Project_C3a_peri-infarct

Project repository C3a peri-infarct study

Step-by-Step guide to replicate the analysis

1. Install AIDAmri and AIDAconnect

2. Download MRI raw data (and proc data for comparison) from G-Node

3. Run AIDAmri pre- and postprocessing steps for T2 and DTI data, see manual

4. Extract the graph theoretical measure global density using AIDAconnect and Matlab

• mergeDTIdata_input.m and plotGlobalParameter(inputDTI, graphCell, 'Density')

6. Run peri-infarct specific Python scripts (4.1_ROI_analysis in AIDAmri)

• Adapt proc_tools.py to your folder structure
• Run python proc_tools.py; python 01_dilate_mask_process.py; python 02_apply_xfm_process.py; python 03_create_seed_rois_process_npflip.py; python 04_examine_roissequentially
• The output of 04_examine_roisis ROIs_count_voxels.txt with the number of voxels inside the peri-infarct mask per brain region (Allen Mouse Brain Atlas label number). This is used to determine which regions to include in the analysis, here: ACA, SSp-ll/m/ul, SSs, MOp, MOs, and GU:

7. Use the iterativeRun_MA_peri-infarct_ROIs.py script to extract diffusion measures (FA, AD, RD, MD) for each atlas region

8. Retrieve the atlas region-specific diffusion measures from the stored .txt files

e.g. C3a_PT_8wks_T2w_DTI/MRI_proc_data/P56/Treatment_C3a/GV_T3_12_1_1_8_20191008_102322/DTI/DSI_studio/GV_T3_12_1_1_8_20191008_102322_T2w_Anno_DTI_mod_peri_scaled_fa0.txt

(first column: atlas number, second column: atlas label, third: value)