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These results were generated with giga_connectome (version {{ data.version }}, https://giga-connectome.readthedocs.io/en/latest/). | ||
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The following steps were followed. | ||
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1. Created subject specific grey matter mask in MNI space. | ||
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2. Sampled the atlas to the space of subject specific grey matter mask in MNI space. | ||
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3. Calculated the conjunction of the customised grey matter mask and resampled atlas to find valid parcels. | ||
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4. Used the new input specific grey matter mask and atlas to extract time series and connectomes for each subject. | ||
The time series data were denoised as follow: | ||
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- Time series extractions through label or map maskers are performed on the denoised nifti file. | ||
- Denoising steps were performed on the voxel level: | ||
- spatial smoothing | ||
- detrend, only if high pass filter was not applied through confounds | ||
- Regress out confounds | ||
- standardize | ||
- Extracted time series from atlas | ||
- Computed correlation matrix | ||
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5. Calculate intranetwork correlation of each parcel. The values replace the diagonal of the connectomes. | ||
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6. Create a group average connectome. |
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"""Module responsible for generating method section.""" | ||
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from pathlib import Path | ||
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from jinja2 import Environment, FileSystemLoader, select_autoescape | ||
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from giga_connectome import __version__ | ||
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def generate_method_section(output_dir: Path): | ||
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env = Environment( | ||
loader=FileSystemLoader(Path(__file__).parent), | ||
autoescape=select_autoescape(), | ||
lstrip_blocks=True, | ||
trim_blocks=True, | ||
) | ||
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template = env.get_template("data/methods/template.jinja") | ||
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output_file = output_dir / "logs" / "CITATION.md" | ||
output_file.parent.mkdir(parents=True, exist_ok=True) | ||
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data = {"version": __version__} | ||
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with open(output_file, "w") as f: | ||
print(template.render(data=data), file=f) |
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from giga_connectome import methods | ||
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def test_generate_method_section(tmp_path): | ||
methods.generate_method_section(output_dir=tmp_path) |
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