Nilearn_SubjectLevelGLM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Designing GLM with additional confounds for (Neural Correlates of Lidocaine Analgesia) NLA Subject Level Stat Analysis for a Pain-stimulus task-based fMRI experiment to study effects of Saline vs Lidocaine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing all the necessary OS, shell scripting and imaging operations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import subprocess\n",
    "import re\n",
    "import glob\n",
    "import argparse\n",
    "import shutil\n",
    "import json\n",
    "import nibabel as nib\n",
    "from nilearn import plotting\n",
    "from nilearn.image import load_img, mean_img, new_img_like, resample_to_img, threshold_img, high_variance_confounds\n",
    "from nilearn import datasets\n",
    "from nilearn import maskers\n",
    "from nilearn import signal\n",
    "\n",
    "from nilearn.glm.first_level import FirstLevelModel\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from nilearn.plotting import plot_stat_map, plot_glass_brain, plot_epi, plot_contrast_matrix\n",
    "from nilearn.plotting import plot_design_matrix, plot_anat, plot_img, plot_roi\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the design matrices from FSL FEAT results for a consolidated GLM matrix with new confounds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "File path and directories used:\n",
    "\n",
    "funcpath = '/localpath/func/' containing all filtered functional data \n",
    "\n",
    "\n",
    "conf = '/localpath/confounds/' containing TSV files that have all regressor columns : Pain, Pain_der, #6_motionparameters, #n_MotionOutliers, #5_Compcor_HVC\n",
    "\n",
    "\n",
    "fsl_des = '/localpath/design/' containing TSV files that have data from design.mat from FSL feat results : Pain, Pain_der, #6_motionparameters, #n_MotionOutliers \n",
    "\n",
    "\n",
    "hvc = '/localpath/design/' containing TSV files that have data from high_variance_confounds : #5_Compcor_HVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list all the files with the suffix mp-confoundsMtx.txt in the path /serverpath/NLA*/Pain_func*\n",
    "mp_path = glob.glob('/serverpath/NLA*/Pain_func*/mp-confoundsMtx.txt')\n",
    "# print(mp_path)\n",
    "mp_path.sort()\n",
    "mp_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since no motion outliers are detected for a fdrms threshold of over 1.5 mm, including only the 6 pre-computed motion parameters from motion analysis in the GLM regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list the files in a directory /localpath/ in the format NLA*.nii.gz\n",
    "def list_files(path):\n",
    "    files = glob.glob(path + 'NLA*.nii.gz')\n",
    "    return files\n",
    "\n",
    "funcpath = '/localpath/func/'\n",
    "files = list_files(funcpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute the mean of the files\n",
    "from nilearn.image import mean_img\n",
    "mean_img_func = mean_img(files)\n",
    "\n",
    "# save the mean image as a nifti file\n",
    "mean_img_func.to_filename('/localpath/mean_img_func.nii.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NLA08_Pain1_Pre_10262023_124336.nii.gz',\n",
       " 'NLA15_Pain1_Pre_10252023_144351.nii.gz',\n",
       " 'NLA10_Pain2_Pre_10252023_144351.nii.gz',\n",
       " 'NLA11_Pain1_Pre_10252023_144351.nii.gz',\n",
       " 'NLA14_Pain2_Pre_10252023_144351.nii.gz',\n",
       " 'NLA26_Pain1_Pre_10252023_144351.nii.gz',\n",
       " 'NLA09_Pain2_Pre_10262023_124336.nii.gz',\n",
       " 'NLA19_Pain1_Pre_10252023_144351.nii.gz',\n",
       " 'NLA04_Pain1_Pre_10262023_124336.nii.gz',\n",
       " 'NLA05_Pain2_Pre_10262023_124336.nii.gz',\n",
       " 'NLA29_Pain1_Pre_10252023_212459.nii.gz',\n",
       " 'NLA33_Pain2_Pre_10252023_212459.nii.gz',\n",
       " 'NLA34_Pain2_Pre_10252023_212459.nii.gz',\n",
       " 'NLA31_Pain1_Pre_10252023_212459.nii.gz',\n",
       " 'NLA02_Pain2_Pre_10262023_124336.nii.gz',\n",
       " 'NLA07_Pain1_Pre_10262023_124336.nii.gz',\n",
       " 'NLA03_Pain1_Pre_10262023_124336.nii.gz',\n",
       " 'NLA35_Pain1_Pre_10252023_212459.nii.gz',\n",
       " 'NLA16_Pain1_Pre_10252023_144351.nii.gz',\n",
       " 'NLA24_Pain2_Pre_10252023_144351.nii.gz',\n",
       " 'NLA13_Pain2_Pre_10252023_144351.nii.gz',\n",
       " 'NLA21_Pain1_Pre_10252023_144351.nii.gz',\n",
       " 'NLA20_Pain2_Pre_10252023_144351.nii.gz',\n",
       " 'NLA12_Pain1_Pre_10252023_144351.nii.gz',\n",
       " 'NLA25_Pain1_Pre_10252023_144351.nii.gz',\n",
       " 'NLA17_Pain2_Pre_10252023_144351.nii.gz',\n",
       " 'NLA27_Pain2_Pre_10262023_161016.nii.gz',\n",
       " 'NLA36_Pain2_Pre_11282023_114924.nii.gz',\n",
       " 'NLA04_Pain2_Pre_10262023_124336.nii.gz',\n",
       " 'NLA19_Pain2_Pre_10252023_144351.nii.gz',\n",
       " 'NLA33_Pain1_Pre_10252023_212459.nii.gz',\n",
       " 'NLA29_Pain2_Pre_10252023_212459.nii.gz',\n",
       " 'NLA05_Pain1_Pre_10262023_124336.nii.gz',\n",
       " 'NLA27_Pain1_Pre_10252023_144351.nii.gz',\n",
