Representation of Spatial Goals in Rat Orbitofrontal Cortex

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1 Neuron, Volume 51 Supplemental Data Representation of Spatial Goals in Rat Orbitofrontal Cortex Claudia E. Feierstein, Michael C. Quirk, Naoshige Uchida, Dara L. Sosulski, and Zachary F. Mainen Experimental Procedures Animal subjects Male Long-Evans rats weighing 35-45g at the time of surgery were used for the present study. Rats were housed in a 12-hour reversed dark-light cycle and tested during the dark period. Animals had free access to food but water was restricted to the behavioral session and approximately 15 additional minutes; drinking time was adjusted to maintain the rats at 85% of their free-drinking weight. All procedures involving animals were carried out in accordance with NIH standards and approved by the Cold Spring Harbor Laboratory Institutional Animal Care and Use Committee. Behavioral task Behavioral training and testing were conducted as described in Uchida and Mainen (23). The behavioral setup consisted of a box with a panel containing three ports: a central port for odor delivery and two lateral ports for water delivery located 57 mm to the left and right of the central port. Each port was equipped with an infrared photodiode and phototransistor; interruption of the beam signaled that the rat has introduced its snout into the port. We used port beam break signals to determine the position of the animal during the task with high temporal precision. Odors were mixed with pure air to produce a 1:2 dilution at a flow rate of 1 L/min using a custom-built olfactometer (for detailed description see Uchida and Mainen, 23). Delivery of odors and water reinforcement were controlled using computer data acquisition hardware (National Instruments) and custom software written in MATLAB (MathWorks, Natick, MA). Rats were trained and tested on a two-alternative choice discrimination task where water was used as a reward. The task is a self-paced or reaction time task. Trial start was signaled by a click (after a minimum inter-trial interval of 4 s). Rats had to withhold from entering the central odor port for >.6 s after trial start; trials in which rats entered the 1

2 port during this no poke period were aborted. Subjects initiated the behavioral sequence by entering the odor sampling port (Figure 1A), which triggered the delivery of an odor with a random delay (stimulus delivery delays were drawn from a uniform distribution [.2-.5] s). Odor was available for up to 1 s. Rats could exit the odor port at any time, and make a movement to either of the two goal ports. Reward was available for correct choices for up to 2 s after the rat left the odor sampling port. Water was delivered with a random delay drawn from a uniform distribution [.2-.5] s. The system was calibrated regularly to ensure that equal amounts of water were delivered at both ports (see Water calibration below and Figure S3 for calibration data). Behavioral sessions consisted of 25-4 trials, spanning 45 to 75 minutes. Rats were trained until a performance of 85% correct trials was achieved. In the two-odor variant of the task, rats were presented with either one of two odors; each odor signaled that water was available in one of the two goal ports. The standard odors for the two odor discrimination were 1-hexanol (neat) and caproic acid (1/1 dilution in mineral oil), except for one rat, for which we used pentanol (1/1) and caproic acid (1/1). Some rats were trained on two variants of the task: a multiple-odor discrimination task and a binary mixture discrimination task. In the multiple odor discrimination task four or six odors were used, such that two or three were associated with reward in the left goal port, and the other two or three odors were associated with reward available on the right goal port. The binary-mixture discrimination task has been described elsewhere (Uchida and Mainen, 23). Briefly, two odors were mixed in six different proportions (1/, 68/32, 56/44, 44/56, 32/68, /1), and the dominant odorant determined the side of reward availability (see Selectivity analysis below for the use of these stimuli in the analysis). Odors used for the multiple-odor discriminations (all dilutions in mineral oil): pentyl acetate 1/1; butyric acid 1/1; s(+)- 2-octanol 1/1; (-) carvone (1/1); pentanol (1/1; valeric acid 1/1. 2

