Running only functionals

[oesteban]

A good option to have would be running FMRIPREP without T1w images under two possible circumstances:

• The dataset does not have T1w images
• The user set a flag --dismiss-t1w to force dismissing the anatomical processing.

(comes from this discussion)

[madeleinesnyder]

Which script would be the best place to start with this? I tried changing run.py and just take away the raise exception when "No T1w images found", but then the workflow messed up. Is there a good junction to insert a "skip this part"?

[oesteban]

I think your target here would be writing an equivalent to init_single_subject_wf:

https://github.com/poldracklab/fmriprep/blob/a6fe5500ad2e49fb57dd6a4c98155be4a41570bd/fmriprep/workflows/base.py#L210-L462

where no anatomical workflows are instantiated and run. Without meaning to discourage you, I must note that this will have a steep learning curve :P. We'll try to help as much as we can, but we can't take this as a priority right now. I can see that in one month this would probably change.

[effigies]

In run.py, there's an --anat-only option, that you can imitate:

https://github.com/poldracklab/fmriprep/blob/a6fe5500ad2e49fb57dd6a4c98155be4a41570bd/fmriprep/cli/run.py#L87-L88

You could do --func-only or --bold-only or --dismiss-t1w (as @oesteban suggests). Any of these seems fine to me.

That will produce an opts.func_only (or bold_only or dismiss_t1w) that will need to be passed to init_fmriprep_wf:

https://github.com/poldracklab/fmriprep/blob/a6fe5500ad2e49fb57dd6a4c98155be4a41570bd/fmriprep/cli/run.py#L298-L327

This option will need to be added to the workflows in fmriprep/workflows/base.py, fmriprep/workflows/bold.py, and fmriprep/workflows/confounds.py.

The effects of --anat-only will be easy to mimic in base.py, and I think confounds.py should be pretty easy to pick out the confounds that require anatomical inputs and disable them.

bold.py is going to be the complicated one, as a bunch of subworkflows and connections should only be created if anatomicals are going to be used. You may want to look at how if freesurfer blocks work, as there are workflows that are based on whether or not the user wants to use FreeSurfer.

[madeleinesnyder]

Cool. I'm trying to puzzle out the bold_reg_wf because i think this is the place I want to say: "if dismiss_t1w, then do the bold_reg_wf with the mni brain and not the t1w because it doesn't exist. My confusion comes from where 't1_brain' is inputted to this workflow. Is there a master function coordinating the 't1_brain', 'bold_mask', etc things inside the workflow.connect function?

On Wed, Oct 11, 2017 at 12:50 PM, Chris Markiewicz <[email protected]

wrote:

In run.py, there's an --anat-only option, that you can imitate:

https://github.com/poldracklab/fmriprep/blob/ a6fe5500ad2e49fb57dd6a4c98155be4a41570bd/fmriprep/cli/run.py#L87-L88

You could do --func-only or --bold-only or --dismiss-t1w (as @oesteban https://github.com/oesteban suggests). Any of these seems fine to me.

That will produce an opts.func_only (or bold_only or dismiss_t1w) that will need to be passed to init_fmriprep_wf:

https://github.com/poldracklab/fmriprep/blob/ a6fe5500ad2e49fb57dd6a4c98155be4a41570bd/fmriprep/cli/run.py#L298-L327

This option will need to be added to the workflows in fmriprep/workflows/base.py, fmriprep/workflows/bold.py, and fmriprep/workflows/confounds.py.

The effects of --anat-only will be easy to mimic in base.py, and I think confounds.py should be pretty easy to pick out the confounds that require anatomical inputs and disable them.

bold.py is going to be the complicated one, as a bunch of subworkflows and connections should only be created if anatomicals are going to be used. You may want to look at how if freesurfer blocks work, as there are workflows that are based on whether or not the user wants to use FreeSurfer.

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[madeleinesnyder]

Or rather-- how are the input node fields (such as inputnode.t1_brain) populated?

On Thu, Oct 12, 2017 at 11:42 AM, Madeleine Snyder < [email protected]> wrote:

Cool. I'm trying to puzzle out the bold_reg_wf because i think this is the place I want to say: "if dismiss_t1w, then do the bold_reg_wf with the mni brain and not the t1w because it doesn't exist. My confusion comes from where 't1_brain' is inputted to this workflow. Is there a master function coordinating the 't1_brain', 'bold_mask', etc things inside the workflow.connect function?

