mean_fMRI.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "33b26c1b",
   "metadata": {},
   "source": [
    "# Quality control"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcd71b9b",
   "metadata": {},
   "source": [
    "After spatial smoothing, generate a mean fMRI image for each subject, then a mean fMRI image across all subjects, and then correlate each subject's mean fMRI to the across-subjects' mean fMRI image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "00050c3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load required packages\n",
    "import nibabel as nib\n",
    "import numpy as np\n",
    "import os\n",
    "import glob\n",
    "from scipy.stats import pearsonr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9dfe4f49",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('/project/3013104.01/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "56d8318e",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of subjects to be used for mean fMRI calculation: 239\n",
      "subject-0001\n",
      "subject-0002\n",
      "subject-0003\n",
      "subject-0004\n",
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      "subject-0239\n"
     ]
    }
   ],
   "source": [
    "# Create a subject list\n",
    "def get_directories_with_prefix(directory, prefix):\n",
    "    matching_directories = []\n",
    "    # Iterate over all entries in the directory\n",
    "    for entry in os.listdir(directory):\n",
    "        # Get the full path of the entry\n",
    "        full_path = os.path.join(directory, entry)\n",
    "        # Check if it is a directory and starts with the given prefix\n",
    "        if os.path.isdir(full_path) and entry.startswith(prefix):\n",
    "            matching_directories.append(entry)\n",
    "    return matching_directories\n",
    "\n",
    "# Provide the directory path and prefix\n",
    "directory_path = '/project/3013104.01/analysis/smoothed/pulled-data-MRI'\n",
    "prefix_to_match = 'subject-'\n",
    "\n",
    "# Get the matching directories as a list\n",
    "subject_list = get_directories_with_prefix(directory_path, prefix_to_match)\n",
    "\n",
    "# Sort the list\n",
    "subject_list.sort()\n",
    "\n",
    "# Print the list of matching directories\n",
    "print(\"Number of subjects to be used for mean fMRI calculation:\", len(subject_list))\n",
    "for subject in subject_list:\n",
    "    print(subject)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c5ae743",
   "metadata": {},
   "source": [
    "## Generate a mean fMRI image for each subject and each session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "61a12fa8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Define the paths to directories\n",
    "sub_directories = ['mri_movietask_1', 'mri_movietask_2', 'mri_movietask_3']\n",
    "output_directory = '/project/3013104.01/analysis/mean_fMRI'\n",
    "data_dir = '/project/3013104.01/analysis/smoothed/pulled-data-MRI'\n",
    "\n",
    "# Specify the index to start the for-loop from\n",
    "start_index = 85  # index to start from in subject_list\n",
    "\n",
    "# Loop over the main directories\n",
    "os.chdir(data_dir)\n",
    "for index, subject in enumerate(subject_list[start_index:], start=start_index):\n",
    "#for subject in subject_list:\n",
    "    # Loop over the subdirectories\n",
    "    for sub_dir in sub_directories:\n",
    "        # Create the full path to the subdirectory\n",
    "        sub_dir_path = os.path.join(subject, sub_dir)\n",
    "        \n",
    "        # Match the .nii.gz files in the subdirectory\n",
    "        file_pattern = os.path.join(sub_dir_path, '*.nii.gz')\n",
    "        nii_files = glob.glob(file_pattern)\n",
    "        \n",
    "        # Loop over the matched .nii.gz files\n",
    "        for nii_file in nii_files:\n",
    "            # Load the NIfTI image\n",
    "            nii_img = nib.load(nii_file)\n",
    "            \n",
    "            # Get the image data as a NumPy array\n",
    "            img_data = nii_img.get_fdata()\n",
    "            \n",
    "            # Calculate the mean along the time dimension\n",
    "            mean_img_data = np.mean(img_data, axis=-1)\n",
    "            \n",
    "            # Create the output directory if it doesn't exist\n",
    "            os.makedirs(output_directory, exist_ok=True)\n",
    "            \n",
    "            # Extract the file name without extension\n",
    "            file_name = os.path.splitext(os.path.basename(nii_file))[0]\n",
    "            \n",
    "            # Construct the output file name with subject and subdirectory\n",
    "            output_file = f\"{subject}_{sub_dir}_{file_name}_mean.nii.gz\"\n",
    "            \n",
    "            # Save the mean image as a new NIfTI file\n",
    "            output_path = os.path.join(output_directory, output_file)\n",
    "            mean_nii_img = nib.Nifti1Image(mean_img_data, nii_img.affine, nii_img.header)\n",
    "            nib.save(mean_nii_img, output_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13764f68",
   "metadata": {},
   "source": [
    "## Generate mean fMRI image across all subjects and sessions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "078dbc58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean fMRI image saved to: mean_fMRI.nii.gz\n"
     ]
    }
   ],
   "source": [
    "# Specify the directory containing the .nii.gz files\n",
    "directory = output_directory\n",
    "os.chdir(directory)\n",
    "\n",
    "# Get a list of all .nii.gz files in the directory\n",
    "file_list = [f for f in os.listdir(directory) if f.endswith(\".nii.gz\")]\n",
    "\n",
    "# Initialize an empty array to store voxel values\n",
    "voxel_data = None\n",
