Selective engagement of prefrontal VIP neurons in reversal learning
Data files
Jun 24, 2025 version files 8.40 GB
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ChRmine.zip
377.57 KB
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fig1C_example_CNO_session.mat
25.20 KB
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fig1C_example_DMSO_session.mat
33.87 KB
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fig1D_example_CNO_session.mat
27.40 KB
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fig1D_exmaple_DMSO_session.mat
31.43 KB
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fig2B_example_DMSO_session.mat
32.10 KB
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Gi.zip
582.35 KB
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Gq.zip
1.18 MB
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matlab_code.zip
805.52 KB
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no_rev_dataname.mat
1.02 KB
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no_rev.zip
3.51 GB
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opto_control.zip
377.51 KB
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README.md
8.87 KB
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rev_d1_dataname.mat
412 B
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rev_dataname.mat
1.54 KB
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rev.zip
4.88 GB
Abstract
To gain insights into neural mechanisms enabling behavioral adaptations to complex and multidimensional environmental dynamics, we examined roles of VIP neurons in mouse medial prefrontal cortex (mPFC) in probabilistic reversal learning. Behaviorally, manipulating VIP neuronal activity left probabilistic classical conditioning unaffected but severely impaired reversal learning. Physiologically, conditioned cue-associated VIP neuronal responses changed abruptly after encountering an unexpected reward. They also conveyed strong reward prediction error signals during behavioral reversal, but not before or after, unlike pyramidal neurons which consistently conveyed error signals throughout all phases. Furthermore, the signal’s persistence across trials correlated with reversal learning duration. These results suggest that mPFC VIP neurons play crucial roles in rapid reversal learning, but not in incremental cue-outcome association learning, by monitoring significant deviations from ongoing environmental contingency and imposing error-correction signals during behavioral adjustments. These findings shed light on the intricate cortical circuit dynamics underpinning behavioral flexibility in complex, multifaceted environments.
https://doi.org/10.5061/dryad.pk0p2ngzs
Description of the data and file structure
These Matlab (.mat) files include behavioral results, calcium imaging results, and behavioral event alignment information mat files and file name sets (in a cell) for the ease of the analysis. I recommend to have all the data in a folder named data_VIP_CC.
Files: fig1C_example_CNO_session.mat, fig1C_example_DMSO_session.mat, fig1D_example_CNO_session.mat, fig1D_exmaple_DMSO_session.mat, fig2B_example_DMSO_session.mat
Description
Representative behavioral sessions used for fig1C, D, and 2B, respectively.
File content
A behavior session data includes following variables:
| Variable name | Description | Dimension |
|---|---|---|
| cylinderTime | Detected cylinder pass time (ms) / trial number / trial epoch | Cylinder event number x 3 |
| lickNum | Anticipatory lick number counting for each trial | Trial number x 1 |
| lickTime | Detected lick time (ms) / trial number / trial epoch | Lick number x 3 |
| nReward | Number of rewarded trials during the session | 1 x 1 |
| nTrial | Number of trials of the session | 1 x 1 |
| odorCue | Odor (0~3) presented at the trial | Trial number x 1 |
| outcomeIdentity | Outcome setting (1 Nothing, 2 Reward, 3 Punish) for each cue (cue 0 in column 1). | Trial number x 4 |
| outcomeProbability | Probability setting (%) for each cue (cue 0 in column 1). | Trial number x 4 |
| stateTime | Time (ms) for baseline, cue, delay, outcome and ITI onset for each trial | Trial number x 5 |
| waterReward | True(1)/False(0) for reward or punishment presentation. | Trial number x 1 |
Files: Gi.zip, Gq.zip, ChRmine.zip, opto_control.zip
Description
Folder with Matlab files with chemogenetic experiment results (Gi, Gq) and optogenetic results (ChRmine animals and control animals), respectively.
Folder organization and file naming
For Gi.zip and Gq.zip, each animal’s data is grouped in a folder with the name of the animal. For optogenetic data, all animal’s data are in a single folder. Behavioral session files are named as AnimalName_Date_SessionStartTime.mat, and contain the variables described above.
Files: no_rev.zip & rev.zip
Description
Folder with the pre-reversal data set and the reversal data set, respectively, for calcium imaging analysis (includes behavioral results files, calcium imaging result files and alignment information files, 3 files for each session).
