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@@ -1,3 +1,157 @@
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-# dHCP_neonatal_HARDI_atlas
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+![tracking performed on the A0 component overlaid onto the tissue component ODFs](components.png)
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+# dHCP neonatal-multi-component-HARDI-atlas
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-dHCP neonatal multi-component HARDI atlas
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+This hosts the multi-component atlas of diffusion MRI data over the neonatal period created for the publication **A framework for multi-component analysis of diffusion MRI data over the neonatal period.**
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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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+
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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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+---
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+
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+
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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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+On macOS, it can be installed via [homebrew](https://brew.sh/):
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+```bash
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+brew tap g-node/pkg
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+brew install g-node/pkg/gin-cli
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+```
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+
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+On ubuntu:
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+```bash
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+wget https://web.gin.g-node.org/G-Node/gin-cli-releases/raw/master/gin-cli-latest.deb
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+sudo dpkg -i gin-cli-latest.deb
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+sudo apt install -f
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+```
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+
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+For standlone binaries and instructions for other operating systems, see [here](https://github.com/G-Node/gin-cli/wiki/downloads).
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+
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+
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+Then, to download this repository simply execute
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+```bash
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+gin get 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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+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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+
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+
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+This repo is organised hierarchically:
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+
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+
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+```
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+├── LICENSE
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+├── README.md
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+└── v1 <-- atlas version (`v1` is as used for the paper)
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+ ├── joint <-- jointly aligned data (single space across time)
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+ │ ├── mean <-- mode of aggregation
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+ │ ├── median <-- mode of aggregation
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+ │ ├── rois <-- regions of interest used for the paper
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+ ├── rigid <-- weekly templates, rigidly aligned
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+ │ ├── mean
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+ │ └── median
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+ └── metadata <-- subject information and response functions
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+ ├── rf_csf_all
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+ ├── rf_native
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+ ├── rf_tissue_32.9
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+ ├── rf_tissue_44.1
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+ ├── sub_ses_ga_32.9
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+ ├── sub_ses_ga_34.0
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+ ├── sub_ses_ga_35.2
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+ ├── sub_ses_ga_35.7
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+ ├── sub_ses_ga_37.1
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+ ├── sub_ses_ga_38.1
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+ ├── sub_ses_ga_39.1
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+ ├── sub_ses_ga_40.1
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+ ├── sub_ses_ga_40.9
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+ ├── sub_ses_ga_42.0
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+ ├── sub_ses_ga_42.8
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+ └── sub_ses_ga_44.1
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+```
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+
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+The files (see [fig. 3](https://www.sciencedirect.com/science/article/pii/S1053811918320329#fig3)):
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+
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+```
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+
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+├── LICENSE
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+├── README.md
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+└── v1
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+ ├── joint
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+ │ ├── mean
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+ │ │ ├── 32.9
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+ │ │ │ ├── adc.mif.gz <-- DTI-fit, ADC map
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+ │ │ │ ├── count.mif.gz <-- sum across masks, useful to modify template mask
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+ │ │ │ ├── fa.mif.gz <-- DTI-fit, FA map
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+ │ │ │ ├── mask.mif.gz
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+ │ │ │ ├── odf_csf_normed_cyo.mif.gz <-- fluid component ODF, intensity, normalised
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+ │ │ │ ├── odf_csf_normed_native.mif.gz <-- subject-native fluid image
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+ │ │ │ ├── odf_o_normed_cyo.mif.gz <-- Ao: older-appearing tissue component
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+ │ │ │ ├── odf_wm_gm_normed_native.mif.gz <-- subject-native tissue image
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+ │ │ │ └── odf_y_normed_cyo.mif.gz <-- Ay: younger-appearing tissue component, normalised
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+ │ │ ├── 34.0
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+...
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+ ├── metadata
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+ │ ├── rf_native <-- subject-specific response functions
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+ │ │ ├── sub-CC00063AN06_ses-15102_36_csf.txt <-- CSF response function for this subject (PMA: 36w)
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+ │ │ ├── sub-CC00063AN06_ses-15102_36_wm_gmi.txt
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+ │ │ ├── sub-CC00065XX08_ses-18600_41_csf.txt
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+ │ │ ├── sub-CC00065XX08_ses-18600_41_wm_gmi.txt
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+ │ │ ├── sub-CC00067XX10_ses-20200_40_csf.txt
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+
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+...
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+ │ ├── rf_csf_all <-- group-average response functions
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+ │ ├── rf_tissue_32.9
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+ │ ├── rf_tissue_44.1
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+ │ ├── sub_ses_ga_32.9 <-- subject identitifers per age group
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+ │ ├── sub_ses_ga_34.0
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+ │ ├── sub_ses_ga_35.2
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+ │ ├── sub_ses_ga_35.7
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+ │ ├── sub_ses_ga_37.1
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+ │ ├── sub_ses_ga_38.1
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+ │ ├── sub_ses_ga_39.1
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+ │ ├── sub_ses_ga_40.1
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+ │ ├── sub_ses_ga_40.9
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+ │ ├── sub_ses_ga_42.0
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+ │ ├── sub_ses_ga_42.8
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+ │ └── sub_ses_ga_44.1
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+ └── rigid
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+ ├── mean
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+ │ ├── 32.9
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+ │ │ ├── adc.mif.gz
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+ │ │ ├── count.mif.gz
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+ │ │ ├── fa.mif.gz
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+ │ │ ├── mask.mif.gz
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+ │ │ ├── odf_csf_normed_cyo.mif.gz
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+ │ │ ├── odf_csf_normed_native.mif.gz
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+ │ │ ├── odf_o_normed_cyo.mif.gz
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+ │ │ ├── odf_wm_gm_normed_native.mif.gz
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+ │ │ └── odf_y_normed_cyo.mif.gz
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+ │ ├── 34.0
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+...
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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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+
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+You can cite this work:
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+
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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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+
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+```bibtex
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+@article{pietsch2019framework,
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+ title={A framework for multi-component analysis of diffusion MRI data over the neonatal period},
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+ author={Pietsch, Maximilian and Christiaens, Daan and Hutter, Jana and Cordero-Grande, Lucilio and Price, Anthony N and Hughes, Emer and Edwards, A David and Hajnal, Joseph V and Counsell, Serena J and Tournier, J-Donald},
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+ journal={NeuroImage},
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+ volume={186},
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+ pages={321--337},
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+ year={2019},
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+ publisher={Elsevier}
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+}
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+```
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