    "\n",
    "# Iterate over each file\n",
    "for file_name in file_list:\n",
    "    file_path = os.path.join(directory, file_name)\n",
    "    \n",
    "    # Load the .nii.gz file\n",
    "    img = nib.load(file_path)\n",
    "    data = img.get_fdata()\n",
    "    \n",
    "    # Add voxel data to the array\n",
    "    if voxel_data is None:\n",
    "        voxel_data = data\n",
    "    else:\n",
    "        voxel_data += data\n",
    "\n",
    "# Compute the mean voxel data\n",
    "mean_data = voxel_data / len(file_list)\n",
    "\n",
    "# Create a NIfTI image object for the mean data\n",
    "mean_img = nib.Nifti1Image(mean_data, img.affine, img.header)\n",
    "\n",
    "# Save the mean image to a file\n",
    "output_path = \"mean_fMRI.nii.gz\"\n",
    "nib.save(mean_img, output_path)\n",
    "\n",
    "print(\"Mean fMRI image saved to:\", output_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af313f4b",
   "metadata": {},
   "source": [
    "## Calculate correlations between each subject-session mean fMRI and the across-subjects mean fMRI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "be33a903",
   "metadata": {},
   "outputs": [],
   "source": [
    "def correlate_nii_files(directory, search_string, reference_file, output_file):\n",
    "    # Find all files in the directory that match the search string\n",
    "    file_list = glob.glob(os.path.join(directory, f\"*{search_string}*.nii.gz\"))\n",
    "\n",
    "    # Load the reference file\n",
    "    reference_data = nib.load(reference_file).get_fdata()\n",
    "\n",
    "    # Open the output file in write mode\n",
    "    with open(output_file, 'w') as f:\n",
    "        # Iterate over each file in the file list\n",
    "        for file_path in file_list:\n",
    "            # Load the current file\n",
    "            current_data = nib.load(file_path).get_fdata()\n",
    "\n",
    "            # Calculate the correlation coefficient and p-value\n",
    "            correlation, p_value = pearsonr(reference_data.flatten(), current_data.flatten())\n",
    "\n",
    "            # Write the file name, correlation coefficient, and p-value to the output file\n",
    "            f.write(f\"{file_path},\")\n",
    "            f.write(f\"{correlation},\")\n",
    "            f.write(f\"{p_value}\\n\")\n",
    "\n",
    "# Set the directory path, search string, reference file path, and output file path\n",
    "directory_path = output_directory\n",
    "search_string = \"subject-\"\n",
    "reference_file_path = output_path\n",
    "output_file_path = \"correlations_to_mean.csv\"\n",
    "\n",
    "# Call the function to correlate the files and save the output to the file\n",
    "correlate_nii_files(directory_path, search_string, reference_file_path, output_file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5a3657d",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Plot correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e3b7b518",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r < 0.8:\n",
      "                                                                                                                      Outliers\n",
      "312  /project/3013104.01/analysis/mean_fMRI/subject-0114_mri_movietask_3_smoothed_filtered_func_data_clean_MNI.nii_mean.nii.gz\n",
      "349  /project/3013104.01/analysis/mean_fMRI/subject-0128_mri_movietask_2_smoothed_filtered_func_data_clean_MNI.nii_mean.nii.gz\n",
      "350  /project/3013104.01/analysis/mean_fMRI/subject-0128_mri_movietask_3_smoothed_filtered_func_data_clean_MNI.nii_mean.nii.gz\n",
      "427  /project/3013104.01/analysis/mean_fMRI/subject-0157_mri_movietask_1_smoothed_filtered_func_data_clean_MNI.nii_mean.nii.gz\n",
      "431  /project/3013104.01/analysis/mean_fMRI/subject-0158_mri_movietask_2_smoothed_filtered_func_data_clean_MNI.nii_mean.nii.gz\n",
      "557  /project/3013104.01/analysis/mean_fMRI/subject-0203_mri_movietask_1_smoothed_filtered_func_data_clean_MNI.nii_mean.nii.gz\n",
      "558  /project/3013104.01/analysis/mean_fMRI/subject-0203_mri_movietask_2_smoothed_filtered_func_data_clean_MNI.nii_mean.nii.gz\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "file_path = 'correlations_to_mean.csv'\n",
    "\n",
    "# Import CSV file using Pandas without headers\n",
    "df = pd.read_csv(file_path, header=None)\n",
    "\n",
    "# Column with values to plot\n",
    "second_column_values = df.iloc[:, 1]\n",
    "\n",
    "# Filter rows where the second column value is less than 0.8\n",
    "filtered_rows = df[df.iloc[:, 1] < 0.8]\n",
    "\n",
    "# Extract values from the first column for the filtered rows\n",
    "first_column_filtered_values = filtered_rows.iloc[:, 0]\n",
    "\n",
    "# Create a scatter plot using Matplotlib\n",
    "plt.scatter(range(1, len(second_column_values) + 1), second_column_values)\n",
    "\n",
    "# Add labels and a title\n",
    "plt.xlabel('Subjects')\n",
    "plt.ylabel('Pearson r')\n",
    "plt.title('Correlations between each mean fMRI and the across-subjects mean fMRI')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n",
    "\n",
    "# Output the filtered values from the first column into a table\n",
    "if not first_column_filtered_values.empty:\n",
    "    table_data = pd.DataFrame({\"Outliers\": first_column_filtered_values})\n",
    "    pd.set_option(\"max_colwidth\", None)  # Display entire contents of each cell\n",
    "    print(\"r < 0.8:\")\n",
    "    print(table_data)\n",
    "else:\n",
    "    print(\"No r values found < 0.8.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e95ca0eb",
   "metadata": {},
   "source": [
    "The outliers above should be added to the excluded list of subject data."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}