       " 'NLA15_Pain2_Pre_10252023_144351.nii.gz',\n",
       " 'NLA10_Pain1_Pre_10252023_144351.nii.gz',\n",
       " 'NLA08_Pain2_Pre_10262023_124336.nii.gz',\n",
       " 'NLA09_Pain1_Pre_10262023_124336.nii.gz',\n",
       " 'NLA11_Pain2_Pre_10252023_144351.nii.gz',\n",
       " 'NLA14_Pain1_Pre_10252023_144351.nii.gz',\n",
       " 'NLA26_Pain2_Pre_10252023_144351.nii.gz',\n",
       " 'NLA16_Pain2_Pre_10252023_144351.nii.gz',\n",
       " 'NLA24_Pain1_Pre_10252023_144351.nii.gz',\n",
       " 'NLA13_Pain1_Pre_10252023_144351.nii.gz',\n",
       " 'NLA21_Pain2_Pre_10252023_144351.nii.gz',\n",
       " 'NLA36_Pain1_Pre_11282023_114924.nii.gz',\n",
       " 'NLA20_Pain1_Pre_10252023_144351.nii.gz',\n",
       " 'NLA12_Pain2_Pre_10252023_144351.nii.gz',\n",
       " 'NLA25_Pain2_Pre_10252023_144351.nii.gz',\n",
       " 'NLA17_Pain1_Pre_10252023_144351.nii.gz',\n",
       " 'NLA02_Pain1_Pre_10262023_124336.nii.gz',\n",
       " 'NLA07_Pain2_Pre_10262023_124336.nii.gz',\n",
       " 'NLA34_Pain1_Pre_10252023_212459.nii.gz',\n",
       " 'NLA31_Pain2_Pre_10252023_212459.nii.gz',\n",
       " 'NLA35_Pain2_Pre_10252023_212459.nii.gz',\n",
       " 'NLA03_Pain2_Pre_10262023_124336.nii.gz']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list the basename of the files \n",
    "def list_basename(files):\n",
    "    basenames = [os.path.basename(x) for x in files]\n",
    "    return basenames\n",
    "\n",
    "basenames = list_basename(files)\n",
    "basenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NLA08_Pain1_Pre_10262023_124336.feat',\n",
       " 'NLA15_Pain1_Pre_10252023_144351.feat',\n",
       " 'NLA10_Pain2_Pre_10252023_144351.feat',\n",
       " 'NLA11_Pain1_Pre_10252023_144351.feat',\n",
       " 'NLA14_Pain2_Pre_10252023_144351.feat',\n",
       " 'NLA26_Pain1_Pre_10252023_144351.feat',\n",
       " 'NLA09_Pain2_Pre_10262023_124336.feat',\n",
       " 'NLA19_Pain1_Pre_10252023_144351.feat',\n",
       " 'NLA04_Pain1_Pre_10262023_124336.feat',\n",
       " 'NLA05_Pain2_Pre_10262023_124336.feat',\n",
       " 'NLA12_Pain2_corrected_NLA_12_4stim.feat',\n",
       " 'NLA29_Pain1_Pre_10252023_212459.feat',\n",
       " 'NLA33_Pain2_Pre_10252023_212459.feat',\n",
       " 'NLA34_Pain2_Pre_10252023_212459.feat',\n",
       " 'NLA31_Pain1_Pre_10252023_212459.feat',\n",
       " 'NLA02_Pain2_Pre_10262023_124336.feat',\n",
       " 'NLA07_Pain1_Pre_10262023_124336.feat',\n",
       " 'NLA03_Pain1_Pre_10262023_124336.feat',\n",
       " 'NLA35_Pain1_Pre_10252023_212459.feat',\n",
       " 'NLA16_Pain1_Pre_10252023_144351.feat',\n",
       " 'NLA24_Pain2_Pre_10252023_144351.feat',\n",
       " 'NLA13_Pain2_Pre_10252023_144351.feat',\n",
       " 'NLA21_Pain1_Pre_10252023_144351.feat',\n",
       " 'NLA20_Pain2_Pre_10252023_144351.feat',\n",
       " 'NLA12_Pain1_Pre_10252023_144351.feat',\n",
       " 'NLA25_Pain1_Pre_10252023_144351.feat',\n",
       " 'NLA17_Pain2_Pre_10252023_144351.feat',\n",
       " 'NLA27_Pain2_Pre_10262023_161016.feat',\n",
       " 'NLA36_Pain2_Pre_11282023_114924.feat',\n",
       " 'NLA36_Pain1_Pre_11282023_114924_z_map_COMPCORmasked.feat',\n",
       " 'NLA04_Pain2_Pre_10262023_124336.feat',\n",
       " 'NLA19_Pain2_Pre_10252023_144351.feat',\n",
       " 'NLA33_Pain1_Pre_10252023_212459.feat',\n",
       " 'NLA29_Pain2_Pre_10252023_212459.feat',\n",
       " 'NLA05_Pain1_Pre_10262023_124336.feat',\n",
       " 'NLA27_Pain1_Pre_10252023_144351.feat',\n",
       " 'NLA15_Pain2_Pre_10252023_144351.feat',\n",
       " 'NLA10_Pain1_Pre_10252023_144351.feat',\n",
       " 'NLA08_Pain2_Pre_10262023_124336.feat',\n",
       " 'NLA09_Pain1_Pre_10262023_124336.feat',\n",
       " 'NLA11_Pain2_Pre_10252023_144351.feat',\n",
       " 'NLA14_Pain1_Pre_10252023_144351.feat',\n",
       " 'NLA26_Pain2_Pre_10252023_144351.feat',\n",
       " 'NLA16_Pain2_Pre_10252023_144351.feat',\n",
       " 'NLA24_Pain1_Pre_10252023_144351.feat',\n",
       " 'NLA13_Pain1_Pre_10252023_144351.feat',\n",
       " 'NLA21_Pain2_Pre_10252023_144351.feat',\n",
       " 'NLA36_Pain1_Pre_11282023_114924.feat',\n",
       " 'NLA36_Pain1_Pre_11282023_114924_z_map.feat',\n",
       " 'NLA20_Pain1_Pre_10252023_144351.feat',\n",
       " 'NLA12_Pain2_Pre_10252023_144351.feat',\n",
       " 'NLA25_Pain2_Pre_10252023_144351.feat',\n",
       " 'NLA17_Pain1_Pre_10252023_144351.feat',\n",
       " 'NLA02_Pain1_Pre_10262023_124336.feat',\n",
       " 'NLA07_Pain2_Pre_10262023_124336.feat',\n",
       " 'NLA34_Pain1_Pre_10252023_212459.feat',\n",
       " 'NLA31_Pain2_Pre_10252023_212459.feat',\n",
       " 'NLA35_Pain2_Pre_10252023_212459.feat',\n",
       " 'NLA03_Pain2_Pre_10262023_124336.feat']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace the .nii.gz with .feat\n",
    "def replace_feat(basenames):\n",
    "    feat = [x.replace('.nii.gz', '.feat') for x in basenames]\n",
    "    return feat\n",
    "\n",
    "feat = replace_feat(basenames)\n",
    "feat\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat = ['NLA08_Pain1_Pre_10262023_124336.feat',\n",
