Merge remote-tracking branch 'refs/remotes/origin/master' into HEAD

code/GetQuantitativeValues_onlyCST.py

@@ -0,0 +1,197 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Nov 11 09:35:42 2024
+
+@author: arefks
+"""
+
+import os
+import glob
+import pandas as pd
+import nibabel as nib
+import numpy as np
+from tqdm import tqdm
+from concurrent.futures import ProcessPoolExecutor
+from scipy.ndimage import binary_dilation
+
+def create_output_dir(output_dir):
+    """Create the output directory if it does not exist."""
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+def save_mask_as_nifti(mask_data, output_file):
+    """Save mask data as NIfTI file."""
+    mask_data_int = mask_data.astype(np.uint8)
+    mask_img = nib.Nifti1Image(mask_data_int, affine=None)
+    nib.save(mask_img, output_file)
+
+def process_single_file(file_info):
+    """Process a single file path."""
+    file_path, session_mapping, output_path, save_nifti_mask = file_info
+    
+    csvdata = []  # Local CSV data collector
+    
+    # Extract information from the file path
+    subject_id = file_path.split(os.sep)[-5]
+    time_point = file_path.split(os.sep)[-4]
+
+    # Map the extracted time_point directly using session_mapping
+    merged_time_point = session_mapping.get(time_point, None)
+
+    if merged_time_point is None:
+        print(f"Warning: No mapping found for {time_point}. Skipping file: {file_path}")
+        return []
+
+    # Extract the q_type from the filename
+    q_type = os.path.basename(file_path).split("_flipped")[0]
+
+    # Attempt to find the stroke mask path
+    try:
+        search_stroke = os.path.join(os.path.dirname(file_path), "*StrokeMask_scaled.nii")
+        stroke_path = glob.glob(search_stroke)[0]
+    except IndexError:
+        stroke_path = None
+
+    # Create the temp path to search for dwi_masks
+    temp_path = os.path.join(os.path.dirname(file_path), "RegisteredTractMasks_adjusted")
+    dwi_masks = glob.glob(os.path.join(temp_path, "CST*dwi_flipped.nii.gz")) + glob.glob(os.path.join(temp_path, "White_matter*dwi_flipped.nii.gz"))
+    white_matter_index = next((i for i, path in enumerate(dwi_masks) if "White_matter" in path), None)
+    
+    if white_matter_index is None:
+        print(f"Warning: White matter mask not found in {temp_path}. Skipping file: {file_path}")
+        return []
+    
+    white_matter_path = dwi_masks.pop(white_matter_index)
+    
+    # Load white matter mask
+    white_matter_img = nib.load(white_matter_path)
+    white_matter_data = white_matter_img.get_fdata()
+
+    # Load DWI data using nibabel
+    dwi_img = nib.load(file_path)
+    dwi_data = dwi_img.get_fdata()
+
+    # Loop through masks and calculate average values
+    pix_dialation = [0, 1, 2, 3, 4]
+    for mask_path in dwi_masks:
+        mask_name = os.path.basename(mask_path).replace("registered", "").replace("flipped", "").replace(".nii.gz", "").replace("__dwi_", "").replace("_dwi_", "")
+        mask_img = nib.load(mask_path)
+        mask_data_pre_dilation = mask_img.get_fdata()
+
+        # Determine if it's an AMBA mask
+        AMBA_flag = "AMBA" in mask_name
+
+        # Calculate the average value within the mask region for each dilation
+        for pp in pix_dialation:
+            if pp != 0:
+                mask_data0 = binary_dilation(mask_data_pre_dilation, iterations=pp)
+            else:
+                mask_data0 = mask_data_pre_dilation > 0
+
+            mask_data = mask_data0 #& (white_matter_data > 0)
+            if stroke_path:
+                stroke_image = nib.load(stroke_path)
+                stroke_data = stroke_image.get_fdata()
+            
+                # Subtracting stroke region from mask except in baseline
+                if merged_time_point != 0:
+                    mask_data = mask_data & (stroke_data < 1)
+            
+                    # Create an empty array to hold the updated mask data
+                    new_mask_data = np.zeros_like(mask_data)
+            
+                    # Iterate through each slice along the third axis and only retain slices where there is data in stroke_data
+                    for z in range(stroke_data.shape[2]):
+                        if np.any(stroke_data[:, :, z]):