3 Water valve calibration For the delivery of water reward we used computer-controlled valves. The volume of water delivered in each trial is determined by the amount of time the valve is open, and the open time of the left and right valves can be controlled independently. To ensure that equal volumes of water reward were delivered at both ports, we calibrated the water valves periodically. For the calibration procedure we collected water from both ports in 5 consecutive valve openings (for a given open valve duration). We weighted the water collected from each of the ports, and estimated the mean water volume delivered in each trial. We did the same for different valve open time durations, and repeated each measurement 3 times. A water calibration curve is shown in Figure S3; it can be seen that for different open time values the amount of water delivered in the left and right ports did not differ significantly. We chose a water valve open duration of 3 ms. Full-curve calibrations were performed weekly. In addition, before each recording session we measured the volume of water delivered by each valve for the chosen open duration. There was no change in the volume dispensed by the valves across days. Surgery Each rat was implanted with a custom-made multi-electrode drive in the left hemisphere in orbitofrontal cortex (3.5 mm anterior to bregma, 2.5 mm lateral to midline). Anesthesia was induced with an injection of ketamine/medetomidine (6/.5 mg/kg, i.p.). Depth of anesthesia was monitored by tail pinch response, whisking, breathing rate and pedal reflex. Medetomidine was supplemented periodically during surgery. Body temperature was maintained using a heating blanket. Surgical procedures were carried out under aseptic conditions to reduce the chance of infection. The rat was placed in a stereotaxic frame, and a small incision was made in the skin with a stainless steel surgical blade. The skull was cleaned and dried, and a craniotomy was performed using a dental drill. The drive was placed over the craniotomy, and the hole sealed with bone wax. The tetrode drive was attached to the skull using several small skull screws (#-8) and cemented with dental acrylic. One of the skull screws was used for providing electrical ground for the recording array. Following surgery rats were administered ketofen as an analgesic, and the incision site treated with topical antibiotic. Rats were allowed to 3

4 recover for 5 to 7 days before resuming water restriction and starting the recordings. During that period, tetrodes were gradually lowered to reach OFC (electrode placements were estimated by depth and later confirmed with histology). Electrophysiological recordings Extracellular recordings were obtained using six independently adjustable tetrodes for recording and an additional reference electrode (which was positioned in superficial cortex). Individual tetrodes consisted of four twisted polyimide-coated nichrome wires (H.P. Reid, Inc., Palm Coast, FL; single wire diameter 12 µm, gold plated to MΩ impedance). Single unit activity was first amplified using a unity-gain op-amp preamplifier fitted to the microdrive array and connected to a bank of eight channel programmable amplifiers (Cheetah acquisition system, Neuralynx, Tucson AZ) via flexible fine wire cables. Signals were band-pass filtered from.6-6 khz and sampled at khz. Individual threshold values were set for each channel such that anytime a recorded voltage crossed the threshold of at least one channel of a tetrode, 32 points were recorded for each of the four tetrode channels. Spike waveforms were than amplified 5 5, times and saved to disk using the Cheetah recording system. The Cheetah system was also configured to record behavioral event flags generated by the behavioral control computer, as well as the photo beam signals from the odor and goal ports. Recordings were obtained for 2-4 weeks with electrode depths adjusted on each recording day so as to sample an independent population of cells across sessions. Histology At the end of the experiment, animals were deeply anesthetized with sodium pentobarbital. Recording sites were marked first by coating electrodes with fluorescent dye (Vybrant DiI, Invitrogen). For three rats, electrolytic lesions were made for each electrode. In the other two rats lesions were not made because the drive was damaged before termination of the experiment. In the latter cases, electrode tracks were recovered using the fluorescence signals. Rats were perfused transcardiacally with paraformaldehyde 4%. Brains were removed and stored in paraformaldehyde 4% until sectioning. Thirty-micron sections were made, Nissl-stained and photographed using a 1.25X lens to determine the location of recording sites. Images were imported into custom-made software, which allowed us to align different sections and determine the location of the electrodes. Depth was estimated using the travel distance registered 4