    " 'NLA15_Pain1_Pre_10252023_144351.feat',\n",
    " 'NLA10_Pain2_Pre_10252023_144351.feat',\n",
    " 'NLA11_Pain1_Pre_10252023_144351.feat',\n",
    " 'NLA14_Pain2_Pre_10252023_144351.feat',\n",
    " 'NLA26_Pain1_Pre_10252023_144351.feat',\n",
    " 'NLA09_Pain2_Pre_10262023_124336.feat',\n",
    " 'NLA19_Pain1_Pre_10252023_144351.feat',\n",
    " 'NLA04_Pain1_Pre_10262023_124336.feat',\n",
    " 'NLA05_Pain2_Pre_10262023_124336.feat',\n",
    " 'NLA12_Pain2_corrected_NLA_12_4stim.feat',\n",
    " 'NLA29_Pain1_Pre_10252023_212459.feat',\n",
    " 'NLA33_Pain2_Pre_10252023_212459.feat',\n",
    " 'NLA34_Pain2_Pre_10252023_212459.feat',\n",
    " 'NLA31_Pain1_Pre_10252023_212459.feat',\n",
    " 'NLA02_Pain2_Pre_10262023_124336.feat',\n",
    " 'NLA07_Pain1_Pre_10262023_124336.feat',\n",
    " 'NLA03_Pain1_Pre_10262023_124336.feat',\n",
    " 'NLA35_Pain1_Pre_10252023_212459.feat',\n",
    " 'NLA16_Pain1_Pre_10252023_144351.feat',\n",
    " 'NLA24_Pain2_Pre_10252023_144351.feat',\n",
    " 'NLA13_Pain2_Pre_10252023_144351.feat',\n",
    " 'NLA21_Pain1_Pre_10252023_144351.feat',\n",
    " 'NLA20_Pain2_Pre_10252023_144351.feat',\n",
    " 'NLA12_Pain1_Pre_10252023_144351.feat',\n",
    " 'NLA25_Pain1_Pre_10252023_144351.feat',\n",
    " 'NLA17_Pain2_Pre_10252023_144351.feat',\n",
    " 'NLA27_Pain2_Pre_10262023_161016.feat',\n",
    " 'NLA36_Pain2_Pre_11282023_114924.feat',\n",
    " 'NLA04_Pain2_Pre_10262023_124336.feat',\n",
    " 'NLA19_Pain2_Pre_10252023_144351.feat',\n",
    " 'NLA33_Pain1_Pre_10252023_212459.feat',\n",
    " 'NLA29_Pain2_Pre_10252023_212459.feat',\n",
    " 'NLA05_Pain1_Pre_10262023_124336.feat',\n",
    " 'NLA27_Pain1_Pre_10252023_144351.feat',\n",
    " 'NLA15_Pain2_Pre_10252023_144351.feat',\n",
    " 'NLA10_Pain1_Pre_10252023_144351.feat',\n",
    " 'NLA08_Pain2_Pre_10262023_124336.feat',\n",
    " 'NLA09_Pain1_Pre_10262023_124336.feat',\n",
    " 'NLA11_Pain2_Pre_10252023_144351.feat',\n",
    " 'NLA14_Pain1_Pre_10252023_144351.feat',\n",
    " 'NLA26_Pain2_Pre_10252023_144351.feat',\n",
    " 'NLA16_Pain2_Pre_10252023_144351.feat',\n",
    " 'NLA24_Pain1_Pre_10252023_144351.feat',\n",
    " 'NLA13_Pain1_Pre_10252023_144351.feat',\n",
    " 'NLA21_Pain2_Pre_10252023_144351.feat',\n",
    " 'NLA36_Pain1_Pre_11282023_114924.feat',\n",
    " 'NLA20_Pain1_Pre_10252023_144351.feat',\n",
    " 'NLA12_Pain2_Pre_10252023_144351.feat',\n",
    " 'NLA25_Pain2_Pre_10252023_144351.feat',\n",
    " 'NLA17_Pain1_Pre_10252023_144351.feat',\n",
    " 'NLA02_Pain1_Pre_10262023_124336.feat',\n",
    " 'NLA07_Pain2_Pre_10262023_124336.feat',\n",
    " 'NLA34_Pain1_Pre_10252023_212459.feat',\n",
    " 'NLA31_Pain2_Pre_10252023_212459.feat',\n",
    " 'NLA35_Pain2_Pre_10252023_212459.feat',\n",
    " 'NLA03_Pain2_Pre_10262023_124336.feat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if the folder is present in the path /serverpath/NLA*/Pain_func*, then copy the design.mat file to the folder /localpath/func/\n",
    "def copy_mat(feat):\n",
    "    for i in feat:\n",
    "        des_mtx = glob.glob('/serverpath/NLA*/Pain_func*/' + i + '/design.mat')\n",
    "        shutil.copy(des_mtx[0], '/localpath/func/' + i + '_design.mat')\n",
    "\n",
    "\n",
    "copy_mat(feat)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert all the .mat files to .tsv files\n",
    "def convert_mat(feat):\n",
    "    for i in feat:\n",
    "        mat = np.loadtxt('/localpath/func/' + i + '_design.mat', skiprows=5)\n",
    "        df = pd.DataFrame(mat)\n",
    "        # headers are in the order of Pain, Pain_derivative, 6_MOtionParameters, n_MotionOutliers\n",
    "        df.to_csv('/localpath/func/' + i[:-5] + '_design.tsv', sep='\\t', index=False, header=False)\n",
    "\n",
    "convert_mat(feat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell below takes the high variance data as confounds computed using the nilearn function high_variance_confounds. It serves to account for the Compcor components in the filtered functional data from FSL preprocessing.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list the .tsv files in the directory /localpath/hvc\n",
    "path = '/localpath/'\n",
    "hvc_files = glob.glob(path + 'hvc/*_hvc.tsv')\n",
    "hvc_files.sort()\n",
    "hvc_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len('/localpath/hvc/NLA36_Pain2_Pre_11282023_114924_hvc.tsv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "fslpath = '/serverpath/HVC/'\n",
    "\n",
    "# get all files in fslpath directory\n",
    "def list_files(path):\n",
    "    files = glob.glob(path + '*.txt')\n",
    "    return files\n",
    "\n",
    "hvc_files_fsl = list_files(fslpath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hvc_files_fsl\n",
    "hvc_files\n",
    "\n",
    "t = os.path.basename(hvc_files[0])\n",
    "t[:11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preparation for FSL GUI FirstLevel Stats: save the TSV files as txt files \n",
    "for i in range(len(hvc_files)):\n",
