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[ENH] add transformation free model #26

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merged 10 commits into from
Jun 16, 2022
Merged

[ENH] add transformation free model #26

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945aca7
[DATALAD] Added subdataset
Remi-Gau May 4, 2022
ed1d364
move dataset
Remi-Gau May 4, 2022
785a576
add models for denoising
Remi-Gau May 4, 2022
18ae1b2
fix with validator and update fitler cutoffs
Remi-Gau May 6, 2022
fb89bd5
update groub by
Remi-Gau May 6, 2022
d644d37
add constant and comments as Description
Remi-Gau May 7, 2022
56335b9
remove duplicate dataset
Remi-Gau May 7, 2022
2cedb6b
Update ds2799/model-ds2799-desc-compcop_smdl.json
Remi-Gau May 7, 2022
ff4aadb
Merge branch 'resting_stage' of https://github.com/Remi-Gau/model-zoo…
Remi-Gau May 7, 2022
56de80a
remove space and add line at end of file
Remi-Gau May 7, 2022
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2 changes: 2 additions & 0 deletions .gitignore
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@@ -1 +1,3 @@
.ipynb_checkpoints/

.vscode
9 changes: 9 additions & 0 deletions .gitmodules
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Expand Up @@ -41,3 +41,12 @@
url = https://gin.g-node.org/markiewicz/ds000003-fmriprep
datalad-id = b099d04a-a2d7-4082-9660-05a9019ed8fe
datalad-url = https://gin.g-node.org/markiewicz/ds000003-fmriprep
[submodule "ds002799"]
path = ds002799
url = https://github.com/OpenNeuroDatasets/ds002799.git
datalad-id = d5b52178-970c-11ea-a09a-1e09d750626b
datalad-url = https://github.com/OpenNeuroDatasets/ds002799.git
[submodule "ds2799/ds002799"]
path = ds2799/ds002799
url = https://github.com/OpenNeuroDatasets/ds002799.git
datalad-id = d5b52178-970c-11ea-a09a-1e09d750626b
1 change: 1 addition & 0 deletions ds002799
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Submodule ds002799 added at 2e4b90
1 change: 1 addition & 0 deletions ds2799/ds002799
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Submodule ds002799 added at 2e4b90
39 changes: 39 additions & 0 deletions ds2799/model-ds2799-desc-compcop_smdl.json
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{
"Name": "ds2799_compcor",
"Description": "scrubbing compcor, see https://nilearn.github.io/stable/modules/generated/nilearn.interfaces.fmriprep.load_confounds_strategy.html",
"BIDSModelVersion": "1.0.0",
"Input": {
"task": [
"rest"
],
"space": [
"MNI152NLin2009cAsym"
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]
},
"Nodes": [
{
"Level": "Run",
"Name": "run",
"GroupBy": [
"run",
"session",
"subject"
],
"Model": {
"Description": "removes effect of 1) realigment parameters, their squares, their derivatives and the derivatives squared and 2) the a_comp_cor noise regressors computer by fmriprep to explain 50% of the noise variance in a noise region.",
"X": [
"rot*",
"trans*",
"a_comp_cor_??",
1
],
"Type": "glm",
"Options": {
"Description": "define a bandpass filter",
"HighPassFilterCutoffHz": 0.01,
"LowPassFilterCutoffHz": 0.15
}
}
}
]
}
42 changes: 42 additions & 0 deletions ds2799/model-ds2799-desc-scrubbing_smdl.json
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{
"Name": "ds2799_scrubbing",
"Description": "scrubbing denoising, see https://nilearn.github.io/stable/modules/generated/nilearn.interfaces.fmriprep.load_confounds_strategy.html",
"BIDSModelVersion": "1.0.0",
"Input": {
"task": [
"rest"
],
"space": [
"MNI152NLin2009cAsym"
]
},
"Nodes": [
{
"Level": "Run",
"Name": "run",
"GroupBy": [
"run",
"session",
"subject"
],
"Model": {
"Description": "1) removes effect of realigment parameters, their squares, their derivatives and the derivatives squared, 2) removes effect of the mean signal measured in CSF, white matter as well as their derivatives and squares and 3) do scrubbing by removing time points flagged as outliers by fmriprep",
"X": [
"rot*",
"trans*",
"white_matter*",
"csf*",
"motion_outlier*",
"non_steady*",
1
],
"Type": "glm",
"Options": {
"Description": "define a bandpass filter",
"HighPassFilterCutoffHz": 0.01,
"LowPassFilterCutoffHz": 0.15
}
}
}
]
}
40 changes: 40 additions & 0 deletions ds2799/model-ds2799-desc-simple_smdl.json
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{
"Name": "ds2799_simple",
"Description": "simple denoising, see https://nilearn.github.io/stable/modules/generated/nilearn.interfaces.fmriprep.load_confounds_strategy.html",
"BIDSModelVersion": "1.0.0",
"Input": {
"task": [
"rest"
],
"space": [
"MNI152NLin2009cAsym"
]
},
"Nodes": [
{
"Level": "Run",
"Name": "run",
"GroupBy": [
"run",
"session",
"subject"
],
"Model": {
"Description": "removes effect of 1) realigment parameters, their squares, their derivatives and the derivatives squared and 2) the mean signal measured in CSF, white matter",
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Added some "comments": I believe we should make extensive / intensive use of them in the model zoo.

"X": [
"rot*",
"trans*",
"white_matter",
"csf",
1
],
"Type": "glm",
"Options": {
"Description": "define a bandpass filter",
"HighPassFilterCutoffHz": 0.01,
"LowPassFilterCutoffHz": 0.15
}
}
}
]
}