DOI: 10.5061/dryad.9s4mw6mkn
title: Data from: Learning of probabilistic punishment as a model of anxiety produces changes in action but not punisher encoding in the dmPFC and VTA

description: Previously, we developed a novel model for anxiety during motivated
 behavior by training rats to perform a task where actions executed to
 obtain a reward were probabilistically punished and observed that after
 learning, neuronal activity in the ventral tegmental area (VTA) and
 dorsomedial prefrontal cortex (dmPFC) represent the relationship between
 action and punishment risk (Park & Moghaddam, 2017). Here we used
 male and female rats to expand on the previous work by focusing on neural
 changes in the dmPFC and VTA that were associated with the learning of
 probabilistic punishment, and anxiolytic treatment with diazepam after
 learning. We find that adaptive neural responses of dmPFC and VTA during
 the learning of anxiogenic contingencies are independent from the punisher
 experience and occur primarily during the peri-action and reward period.
 Our results also identify peri-action ramping of VTA neural calcium
 activity, and VTA-dmPFC correlated activity, as potential markers for the
 anxiolytic properties of diazepam.
description: <strong>Subjects</strong> Male and
 female Long-Evans (bred in house n=8) and Sprague-Dawley (Charles River
 n=5) rats were used. Animals were pair-housed on a reverse 12 h:12 h
 light/dark cycle. All experimental procedures and behavioral testing were
 performed during the dark (active) cycle. All studies included both
 strains of male (n=7) and female (n=6) rats. All experimental procedures
 were approved by the OHSU Institutional Animal Use and Care Committee and
 were conducted in accordance with the National Institutes of Health Guide
 for the Care and Use of Laboratory Animals.
 <strong>Initial Training & Punishment Risk Task
 (PRT)</strong> The PRT follows previously
 published methods (Park & Moghaddam, 2017; Chowdhury et al.,
 2019). Rats were trained to make an instrumental response to receive a
 45-mg sugar pellet (BioServe) under fixed ratio one schedule of
 reinforcement (FR1). The availability of the nosepoke for reinforcement
 was signaled by a 5-s tone. After at least three FR1 training sessions,
 PRT sessions began. PRT sessions consisted of three blocks of 30 trials
 each. The action-reward contingency remained constant, with one nose-poke
 resulting in one sugar pellet. However, there was a probability of
 receiving a footshock (300 ms electrical footshock of 0.3 mA) after the
 FR1 action, which increased over the blocks (0%, 6%, or 10% in blocks 1, 2
 and 3, respectively). To minimize generalization of the action-punishment
 contingency, blocks were organized in an ascending footshock probability
 with 2-min timeouts between blocks. Punishment trials were pseudo-randomly
 assigned, with the first footshock occurring within the first five trials.
 All sessions were terminated if not completed in 180 mins.
 <strong>Fiber Photometry Analysis</strong>
 Peri-event analysis: Signals from the 465 (GCaMP6s) and 560
 (tdTomato) streams were processed in Python (Version 3.7.4) using
 custom-written scripts similar to previously published methods (Jacobs
 & Moghaddam, 2020). Briefly, 465 and 560 streams were low pass
 filtered at 3 Hz using a butterworth filter and subsequently broken up
 based on the start and end of a given trial. The 560 signal was fitted to
 the 465 using a least-squares first order polynomial and subtracted from
 465 signal to yield the change in fluorescent activity (ΔF/F= 465 signal -
 fitted 560 signal/ fitted 560 signal). Peri-event z-scores were computed
 by comparing the ΔF/F after the behavioral action to the 4-2 sec baseline
 ΔF/F prior to a given epoch. To investigate potential different neural
 calcium responses to receiving the footshock vs. anticipation, punished
 (i.e. shock) trials and unpunished trials were separated. Trials with a
 z-score value &gt; 40 were excluded. From approximately 3,000 trials
 analyzed, this occurred on &lt; 1% of trials. Area
 under the curve (AUC) analyses: To represent individual data we calculated
 the AUCs for each subject. To quantify peri-cue and peri-action changes we
 calculated a change or summation score between 1 sec before (pre-event)
 and 1 sec after (post-event) cue onset or action execution. For the reward
 period, we calculated a change score by comparing 2 sec after reward
 delivery to the 1 sec prior to reward delivery. For punished trials,
 response to footshock was calculated as the change from 1 sec following
 footshock delivery compared to the 1 sec before footshock. Outliers were
 removed using GraphPad Prism’s ROUT method (Q=1%; Motulsky &
 Brown, 2006) which removed only three data points from the
 analysis. Time Lagged Cross-Correlation Analysis:
 Cross-correlation analysis has been used to identify networks from
 simultaneously measured fiber photometry signals (Sych et al., 2019). For
 rats with properly placed fibers in the dmPFC and VTA, correlations
 between photometry signals arising in the VTA and dmPFC were calculated
 for the peri-action, peri-footshock and peri-reward periods using the
 z-score normalized data. The following equation was used to normalize
 covariance scores for each time lag to achieve a correlation coefficient
 between -1 and 1:     Coef =
 Cov/(s<sup>1</sup>*s<sup>2</sup>*n)
 Where <em>Cov</em> is the covariance from the dot
 product of the signal for each timepoint,
 <em>s<sup>1</sup></em> and
 <em>s<sup>2</sup></em> are the standard deviations
 of the dmPFC and VTA streams, respectively, and <em>n</em> is
 the number of samples. An entire cross-correlations function was derived
 for each trial and epoch. Comparison to
 Electrophysiology Results: Fiber photometry data for the third PRT session
 were compared to the average of the 50 msec binned single unit data (see
 Figure 4 of Park & Moghaddam, 2017). This third PRT session
 corresponds to the session electrophysiology data were collected from. To
 overlay data from the two techniques, data were lowpass filtered at 3 Hz
 and photometry data were downsampled to 20 Hz (to match the 50 msec
 binning). Data from both streams were then min-max normalized between 0
 and 1 at the corresponding cue and action+reward epochs.
