Differential stability of task variable representations in retrosplenial cortex
Data files
Aug 07, 2024 version files 1.79 GB
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exampleExperiment.zip
86.70 MB
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README.md
7.53 KB
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stabilityData.mat
1.70 GB
Aug 10, 2024 version files 1.79 GB
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exampleExperiment.zip
86.70 MB
-
README.md
7.39 KB
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stabilityData.mat
1.70 GB
Abstract
Cortical neurons store information across different timescales, from seconds to years. Although information stability is variable across regions, it can vary within a region as well. Association areas are known to multiplex behaviorally relevant variables, but the stability of their representations is not well understood. Here, we longitudinally recorded the activity of neuronal populations in the mouse retrosplenial cortex (RSC) during the performance of a context-choice association task. We found that the activity of neurons exhibits different levels of stability across days. Using linear classifiers, we quantified the stability of three task-relevant variables. We find that RSC representations of context and trial outcome display higher stability than motor choice, both at the single cell and population levels. Together, our findings show an important characteristic of association areas, where diverse streams of information are stored with varying levels of stability, which may balance representational reliability and flexibility according to behavioral demands.
README
This README.txt file was generated on 2024-08-10 by Luis M. Franco
GENERAL INFORMATION
- Title of Dataset: Differential Stability of Task Variable Representations in Retrosplenial Cortex
- Author Information A. Principal Investigator Contact Information Name: Michael J. Goard Institution: University of California Santa Barbara (UCSB) Address: 6131 Biological Science Building II, University of California Santa Barbara (UCSB), Santa Barbara, CA 93106-5060 Email: michael.goard@lifesci.ucsb.edu
B. Associate or Co-investigator Contact Information
Name: Luis M. Franco
Institution: University of Oregon (UO)
Address: Institute of Neuroscience, University of Oregon (UO), 1254 University of Oregon, Eugene, OR 97403-1254
Email: luisfran@uoregon.edu
3. Date of data collection: stabilityData.mat ---> 2018-09-11 to 2019-06-14
exampleExperiment [folder] ---> 2018-09-11 to 2018-09-15
4. Geographic location of data collection: Santa Barbara, CA, USA
5. Information about funding sources that supported the collection of the data: This work was supported by the Harvey Karp Discovery Award (L.M.F.) and UC MEXUS-CONACYT Postdoctoral Fellowship (L.M.F.), NIH R01 NS121919 (M.J.G.), NSF 1707287 (M.J.G.), the Larry L. Hillblom Foundation (M.J.G.), and the Whitehall Foundation (M.J.G.).
SHARING/ACCESS INFORMATION
- Licenses/restrictions placed on the data: If you find this dataset useful, please thank the authors by citing their work: Franco and Goard, 2024.
- Links to publications that cite or use the data: https://www.nature.com/articles/s41467-024-51227-7
- Links to other publicly accessible locations of the data: No other publicly accessible locations.
- Links/relationships to ancillary data sets: Not available.
- Was data derived from another source? No.
- Recommended citation for this dataset: Franco and Goard, 2024.
DATA & FILE OVERVIEW
- File List: stabilityData.mat
- Relationship between files, if important: Dataset of neuronal activity and task variable decoding in longitudinal experiments.
- Additional related data collected that was not included in the current data package: No additional data.
- Are there multiple versions of the dataset? A partially overlapping dataset from single day experiments (not longitudinal) was previosuly published by us here: https://datadryad.org/stash/dataset/doi:10.25349/D99W4T
METHODOLOGICAL INFORMATION
- Description of methods used for collection/generation of data: https://www.nature.com/articles/s41467-024-51227-7
- Methods for processing the data: https://www.nature.com/articles/s41467-024-51227-7
- Instrument- or software-specific information needed to interpret the data: Data are saved as a .mat file that can be read in Matlab.
- Standards and calibration information, if appropriate: Not available.