+                            new_mask_data[:, :, z] = mask_data[:, :, z]
+            
+                    # Replace mask_data with the new filtered mask
+                    mask_data = new_mask_data
+            
+                strokeFlag = "Stroke"
+            else:
+                strokeFlag = "Sham"
+
+                
+            # Calculate average DWI value within the mask
+            masked_data = dwi_data[mask_data > 0]
+            non_zero_masked_data = masked_data[masked_data != 0]  # Exclude zero values
+            average_value = np.nanmean(non_zero_masked_data)  # Calculate mean only on non-zero elements
+            
+            # Calculate the number of voxels in the mask
+            num_voxels = non_zero_masked_data.size
+
+            # Save the mask as NIfTI file if the flag is set
+            if save_nifti_mask and pp > 0 and merged_time_point in [3] and q_type == "fa":
+                output_nifti_file = os.path.join(output_path,
+                                                  f"{subject_id}_tp_{merged_time_point}_{q_type}_{mask_name}_{pp}.nii.gz")
+                save_mask_as_nifti(mask_data, output_nifti_file)
+
+            # Append data to local CSV data
+            csvdata.append([file_path, subject_id, time_point, merged_time_point, q_type, mask_name, pp, average_value, strokeFlag, AMBA_flag, num_voxels])
+
+    return csvdata
+
+def process_files(input_path, output_path, save_nifti_mask):
+    """Process DWI files and generate data using parallel processing."""
+    session_mapping = {
+        "ses-Baseline": 0, "ses-Baseline1": 0, "ses-pre": 0,
+        "ses-P1": 3, "ses-P2": 3, "ses-P3": 3, "ses-P4": 3, "ses-P5": 3,
+        "ses-P6": 7, "ses-P7": 7, "ses-P8": 7, "ses-P9": 7, "ses-P10": 7,
+        "ses-P11": 14, "ses-P12": 14, "ses-P13": 14, "ses-P14": 14, 
+        "ses-P15": 14, "ses-P16": 14, "ses-P17": 14, "ses-P18": 14, 
+        "ses-P19": 21, "ses-D21": 21, "ses-P20": 21, "ses-P21": 21,
+        "ses-P22": 21, "ses-P23": 21, "ses-P24": 21, "ses-P25": 21,
+        "ses-P26": 28, "ses-P27": 28, "ses-P28": 28, "ses-P29": 28, 
+        "ses-P30": 28, "ses-P42": 42, "ses-P43": 42,
+        "ses-P56": 56, "ses-P57": 56, "ses-P58": 56,
+        "ses-P151": 28
+    }
+
+    # Get all relevant file paths
+    file_paths = glob.glob(os.path.join(input_path, "**", "dwi", "DSI_studio", "*_flipped.nii.gz"), recursive=True)
+
+    # Prepare arguments for parallel processing
+    arguments = [(file_path, session_mapping, output_path, save_nifti_mask) for file_path in file_paths]
+
+    # Initialize a list to store results
+    csv_data = []
+
+    # Use ProcessPoolExecutor to parallelize the file path processing
+    with ProcessPoolExecutor(max_workers=6) as executor:
+        # Process files in parallel
+        results = list(tqdm(executor.map(process_single_file, arguments), total=len(file_paths), desc="Processing files"))
+
+    # Aggregate the results
+    for result in results:
+        csv_data.extend(result)
+
+    # Create a DataFrame
+    df = pd.DataFrame(csv_data,
+                      columns=["fullpath", "subjectID", "timePoint", "merged_timepoint", "Qtype", "mask_name",
+                               "dialation_amount", "Value", "Group", "is_it_AMBA", "num_voxels"])
+
+    return df
+
+def main():
+    # Use argparse to get command-line inputs
+    import argparse
+    parser = argparse.ArgumentParser(description="Process DWI files and generate data.")
+    parser.add_argument("-i", "--input", type=str, required=True, help="Input directory containing DWI files.")
+    parser.add_argument("-o", "--output", type=str, required=True, help="Output directory to save results.")
+    parser.add_argument("-n", "--nifti-mask", action="store_true", default=False, help="Set to save masks as NIfTI files.")
+    args = parser.parse_args()
+
+    # Create the output directory if it does not exist
+    create_output_dir(args.output)
+
+    # Process files
+    df = process_files(args.input, args.output, args.nifti_mask)
+
+    # Save the DataFrame as CSV
+    csv_file = os.path.join(args.output, "Quantitative_results_from_dwi_processing.csv")
+    df.to_csv(csv_file, index=False)
+    print("CSV file created at:", csv_file)
+
+if __name__ == "__main__":
+    main()