5 while lowering the electrodes and the distance to the brain surface observed in the sections. Neuronal data analysis Spike sorting Multiple single units were isolated off-line by manually clustering spike features derived from the sampled waveforms using MCLUST software (A.D. Redish). In a process analogous to triangulation, the principal features used for the initial isolation of all cells were the relative ratios of spike peaks recorded across the four leads of the tetrode. Further refinement of spike clusters was primarily obtained through the use of additional features derived from the tetrode channels with the strongest amplitude signal. These features typically included spike width, spike valley, the fast Fourier transform of the spike (FFT), energy of waveform (L1-norm) and the first principal component of the extracted waveforms (PCA1). Only cells that produced a minimum of 1 spikes with less than.5% refractory period violations (refractory period >1 ms) were used for subsequent analysis. In addition, to ensure that we used well-isolated cells, we calculated an isolation distance (ID) for each cluster (Schmitzer-Torbert et al., 25) using the following features: peak, energy, FFT, PCA1. The ID is defined as the Mahalanobis distance from the center of an identified cluster within which as many spikes belong to the specified cluster as others. Clusters of ID < 2 were excluded from analysis. Peri-stimulus time histograms (PSTHs) Trials were grouped according to odor identity, response direction or trial outcome. For all trials of a given type in a session, spikes were aligned to a trigger event (depending of the epoch of interest). Spikes were binned in 2 ms windows, and bins were averaged across trials. The average firing rate was then smoothed using a Gaussian filter with σ = 1 ms. Use of ROC analysis to classify neuronal responses To quantify selectivity for task variables (stimulus, response direction, outcome), we used a measurement based on ROC ( receiver operator characteristic ) analysis (Green and Swets, 1966). This analysis originates in signal detection theory, and has been 5

6 widely used for comparing neuronal responses to different stimuli and for estimating how well the activity of a neuron can be used to classify, on a trial by trial basis, the stimulus that originated it (for an example, see Britten et al., 1992). The area under the ROC curve provides a measure of the discriminability of two signals, and is directly related to the overlap of the two distributions of responses compared. To clarify the meaning of the ROC area, suppose the following: two types of stimuli are presented, stimulus A and stimulus B; we can then observe the distribution of neuronal responses for trials in which stimulus A was presented and the distribution of responses when stimulus B was presented. The ROC area can be interpreted as the probability with which an ideal observer (i.e. one that knows the distributions of responses) given the firing rate of a particular trial, can correctly determine what stimulus originated it, that is, correctly assign the observed response to one of the distributions. This is determined by the overlap of the distributions: an ROC area of.5 corresponds to completely overlapping distributions, and therefore stimuli that cannot be discriminated; an area of 1 corresponds to stimuli that can be perfectly discriminated. This analysis is appropriate for binary variables and makes no assumptions about the underlying distributions. Epoch modulation We defined modulation as a significant change in firing rate relative to baseline. A unit was considered to modulate its activity during a given epoch if the distribution of firing rates during that epoch for at least one of the four trial conditions (see below) was distinguishable from the distribution of baseline firing rates using receiver-operator characteristic (ROC) signal detection analysis (Green and Swets, 1966). Baseline was defined as the period from 35 ms to 1 ms before the entry in the odor port, provided that no entries into any of the ports occurred during this period. With two odor stimuli, A and B, the four trial types are correct A, correct B, error A, and error B. Modulation analysis was performed both using whole-epoch windows and using windows of fixed length (25 ms) to avoid the possible influence of epochs of different duration (see odor sampling duration and movement time distributions, Figure 1D). However, the results did not differ for the windows used, and the numbers for the fixed-length windows are reported here. Selectivity in multiple-odor and binary mixture experiments 6

