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Copy file name to clipboardexpand all lines: .zenodo.json
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{
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"creators": [
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{
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"affiliation": "Montreal Neurological Institute and Hospital",
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"name": "Notter, Michael Philipp",
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"affiliation": "The Laboratory for Investigative Neurophysiology (The LINE), Department of Radiology and Department of Clinical Neurosciences, Lausanne, Switzerland; Center for Biomedical Imaging (CIBM), Lausanne, Switzerland",
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"orcid": "0000-0002-5866-047X"
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},
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{
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"name": "Gale, Dan",
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"affiliation": "Centre for Neuroscience Studies, Queen's University, Kingston, Canada",
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"orcid": "0000-0002-9082-1659"
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},
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{
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"name": "Herholz, Peer",
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"affiliation": "Laboratory for Multimodal Neuroimaging, Philipps-University Marburg, Hesse, Germany; International Laboratory for Brain, Music and Sound Research, Université de Montréal & McGill University, Montréal, Canada",
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"orcid": "0000-0002-9840-6257"
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},
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{
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"name": "Markello, Ross",
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"affiliation": "McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada",
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"orcid": "0000-0003-1057-1336"
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},
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{
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"affiliation": "The Laboratory for Investigative Neurophysiology (The LINE), Department of Radiology and Department of Clinical Neurosciences, Lausanne, Switzerland; Center for Biomedical Imaging (CIBM), Lausanne, Switzerland",
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"name": "Notter, Michael P.",
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"orcid": "0000-0002-5866-047X"
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"name": "Notter-Bielser, Marie-Laure",
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"affiliation": "Centre Leenaards de la Mémoire, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland",
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"orcid": "0000-0001-5507-4304"
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},
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{
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"name": "Whitaker, Kirstie",
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"affiliation": "Alan Turing Institute, London, UK; Department of Psychiatry, University of Cambridge, Cambridge, UK",
Copy file name to clipboardexpand all lines: README.md
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# AtlasReader
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This package provides a Python interface for generating coordinate tables and region labels from statistical MRI images.
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This package provides a Python interface for generating coordinate tables and
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region labels from statistical MRI images.
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If you are using `atlasreader` in your publication, please cite the following paper:
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Markello R., Gale D., Herholz P., Whitaker K., & Notter M. P. (2019). AtlasReader: Quick generation of coordinate tables and region labels for statistical MRI images. *The Journal of Open Source Software*, ZZZ, YYY.
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Notter M. P., Gale D., Herholz P., Markello R. D., Notter-Bielser M.-L., & Whitaker K. (2019). AtlasReader: A Python package to generate coordinate tables, region labels, and informative figures from statistical MRI images. *The Journal of Open Source Software*, ZZZ, YYY.
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***TODO: This reference needs to be updated after paper is published. Don't forget to add the DOI and link to the paper.***
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## Installation
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This package requires Python >= 3.6. Provided you have `pip` at your disposal,
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installing `atlasreader` is as simple as this:
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```bash
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pip install atlasreader
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```
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If you want to build `atlasreader` directly from source code, use the
`atlasreader` can either be run through the command line interface or directly within Python. The commands to do so are rather straight forward. Let's say you want to apply `atlasreader` to a statistical image called `file_name = 'stat_img.nii'`, and only want to keep clusters if they have more than 5 voxels:
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`atlasreader` can either be run through the command line interface or directly
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within Python. The commands to do so are rather straight forward. Let's say you
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want to apply `atlasreader` to a statistical image called
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`file_name = 'stat_img.nii'`, and only want to keep clusters if they have more
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than 5 voxels:
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#### Python
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```python
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After executing `atlasreader` on a given image, four kinds of outputs are generated:
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1. An **overview figure** that shows the results within the whole brain at once
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2.**For each cluster**, an **informative figure** showing the sagittal, coronal and transversal plane centered at this center of the cluster
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3. A **csv file** containing relevant information about the **peak** of each cluster. This table contains the location of each cluster's peak, it's signal value at this location, the cluster extent (in mm, not in number of voxels) and the atlas correspondence of the peak.
4. A **csv** file containing relevant information about each **cluster**. This table contains the location of each cluster's peak, the mean value within the cluster, the cluster extend (in mm, not in number of voxels), as well as the membership of each cluster, given a particular atlas.
2.**For each cluster**, an **informative figure** showing the sagittal, coronal
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and transversal plane centered on the main peak of the cluster
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3. A **csv file** containing relevant information about the **peak** of each
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cluster. This table contains the cluster association and location of each
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peak, its signal value at this location, the cluster extent (in mm, not in
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number of voxels), as well as the membership of each peak, given a
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particular atlas.
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4. A **csv** file containing relevant information about each **cluster**. Table
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showing relevant information for the cluster extent of each ROI. This table
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contains the cluster association and location of each peak, the mean value
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within the cluster, the cluster extent (in mm, not in number of voxels), as
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well as the membership of each cluster, given a particular atlas.
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### Additional parameters
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`atlasreader` has many additional parameters that allow you to change the way the clusters are generated and what kind of outputs are generated:
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`atlasreader` has many additional parameters that allow you to change the way
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the clusters are generated and what kind of outputs are generated:
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-**filename**: Niimg_like
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A 3D statistical image.
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-**stat_plot_kws**: dict or None, optional
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Additional keyword arguments to pass to `nilearn.plotting.plot_stat_map`.
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For a more detailed explanation about the toolbox and the effect of the parameters above, see the [example notebook](https://github.com/miykael/atlasreader/blob/master/notebooks/atlasreader.ipynb).