On Wed, Oct 11, 2017 at 12:50 PM, Chris Markiewicz < [email protected]> wrote:

In run.py, there's an --anat-only option, that you can imitate:

https://github.com/poldracklab/fmriprep/blob/a6fe5500ad2e49f b57dd6a4c98155be4a41570bd/fmriprep/cli/run.py#L87-L88

You could do --func-only or --bold-only or --dismiss-t1w (as @oesteban https://github.com/oesteban suggests). Any of these seems fine to me.

That will produce an opts.func_only (or bold_only or dismiss_t1w) that will need to be passed to init_fmriprep_wf:

https://github.com/poldracklab/fmriprep/blob/a6fe5500ad2e49f b57dd6a4c98155be4a41570bd/fmriprep/cli/run.py#L298-L327

This option will need to be added to the workflows in fmriprep/workflows/base.py, fmriprep/workflows/bold.py, and fmriprep/workflows/confounds.py.

The effects of --anat-only will be easy to mimic in base.py, and I think confounds.py should be pretty easy to pick out the confounds that require anatomical inputs and disable them.

bold.py is going to be the complicated one, as a bunch of subworkflows and connections should only be created if anatomicals are going to be used. You may want to look at how if freesurfer blocks work, as there are workflows that are based on whether or not the user wants to use FreeSurfer.

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[oesteban]

They are set in by connecting this node:

https://github.com/poldracklab/fmriprep/blob/a6fe5500ad2e49fb57dd6a4c98155be4a41570bd/fmriprep/workflows/base.py#L363-L364

to those fields in the top-level workflow, eg.:

https://github.com/poldracklab/fmriprep/blob/a6fe5500ad2e49fb57dd6a4c98155be4a41570bd/fmriprep/workflows/base.py#L407-L408

sets the anatomical inputs.

[effigies]

To narrowly answer your question, t1_brain is set in bold_reg_wf in this line:

https://github.com/poldracklab/fmriprep/blob/0e2539e0cd472c6e225d73e0c62f91b798131544/fmriprep/workflows/bold.py#L353

I will say that registering BOLD to MNI is probably going to be the most difficult thing to attempt in our current setup. bold_reg_wf attempts to find affine transforms using FLIRT or mri_coreg/bbregister, which is then concatenated with a T1-MNI transform found by ANTs (in bold_mni_trans_wf).

If you want to estimate a nonlinear transform directly from BOLD to a template T2w or EPI image, that would probably be best handled by a new sub-workflow that bypasses both bold_reg_wf and bold_mni_trans_wf entirely, rather than repurposing these.

[madeleinesnyder]

Ok, my intuition is to insert:

if dismiss_t1w: [new workflow code here] return workflow

just before line 346 in bold.py

is this on the right track?

On Thu, Oct 12, 2017 at 11:51 AM, Oscar Esteban [email protected] wrote:

They are set in by connecting this node:

https://github.com/poldracklab/fmriprep/blob/ a6fe5500ad2e49fb57dd6a4c98155be4a41570bd/fmriprep/workflows/ base.py#L363-L364

to those fields in the top-level workflow, eg.:

https://github.com/poldracklab/fmriprep/blob/ a6fe5500ad2e49fb57dd6a4c98155be4a41570bd/fmriprep/workflows/ base.py#L407-L408

sets the anatomical inputs.

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[oesteban]

Yes, that's the right direction. The problem, as Chris mentioned, resides on the fact that currently, BOLD workflows depend on anatomical inputs.

Since the anatomical workflows do not depend on BOLD workflows, it was relatively easy to use the approach you mentioned.

So the hard problem here is where you wrote: "[new workflow code here]" because you'll indeed need a new workflow (without dependencies on anatomical) here.

[madeleinesnyder]

Ok, Really the problem I want to solve is that if there are no T1w images available for a certain scan, the whole thing won't run at all. I wanted to create a functional only workflow to solve this problem.

On Thu, Oct 12, 2017 at 12:11 PM, Oscar Esteban [email protected] wrote:

Yes, that's the right direction. The problem, as Chris mentioned, resides on the fact that currently, BOLD workflows depend on anatomical inputs.

Since the anatomical workflows do not depend on BOLD workflows, it was relatively easy to use the approach you mentioned.

So the hard problem here is where you wrote: "[new workflow code here]" because you'll indeed need a new workflow (without dependencies on anatomical) here.