code/GetQuantitativeValues_only_in_stroke_slices.py

@@ -0,0 +1,204 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Nov 11 12:49:37 2024
+
+@author: arefks
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Nov 11 09:35:42 2024
+
+@author: arefks
+"""
+
+import os
+import glob
+import pandas as pd
+import nibabel as nib
+import numpy as np
+from tqdm import tqdm
+from concurrent.futures import ProcessPoolExecutor
+from scipy.ndimage import binary_dilation
+
+def create_output_dir(output_dir):
+    """Create the output directory if it does not exist."""
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+def save_mask_as_nifti(mask_data, output_file):
+    """Save mask data as NIfTI file."""
+    mask_data_int = mask_data.astype(np.uint8)
+    mask_img = nib.Nifti1Image(mask_data_int, affine=None)
+    nib.save(mask_img, output_file)
+
+def process_single_file(file_info):
+    """Process a single file path."""
+    file_path, session_mapping, output_path, save_nifti_mask = file_info
+    
+    csvdata = []  # Local CSV data collector
+    
+    # Extract information from the file path
+    subject_id = file_path.split(os.sep)[-5]
+    time_point = file_path.split(os.sep)[-4]
+
+    # Map the extracted time_point directly using session_mapping
+    merged_time_point = session_mapping.get(time_point, None)
+
+    if merged_time_point is None:
+        print(f"Warning: No mapping found for {time_point}. Skipping file: {file_path}")
+        return []
+
+    # Extract the q_type from the filename
+    q_type = os.path.basename(file_path).split("_flipped")[0]
+
+    # Attempt to find the stroke mask path
+    try:
+        search_stroke = os.path.join(os.path.dirname(file_path), "*StrokeMask_scaled.nii")
+        stroke_path = glob.glob(search_stroke)[0]
+    except IndexError:
+        stroke_path = None
+
+    # Create the temp path to search for dwi_masks
+    temp_path = os.path.join(os.path.dirname(file_path), "RegisteredTractMasks_adjusted")
+    dwi_masks = glob.glob(os.path.join(temp_path, "*dwi_flipped.nii.gz")) 
+    white_matter_index = next((i for i, path in enumerate(dwi_masks) if "White_matter" in path), None)
+    
+    if white_matter_index is None:
+        print(f"Warning: White matter mask not found in {temp_path}. Skipping file: {file_path}")
+        return []
+    
+    white_matter_path = dwi_masks.pop(white_matter_index)
+    
+    # Load white matter mask
+    white_matter_img = nib.load(white_matter_path)
+    white_matter_data = white_matter_img.get_fdata()
+
+    # Load DWI data using nibabel
+    dwi_img = nib.load(file_path)
+    dwi_data = dwi_img.get_fdata()
+
+    # Loop through masks and calculate average values
+    pix_dialation = [0, 1, 2, 3, 4]
+    for mask_path in dwi_masks:
+        mask_name = os.path.basename(mask_path).replace("registered", "").replace("flipped", "").replace(".nii.gz", "").replace("__dwi_", "").replace("_dwi_", "")
+        mask_img = nib.load(mask_path)
+        mask_data_pre_dilation = mask_img.get_fdata()
+
+        # Determine if it's an AMBA mask
+        AMBA_flag = "AMBA" in mask_name
+
+        # Calculate the average value within the mask region for each dilation
+        for pp in pix_dialation:
+            if pp != 0:
+                mask_data0 = binary_dilation(mask_data_pre_dilation, iterations=pp)
+            else:
+                mask_data0 = mask_data_pre_dilation > 0
+
+            mask_data = mask_data0 #& (white_matter_data > 0)
+            if stroke_path:
+                stroke_image = nib.load(stroke_path)
+                stroke_data = stroke_image.get_fdata()
+            
+                # Subtracting stroke region from mask except in baseline
+                if merged_time_point != 0:
+                    mask_data = mask_data & (stroke_data < 1)
+            
+                    # Create an empty array to hold the updated mask data
+                    new_mask_data = np.zeros_like(mask_data)
+            
+                    # Iterate through each slice along the third axis and only retain slices where there is data in stroke_data
+                    for z in range(stroke_data.shape[2]):
+                        if np.any(stroke_data[:, :, z]):
+                            new_mask_data[:, :, z] = mask_data[:, :, z]