- Environmental/experimental conditions: For a description of experimental methodology: https://www.nature.com/articles/s41467-024-51227-7
- Describe any quality-assurance procedures performed on the data: https://www.nature.com/articles/s41467-024-51227-7
- People involved with sample collection, processing, analysis and/or submission: Luis M. Franco and Michael J. Goard
DATA-SPECIFIC INFORMATION FOR: stabilityData.mat
This is a .mat file containing the following matrices:
cellPreference: [4701×5×4 double] ---> preference of cells for each task variable
1. 4701 cells
2. Day of experiment
3. Task variable: 1. Context (1 or 2), 2. Motor (left or right), 3. Post-decision outcome (correct or incorrect), 4. Post-trial outcome (correct or incorrect)
cellsPerSession: [18×1 double] ---> number of cells in each of the 18 experimental 5-day sessions
cellsWithEnoughPerformance: [4701×5 double] ---> logical indices for cells from experiments where mice had a behavioral performance above chance
1. 4701 cells
2. Day of experiment
cellsWithEnoughTrials: [4701×5 double] ---> logical indices for cells with 2 or more trials of each trial type
1. 4701 cells
2. Day of experiment
cellsWithSignificantDecoding: [4701×5×4 double] ---> logical indices for cells with significan decoding of each task variable
1. 4701 cells
2. Day of experiment
3. Task variable: 1. Context, 2. Motor, 3. Post-decision outcome, 4. Post-trial outcome
goodROIs: [4701×4 double] ---> logical indices for cells with stable ROIs across sessions
1. 4701 cells
2. Day of experiment to which the ROI in day 1 is compared to. 1. Day 2, 2. Day 3, 3. Day 4, 4. Day 5
neuronalActivity: [4701×90×69×4x5 double] ---> calcium activity in individual neurons (DFF)
1. 4701 cells
2. 90 time bins (acquired at 10 Hz)
3. Trials (padded with NaNs for missing data)
4. Trial type: 1. Left-Yellow, 2. Left-Blue, 3. Right-Yellow, 4. Right-Blue
5. Day of experiment
neuronalDecoding: [4701×90×100×5x3 double] ---> task variable decoding in individual neurons (measured as the performance of support vector machine classifiers trained with the activity of individual neurons)
1. 4701 cells
2. 90 time bins (acquired at 10 Hz)
3. 100 iterations Day of experiment
4. Day of experiment
5.Task variable: 1. Context, 2. Motor, 3. Outcome
populationDecoding: [90×100×5x5x4 double] ---> population decoding of task variables (measured as the performance of support vector machine classifiers trained with the activity of populations of neurons)
1. 90 time bins (acquired at 10 Hz)
2. 100 iterations
3. Reference day when classifiers are trained
4. Day when classifiers are tested
5. Task variable: 1. Context, 2. Motor, 3. Post-decision outcome, 4. Post-trial outcome
reliableCells: [4701×5 double] ---> logical indices for cells with reliable responses
1. 4701 cells
2. Day of experiment
significantActivityDifference: [4701×5×4 double] ---> logical indices for cells with significant activity difference in each task variable
1. 4701 cells
2. Day of experiment
3. Task variable: 1. Context, 2. Motor, 3. Post-decision outcome, 4. Post-trial outcome
DATA-SPECIFIC INFORMATION FOR: exampleExperiment [folder]
This folder contains 10 different .mat files corresponding to 5 consecutive imaging sessions (2 experimental blocks per day)
These imaging sessions were performed on the same region of the retrosplenial cortex in mouse 'sLMF009'
Each .mat file contains a structure array called 'mouseData' with fields:
averageProjection: [750×750 double] ---> average projection of the imaging field (750 x 750 pixels; 425 × 425 μm)
activityMap: [750×750 double] ---> kurtosis map of the imaging field (750 x 750 pixels; 425 × 425 μm)
cellROIs: {317×1 cell} ---> cell array with the pixel indices for each of the 317 ROIs indentified in this experiment.
The data in this example experiment can be used to test our custom code for autoamted detection of ROIs across multiple imaging sessions available here:
https://github.com/ucsb-goard-lab/defineCellROIs?tab=readme-ov-file