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[effigies]

I'll try to pare this down to the minimum things you need to do just to get it running, and we can work from there to build out the remaining needed functionality, such as registration to MNI. Assuming you have a variable dismiss_t1w.

Split this chunk:

https://github.com/poldracklab/fmriprep/blob/0e2539e0cd472c6e225d73e0c62f91b798131544/fmriprep/workflows/bold.py#L346-L390

Into two bits, to be schematic as possible:

workflow.connect([
    # Only connect workflows that do not require T1w processing
    ])
if not dismiss_t1w:
    workflow.connect([
        # Connect up the rest
        ])

These lines take the following form:

(from_node, to_node, [('from_name', 'to_name')]),

Nodes that do not require T1w preprocessing are:

• inputnode, outputnode
• bold_reference_wf
• bold_stc_wf
• bold_hmc_wf
• func_reports_wf
• summary

For the moment, I would entirely disable fieldmaps by commenting out:

https://github.com/poldracklab/fmriprep/blob/0e2539e0cd472c6e225d73e0c62f91b798131544/fmriprep/workflows/bold.py#L413-L480

And replace with:

if True:

Comment these lines out, as well:

https://github.com/poldracklab/fmriprep/blob/0e2539e0cd472c6e225d73e0c62f91b798131544/fmriprep/workflows/bold.py#L487-L489 https://github.com/poldracklab/fmriprep/blob/0e2539e0cd472c6e225d73e0c62f91b798131544/fmriprep/workflows/bold.py#L492-L579

Then pacify summary:

https://github.com/poldracklab/fmriprep/blob/0e2539e0cd472c6e225d73e0c62f91b798131544/fmriprep/workflows/bold.py#L280-L286

by adding the following lines:

if dismiss_t1w:
    summary.inputs.fallback = False
    summary.inputs.confounds_list = []

Finally, disable some reports by commenting out these lines:

https://github.com/poldracklab/fmriprep/blob/0e2539e0cd472c6e225d73e0c62f91b798131544/fmriprep/workflows/bold.py#L1605-L1612

I think that should technically run. You will get some minimal reports, but no derivatives. If you want BOLD series in their original BOLD space, that will need a new workflow, as will an MNI registration workflow.

Once you have this, start a PR, and we can comment directly on that, which should make things easier.

[madeleinesnyder]

Wow this a lot to go on thank you so much! I'll start cracking

On Thu, Oct 12, 2017 at 12:35 PM, Chris Markiewicz <[email protected]

wrote:

I'll try to pare this down to the minimum things you need to do just to get it running, and we can work from there to build out the remaining needed functionality, such as registration to MNI. Assuming you have a variable dismiss_t1w.

Split this chunk:

https://github.com/poldracklab/fmriprep/blob/ 0e2539e0cd472c6e225d73e0c62f91b798131544/fmriprep/workflows/ bold.py#L346-L390

Into two bits, to be schematic as possible:

workflow.connect([ # Only connect workflows that do not require T1w processing ])if not dismiss_t1w: workflow.connect([ # Connect up the rest ])

These lines take the following form:

(from_node, to_node, [('from_name', 'to_name')]),

Nodes that do not require T1w preprocessing are:

• inputnode, outputnode
• bold_reference_wf
• bold_stc_wf
• bold_hmc_wf
• func_reports_wf
• summary

For the moment, I would entirely disable fieldmaps by commenting out:

https://github.com/poldracklab/fmriprep/blob/ 0e2539e0cd472c6e225d73e0c62f91b798131544/fmriprep/workflows/ bold.py#L413-L480

And replace with:

if True:

Comment these lines out, as well:

https://github.com/poldracklab/fmriprep/blob/ 0e2539e0cd472c6e225d73e0c62f91b798131544/fmriprep/workflows/ bold.py#L487-L489 https://github.com/poldracklab/fmriprep/blob/ 0e2539e0cd472c6e225d73e0c62f91b798131544/fmriprep/workflows/ bold.py#L492-L579

Then pacify summary:

https://github.com/poldracklab/fmriprep/blob/ 0e2539e0cd472c6e225d73e0c62f91b798131544/fmriprep/workflows/ bold.py#L280-L286

by adding the following lines:

if dismiss_t1w: summary.inputs.fallback = False summary.inputs.confounds_list = []

Finally, disable some reports by commenting out these lines:

https://github.com/poldracklab/fmriprep/blob/ 0e2539e0cd472c6e225d73e0c62f91b798131544/fmriprep/workflows/ bold.py#L1605-L1612

I think that should technically run. You will get some minimal reports, but no derivatives. If you want BOLD series in their original BOLD space, that will need a new workflow, as will an MNI registration workflow.