+            
+                    # Replace mask_data with the new filtered mask
+                    mask_data = new_mask_data
+            
+                strokeFlag = "Stroke"
+            else:
+                strokeFlag = "Sham"
+
+                
+            # Calculate average DWI value within the mask
+            masked_data = dwi_data[mask_data > 0]
+            non_zero_masked_data = masked_data[masked_data != 0]  # Exclude zero values
+            average_value = np.nanmean(non_zero_masked_data)  # Calculate mean only on non-zero elements
+            
+            # Calculate the number of voxels in the mask
+            num_voxels = non_zero_masked_data.size
+
+            # Save the mask as NIfTI file if the flag is set
+            if save_nifti_mask and pp > 0 and merged_time_point in [3] and q_type == "fa":
+                output_nifti_file = os.path.join(output_path,
+                                                  f"{subject_id}_tp_{merged_time_point}_{q_type}_{mask_name}_{pp}.nii.gz")
+                save_mask_as_nifti(mask_data, output_nifti_file)
+
+            # Append data to local CSV data
+            csvdata.append([file_path, subject_id, time_point, merged_time_point, q_type, mask_name, pp, average_value, strokeFlag, AMBA_flag, num_voxels])
+
+    return csvdata
+
+def process_files(input_path, output_path, save_nifti_mask):
+    """Process DWI files and generate data using parallel processing."""
+    session_mapping = {
+        "ses-Baseline": 0, "ses-Baseline1": 0, "ses-pre": 0,
+        "ses-P1": 3, "ses-P2": 3, "ses-P3": 3, "ses-P4": 3, "ses-P5": 3,
+        "ses-P6": 7, "ses-P7": 7, "ses-P8": 7, "ses-P9": 7, "ses-P10": 7,
+        "ses-P11": 14, "ses-P12": 14, "ses-P13": 14, "ses-P14": 14, 
+        "ses-P15": 14, "ses-P16": 14, "ses-P17": 14, "ses-P18": 14, 
+        "ses-P19": 21, "ses-D21": 21, "ses-P20": 21, "ses-P21": 21,
+        "ses-P22": 21, "ses-P23": 21, "ses-P24": 21, "ses-P25": 21,
+        "ses-P26": 28, "ses-P27": 28, "ses-P28": 28, "ses-P29": 28, 
+        "ses-P30": 28, "ses-P42": 42, "ses-P43": 42,
+        "ses-P56": 56, "ses-P57": 56, "ses-P58": 56,
+        "ses-P151": 28
+    }
+
+    # Get all relevant file paths
+    file_paths = glob.glob(os.path.join(input_path, "**", "dwi", "DSI_studio", "*_flipped.nii.gz"), recursive=True)
+
+    # Prepare arguments for parallel processing
+    arguments = [(file_path, session_mapping, output_path, save_nifti_mask) for file_path in file_paths]
+
+    # Initialize a list to store results
+    csv_data = []
+
+    # Use ProcessPoolExecutor to parallelize the file path processing
+    with ProcessPoolExecutor(max_workers=6) as executor:
+        # Process files in parallel
+        results = list(tqdm(executor.map(process_single_file, arguments), total=len(file_paths), desc="Processing files"))
+
+    # Aggregate the results
+    for result in results:
+        csv_data.extend(result)
+
+    # Create a DataFrame
+    df = pd.DataFrame(csv_data,
+                      columns=["fullpath", "subjectID", "timePoint", "merged_timepoint", "Qtype", "mask_name",
+                               "dialation_amount", "Value", "Group", "is_it_AMBA", "num_voxels"])
+
+    return df
+
+def main():
+    # Use argparse to get command-line inputs
+    import argparse
+    parser = argparse.ArgumentParser(description="Process DWI files and generate data.")
+    parser.add_argument("-i", "--input", type=str, required=True, help="Input directory containing DWI files.")
+    parser.add_argument("-o", "--output", type=str, required=True, help="Output directory to save results.")
+    parser.add_argument("-n", "--nifti-mask", action="store_true", default=False, help="Set to save masks as NIfTI files.")
+    args = parser.parse_args()
+
+    # Create the output directory if it does not exist
+    create_output_dir(args.output)
+
+    # Process files
+    df = process_files(args.input, args.output, args.nifti_mask)
+
+    # Save the DataFrame as CSV
+    csv_file = os.path.join(args.output, "Quantitative_results_from_dwi_processing_only_in_stroke_slices.csv")
+    df.to_csv(csv_file, index=False)
+    print("CSV file created at:", csv_file)
+
+if __name__ == "__main__":
+    main()