    "    df = pd.read_csv(hvc_files[i], sep='\\t', header=None)\n",
    "    t = os.path.basename(hvc_files[i])\n",
    "    df.to_csv(fslpath + t[:11] + '_hvc.txt', sep='\\t', index=False, header=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The HVC data are stored as FSL compatible .txt format to provide in the GUI for appending to the previously designed GLM matrix for their corresponding functional data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "design_files = glob.glob(path + '/design/*_design.tsv')\n",
    "design_files.sort()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New TSVs with old GLM with HVC data combined as new regressors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for every corresponding file in the hvc_files and design_files, concatenate the two files and save it as a new TSV file \n",
    "# in the directory /localpath/confounds/\n",
    "def concat_files(hvc_files, design_files):\n",
    "    for i in range(len(hvc_files)):\n",
    "        hvc = pd.read_csv(hvc_files[i], sep='\\t', header=None)\n",
    "        design = pd.read_csv(design_files[i], sep='\\t', header=None)\n",
    "        concat = pd.concat([design, hvc], axis=1)\n",
    "        concat.to_csv(path + 'confounds/' + os.path.basename(hvc_files[i])[:-8] + '_confounds.tsv', sep='\\t', index=False, header=False)\n",
    "\n",
    "concat_files(hvc_files, design_files)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract a subset of functional data and confounds data for nilearn's GLM modeling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "files_trial = ['/localpath/func/NLA04_Pain1_Pre_10262023_124336.nii.gz',\n",
    " '/localpath/func/NLA04_Pain2_Pre_10262023_124336.nii.gz',\n",
    " '/localpath/func/NLA05_Pain1_Pre_10262023_124336.nii.gz',\n",
    " '/localpath/func/NLA05_Pain2_Pre_10262023_124336.nii.gz']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "conf = glob.glob(path + '/confounds/*_confounds.tsv')\n",
    "conf.sort()\n",
    "conf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "design_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "conf_trial = ['/localpath/confounds/NLA04_Pain1_Pre_10262023_124336_confounds.tsv',\n",
    " '/localpath/confounds/NLA04_Pain2_Pre_10262023_124336_confounds.tsv',\n",
    " '/localpath/confounds/NLA05_Pain1_Pre_10262023_124336_confounds.tsv',\n",
    " '/localpath/confounds/NLA05_Pain2_Pre_10262023_124336_confounds.tsv']\n",
    "\n",
    "# fsl_des = ['/localpath/design/NLA04_Pain1_Pre_10262023_124336_design.tsv',\n",
    "#  '/localpath/design/NLA04_Pain2_Pre_10262023_124336_design.tsv',\n",
    "#  '/localpath/design/NLA05_Pain1_Pre_10262023_124336_design.tsv',\n",
    "#  '/localpath/design/NLA05_Pain2_Pre_10262023_124336_design.tsv']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The First Level Modeling accepts a collective dataframe of the individual dataframes from TSV files for confounds. The trial dataframe has the corresponding information in the sequence of the list of the functional files given as input to the model. It can have a custom number of regressor columns for each functional run in case of the presence of motion outlier regressors based on the particular fMRI run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[           0         1         2         3         4         5         6   \\\n",
       " 0    0.000432  0.001321  0.000726 -0.000476  0.000012  0.020656  0.029362   \n",
       " 1    0.001945  0.001217  0.000891 -0.000265 -0.000028  0.007501 -0.100906   \n",
       " 2    0.003251  0.000965  0.000649 -0.000144 -0.000120  0.003583 -0.087700   \n",
       " 3    0.004261  0.000634 -0.000450  0.000155 -0.000227 -0.012286 -0.048722   \n",
       " 4    0.004904  0.000235 -0.000631  0.000173 -0.000112 -0.011101  0.015213   \n",
       " ..        ...       ...       ...       ...       ...       ...       ...   \n",
       " 220  0.031672  0.004728  0.000571  0.000001  0.000057  0.002154 -0.077414   \n",
       " 221  0.036030  0.003605  0.000272 -0.000014 -0.000047 -0.004087 -0.117642   \n",
       " 222  0.039272  0.002497  0.000020  0.000034 -0.000030 -0.003919 -0.002073   \n",
       " 223  0.041413  0.001403  0.000034  0.000107 -0.000016  0.001866  0.035468   \n",
       " 224  0.042468  0.000860 -0.000013 -0.000116 -0.000095 -0.002001  0.111564   \n",
       " \n",
       "            7         8         9         10        11        12  \n",
       " 0    0.302782 -0.094381 -0.166883 -0.162030 -0.333127 -0.231073  \n",
       " 1    0.155551 -0.058405 -0.146095 -0.050742 -0.184067 -0.205159  \n",
       " 2   -0.013057  0.015410 -0.119602 -0.036276 -0.094559 -0.157765  \n",
       " 3   -0.157131  0.102142 -0.050493 -0.024713 -0.059901  0.104490  \n",
       " 4   -0.164436  0.089575  0.013611 -0.023552  0.011295  0.169177  \n",
       " ..        ...       ...       ...       ...       ...       ...  \n",
       " 220  0.051924 -0.034976 -0.066250  0.002708  0.006018 -0.039940  \n",
