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@@ -5,13 +5,19 @@ This hosts the multi-component atlas of diffusion MRI data over the neonatal per
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doi: [10.1016/j.neuroimage.2018.10.060](https://www.sciencedirect.com/science/article/pii/S1053811918320329)
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using data collected as part of the [developing Human Connectome Project](http://www.developingconnectome.org/) (dHCP).
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-The weekly templates can be downloaded individually from [here](https://gin.g-node.org/maxpietsch/dHCP_neonatal_HARDI_atlas/src/master/v1/rigid/mean).
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-
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---
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+## Download
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+
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+### Manually
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+
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+The weekly templates can be downloaded individually from [here](https://gin.g-node.org/maxpietsch/dHCP_neonatal_HARDI_atlas/src/master/v1/rigid/mean).
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+
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+### via the GIN command line interface
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-To conveniently get local access to all data, I'd recommend using the [GIN-CLI client](http://https://github.com/G-Node/gin-cli).
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+
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+This describes how to download the data using the [GIN-CLI client](http://https://github.com/G-Node/gin-cli).
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On macOS, it can be installed via [homebrew](https://brew.sh/):
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```bash
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@@ -37,6 +43,33 @@ cd dHCP_neonatal_HARDI_atlas
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Initially, place-holder files are downloaded instead of larger files (.git/annex). If you want to download all files use `gin download --content`, for individual files or directories use `gin get-content <location>`, for downloading all rigidly aligned templates use `gin get-content v1/rigid/mean`, check the download status with `gin ls`. See [GIN CLI Usage Tutorial](https://gin.g-node.org/G-Node/Info/wiki/GIN+CLI+Usage+Tutorial) for details.
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+### via Datalad
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+
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+If needed, install [datalad](https://www.datalad.org/get_datalad.html), then clone this repo with
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+
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+```bash
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+datalad clone https://gin.g-node.org/maxpietsch/dHCP_neonatal_HARDI_atlas
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+cd dHCP_neonatal_HARDI_atlas
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+```
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+
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+Finally, download any files needed. For instance, the group-average templates can be downloaded via `datalad get v1/rigid/mean`.
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+
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+
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+
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+## Data
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+
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+Response functions and `.mif.gz` images can be processed, converted and inspected with MRtrix3 (https://www.mrtrix.org/).
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+
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+Note that mean and median aggregation was perfomed using (Not-a-Number-) masked data using the
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+brain masks as inclusion criteria for the aggregation operation. This prevents
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+bias due to cropped data and allows retrospectively thresholding the template
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+voxels by the number of subjects using the count.mif.gz images.
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+For instance, the following MRtrix3 command replaces voxel values to which fewer than 5 subjects contribute by NaNs:
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+`mrcalc count.mif.gz 5 -ge odf_o_normed_cyo.mif.gz nan -if odf_o_normed_cyo_thresholded.mif.gz`
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+
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+
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+### Organisation
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+
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This repo is organised hierarchically:
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@@ -132,15 +165,9 @@ The files (see [fig. 3](https://www.sciencedirect.com/science/article/pii/S10538
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...
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```
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-Note that mean and median aggregation was perfomed using (Not-a-Number-) masked data using the
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-brain masks as inclusion criteria for the aggregation operation. This prevents
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-bias due to cropped data and allows retrospectively thresholding the template
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-voxels by the number of subjects using the count.mif.gz images.
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-For instance, the following replaces voxels to which fewer than 5 subjects contribute by NaNs:
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-`mrcalc count.mif.gz 5 -ge odf_o_normed_cyo.mif.gz nan -if odf_o_normed_cyo_thresholded.mif.gz`
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-
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-You can cite this work:
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+## Cite
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+Please cite the following publication:
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Pietsch, Maximilian, et al. "A framework for multi-component analysis of diffusion MRI data over the neonatal period." NeuroImage 186 (2019): 321-337.
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