code/plotting_quantitative_dti_values.py

@@ -100,7 +100,7 @@ code_dir = os.path.dirname(os.path.abspath(__file__))
 parent_dir = os.path.dirname(code_dir)
 
 # Define the path for the input CSV file
-input_file_path = os.path.join(parent_dir, 'output', "Quantitative_outputs","old", 'Quantitative_results_from_dwi_processing.csv')
+input_file_path = os.path.join(parent_dir, 'output', "Quantitative_outputs","onlyCST", 'Quantitative_results_from_dwi_processing_withOutWM_and_partialCST.csv')
 
 # Define the path for the output folder to save plots
 plots_output_dir = os.path.join(parent_dir, 'output', 'Figures', 'pythonFigs')

code/ttest_group_differences.py

@@ -17,7 +17,7 @@ code_dir = os.path.dirname(os.path.abspath(__file__))
 parent_dir = os.path.dirname(code_dir)
 
 # Load the CSV data
-input_file_path = os.path.join(parent_dir, 'output', "Quantitative_outputs", 'Quantitative_results_from_dwi_processing_merged_with_behavior_data.csv')
+input_file_path = os.path.join(parent_dir, 'output', "Quantitative_outputs", "onlyCST",'Quantitative_results_from_dwi_processing_withOutWM_and_partialCST.csv')
 df = pd.read_csv(input_file_path, low_memory=False)
 
 # Initialize an empty list to store results
@@ -106,7 +106,7 @@ with ThreadPoolExecutor(max_workers=6) as executor:
 results_df = pd.DataFrame(results)
 
 # Define output path for the new CSV
-output_file_path = os.path.join(parent_dir, 'output', "Quantitative_outputs", 'Significance_stroke_vs_sham_difference_withoutWMmask.csv')
+output_file_path = os.path.join(parent_dir, 'output', "Quantitative_outputs", 'Significance_stroke_vs_sham_difference_withoutWMmask_partialCST.csv')
 
