800 14101 4-Hydroxycoumarin 1076-38-6 0 2000 7895 2-pentanone 107-87-9 1600 1600 264 Butyric acid 107-92-6 2000 0 342 meta-cresol 108-39-4 242 600 7966 cyclohexanol 108-93-0 2600 620 7967 Cyclohexanone 108-94-1 1800 0 996 phenol 108-95-2 317 270 7991 Valeric acid 109-52-4 1844 600 7997 propyl acetate 109-60-4 0.00 0 ? 8007 Butylamine 109-73-9 360 0 8051 2-heptanone 110-43-0 1670.00 2407 8060 pentylamine 110-58-7 470 0 8063 Valeraldehyde 110-62-3 5660.00 6400 8068 methyl butyl amine 110-68-9 420 0 8082 Piperidine 110-89-4 400.00 30 9862 6-methyl-5-hepten-2-one 110-93-0 4100 3690 8093 2-Octanone 111-13-7 0 3832
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