Once you have this, start a PR, and we can comment directly on that, which should make things easier.

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[madeleinesnyder]

I thought of another workaround that might be easier:

Would it be ok to use a dummy T1 dataset as a placeholder to get the pipeline to run, and have the --output-space be template so that the dummy T1 isn't used for coregistration? Or would the dummy be automatically used in other parts of the pipeline even if the output space is "template"?

On Thu, Oct 12, 2017 at 12:41 PM, Madeleine Snyder < [email protected]> wrote:

Wow this a lot to go on thank you so much! I'll start cracking

On Thu, Oct 12, 2017 at 12:35 PM, Chris Markiewicz < [email protected]> wrote:

I'll try to pare this down to the minimum things you need to do just to get it running, and we can work from there to build out the remaining needed functionality, such as registration to MNI. Assuming you have a variable dismiss_t1w.

Split this chunk:

https://github.com/poldracklab/fmriprep/blob/0e2539e0cd472c6 e225d73e0c62f91b798131544/fmriprep/workflows/bold.py#L346-L390

Into two bits, to be schematic as possible:

workflow.connect([ # Only connect workflows that do not require T1w processing ])if not dismiss_t1w: workflow.connect([ # Connect up the rest ])

These lines take the following form:

(from_node, to_node, [('from_name', 'to_name')]),

Nodes that do not require T1w preprocessing are:

• inputnode, outputnode
• bold_reference_wf
• bold_stc_wf
• bold_hmc_wf
• func_reports_wf
• summary

For the moment, I would entirely disable fieldmaps by commenting out:

https://github.com/poldracklab/fmriprep/blob/0e2539e0cd472c6 e225d73e0c62f91b798131544/fmriprep/workflows/bold.py#L413-L480

And replace with:

if True:

Comment these lines out, as well:

https://github.com/poldracklab/fmriprep/blob/0e2539e0cd472c6 e225d73e0c62f91b798131544/fmriprep/workflows/bold.py#L487-L489 https://github.com/poldracklab/fmriprep/blob/0e2539e0cd472c6 e225d73e0c62f91b798131544/fmriprep/workflows/bold.py#L492-L579

Then pacify summary:

https://github.com/poldracklab/fmriprep/blob/0e2539e0cd472c6 e225d73e0c62f91b798131544/fmriprep/workflows/bold.py#L280-L286

by adding the following lines:

if dismiss_t1w: summary.inputs.fallback = False summary.inputs.confounds_list = []

Finally, disable some reports by commenting out these lines:

https://github.com/poldracklab/fmriprep/blob/0e2539e0cd472c6 e225d73e0c62f91b798131544/fmriprep/workflows/bold.py#L1605-L1612

I think that should technically run. You will get some minimal reports, but no derivatives. If you want BOLD series in their original BOLD space, that will need a new workflow, as will an MNI registration workflow.

Once you have this, start a PR, and we can comment directly on that, which should make things easier.

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[oesteban]

Unfortunately, the BOLD is mapped to MNI through the T1w space :(. I'm afraid that would not work.

[madeleinesnyder]

Rats ok I'll work on chagning the pipeline then

On Thu, Oct 12, 2017 at 2:52 PM, Oscar Esteban [email protected] wrote:

Unfortunately, the BOLD is mapped to MNI through the T1w space :(. I'm afraid that would not work.

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[chrisgorgo]

What about directly coregistering the BOLD images to an EPI MNI template?

[madeleinesnyder]

Would that work? If I put in the EPI MNI template as the dummy?

On Thu, Oct 12, 2017 at 2:55 PM, Chris Filo Gorgolewski < [email protected]> wrote:

What about directly coregistering the BOLD images to an EPI MNI template?

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[oesteban]

@madeleinesnyder, @chrisfilo both statements from you are the shortest path to the goal.

[oesteban]

Sorry, @madeleinesnyder that means that the BOLD is co-registered to some image in MNI space. Probably the T2w image is a good target.

[oesteban]

An example of how that is done on mriqc:

https://github.com/poldracklab/mriqc/blob/master/mriqc/workflows/functional.py#L680-L749

However, in this case you will want to use the 'precise' settings set because you don't want a quick and dirty registration.

