Journal Club at EPFL: Standardizing neuroimaging workflows

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# Standardizing neuroimaging workflows

### Oscar Esteban
#### CHUV | Lausanne University Hospital

###### [www.nipreps.org/assets/EPFL2023](https://www.nipreps.org/assets/EPFL2023)
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# Standardizing neuroimaging workflows

### Oscar Esteban
#### CHUV | Lausanne University Hospital

###### [www.nipreps.org/assets/EPFL2023](https://www.nipreps.org/assets/EPFL2023)
]

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<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0; height: 20px; padding-top: 6px;" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a>
 Standardizing neuroimaging workflows

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# Outline

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* What does *neuroimaging workflow* mean?

* What aspects of neuroimaging workflows can be *standardized*?

* Why standardizing?

* Buy my book (introducing *NiPreps*)

* Going meta: standardizing standard brains (*TemplateFlow*)
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# Define (neuroimaging) workflow

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* Workflow as a recipe or a path on a roadmap:
  * Define the *input* and the *output*
  * Analysis tasks or steps
  * Computation graph (*connect* tasks/steps)
  * Execution of the graph

* How to describe the recipe:
  * Workflow Description Language (WDL): *Nextflow*
  * Explicit programming: *Nipype*, *Scikit-Learn*'s ``Pipeline``
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# WDL Example (Nextflow)

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```WDL
params.input_dir = 'data'
params.output_dir = 'results'

process fsl_preprocess {
  input:
  file input_image from "$input_dir/input.nii.gz"

output:
  file "${params.output_dir}/preprocessed.nii.gz"

script:
  """
  bet $input_image ${params.output_dir}/brain.nii.gz
  flirt -in $input_image -ref MNI152_T1_2mm_brain.nii.gz -omat ${params.output_dir}/transformation.mat
  """

}

process fsl_stats {
  input:
  file input_image from fsl_preprocess.output

output:
  file "${params.output_dir}/stats.nii.gz"

script:
  """
  fslstats $input_image -m > ${params.output_dir}/mean.txt
  fslstats $input_image -p 50 > ${params.output_dir}/median.txt
  """

}

workflow {
  fsl_preprocess | fsl_stats
}
```
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.pull-left[

<img src="../card-nipype.svg" width="100%" />

``` Python
from nipype.interfaces.fsl import BET
brain_extract = BET(
  in_file="/data/coolproject/sub-01/ses-01/anat/sub-01_ses-01_T1w.nii",
  out_file="/out/sub-01/ses-01/anat/sub-01_ses-01_desc-brain_T1w.nii"
)
brain_extract.run()
```
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<img src="https://nipype.readthedocs.io/en/latest/_images/nipype_architecture_overview2.png" width="65%" />

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# WDL Example - Translation into Nipype

```Python
import nipype.interfaces.fsl as fsl
import nipype.pipeline.engine as pe

input_image = 'input.nii.gz'
output_dir = 'results'

preprocess = pe.Node(fsl.BET(), name='preprocess')
preprocess.inputs.in_file = input_image
preprocess.inputs.out_file = f'{output_dir}/brain.nii.gz'

stats = pe.Node(fsl.ImageStats(), name='stats')
stats.inputs.in_file = f'{output_dir}/brain.nii.gz'
stats.inputs.op_string = '-m -p 50'

workflow = pe.Workflow(name='my_workflow')
workflow.connect([(preprocess, stats, [('out_file', 'in_file')])])
workflow.base_dir = 'working_dir'
workflow.run()
```

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# Facets to standardize

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* **Outer interface** (inputs and outputs). Also, consider an internal data structure format for high throughput.
* **Implementation** (WDL vs. programmed): code styling, best practices, BIDS-Apps.
* **Modularize** (see *NiPreps* and *TemplateFlow*).
* Use **containers** to ensure software delivery and reproducibility.
* Implement testing and **continuous integration** to catch errors early and streamline development.
* **Version** the workflow and its components to ensure compatibility and track changes, issue **LTS**.
* Promote **community** and social standardization through collaboration, documentation, telemetry, and open-source practices.
* **Visual reporting**
]]

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# BIDS-Apps: subject-wise parallelization

<a href="https://doi.org/10.1371/journal.pcbi.1005209">
<img src="../journal.pcbi.1005209.g002.png" width="90%" /> 
(Gorgolewski et al., 2017)
</a>

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## The individual report

<video controls="controls" width="70%"
 name="Video Name" src="../fmriprep-report.mov"></video>

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# Why standardizing?

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* Pushing the *truck factor* above 1.0.
* Engage users
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# "Analysis-grade" data

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The *NeuroImaging PREProcessing toolS* (*[NiPreps](https://nipreps.org).org*) augment scanners to produce *analysis-grade* data (= **directly consumable by analyses**)
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***Analysis-grade* data** is an analogy to the concept of "*sushi-grade (or [sashimi-grade](https://en.wikipedia.org/wiki/Sashimi)) fish*" in that both are:

.large[**minimally preprocessed**,]

and

.large[**safe to consume** directly.]
]

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<img align="right" style='margin-right: 50px' src="https://1.bp.blogspot.com/-Osh4H4WXka0/WlMJmVgkZTI/AAAAAAAAEMY/GynUzSomJ-EBiyqv2m-maiOyKSM7SOmNACLcBGAs/s400/yellowfin%2Btuna%2Bsteaks%2Bnutrition.jpg" />
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<img src="../nipreps-chart.png" width="63%" /> 
NiPreps (<a href="https://doi.org/10.31219/osf.io/ujxp6">Esteban et al., 2020</a>)

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<img src="https://github.com/oesteban/fmriprep/raw/f4c7a9804be26c912b24ef4dccba54bdd72fa1fd/docs/_static/fmriprep-21.0.0.svg" width="95%" />

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# *TemplateFlow*

<img src="https://www.templateflow.org/assets/templateflow_fig-birdsview.png" width="60%" /> 
(<a href="https://doi.org/10.1038/s41592-022-01681-2">Ciric et al., 2022</a>)

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# *TemplateFlow* | Client

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# Conclusion

<img src="https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41596-020-0327-3/MediaObjects/41596_2020_327_Fig1_HTML.png?as=png" width="90%" /> 
(<a href="https://doi.org/10.1038/s41596-020-0327-3">Esteban et al., 2020</a>)

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# Thanks!

### Questions?

#### NiPreps Hackathons 2023 - ISMRM and OHBM
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