https://github.com/mvdoc/famface
Analysis code for "The neural representation of personally familiar and unfamiliar faces in the distributed system for face perception" by Visconti di Oleggio Castello, Halchenko, et al., 2017, Scientific Reports
https://github.com/mvdoc/famface
experiment fmri mvpa neuroscience science
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Analysis code for "The neural representation of personally familiar and unfamiliar faces in the distributed system for face perception" by Visconti di Oleggio Castello, Halchenko, et al., 2017, Scientific Reports
- Host: GitHub
- URL: https://github.com/mvdoc/famface
- Owner: mvdoc
- License: other
- Created: 2017-01-06T15:34:51.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2018-01-31T22:52:31.000Z (over 7 years ago)
- Last Synced: 2025-03-27T02:39:07.962Z (3 months ago)
- Topics: experiment, fmri, mvpa, neuroscience, science
- Language: Jupyter Notebook
- Homepage:
- Size: 15.6 MB
- Stars: 6
- Watchers: 5
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# Analysis scripts for "The neural representation of personally familiar and unfamiliar faces in the distributed system for face perception"
This repository contains preprocessing and analysis scripts for Visconti di Oleggio Castello, M., Halchenko, Y. O., Guntupalli, J. S., Gors, J. D., & Gobbini, M. I. (2017). The neural representation of personally familiar and unfamiliar faces in the distributed system for face perception. *Scientific Reports*, 7(1), 12237. https://doi.org/10.1038/s41598-017-12559-1
The dataset is available through [DataLad](http://datasets.datalad.org/?dir=/labs/gobbini/famface). Once datalad is installed in your system, you can get the data with
```bash
# install the dataset without downloading any data
datalad install -r ///labs/gobbini/famface
# download the data
datalad get famface
```
**NOTA BENE:** The latest release of this dataset is in BIDS format, however the
scripts are still configured to run with the old OpenfMRI format. You
can checkout the old file structure as follows
```
cd famface/data
git checkout openfmri-v1.0.0
```
## Setting up the environment
We recommend using either a [NeuroDebian](http://neuro.debian.net/)
virtual machine, or a container (Docker or Singularity) with NeuroDebian
installed to replicate these analyses. In particular, the Python scripts
might rely on specific versions of python packages. For example, the
preprocessing script `fmri_ants_openfmri.py` won't work with newer
versions of Nipype (> 0.11.0) because of recent refactoring. We kept track of the
versions of the most important Python packages in the `requirements.txt`
file. If you're using conda, you can get started as follows:
```
conda create --name famface python=2.7
pip install -r requirements.txt
```
You should also have FSL and ANTs installed.
### Using the singularity image
We provide a [Singularity](http://singularity.lbl.gov/) definition file
that can be used to build a container with all the necessary packages to
run the analyses (except MATLAB--testing in progress with Octave).
Once Singularity is installed on your system, the image can be built as
follows (assuming singularity 2.4.x)
```bash
sudo singularity build famface.simg Singularity
```
alternatively, the image can be pulled from Singularity Hub with
```bash
singularity pull --name famface.simg shub://mvdoc/famface
```
Inside the container we provide the mountpoints `/data` and `/scripts`,
so for example one could run the preprocessing for one participant as
follows
```bash
singularity run -e -c \
-B $PWD:/scripts \
-B /path/to/famface:/data \
famface.simg \
python /scripts/fmri_ants_openfmri.py \
-d /data/data -s sub001 \
--hpfilter 60.0 \
--derivatives \
-o /data/derivatives/output \
-w /data/workdir -p MultiProc
```
Here we are assuming that `/path/to/famface` is the path to the
`famface` directory as pulled from datalad, which contains a `data`
directory. Note that all the paths passed to the script need to be relative to
the filesystem inside the container.
Running the following will instead enter the container for interactive
analyses:
```bash
singularity run -e -c \
-B $PWD:/scripts \
-B /path/to/famface:/data \
famface.simg
```
Please note that some paths in the scripts might be hardcoded, so they
need to be changed prior to running the scripts.