       " 221 -0.043109  0.033405 -0.054131  0.008333 -0.027780  0.046069  \n",
       " 222 -0.058972  0.032479  0.011843 -0.006764  0.011834  0.009982  \n",
       " 223 -0.055174  0.027860  0.041406 -0.011701  0.050111 -0.073965  \n",
       " 224 -0.058854  0.017197  0.043095  0.000787  0.032300 -0.052017  \n",
       " \n",
       " [225 rows x 13 columns],\n",
       "            0         1         2         3         4         5         6   \\\n",
       " 0    0.000432  0.001321 -0.000088  0.000059 -0.000040 -0.003324 -0.030606   \n",
       " 1    0.001945  0.001217 -0.000008  0.000071 -0.000033 -0.005475 -0.028137   \n",
       " 2    0.003251  0.000965 -0.000027  0.000347 -0.000011 -0.002994 -0.026488   \n",
       " 3    0.004261  0.000634 -0.000266  0.000061 -0.000033 -0.003743 -0.019374   \n",
       " 4    0.004904  0.000235 -0.000180  0.000022 -0.000041 -0.006688 -0.009427   \n",
       " ..        ...       ...       ...       ...       ...       ...       ...   \n",
       " 220  0.031672  0.004728  0.000254  0.000068  0.000154 -0.000907  0.013379   \n",
       " 221  0.036030  0.003605  0.000307  0.000063  0.000046 -0.005026  0.016536   \n",
       " 222  0.039272  0.002497  0.000116 -0.000052  0.000075 -0.002904  0.005915   \n",
       " 223  0.041413  0.001403 -0.000370 -0.000104  0.000037 -0.001950  0.021718   \n",
       " 224  0.042468  0.000860 -0.000466 -0.000267 -0.000020  0.003450  0.043975   \n",
       " \n",
       "            7         8         9         10        11        12  \n",
       " 0   -0.111476 -0.092677  0.161081  0.136012 -0.319496  0.204789  \n",
       " 1   -0.091323 -0.056318  0.060719  0.086841 -0.148130  0.123764  \n",
       " 2   -0.094966 -0.030006  0.009044  0.055431 -0.039436  0.093421  \n",
       " 3   -0.089885 -0.021615 -0.009951  0.029974  0.024404  0.063178  \n",
       " 4   -0.094324 -0.018358 -0.017972  0.005396  0.063890  0.030457  \n",
       " ..        ...       ...       ...       ...       ...       ...  \n",
       " 220 -0.069581 -0.017841 -0.032516 -0.026032  0.053742  0.007534  \n",
       " 221 -0.072753 -0.016078 -0.037596 -0.019136  0.051405  0.016256  \n",
       " 222 -0.057450 -0.007137 -0.044501 -0.002271  0.029012  0.027883  \n",
       " 223  0.024146  0.012621 -0.037453 -0.010038 -0.036831  0.041701  \n",
       " 224  0.127388  0.053749 -0.019539 -0.008620 -0.086595  0.017745  \n",
       " \n",
       " [225 rows x 13 columns],\n",
       "            0         1         2         3         4         5         6   \\\n",
       " 0    0.000432  0.001321  0.000533  0.000065 -0.000293 -0.013401 -0.020333   \n",
       " 1    0.001945  0.001217  0.000043  0.000200 -0.000289 -0.016945 -0.034676   \n",
       " 2    0.003251  0.000965 -0.000091 -0.000046 -0.000134 -0.013214  0.029979   \n",
       " 3    0.004261  0.000634 -0.000518  0.000163 -0.000124 -0.004758  0.000661   \n",
       " 4    0.004904  0.000235 -0.000391  0.000222 -0.000164 -0.004140 -0.052890   \n",
       " ..        ...       ...       ...       ...       ...       ...       ...   \n",
       " 220  0.031672  0.004728 -0.000020 -0.000083  0.000183  0.007787 -0.056290   \n",
       " 221  0.036030  0.003605 -0.000206 -0.000127  0.000155  0.007833 -0.041654   \n",
       " 222  0.039272  0.002497 -0.000401 -0.000530 -0.000021  0.006703  0.036625   \n",
       " 223  0.041413  0.001403 -0.000272 -0.000189  0.000035  0.007247 -0.042723   \n",
       " 224  0.042468  0.000860  0.000296 -0.000283 -0.000084  0.009615 -0.013767   \n",
       " \n",
       "            7         8         9         10        11        12  \n",
       " 0   -0.042098 -0.142695  0.030270  0.029117 -0.081321  0.116814  \n",
       " 1   -0.009014 -0.033474  0.026708 -0.000526 -0.027987  0.100078  \n",
       " 2    0.001398  0.011521  0.031087 -0.002823 -0.000100  0.071719  \n",
       " 3    0.009403  0.048702  0.028689 -0.003907 -0.001128  0.069658  \n",
       " 4    0.011102  0.076541  0.009990 -0.011471  0.033167  0.070910  \n",
       " ..        ...       ...       ...       ...       ...       ...  \n",
       " 220 -0.018209  0.044867 -0.003723 -0.037732  0.045188 -0.003608  \n",
       " 221 -0.008466  0.036216  0.004170 -0.020382  0.071390 -0.020797  \n",
       " 222  0.037002  0.015923  0.022958  0.016648  0.027219 -0.031775  \n",
       " 223 -0.014252 -0.003964  0.028778 -0.003950  0.042868 -0.014664  \n",
       " 224 -0.022607 -0.014319  0.027347  0.009997  0.041188 -0.026191  \n",
       " \n",
       " [225 rows x 13 columns],\n",
       "            0         1         2         3         4         5         6   \\\n",
       " 0    0.000432  0.001321 -0.000659  0.000699  0.000095  0.022439 -0.036524   \n",
       " 1    0.001945  0.001217 -0.000201  0.000803  0.000179  0.020151 -0.065584   \n",
       " 2    0.003251  0.000965 -0.000344  0.000849  0.000098  0.019217 -0.025724   \n",
       " 3    0.004261  0.000634 -0.000274 -0.000133  0.000352  0.001286  0.120114   \n",