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## Installation
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This package requires Python >= 3.6. Provided you have `pip` at your disposal,
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installing `atlasreader` is as simple as this:
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```bash
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pip install atlasreader
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```
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If you want to build `atlasreader` directly from source code, use the
For a more detailed explanation about the toolbox and the effect of the
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parameters above, see the [example notebook](https://github.com/miykael/atlasreader/blob/master/notebooks/atlasreader.ipynb).
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## How to get involved
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We're thrilled to welcome new contributors!
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If you're interested in getting involved, you should start by reading our [contributing guidelines](CONTRIBUTING.md).
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If you're interested in getting involved, you should start by reading our
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[contributing guidelines](CONTRIBUTING.md).
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Once you're done with that, you can take a look at our list of active [issues](https://github.com/rmarkello/pyls/issues) and let us know if there's something you'd like to begin working on.
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Once you're done with that, you can take a look at our list of active
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[issues](https://github.com/miykael/atlasreader/issues) and let us know if
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there's something you'd like to begin working on.
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If you've found a bug, are experiencing a problem, or have a question, create a new [issue](https://github.com/miykael/atlasreader/issues) with some information about it!
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If you've found a bug, are experiencing a problem, or have a question, create a
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new [issue](https://github.com/miykael/atlasreader/issues) with some information
Copy file name to clipboardexpand all lines: notebooks/atlasreader.ipynb
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"\n",
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"If you use `atlasreader` in your publication, please cite the following paper:\n",
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"\n",
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" Markello R., Gale D., Herholz P., Whitaker K., & Notter M. P. (2019). AtlasReader: Quick generation of coordinate tables and region labels for statistical MRI images. The Journal of Open Source Software, ZZZ, YYY.\n",
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" Notter M. P., Gale D., Herholz P., Markello R. D., Notter-Bielser M.-L., & Whitaker K. (2019). AtlasReader: A Python package to generate coordinate tables, region labels, and informative figures from statistical MRI images. *The Journal of Open Source Software*, ZZZ, YYY.\n",
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"\n",
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"**TODO: This reference needs to be updated after paper is published. Don't forget to add the DOI and link to the paper.**"
abstract = {Matplotlib is a 2D graphics package used for Python
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for application development, interactive scripting, and
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publication-quality image generation across user
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interfaces and operating systems.},
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publisher = {IEEE COMPUTER SOC},
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doi = {10.1109/MCSE.2007.55},
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year = 2007
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}
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@article{fonov2011unbiased,
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title={Unbiased average age-appropriate atlases for pediatric studies},
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author={Fonov, Vladimir and Evans, Alan C and Botteron, Kelly and Almli, C Robert and McKinstry, Robert C and Collins, D Louis and Brain Development Cooperative Group and others},
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journal={Neuroimage},
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volume={54},
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number={1},
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pages={313--327},
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year={2011},
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publisher={Elsevier},
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doi= {10.1016/j.neuroimage.2010.07.033},
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}
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@article{nibabel,
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author = {Matthew Brett and
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Michael Hanke and
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Chris Markiewicz and
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Marc-Alexandre Côté and
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Paul McCarthy and
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Satrajit Ghosh and
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Demian Wassermann and
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Stephan Gerhard and
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Yaroslav Halchenko and
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Eric Larson and
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Gregory R. Lee and
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Erik Kastman and
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Cindee M and
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Félix C. Morency and
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moloney and
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Ariel Rokem and
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Michiel Cottaar and
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Jarrod Millman and
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Ross Markello and
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jaeilepp and
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Chris Cheng and
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Alexandre Gramfort and
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Robert D Vincent and
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Jasper J.F. van den Bosch and
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Krish Subramaniam and
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Pradeep Reddy Raamana and
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Mathias Goncalves and
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Nolan Nichols and
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embaker and
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Basile},
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title = {nibabel: Access a cacophony of neuro-imaging file formats, version 2.3.0},
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month = jun,
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year = 2018,
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doi = {10.5281/zenodo.1287921},
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url = {https://doi.org/10.5281/zenodo.1287921}
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}
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@article{nilearn,
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title={Machine learning for neuroimaging with scikit-learn},
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author={Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Ga{\"e}l},
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journal={Frontiers in neuroinformatics},
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volume={8},
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pages={14},
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year={2014},
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publisher={Frontiers},
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doi={10.3389/fninf.2014.00014},
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}
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@article{numpy,
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title = {Python for scientific computing},
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author = {Oliphant, Travis E},
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journal = {Computing in Science \& Engineering},
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volume = {9},
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number = {3},
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year = {2007},
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doi = {10.1109/MCSE.2007.58},
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publisher = {IEEE}
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}
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@inproceedings{pandas,
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title={Data structures for statistical computing in python},
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author={McKinney, Wes and others},
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booktitle={Proceedings of the 9th Python in Science Conference},
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volume={445},
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pages={51--56},
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year={2010},
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doi={10.3389/fninf.2014.00014},
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organization={Austin, TX}
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}
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@article{scipy,
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author = {Eric Jones and Travis Oliphant and Pearu Peterson and others},
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title = {{SciPy}: Open source scientific tools for {Python}},
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year = {2001--},
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url = "http://www.scipy.org/",
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note = {[Online; accessed 24. Sept. 2018]}
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}
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@article{scikitlearn,
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title={Scikit-learn: Machine learning in Python},
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author={Pedregosa, Fabian and Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and others},
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journal={Journal of machine learning research},
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pages={2825--2830},
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year={2011}
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}
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@article{scikitimage,
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title={scikit-image: image processing in Python},
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author={Van der Walt, Stefan and Sch{\"o}nberger, Johannes L and Nunez-Iglesias, Juan and Boulogne, Fran{\c{c}}ois and Warner, Joshua D and Yager, Neil and Gouillart, Emmanuelle and Yu, Tony},
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