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d_hcp
rcruces edited this page Aug 31, 2023
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1 revision
- N = 505
- Data location: /host/oncilla/dHCP_data/ORIGINAL_dHCP_DATA/dhcp-rel-2
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Subject information: $Data location/by9433_9_15_2019_16_45_34.csv
- Subject ID
- Sex (M/F)
- Age at birth (in weeks)
- Age at scan (in weeks)
- MR Sessions (# of sessions. IF >1, the age at scan is the age at the second session)
- Etc. (information whether there is missing data)
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Anatomical data: $Data location/dhcp_anat_pipeline/sub-?/ses-?/anat
- Preprocessed T1w and T2w with their masks
- Surfaces of white, midthickness, pial, and inflated
- Surface features of curvature, sulcal depth, thickness, and myelin content
- 5TT files
- Parcellations (first, schaefer 100-400, vosdewael 100-200)
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Functional data: $Data location/dhcp_fmri_pipeline/sub-?/ses-?/func
- Preprocessed rs-fMRI data with its mask
- Time series mapped onto each parcellation (schaefer 100-400, vosdewael 100-200)
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Diffusion data: $Data location/dhcp_dmri_pipeline/sub-?/ses-?/dwi
- Preprocessed DWI data with its mask
- b-value and b-vector
- Fiber orientation distributions and tissue response functions
- Connectome and edge length files using each parcellation (schaefer 100-400, vosdewael 100-200)
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Load features
feat = mica_load_features(micasoftPath,DBName,SubjName,Mod,AtlasName);
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Inputs
micasoftPath: Full directory name of micasoft
DBName: Name of the database (e.g., dHCP2)
SubjName: Subject ID (e.g., CC00060XX03)
Mod: Modality (e.g., struc, func, diff)
AtlasName: Name of the parcellation (e.g., vos100, vos200, schaefer100, schaefer200, schaefer300, schaefer400) -
Outputs
feat: Output features
If Mod = struc, curvature, sulcal depth, and thickness will be generated (feat.?_curv; feat.?_sulc; feat.?_thick)
If Mod = func, time series mapped onto the atlas will be generated (feat)
If Mod = diff, diffusion connectome and edge length matrices mapped onto the atlas will be generated (feat.connectome; feat.edgelength) - [Code request]: Ask to Bo-yong