       " 4    0.004904  0.000235  0.000891 -0.000992  0.000236 -0.025016  0.144337   \n",
       " ..        ...       ...       ...       ...       ...       ...       ...   \n",
       " 220  0.031672  0.004728 -0.000668  0.000312 -0.000709 -0.015478 -0.051426   \n",
       " 221  0.036030  0.003605 -0.001112  0.000732 -0.000218 -0.023183 -0.041279   \n",
       " 222  0.039272  0.002497 -0.000125  0.000417  0.000229 -0.017085  0.075083   \n",
       " 223  0.041413  0.001403  0.000672  0.000696 -0.000171 -0.018009  0.025214   \n",
       " 224  0.042468  0.000860  0.000786  0.000317 -0.000040 -0.007796 -0.132225   \n",
       " \n",
       "            7         8         9         10        11        12  \n",
       " 0   -0.080553 -0.033616 -0.128406 -0.077873  0.022215  0.057488  \n",
       " 1   -0.108292  0.050369 -0.088974 -0.034154  0.018825  0.029407  \n",
       " 2   -0.058415  0.084662 -0.035441  0.028063  0.044782  0.113769  \n",
       " 3    0.051220  0.028681  0.088271  0.068477  0.001371  0.125843  \n",
       " 4    0.112666 -0.008066  0.214538  0.012553 -0.072899 -0.022222  \n",
       " ..        ...       ...       ...       ...       ...       ...  \n",
       " 220  0.003697  0.006803 -0.048441  0.035599  0.060830 -0.033226  \n",
       " 221 -0.012905  0.029644 -0.059077  0.058030  0.060642  0.033602  \n",
       " 222  0.003157  0.011551  0.022157  0.069559 -0.013600  0.104654  \n",
       " 223  0.016781  0.019874  0.058197 -0.002903 -0.110753 -0.013279  \n",
       " 224 -0.063298  0.111115 -0.012230 -0.070303 -0.056690 -0.082904  \n",
       " \n",
       " [225 rows x 13 columns]]"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "des_trials = [pd.read_csv(x, sep='\\t', header=None) for x in conf_trial]\n",
    "des_trials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a list of dataframes from the confounds files\n",
    "def list_df(path):\n",
    "    conf = glob.glob(path + 'confounds/*_confounds.tsv')\n",
    "    conf.sort()\n",
    "    df_list = [pd.read_csv(x, sep='\\t', header=None) for x in conf]\n",
    "    return df_list\n",
    "\n",
    "design_fsl_hvc = list_df(path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(des_trials)\n",
    "des_trials[1].shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "statspath = '/localpath/stats/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Average of the functional data for underlay; used interchangeably with the anatomical underlay for stat maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_img_func = mean_img(files_trial)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running First Level Model with added HVC confounds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "nilearn finds errors with multicollinearity when there are seemingly one or more columns in the regressors that are linear combination of others. VIF (Variance Inflation Factor) uses statsmodel from sklearn to detect columns with VIF > 5 (as suggested by GitHub Copilot's snippet) in the trial run below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 284x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.11/site-packages/nilearn/glm/_utils.py:344: RuntimeWarning: divide by zero encountered in divide\n",
      "  return np.where(X <= 0, 0, 1.0 / X)\n",
      "/usr/local/lib/python3.11/site-packages/nilearn/plotting/displays/_slicers.py:382: UserWarning: empty mask\n",
      "  get_mask_bounds(new_img_like(img, not_mask, affine))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 730x260 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropping columns with high VIF: [ 2  3  5  6  7  8 10]\n"
     ]
    },
    {
     "data": {
      "image/png": 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k55944gl06dIFZ5xxBkpLS1FfX/+VccrKypCbm5t4FBcXi+0zUUsZSnlxjb/1bNvGhRdeiKqqKvztb39LPP/www+jZ8+eKCoqwo4dO7Bw4UKMGDECf/nLX7401slazuLiYvQtvR2+UEhm/4W/cgJAPFN2G6YHX2utkOwioH7B9WTscBh7/uvnqK6uRk5O6/tstP5a6zRnzhy88847SYkJANdee23i/wcNGoTu3btj/Pjx2L17N/r27XvSWMFgEMFgsOkvUn3xXGkeLJ4rfd+rLyz4AeNy7JT4Wjt37lw899xzeOWVV3DKKad85WtHjhwJANi1a5cXu0YkRuuWUymFefPm4emnn8bGjRvRu3fvr/03FRUVAIDu3bsL7x2RLK2Tc86cOVi1ahX++te/Ijs7G5WVlQCA3NxcpKenY/fu3Vi1ahXOO+88dO7cGTt27MD8+fMxZswYDB48uI33nqh1tE7OFStWADgx0MBp5cqVmDFjBgKBAF5++WUsX74cdXV1KC4uxtSpU3HLLbe0wd4SuUvr5Py6C8nFxcXYtGmTa9szFGB4cdFDivDFLF9UNj4AxKVGgaSglLggRNQRMTmJNMXkJNKU1jWn51J8EELg03bwWSs8CMkSnKTMtnmzNVGHwOQk0hSTk0hTrDmdUrzmlO4iNCzZ+ADEz78tOIGY7fINXmw5iTTF5CTSFJOTSFOsOR0MW25srfLgYzAuvAhQWq38TAiGJbsNJXgzt9ux2XISaYrJSaQpJieRplhzesSLhWdzvvWpaPzwG51F4wOAEZOtOe1suc5a23Q3NltOIk0xOYk0xeQk0hRrTicDYvcTWlnyA1Nr62Rmq29keFA3B6pk2wuz53Gx2FZ95Otf1AxsOYk0xeQk0hSTk0hTTE4iTfGCkIPtB4w0mdi+7JhMYId4VHiJrlz5Gbc7vSvbXvhCcjNjW7a7sdlyEmmKyUmkKSYnkaZYczrYQQUITQAVCsnXnHXhdNH4/i7udrKfjBGXPYb6SEAsthXhBF9EHQKTk0hTTE4iTbHmdJCsOdN88gPf/aG4aPzunatF4wPAsS4ZovFtW+5mbtvlbmC2nESaYnISaYrJSaQp1pwOdlABIZma01byEzKbPtmxr31zPxGNDwD7+nSR3cAncjWt3eBuW8eWk0hTTE4iTTE5iTTFmtNBBS2ooEx/ZNyS/xy09sn2ERacKjc5ViNflmxfbdpOubG7VsTddGLLSaQpJieRppicRJpizelgBGwYAZm+wlhMeH4fAKFPZD9rI7b828VqkD1P+XvlJsa2oryfk6hDYHISaYrJSaQpJieRpnhByMHvt2CmyQxCsDy4ICQ9tn7bkR6yGwBgHpd9S0Zy5U6SFXE3NltOIk0xOYk0xeQk0hRrTgdfmgWfUM0ZaxBaIclDBz/JFd+GnS57w3gsW649slyer5otJ5GmmJxEmmJyEmmKNaeDz6fgk5okKyY/wVdarWz8hmq5RYASQrKTb9uCpb/t8q6z5STSFJOTSFNMTiJNseZ08Js2fKZQzWnJ15wNBXI3EgOAr07+szyeITvBl/IJniOXY7PlJNIUk5NIU0xOIk2x5nQwTRumUM1pRuQ/Bw3BhWEBwIjL1rQAgBjbi0Y8E0SaYnISaYrJSaQp1pwe8dd6sHiubBchDA8WAI4J182QLJtdjs2Wk0hTTE4iTTE5iTTF5CTSFC8IOcQsH2xLZvLnwHH5iylWUDa+IXzBCQAM4cEaBi8IEVFrMTmJNMXkJNIUa06HaNQHn1/mlKQLT74FAP462fhR+TmlxQdSWCG52G7fMsGWk0hTTE4iTTE5iTTFmtMhHvXDFqo5s6LyNypbAfm+VGmmywvQfpEdkPs7KJMTfBF1CExOIk0xOYk0xZrTwQ77AUPolHgwN1a4QDa+GZGNDwCG7DpGgOQN4y7HZstJpCkmJ5GmmJxEmmLN6WBETBimzOeVkrlNNIktuUgPABMe9KMKNxeSNa3bsdlyEmmKyUmkKa2Tc/HixTAMI+kxYMCAxO/D4TDmzJmDzp07IysrC1OnTsWhQ4facI+J3KN9zTlw4EC8/PLLiZ/9jrGv8+fPx/PPP48//elPyM3Nxdy5czFlyhS89tprLdqWGTVgmjJ1lZ0mX69JjhsFAPO4aHgAst2QqUb75PT7/SgsLGzyfHV1NR555BGsWrUK3/ve9wAAK1euxGmnnYbXX38d55xzjte7SuQqrb/WAsCHH36IoqIi9OnTB9OmTcPevXsBANu3b0csFsOECRMSrx0wYAB69OiB8vLyr4wZiURQU1OT9CDSjdbJOXLkSDz22GNYu3YtVqxYgT179uDcc8/F8ePHUVlZiUAggLy8vKR/061bN1RWVn5l3LKyMuTm5iYexcXFgkdB1DJaf60tKSlJ/P/gwYMxcuRI9OzZE08++STS09NbHLe0tBQLFixI/FxTU8MEJe1onZxflJeXh1NPPRW7du3C97//fUSjUVRVVSW1nocOHTppjeoUDAYRDDadgdmMGTB9QheEAiJhk4hOmAyPJpWWWVg8QWn9XTFZCu0qUFtbi927d6N79+4YNmwY0tLSsH79+sTvd+7cib1792LUqFFtuJdE7tC65fzJT36CCy64AD179sSBAwfwX//1X/D5fLjyyiuRm5uLWbNmYcGCBcjPz0dOTg7mzZuHUaNG8UottQtaJ+f+/ftx5ZVX4ujRo+jatStGjx6N119/HV27dgUA3HvvvTBNE1OnTkUkEsGkSZPw4IMPtvFeE7nDUEp5cBuw3mpqapCbm4s+t/wSZkhm1uE0D1a2jnSW/VNmfSx/DLXFsscgWZfb4TD2LP45qqurkZOT0+p4KVVzEnUkTE4iTTE5iTSl9QUhrxlxA2ZcauC7SFhPmTH5yxPSN6X76uXqZhXlBF9EHQKTk0hTTE4iTbHmdDCU3NhO5cGZtoKyNaHP5ZrqZKTHB1shuQ3YLs8czpaTSFNMTiJNMTmJNMWa08n+7CERWrgeBAC/YB8eIH+vJQDxBZ+U4MTbbsdmy0mkKSYnkaaYnESaYs3pYCi5fjYvFjLyC98zqkz5ull68VzDFjxHMY6tJeoQmJxEmmJyEmmKyUmkKV4QclIQ6wT3YvWsWLbsBRvbg4tapvDE1al00ztbTiJNMTmJNMXkJNIUa06veFBz+iKy8b1YjCl4VPZE1QyQK2rtBndHULDlJNIUk5NIU0xOIk2x5vSKBzWndB9ePEP+IALVHX5drQS2nESaYnISaYrJSaQp1pztiBL+qI1lycYHgOAx2fhpVXIDhO2wu7HZchJpislJpCkmJ5GmWHO2I2ZMNr7kIkCN6rvL9qVmHJSLbUU4wRdRh8DkJNIUk5NIU6w52xPhktBOk6854xmy8a2QYOwwFzIi6hCYnESaYnISaYrJSaQpXhCib8yLldKkb0qPdJOc4Mvd2Gw5iTTF5CTSFJOTSFOsOb3iwbxVceGFjJRf/iCsoPAGJAdSxDkIgahDYHISaYrJSaQp1pztiBIemK7SbNH4AKDiwp2pUcH2yOXYbDmJNMXkJNIUk5NIU6w5HQx14pGqlE945z1YjAnyZW3KYMtJpCkmJ5GmmJxEmmLN6RUvatmAcMEW96DoFN6Er06uPTLC7Ock6hCYnESaYnISaYo1p5OCN7WhFOluTslxqZ+RXgDYjMkVtYbFhYyIOgQmJ5GmmJxEmmJyEmmKF4Q84sWYcfkLQvJHYXDgewJbTiJNMTmJNMXkJNIUa04HyZutlRdFpycbkWXYsscgeYrcjs2Wk0hTTE4iTTE5iTTFmtMjdtCDEfXS/ZweHIJhycZXKfSOZ8tJpCkmJ5GmmJxEmkqhb+DylCl3s6+dFZcJ7ODLkN2GqhVeZAjyY2vtkNwGbOVubLacRJpichJpislJpCnWnA62HzCEzkhaZkwmsINty37WKvmSU3wbSnDibWWx5iTqEJicRJpichJpijWngx1QgNAY2FB6VCSu0/HKbNkNBOUn+FENskWnIXgMhs2ak6hDYHISaUr75OzVqxcMw2jymDNnDgBg7NixTX53/fXXt/FeE7We9jXnG2+8Acv6/Ca/d955B9///vdx2WWXJZ6bPXs2li5dmvg5IyPD030kkqB9cnbt2jXp52XLlqFv37747ne/m3guIyMDhYWFrd6WFQJUqNVhTspvyl9MCR6WvZgS6S4/eF+acnklMMnY2n+tdYpGo/jDH/6AmTNnwjA+PxFPPPEEunTpgjPOOAOlpaWor6//yjiRSAQ1NTVJDyLdaN9yOq1ZswZVVVWYMWNG4rl/+7d/Q8+ePVFUVIQdO3Zg4cKF2LlzJ/7yl798aZyysjIsWbLEgz0majlDKZUyy8VOmjQJgUAAzz777Je+ZsOGDRg/fjx27dqFvn37nvQ1kUgEkUgk8XNNTQ2Ki4vR879/CTMk8702p/8xkbhO9RX5ovG9+FrrPyrbXsS7yo1xthvC2D9nMaqrq5GTk9PqeCnTcn788cd4+eWXv7JFBICRI0cCwFcmZzAYRDAYbPK8HbQBoZtxlQcTPtvSf02f/Oe49CRihuAgB7djp0zNuXLlShQUFOD888//ytdVVFQAALp37+7BXhHJSYmW07ZtrFy5EtOnT4ff//ku7969G6tWrcJ5552Hzp07Y8eOHZg/fz7GjBmDwYMHt+EeE7VeSiTnyy+/jL1792LmzJlJzwcCAbz88stYvnw56urqUFxcjKlTp+KWW25poz0lck9KJOfEiRNxsutWxcXF2LRpk2vbUUEbSmhgdMzy4E7llClSvpzUBGuNzIhg7e/y4sLt4M9J1D4xOYk0xeQk0lRK1JxeMYIWjKDMSjrRqHzNaUtvQnBcaiPxmjMmeAwux2bLSaQpJieRppicRJpizeng89sw02T6Oa24B/2c0mNf4/I1p/TYWlNwnjXlcmy2nESaYnISaYrJSaQpJieRpnhByMH0W/D5ZQYhxGub3tzttjSXB15/kSE9QsADkjekK5djp/7ZJmqnmJxEmmJyEmmKNaeD36/g88sMQoh40IEfy5WplxsFjskPpLBCsqMQTMG63HD5rcOWk0hTTE4iTTE5iTTFmtPBZ9rwSS045EHNaUZkP2t9DfLHYAVk40v2c7odmy0nkaaYnESaYnISaYo1p4NkzSldD3rBJ3ijcmIbYdn4sTy5RYxtl987qf+OIWqnmJxEmmJyEmmKNadH/LXyfYRpwtsQn7QaQPbHsvGPFsmNP1aGu7HZchJpislJpCkmJ5GmWHM6xCwfbKFFbgM1HoytjcnGj3SSjQ8AgVrZ+zkz8hrEYluBiKvx2HISaYrJSaQpJieRppicRJriBSGHWNQHyy9zSjKFL3QAQEOB7EUnKyh/DAe/KzcwHQB+POA1sdjh2jh+7mI8tpxEmmJyEmmKyUmkKdacDrGoH6ZP5pRIrqjcKHvEEdH4x97vLBofAPZc9LBo/Is/nCQWO1YXBbDRtXhsOYk0xeQk0hSTk0hTrDkdVNgHZcgMfDfkuwhx9FiW7Aa6uTuw+2Qeqvp/ovHffrO3WGw77O7sZGw5iTTF5CTSFJOTSFOsOR2MsAnDkPm8Uh58DJo+2cI2lC7fWXvPPyaIxs/4l9wfwnJ54nC2nESaYnISaYrJSaQp1pwOZtSEacp8XlkB+Qm+7EMh2fg9hGcQA6D2ZojGD59VLxbbrmc/J1GHwOQk0hSTk0hTrDkdfFFAqOSEHZCJ6xQ8KvtZG+mWJhofACDcV6tsudrfdjk2W04iTTE5iTTF5CTSFJOTSFO8IORgxAyYPpkLBl5cEIp0kZ2Q2VAeDKQICV8QapB7y9thd2Oz5STSFJOTSFNMTiJNseZ0MCzAiMvEtj0409Iloc+UrWkBQHCMAADAqJeZwA0AjAZ3Y7PlJNIUk5NIU0xOIk2x5nQwrBMPCV70c0rPXO1PEzo5DkIlf0piy0mkKSYnkaaYnESaYs3pYCi5sk0J30QMAJDu5/TJ93MaEdmDMGNy8d3ed7acRJpichJpislJpCnWnE7qs4dEaLkhnZ+Tv91SnC8sexCiXcEud9Ky5STSFJOTSFNMTiJNseZ0EO3n9OBjUJke9KUKS+mak/2cRB0Dk5NIU0xOIk0xOYk0xQtCToKDEDwZICC8jVhMfiSFT3jxbF9ULrYRcTceW04iTTE5iTTF5CTSFGtOJ8mB7zJhk5gR2c/aWFT+7SI9h5i/Xu4vYUTdjc2Wk0hTTE4iTbVpcm7evBkXXHABioqKYBgG1qxZk/R7pRRuu+02dO/eHenp6ZgwYQI+/PDDpNccO3YM06ZNQ05ODvLy8jBr1izU1tZ6eBREMtq05qyrq8OQIUMwc+ZMTJkypcnv77zzTvz617/