81 lines
1.8 KiB
Python
81 lines
1.8 KiB
Python
"""
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HCP FC Extraction
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======================
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Authors: Leonard Sasse
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License: BSD 3 clause
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"""
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from junifer.api import run
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datagrabber = {
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"kind": "HCPOpenAccess",
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"modality": "fMRI",
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"preprocessed": "ICA+FIX",
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"space": "volumetric",
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}
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custom_confound_strategy = {
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"filter": "butterworth",
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"detrend": True,
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"high_pass": 0.01,
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"low_pass": 0.08,
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"standardize": True,
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"confounds": ["csf", "wm", "gsr"],
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"derivatives": True,
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"squares": True,
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"other": [],
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}
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markers = [
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{
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"name": "Power264_FCPearson",
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"kind": "FunctionalConnectivity",
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"parcellation": "Power264",
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"method": "Pearson",
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"confound_strategy": "Params36",
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},
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{
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"name": "Schaefer400x17_FCPearson",
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"kind": "FunctionalConnectivity",
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"parcellation": "Schaefer400x17",
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"method": "Pearson",
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"confound_strategy": "Params24",
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},
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{
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"name": "Power264_FCSpearman",
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"kind": "FunctionalConnectivity",
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"parcellation": "Power264",
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"method": "Spearman",
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"confound_strategy": "ICAAROMA",
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},
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{
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"name": "Schaefer400x17_FCSpearman",
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"kind": "FunctionalConnectivity",
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"parcellation": "Schaefer400x17",
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"method": "Spearman",
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"confound_strategy": "path/to/predefined/confound_file.tsv",
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},
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{
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"name": "Schaefer400x17_FCSpearman",
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"kind": "FunctionalConnectivity",
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"parcellation": "Schaefer400x17",
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"method": "Spearman",
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"confound_strategy": custom_confound_strategy,
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},
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]
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storage = {
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"kind": "SQLiteFeatureStorage",
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"uri": "/data/project/juniferexample",
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}
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run(
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workdir="/tmp",
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datagrabber=datagrabber,
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elements=[("100408", "REST1", "LR")],
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markers=markers,
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storage=storage,
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)
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