7 In experiments in which multiple odors or binary mixtures were presented, not all trials were included in the analysis, but subsets were selected as follows: For stimulus selectivity: in binary mixture experiments, only trials in which the pure odors were presented were included in the analysis. In multiple odor experiments, only a block in which a single pair of odors was presented was used for the analysis. For direction and outcome selectivity: for both multiple-odor and mixture experiments, trials in which left odors (or left mixture ratios ) were presented were pooled together, and the same was done with right odors. Inspection of individual units showed that this was a valid approach given that direction and outcome selective neurons were rarely further modulated by odor identity (for an example of this, see Figures S2 and 8A). Choice probability analysis in multiple-odor and binary mixture experiments For multiple-odor experiments, only the first block of the session (where only 2 odors were presented) was included in the analysis. For mixture experiments, stimuli were grouped according to response category and choice probability was calculated by comparing left and right trials for the category. Permutation procedure For the calculation of preference and selectivity values, the actual value was computed for each cell by comparing the two distributions of firing rates for the relevant conditions (for example, A and B trials). The hypothesis we test is whether the actual value could be obtained if the firing rates for A and B trials do not truly belong to different distributions, but to the same one. The firing rates were assigned randomly to two groups, and the resulting distributions were compared and the value calculated. This process was repeated many times (1 iterations) to obtain a distribution of random values. The actual value was then compared to the random distribution. The significance value was defined as the fraction of random values exceeding the actual value. No correction for multiple comparisons was applied. 7

8 Results General firing properties The distribution of firing rates in each epoch was broad (ranging from <.1 Hz to > 1 Hz) and approximately log-normal (Figure S1A). The average firing rate of OFC units was low (mean 3.54 Hz, n = 544 cells), but a large fraction of these units responded during the task with substantial modulation of their firing rates. Considering a loose measure of responsiveness a significant difference in average firing rate from baseline (see Epoch modulation above) a large majority of units (87%) responded during one or more task epochs (stimulus: 57%; response: 56%; outcome: 75%). OFC responses were not concentrated in a particular phase of the task. The distribution of firing rates across the population was similar across task epochs (Figure S1A) and increases and decreases in firing rate were roughly balanced in each epoch (Figure S1B). Thus, OFC neurons responded to the task primarily with a redistribution of activity rather than an overall increase in activity. Units responded in more than one of the defined temporal epochs (24% modulated in 1 epoch; 27% in 2 epochs; 36% in all 3 epochs). Regression analysis for firing rate on current and previous trials choices Left/right selectivity during the movement period could arise due to drifting in the relative value of the left and right choice ports, even though efforts were made to make sure the same amount of water was delivered at both ports. Changes in the relative value of the ports should be reflected in choice biases. The history of choices for each session was defined by constructing a vector, with 1, 1 or assigned for each trial s choice (right, left or no choice, respectively). The choice history is considered to be a measure of the choice bias. Autocorrelation of the choice history vectors showed very little evidence for slowly drifting biases, suggesting that rats were not tracking changes in relative port value. To determine the influence of current and past choices on the response period firing rate during the current trial, we performed a multiple linear regression analysis. 8

9 We tested different models, including only the current trial s choice and incorporating up to 9 trials back one at a time (a total of 1 models). We regressed firing rate (FR) on choices using least squares minimization for the following set of equations: FR = b t= * C t= FR = b t= * C t= + b t=-1 * C t= -1 for the model with the current choice (t=) only. for the model with the current and previous trial s choices, and so on... FR = b t= * C t= + b t=-1 * C t= b t=-9 * C t=-9 where FR is the firing rate for the current trial, C t are the choices, and b t the regression coefficients. For each of the equations (for each model), we calculated an R 2 value by using a crossvalidation procedure as follows. Ninety percent of the data was used to estimate the regression coefficients; those coefficients were then used on the remaining 1% of the data to estimate the firing rates, and the estimated firing rates compared to the actual firing rates. This prediction error (difference between estimated and actual firing rates) was used to compute an R 2 (this R 2 tells us how well the model does in explaining the variance of data that was actually not used to train the model). This procedure was repeated 1 times, each time taking a different subset of the data. The mean R 2 is reported. The procedure described above gave us an R 2 value for each model (and for each cell). To select for the best model, we used the one standard error rule (Hastie et al., 21). Briefly, for each cell, we find the maximum R 2 amongst the 1 models, and then look for the most parsimonious model (that is, the one with the least number of predictors) that gives an R 2 that falls within 1 SEM of the maximum R 2. We designate that model as the one that best explains the variance of the firing rate. An example of this analysis is shown in Figure S4A. It can be seen that adding past choices does not improve the model s ability to explain the variance of the firing rate during the movement period. A few cells modulated their firing rates according to current 9