[madeleinesnyder]

Ok, so on a broad level:

If no t1 data for a subject: don't just abort; enter workflow that does epi_mni_align

Which are the parts of the current workflow that are analogs to where I should insert the epi_mni_align node?

On Thu, Oct 12, 2017 at 3:00 PM, Oscar Esteban [email protected] wrote:

An example of how that is done on mriqc:

https://github.com/poldracklab/mriqc/blob/master/ mriqc/workflows/functional.py#L680-L749

However, in this case you will want to use the 'precise' settings set because you don't want a quick and dirty registration.

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[oesteban]

There are some other workflows you'll probably need. Chris pointed them out before:

• bold_reference_wf - generates a reference image in BOLD space, this is probably the reference you will want to use in the registration to MNI space.
• bold_stc_wf - slice timing correction
• bold_hmc_wf - head motion estimation
• func_reports_wf - arranges the reports so they are collated in the final big html file.
• summary - creates that summary part for the big html file.

You'll need to use those, plus the epi_mni_align workflow. Finally, we'll point you to the last bit, where the transforms from bold_hmc_wf and epi_mni_align are combined to generate the final _preproc file.

[madeleinesnyder]

Ok. Should I create a new python script or add all of this to bold.py?

On Thu, Oct 12, 2017 at 3:27 PM, Oscar Esteban [email protected] wrote:

There are some other workflows you'll probably need. Chris pointed them out before:

• bold_reference_wf - generates a reference image in BOLD space, this is probably the reference you will want to use in the registration to MNI space.
• bold_stc_wf - slice timing correction
• bold_hmc_wf - head motion estimation
• func_reports_wf - arranges the reports so they are collated in the final big html file.
• summary - creates that summary part for the big html file.

You'll need to use those, plus the epi_mni_align workflow. Finally, we'll point you to the last bit, where the transforms from bold_hmc_wf and epi_mni_align are combined to generate the final _preproc file.

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[oesteban]

Let's start in that bold.py and then we can help you if it grows too large

[madeleinesnyder]

Ok this amount of change merits inserting the dismiss_t1w flag. Should I include the following workflows in the dismiss_t1w workflow?:

bold_stc_wf nonlinear_sdc_wf bold_mni_trans_wf bold_surf_wf

Thank you so much, for all of your help, I really appreciate it!

On Thu, Oct 12, 2017 at 3:35 PM, Oscar Esteban [email protected] wrote:

Let's start in that bold.py and then we can help you if it grows too large

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[oesteban]

I think

• bold_stc_wf
• nonlinear_sdc_wf
• bold_mni_trans_wf

The surf one does not make sense since the surfaces are reconstructed on the T1.

[madeleinesnyder]

OK cool, and should bold_confounds be included?

On Thu, Oct 12, 2017 at 4:01 PM, Oscar Esteban [email protected] wrote:

I think

• bold_stc_wf
• nonlinear_sdc_wf
• bold_mni_trans_wf

The surf one does not make sense since the surfaces are reconstructed on the T1.

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[oesteban]

Yes, but taking care of not including aCompCor (anatomical Comp Cor)

On Thu, Oct 12, 2017 at 4:09 PM, Maddie Snyder [email protected] wrote:

OK cool, and should bold_confounds be included?

On Thu, Oct 12, 2017 at 4:01 PM, Oscar Esteban [email protected] wrote:

I think

• bold_stc_wf
• nonlinear_sdc_wf
• bold_mni_trans_wf

The surf one does not make sense since the surfaces are reconstructed on the T1.

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[madeleinesnyder]

Ok I got it working. I'll do a few test data runs and then maybe post it.

On Thu, Oct 12, 2017 at 4:13 PM, Oscar Esteban [email protected] wrote:

Yes, but taking care of not including aCompCor (anatomical Comp Cor)

On Thu, Oct 12, 2017 at 4:09 PM, Maddie Snyder [email protected] wrote:

OK cool, and should bold_confounds be included?

On Thu, Oct 12, 2017 at 4:01 PM, Oscar Esteban <[email protected]

wrote:

I think

• bold_stc_wf
• nonlinear_sdc_wf
• bold_mni_trans_wf

The surf one does not make sense since the surfaces are reconstructed on the T1.

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[chrisgorgo]

I asked around for manage to get this ENIGMA EPI template which seems to be a good candidate for coregistration: https://neurovault.org/images/55594/

@oesteban should we add it to niworkflows?