## Preprocessing and GLM modeling
- [`fmri_ants_openfmri.py`](fmri_ants_openfmri.py): nipype pipeline to
perform preprocessing (spatial normalization to MNI 2 mm using ANTs,
first and second level univariate analysis using FSL). Based on the
example with the same name from stock Nipype
- [`pymvpa_hrf.py`](pymvpa_hrf.py): script to run a GLM using PyMVPA and
Nipy, to extract betas used for multivariate analysis
- [`make_unionmask.py`](make_unionmask.py): script to make a union mask
- [`stack_betas.py`](stack_betas.py): script to stack betas for
multivariate analysis
## Analysis
### GLM
- [`group_multregress_openfmri.py`](group_multregress_openfmri.py):
nipype pipeline to perform third (group) level univariate analysis
with FSL. Based on the pipeline provided by Satra Ghosh and Anne Park
(our thanks to them!)
### MVPC
- [`run_sl.py`](run_sl.py): main script to run searchlight analyses
- [`pymvpa2cosmo.py`](pymvpa2cosmo.py): script to convert PyMVPA
datasets into CoSMoMVPA datasets for statistical testing using TFCE
- [`run_tfce_mvdoc_fx.m`](run_tfce_mvdoc_fx.m): script to run TFCE on
accuracy maps using CoSMoMVPA
- [`ev_roi_clf.py`](ev_roi_clf.py): script to run additional decoding analyses in probabilistic masks of early visual areas
- [`permutation_testing_ev.Rmd`](permutation_testing_ev.Rmd): RMarkdown notebook that plots the results of the analysis in probabilistic masks of early visual areas (see also pre-computed HTML output [`permutation_testing_ev.nb.html`](permutation_testing_ev.nb.html))
- [`hmax_decoding_familiarvsunfamiliar.ipynb`](hmax_decoding_familiarvsunfamiliar.ipynb): Jupyter notebook with decoding analysis on features extracted from the HMAX model.
- [`hmax_familiarvsunfamiliar-id.Rmd`](hmax_familiarvsunfamiliar-id.Rmd): RMarkdown notebook used to analyze the decoding of images using HMAX features (see also pre-computed HTML output [`hmax_familiarvsunfamiliar-id.nb.html`](hmax_familiarvsunfamiliar-id.nb.html))
### Similarity of Representational Geometries
- [`notebooks/define_rois_mds.ipynb`](notebooks/define_rois_mds.ipynb):
notebook used to obtain non-overlapping spherical ROIs in both the
task data and the movie hyperaligned data
- [`compute_dsmroi_firstlev.py`](compute_dsmroi_firstlev.py): script to
compute first-level cross-validated representational dissimilarity matrices
- [`notebooks/compute_dsmroi_hpal.ipynb`](notebooks/compute_dsmroi_hpal.ipynb):
notebook used to compute the similarity of representational geometries
using hyperaligned movie data
- [`notebooks/plot_mds.ipynb`](notebooks/plot_mds.ipynb):
notebook used to generate MDS and circular graph plots for task and
hyperaligned data
- [`notebooks/get_between-within_correlations.ipynb`](notebooks/get_between-within_correlations.ipynb):
notebook used to obtain dataframes with correlations between/within
systems for each subject (task data) or pair of subjects (hyperaligned
data)
- [`mds_betweenwithin_corr.Rmd`](mds_betweenwithin_corr.Rmd): RMarkdown
notebook with additional analyses on correlations of RDMS
between/within systems (see rendering in
[`mds_betweenwithin_corr.nb.html`](mds_betweenwithin_corr.nb.html))
## Auxiliary and miscellaneous files
- [`mds_rois.py`](mds_rois.py): contains functions to run MDS analyses
- [`expdir.py`](expdir.py): to fetch directories used in analyses
- [`notebooks/scatterplots.ipynb`](notebooks/scatterplots.ipynb): notebook used to plot scatterplots shown in the supplementary material
## Response to Reviewers
- [response_reviewers_ev.ipynb](notebooks/response_reviewers_ev.ipynb): Is the dorsal stream also close to EV areas?
- [response_reviewers_modelrsa.ipynb](notebooks/response_reviewers_modelrsa.ipynb): Can we say more about why the representations differ between areas?
- [response_reviewers_similarity_taskmovie.ipynb](notebooks/response_reviewers_similarity_taskmovie.ipynb): How similar are the second-order representational geometries between the task data and the movie data?