10 and previous trials choices, as the example cell shown in Figure S4B; for this cell, both the current trial and one trial back had influence on the firing rate. We performed this analysis on all direction-selective cells. We calculated the average normalized coefficients for the population of direction-selective cells using a model with 4 predictors (4 trials back: current trial and 3 trials back); only the regression coefficient for the current trial s choice was different from (Figure S4C). For most cells the best model was that including only the current trial s choice (Figure 7B-C); for very few cells (8/221 or 3.6%) the best model included both the current and the previous trials choices. Figure 7C compares the R 2 obtained for the models including one trial (the current choice) vs. the R 2 obtained using 2 trials (the current and the previous choices); it can be seen that for very few cells (those above the diagonal) the previous choice has some influence on the firing rate in the current trial. Distribution of direction-selective cells across recording locations We also considered whether direction-selective cells could represent a distinct subpopulation of OFC neurons, perhaps being localized to a specific layer or subregion of OFC. To test this and to exclude the possibility that the results were biased by recordings from more superficial motor cortical regions, we examined the correlation between recording depth and direction tuning. The magnitude of direction selectivity did not vary systematically with recording depth (3.5 mm to 6.5 mm, corresponding to the depth of OFC; see Figure S5). 1

11 Supplemental Figures A 2 Baseline Stimulus Response Outcome Cells Firing rate (Spikes/s) B 28 Stimulus Response Outcome Cells Fractional change relative to baseline Figure S1. Firing statistics of OFC neurons. (A) Distribution of firing rates. Mean firing rate (across trials) was computed for each cell in four behavioral epochs, and plotted in logarithmic scale. (B) Firing rate modulation. Histograms show the distribution of the fractional firing rate change relative to baseline (on a log scale) in the different epochs. The fractional change was defined as: firing rate epoch / firing rate baseline. For each cell, the fractional change in each epoch was computed separately for correct A trials and correct B trials. Mean firing rates for each subset of trials was compared to mean baseline firing rate. Thus, for each cell we obtained 2 values of fractional modulation. Note that cells both increased and decreased their firing rates relative to baseline. 11

12 Odor port Odor valve Goal port Water valve L Odor R Response Left Right Odor 1 n = 69 Odor 3 n = Correct Odor 2 n = 17 Odor 4 n = Spikes/s Odor 3 n = 6 Odor 1 n = Error Odor 4 n = 17 Odor 2 n = Time - Odor Port Out (s) Time - Odor Port Out (s) Figure S2. Direction selectivity in multiple-odor discrimination. Example of a direction-selective neuron recorded during a four-odor discrimination session. Odors 1 and 2 were rewarded when left choices were made, and odors 3 and 4 were rewarded for choices to the right goal port. (Top) Left, schematic of the timing of events in a trial, showing the analysis period (response, shaded box). Right, in the response period the rat is moving from the central port to the left or right goal ports. (Raster plots) Trials are grouped according to stimulus and direction. Only 2 representative trials are shown in each raster plot, sorted according to movement time (gray shaded area represents response period). Perievent histograms are overlaid on raster plots (see Figure 3A). Raster plots 12