 # Save results to CSV
 results_df.to_csv(output_file_path, index=False)

code/volcano_plot.py

@@ -14,8 +14,8 @@ code_dir = os.path.dirname(os.path.abspath(__file__))
 parent_dir = os.path.dirname(code_dir)
 
 # Define the path for the input CSV files
-original_file_path = os.path.join(parent_dir, 'output', 'Quantitative_outputs', 'old', 'Quantitative_results_from_dwi_processing.csv')
-results_file_path = os.path.join(parent_dir, 'output', 'Quantitative_outputs', 'Significance_stroke_vs_sham_difference.csv')
+original_file_path = os.path.join(parent_dir, 'output', 'Quantitative_outputs',"onlyCST", 'Quantitative_results_from_dwi_processing_withOutWM_and_partialCST.csv')
+results_file_path = os.path.join(parent_dir, 'output', 'Quantitative_outputs',"onlyCST", 'Significance_stroke_vs_sham_difference_withoutWMmask_partialCST.csv')
 
 # Define the path for the output folders to save plots
 plots_output_dir = os.path.join(parent_dir, 'output', 'Figures', 'pythonFigs')

output/Quantitative_outputs/Quantitative_results_from_dwi_processing_only_in_stroke_slices.csv

@@ -0,0 +1 @@
+/annex/objects/MD5E-s235811101--07b5d750a8d6b775f37029b451169ebe.csv

output/Quantitative_outputs/old/Quantitative_results_from_dwi_processing.csv

@@ -1 +0,0 @@
-/annex/objects/MD5E-s241690371--8a191eeb1b83fc742fd0447859e21fc5.csv

output/Quantitative_outputs/old/Quantitative_results_from_dwi_processing_merged_with_behavior_data.csv

@@ -1 +0,0 @@
-/annex/objects/MD5E-s335815237--03564a439fd41d8d78171733f4986a7b.csv

output/Quantitative_outputs/onlyCST/Significance_stroke_vs_sham_difference_withoutWMmask_partialCST.csv

@@ -0,0 +1,721 @@
+mask_name,Qtype,merged_timepoint,dialation_amount,Pvalue
+CST_MOp-int-py_contralesional_CCasROA,fa,7,0,6.652607466913161e-19
+CST_MOp-int-py_contralesional_CCasROA,fa,0,2,0.7675523372233184
+CST_MOp-int-py_contralesional_CCasROA,fa,0,3,0.8237722763001227
+CST_MOp-int-py_contralesional_CCasROA,fa,0,4,0.5790896324861969
+CST_MOp-int-py_contralesional_CCasROA,fa,0,0,0.637475189253601
+CST_MOp-int-py_contralesional_CCasROA,fa,7,2,5.178660627622594e-18
+CST_MOp-int-py_contralesional_CCasROA,fa,7,4,7.342107433974614e-17
+CST_MOp-int-py_contralesional_CCasROA,fa,0,1,0.54630703998025
+CST_MOp-int-py_contralesional_CCasROA,fa,7,3,1.8764913471523474e-17
+CST_MOp-int-py_contralesional_CCasROA,fa,7,1,5.669817717417617e-11
+CST_MOp-int-py_contralesional_CCasROA,fa,28,1,4.879588057610322e-09
+CST_MOp-int-py_contralesional_CCasROA,fa,28,2,5.705180168639873e-10
+CST_MOp-int-py_contralesional_CCasROA,fa,28,3,7.137930927315022e-10
+CST_MOp-int-py_contralesional_CCasROA,fa,28,4,3.6601592783846288e-09
+CST_MOp-int-py_contralesional_CCasROA,fa,14,0,5.820317749237909e-16
+CST_MOp-int-py_contralesional_CCasROA,fa,14,1,6.905513983204099e-20
+CST_MOp-int-py_contralesional_CCasROA,fa,28,0,1.8501032515774086e-08
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