 To assess the similarity of the two signals, we performed a
 Pearson correlation analysis between the normalized single unit and fiber
 photometry data for cue or action+reward epochs at each risk block, as
 well as between randomly shuffled photometry signals with single-unit
 response as a control. For significant Pearson correlations, we performed
 cross-correlation analysis (see above) to investigate if the photometry
 signal lagged behind electrophysiology given the slower kinetics of GCAMP6
 compared to single-unit approaches (Chen et al., 2013).
 <strong>Statistical Analysis</strong>
 For FR1 training, trial completion was measured as the number of
 food pellets earned. Data were assessed for the first 3-4 training
 sessions. Action and reward latencies were defined as time from cue onset
 to action execution or from food delivery until retrieval, respectively.
 Values were assessed using a mixed-effects model with the training as a
 factor and post-hoc tests were performed using the Bonferroni correction
 where appropriate. For the PRT, trial completion was
 measured as the percentage of completed trials (of the 30 possible) for
 each block. Action latencies were defined as time from cue onset to action
 execution. Data were analyzed using a two-way RM ANOVA or mixed effects
 model. Because there were missing data for non-random reasons (e.g.
 failure to complete trials in response to punishment risk) we took the
 average of risk blocks (blocks 2 and 3) and the no-risk block (block 1) to
 permit repeated measures analysis. We used mixed effects model if data
 were missing for random reasons. Risk and session were used as factors and
 <em>post</em>-<em>hoc</em> tests were performed
 using the Bonferroni correction where appropriate. When only two groups
 were compared a paired t-test or Wilcoxon test was performed after
 checking normality assumption through the Shapiro-Wilk test.
 To assess changes in neural calcium activity, we utilized a
 permutation-based approach as outlined in (Jean-Richard-dit-Bressel et
 al., 2020) using Python (Version 3). An average response for each subject
 for a given time point in the cue, action, or reward delivery period was
 compared to either the first PRT or saline session. For each time point, a
 null distribution was generated by shuffling the data, randomly selecting
 the data into two groups, and calculating the mean difference between
 groups. This was done 1,000 times for each time-point and a p-value was
 obtained by determining the percentage of times a value in the null
 distribution of mean differences was greater than or equal to the observed
 difference in the unshuffled data (one-tailed for comparisons to 0% risk
 and FR1 data, two-tailed for all other comparisons). To control for
 multiple comparisons we utilized a consecutive threshold approach based on
 the 3 Hz lowpass filter window (Jean-Richard-dit-Bressel et al., 2020;
 Pascoli et al., 2018), where a p-value &lt; 0.05 was required for 14
 consecutive samples to be considered significant. To
 assess AUC changes in photometry data, we compared all risk blocks and all
 sessions using ANOVA with factors risk block and session. Because not all
 subjects completed learning and diazepam data, we used an ordinary two-way
 ANOVA. Significant main effects and interactions were assessed with
 <em>post</em>-<em>hoc</em> Bonferroni multiple
 comparison tests. To assess correlated activity changes
 as a function of risk or session, we took the peak and 95% confidence
 interval for the overall cross-correlation function. These values were
 compared by a two-way ANOVA with factors risk and session and utilized a
 <em>post-hoc</em> Bonferroni correction.
 Other than permutation tests, all statistical tests were done
 using GraphPad Prism (Version 8) and an α of 0.05. Results for all
 statistical tests and corresponding figures can be found in Table 1 or
 supplemental figures. <strong>Excluded
 Data</strong> Outliers from latency analysis were
 removed when a data point was &gt; 5 SDs above the mean across all
 blocks. This removed one data point from the analysis. In FR1 studies,
 data from one rat’s third and fourth session were excluded because the
 camera became misaligned with the patch cord and thus the last (fifth) FR1
 session was used for analysis. In PRT studies, data from the dmPFC of one
 session for a rat was excluded due to lack of timestamp collection and one
 block of a session was excluded for two other rats because the control
 560-nm LED failed for the dmPFC. Four rats with VTA
 placement were excluded because fibers were placed outside the VTA or
 GCaMP6s expression was not observed. Several rats did not complete all
 phases of the experiment due to lost fiber implants, leaving the final
 sample sizes as n=9 and n=7 for dmPFC in learning and diazepam treatment
 stages, respectively, and n=4 for VTA in learning and diazepam treatment
 stages.  <strong>Analysis
 Scripts</strong> Data were analyzed using
 custom-written scripts in Python or R. Please see https://github.com/MoghaddamLab/Jacobs2022-eLife for analysis codes.
description: Source data may be opened using Microsoft Excel. One could also
 read them into Python or R which are open source.