13 and histograms are aligned to movement out of the odor port. (Left column) Trials in which the rat moved to the left (orange). (Right column) Trials in which the rat moved to the right (blue). Odors are indicated on top of each plot. The four upper plots correspond to correct trials, while the four lower plots correspond to error trials. Odor 1: 1-hexanol; odor 2: valeric acid (1/1); odor 3: caproic acid (1/1); odor 4: pentanol (1/1) s Mean right volume (µl) s.5 s s Mean left volume (µl) Figure S3. Water valve calibration. Mean volume of water (µl) delivered per trial for the left and right water valves, averaged across 3 repeated measurements. Each measurement was obtained by opening 5 times the valves and calculating the amount delivered in a single trial (see above). Bars represent 1 SD across the 3 measurements. Each dot represents a valve opening duration (.3,.4,.5 and.6 seconds). Dotted line corresponds to equal volume of water in both ports. 13

14 A N1_4624_6.3 C B Regression coefficient Regression coefficient Trial (relative to current) w1_4731_ R 2 R Trials included in the model (current + trials back) Average normalized coefficients Trial (relative to current) Trial (relative to current) Trials included in the model (current + trials back) Figure S4. Multiple regression analysis on choice history. (A) Analysis of a direction-selective cell for which only the current trial s choice contributed to explaining the variance in the firing rate. Firing rate for the response period in the present trial was regressed on the current and past choices. (Left) Regression coefficients for the model using 4 predictors (current trial, x =, and 3 trials back). Only the coefficient for the current trial differs significantly from (arrowhead). (Right) R 2 (computed using the cross-validation procedure described in Supplemental Data) was calculated for different models, including only the current trial s choice (1 trial included, x = 1) or increasingly adding trials back (x = 2, current trial and 1 trial back, etc.). R 2 was computed using the cross-validation procedure described in Supplemental Data. Error bars show 1 SEM for the R 2 based on the cross-validation. Dotted lines show +/- 1 SEM around the maximum R 2. Note that for this cell the best model includes only the current trial s choice (arrowhead), as R 2 does not increase by adding more predictors. (B) Example of one of the few direction-selective cells for which both the current and the previous trial choices contributed to the firing rate. (Left) Regression coefficients for the model that includes the current trial and 3 trials back. Note that in this case both the coefficient for the current trial (x = ) and the previous trial (x = -1) depart from. (Right) R 2 for the different 14

15 regression models, as in (A). Note that in this case the best model is the one including 2 trials the current and 1 trial back (arrowhead) -; adding a second predictor to the model (the previous trial s choice, x = 2) produces an increase in R 2. (C) Average of the regression coefficients for the model that includes the current choice and 3 trials back. For each cell, regression coefficients were normalized to the first coefficient (the coefficient for the current trial), and then the coefficients were averaged across cells. Note that for the population of cells only the first regression coefficient (for the current trial s choice) differs from. 7 N.S. p <.1 6 Depth (mm) Direction selectivity Figure S5. Direction selectivity was found at all recording depths. Direction selectivity values (based on ROC analysis, see Experimental Procedures in the main text) for all cells as a function of recording depth. Recording locations were estimated from electrode depths. Each dot represents a cell; red: significant direction selectivity (p <.1); black: not significant. 15

16 References Britten, K. H., Shadlen, M. N., Newsome, W. T., and Movshon, J. A. (1992). The analysis of visual motion: a comparison of neuronal and psychophysical performance. J Neurosci 12, Green, D. M., and Swets, J. A. (1966). Signal detection theory and psycophysics (New York, Wiley). Hastie, T., Tibshirani, R., and Friedman, J. (21). The elements of statistical learning; data mining, inference, and prediction (New York, Springer-Verlag). Schmitzer-Torbert, N., Jackson, J., Henze, D., Harris, K. D., and Redish, A. D. (25). Quantitave measures of cluster quality for use in extracellular recordings. Neuroscience 131, Uchida, N., and Mainen, Z. F. (23). Speed and accuracy of olfactory discrimination in the rat. Nature Neuroscience 6,