File naming
Behavioral session files are named as AnimalName_Date_SessionStartTime.mat
Calcium imaging result files are named as AnimalName_Date_SessionInfo_ProcessingInfo_cnmfe.mat. Within the processing information, MC is for motion-corrected.
Behavioral events were aligned with the calcium imaging files, and this alignment information is stored in alignment files, named as AnimalName_date_aligned.mat
File content
Behavioral session files contain the variables described above. The reversal data set also includes the previous day’s behavior data folder (VIP 1day before). This is used for ANCCR fitting process, by concatenating two behavioral sessions to include more trials to the fitting.
Calcium imaging result files are CNMF-E result files (https://elifesciences.org/articles/28728), with a neuron structure variable. neuron.C_raw, the single-cell sorted calcium transient was used mainly for the analysis. Requires having the CNMF-E package (https://github.com/zhoupc/CNMF_E) in the path to load the data.
Alignment information file includes the following variables:
| Variable name | Description | Dimension |
|---|---|---|
| state_frame_num | Frame number for baseline, cue, delay, outcome and ITI onset for each trial | Trial number x 5 |
| trial_idx_rec | Trial index well recorded (without frame drops) | 1 x Recorded trial # |
Files: no_rev_dataname.mat & rev_dataname.mat
Description
File with the mat file names of behavior, calcium imaging and alignment for calcium imaging analysis (pre-reversal data and reversal data, respectively). If the data files are in the path, using this information, a proper loading of the data can be made. Also behavioral performance information and manually curated cell indices are included.
File content
| Variable name | Description | Dimension |
|---|---|---|
| aligned_file | Alignment information mat file name for each session. | 1 x session number |
| beh_file | Behavioral session mat file name for each session. | 1 x session number |
| cell_file | Calcium imaging result mat file name for each session. | 1 x session number |
| neuron_drop | Manually curated indices of neurons to exclude. | 1 x session number |
| thr_pass | Number of trials to cue discrimination criteria, for each session. | 1 x session number |
| rev_trial_anova_win (only for reversal data) | Number of trials to reversal success criteria, for each session. | 1 x session number |
File: rev_d1_dataname.mat
Description
The reversal day-1’s behavior data name set. This is used for ANCCR fitting process, by concatenating two behavioral sessions to include more trials to the fitting.
File content
A beh_file variable with the reversal day-1 behavioral mat file name, for each session (animal).
Access information
Other publicly accessible locations of the data:
Code/Software
- https://github.com/youngju0565/VIP_reversal
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The Matlab codes can be also downloaded from the matlab_code.zip file in this dataset.
- MATLAB version used: R2017a
- MATLAB path setting
- Make sure that “/functions” folder and data in “/data_VIP_CC” folder are on the MATLAB path.
- Required MATLAB functions
- Functions included in the “/functions” folder:
- othercolor (Joshua Atkins (2011)), stateTime_zerofil, recorded_trial_types, behmat2eventlog, fun_ANCCR_3var_Ras, compare_scatter_cuedep_fitline_r_vip
- othercolor: Generates a matrix with the RGB codes of the colors used for reproduce the manuscript’s plots.
- stateTime_zerofil: Fixes zero-value errors for the stateTime variable in a behavioral session data mat file.
- recorded_trial_types: Sorts the trial numbers according to the cue and outcome appearance.
- behmat2eventlog: Converts a behavioral mat file data to an ANCCR-package readable eventlog form. Used for ANCCR fitting.
- fun_ANCCR_3var_Ras: The cost function for ANCCR fitting.
- compare_scatter_cuedep_fitline_r_vip: Plots a scatter plot with a regression line.
- cbrewer (by Charles Robert (2011) from MATLAB exchanges, included in https://www.mathworks.com/matlabcentral/fileexchange/49692-gptoolbox (See https://github.com/scottclowe/cbrewer2 for details.)), ANCCR (https://github.com/namboodirilab/ANCCR) and CNMF-E (https://github.com/zhoupc/CNMF_E) packages are also required. (not included in this repository) (include them on your MATLAB path)
- othercolor (Joshua Atkins (2011)), stateTime_zerofil, recorded_trial_types, behmat2eventlog, fun_ANCCR_3var_Ras, compare_scatter_cuedep_fitline_r_vip
- Functions included in the “/functions” folder: