This repository contains analysis code for the following paper:
Waskom M.L., Wagner A.D. (2017). Distributed representation of context by intrinsic subnetworks in prefrontal cortex. Proceedings of the National Academy of the Sciences, USA.
The paper can be accessed at the PNAS website.
Data can be accessed at the Stanford Digital Repository.
A high-level overview of the commands that were executed to perform the analyses can be found in commands.md. Some of these commands execute lyman workflows using the parameters defined in the lyman/ subdirectory. Others execute experiment-specific code that are included in this repository. The experiment-specific code falls into a few different categories:
roi_cache.py: Extracts timeseries data from the regions of interest that are analyzed in the paper.
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decoding_analysis.py: Performs the decoding analyses and estimation of context preferences. -
spatial_analysis.py: Performs analyses of spatial distribution of context preferences. -
correlation_analysis.py: Performs analyses relating to spontaneous correlations.
compile_data.py: Reads the individual outputs of the analysis scripts and compiles some summary data into tidy.csvfiles (stored in thedata/subdirectory).
paper_statistics.ipynb: The statistical results reported in the paper were produced by analyses performed in this notebook.
These scripts generate each of the figures in the paper. They are contained in .py files with names corresponding to the relevant figure.
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surfutils.py: Functions for going between volumetric and surface representations of data. -
plotutils.py: Functions that are useful for generating consistent figures.
lyman/environment.yml: Acondaenvironment file that should be able to reproduce all relevant software versions used for the analyses in the paper.
Copyright (c) 2017, Michael Waskom
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