G73//e/Tu3Ru33norJk2ahPfeew+hUAgAMG3aNBw8eBDr1q1DLBbDNddcg2uvvRarVq3y+nC+mgf9nNID39XxNNH4Xkirk4ttutyH2qbJWVJSgpKSkpP+TimF5cuX45ZbbsFFF10EAHj88cfRrVs3rFmzBldccQXef/99rF27Fm+88QbOPvtsAMB9992H8847D7/61a9QVFR00tiRSASRyOc9xjU1NS4fGVHraVtz7tmzB5WVlZgwYULiudzcXIwcORLl5eUAgPLycuTl5SUSEwAmTJgA0zSxdevWL41dVlaG3NzcxKO4uFjuQIhaSNvkrKysBAB069Yt6flu3bolfldZWYmCgoKk3/v9fuTn5ydeczKlpaWorq5OPPbt2+fy3hO1Xofs5wwGgwgGg229G65TIdlJn9OOyb9dbOHhu2ZMri5X8Q7Sz1lYWAgAOHToUNLzhw4dSvyusLAQhw8fTvp9PB7HsWPHEq8hSlXaJmfv3r1RWFiI9evXJ56rqanB1q1bMWrUKADAqFGjUFVVhe3btydes2HDBti2jZEjR3q+z0RuatOvtbW1tdi1a1fi5z179qCiogL5+fno0aMHbrrpJvz3f/83vvWtbyW6UoqKinDxxRcDAE477TRMnjwZs2fPxkMPPYRYLIa5c+fiiiuu+NIrtUSpolnJGYvFcN111+HWW29F7969W73xbdu2Ydy4cYmfFyxYAACYPn06HnvsMdx8882oq6vDtddei6qqKowePRpr165N9HECwBNPPIG5c+di/PjxME0TU6dOxa9//esW7Y/kBF9eMKKyX4TSquU7a23hSwHxdLljsHzuxjaUUs16O+bm5qKiosKV5NRFTU0NcnNzMWDe7fAFQ1//D1qgoUA+661M2QtC6f+Sv9laOjmz9sr9HaxoGP/4/36O6upq5OTktDpesz9qL7744ibD7IjIfc2uOb/1rW9h6dKleO211zBs2DBkZmYm/f7GG290beeIOrJmJ+cjjzyCvLw8bN++PekqKQAYhsHkbEMqXfZmyLRa+a+1DVnC44MFD8Ht2M1Ozj179ri7B0R0Ui2+vBeNRrFz507E4y6ve0ZEAFqQnPX19Zg1axYyMjIwcOBA7N27FwAwb948LFu2zPUdJOqomp2cpaWl+Mc//oGNGzcm9TdOmDABf/zjH13dOaKOrNk155o1a/DHP/4R55xzDgzj807XgQMHYvfu3a7uHDWP0SB7wSatTr6vtjYgGz+WKTgIwd/Gq4wdOXKkyW1awIlZDZzJSkSt0+zkPPvss/H8888nfm5MyN/97neJAelE1HrN/lp7++23o6SkBO+99x7i8Tj+53/+B++99x62bNmCTZs2SewjUYfU7OQcPXo0KioqsGzZMgwaNAgvvfQSzjrrLJSXl2PQoEES++idVF/IKFt2FSB/g3BBCIifJysoWHO6HK9Ft4z17dsXv/3tb13eFSJyalFyWpaFp59+Gu+//z4A4PTTT8dFF10Ev79DznpCJKLZ2fTuu+/iwgsvRGVlJfr37w8AuOOOO9C1a1c8++yzOOOMM1zfSaKOqNnJ+e///u8YOHAgtm3bhk6dOgEAPv30U8yYMQPXXnsttmzZ4vpOtgeW8ORbAMTrNdPlCaxOTvYg4pmC93P63I3d7OSsqKhISkwA6NSpE375y19i+PDhru4cUUfW7H7OU089tcmMeABw+PBh9OvXz5WdIqJvmJw1NTWJR1lZGW688UY89dRT2L9/P/bv34+nnnoKN910E+644w7p/SXqML7R19q8vLykoXlKKVx++eWJ5xqnIbrgggtgWcKrnwpSPrmbcVWm/HkxqlJ/oSFfg2zNGeki93ewG9yN/Y2S85VXXnF1o0T09b5Rcn73u9+V3g8i+oIWjRoIh8PYsWMHDh8+DNtO7iK48MILXdkxoo6u2cm5du1aXH311fjkk0+a/M4wjJSuOW0/YAgNcgpkubyy6kmkfZglGt8KyPfV+sLSnbWpE7vZ4ebNm4fLLrsMBw8ehG3bSY9UTkwi3TQ7OQ8dOoQFCxY0WTeTiNzV7OS89NJLsXHjRoFdISKnZldY999/Py677DK8+uqrGDRoENLSkvvWUnlSaSsIQGapFISCsvdaAkBUehGgkPxNqcFPZeNHT5f7Oxg+d2M3Ozn/93//Fy+99BJCoRA2btyYNDiBM74TuafZyfnzn/8cS5YswaJFi2Ca2q69S5Tymp1d0WgUP/zhD5mYRMKanWHTp0/n5NFEHmj211rLsnDnnXfixRdfxODBg5tcELrnnntc2zmv2UEFBGVuxvX75PuAw2myN0NHOslfEPLXy8ZPC8it7WO5vG5Qs5Pz7bffxtChQwEA77zzTtLvOKk0kXuanZy8Q4XIG7yqQ6SpZrec48aN+8qvrxs2bGjVDrUlK6Sg0mXqNqXkv/LHhVeFNiz5Y4jmyh5DVHClJLvB3RsDmp2cZ555ZtLPsVgMFRUVeOeddzB9+nS39ouow2t2ct57770nfX7x4sWora1t9Q4R0Qmu1Zw/+tGP8Oijj7oVjqjDc+3W4vLy8qSVrlORCtpQQZkbimOW7MK2AGBGhCdkzpK/2doOydacpuQkaOE2mODLacqUKUk/K6Vw8OBBbNu2DbfeeqtrO0bU0TU7OXNzc5N+Nk0T/fv3x9KlSzFx4kTXdoyoo2t2cq5cuVJiP4joC1pcc0aj0ZPOvtejR49W71SbCVonHgKiUfnlEdMPydacxwVvVPZK+n65fk4r4u6Ynma/Y/75z39i1qxZTVYTU0ql/Ox7RDppdnJec8018Pv9eO6559C9e3cOdicS0qIlALdv344BAwZI7A8RfabZyXn66aefdELp9sCXZsMUmjg5HpPv5wxI38bgwW0S/sOyizFJjt21w+7GbvbpvuOOO3DzzTdj48aNOHr0aNLygDU1Na7uHFFH1uyWc8KECQCA8ePHJz3PC0JE7uLN1kSaanZytuflAH1+C6ZfqJ+zXn5hW1t6Ex7cz6n8smNrrVzJxXPdnUOIMyEQaYrJSaQpJieRppicRJqSH42dQnx+Gz6pC0Ix4SXAAASrZC+m1HtwQciSvqHbJ3iOXI7dosVzr7rqKhQVFcHv98Pn8yU9iMgdzW45Z8yYgb179+LWW2/lwHciQc1Ozr/97W949dVXm0yRSUTuanZyFhcXQynZ2qatmKaCaQodmwf1GqT/LPLze8HIkB3+qcKCpVfU3eurzY62fPlyLFq0CB999JGrO0JEyZrdcv7whz9EfX09+vbti4yMjCZLAB47dsy1nSPqyJqdnMuXLxfYDSL6omYnZ3teD8U0FExDpnAzw/LjPdKPyhaFn4pGP0EJ1+ZGVC6+27FbNQghHA4jGo0mPZeTk9OqHSKiE5r9cV5XV4e5c+eioKAAmZmZ6NSpU9KDiNzR7OS8+eabsWHDBqxYsQLBYBC/+93vsGTJEhQVFeHxxx+X2EeiDqnZX2ufffZZPP744xg7diyuueYanHvuuejXrx969uyJJ554AtOmTZPYz5Tnr5Xv5wx3Eq7XlHxHp4rJ1uZp1XLxbZevKzQ72rFjx9CnTx8AJ+rLxq6T0aNHY/Pmza7uHFFH1uzk7NOnD/bs2QMAGDBgAJ588kkAJ1rUvLw8V3eOqCNrdnJec801+Mc//gEAWLRoER544AGEQiHMnz8fP/3pT13fQaKOqtk15/z58xP/P2HCBHzwwQfYvn07+vXrh8GDB7u6c16zbBOwZWqSQI18zSk+wZcXQ6qFtyF5jmyXhwW3+mbrnj17omfPnm7sCxE5NLuZuPHGG/HrX/+6yfP3338/brrpJjf2iYjQguT885//jO985ztNnv/2t7+Np556ypWdIqIWfK09evRok6XngRPdKqm+wFEsasLyy9zvl1UrX7DFsoTrWg/u5zSF+zmV4O2cbsdu9pno168f1q5d2+T5F154IdH/SUSt1+yWc8GCBZg7dy6OHDmC733vewCA9evX4+677+btZEQuanZyzpw5E5FIBL/85S/xi1/8AgDQq1cvrFixAldffbXrO0jUUbWoK+WGG27ADTfcgCNHjiA9PR1ZWVlu7xdRh9fs5GxoaIBSChkZGejatSs+/vhj/O53v8Ppp5+OiRMnSuyjZ+JRP0yfzDzbZvTrX0NA6LDsBaH6YndXAnOyTXdHITT7TFx00UWJW8OqqqowYsQI3H333bjooouwYsUKV3eOqCNrdnK++eabOPfccwEATz31FAoLC/Hxxx/j8ccfP+ngBCJqmWYnZ319PbKzswEAL730EqZMmQLTNHHOOefg448/dn0HiTqqZhdY/fr1w5o1a3DJJZfgxRdfTAyEP3z4cMrPH6Qa/FBCazsZHnTgS3awA/BkTTp/vXD843InyXZ5wupmn+7bbrsNP/nJT9CrVy+MHDkSo0aNAnCiFR06dKirO0fUkTW7mbj00ksxevRoHDx4EEOGDEk8P378eFxyySWu7hxRR9ai73CFhYUoLCxMem7EiBGu7BARncDFcx2MiAnDlCmsxOtBAPEM6TuVZcMD8rW5KdfNCbgcm8vOE2mKyUmkqTZNzs2bN+OCCy5AUVERDMPAmjVrEr+LxWJYuHAhBg0ahMzMTBQVFeHqq6/GgQMHkmL06tULhmEkPZYtW+bxkRC5r01rzrq6OgwZMgQzZ87ElClTkn5XX1+PN998E7feeiuGDBmCTz/9FD/+8Y9x4YUXYtu2bUmvXbp0KWbPnp34uXGQRHP5IgZMQ+aGZfHJtwDEsoRrTsVJyryM3abJWVJSgpKSkpP+Ljc3F+vWrUt67v7778eIESOwd+9e9OjRI/F8dnZ2k6vHXyUSiSASiSR+rqmpaeaeE8lLqZqzuroahmE0mbx62bJl6Ny5M4YOHYq77roL8fhXXzYrKytDbm5u4lFcXCy410QtkzJdKeFwGAsXLsSVV16ZNEzwxhtvxFlnnYX8/Hxs2bIFpaWlOHjwIO65554vjVVaWooFCxYkfq6pqWGCknZSIjljsRguv/xyKKWa3JbmTLLBgwcjEAjguuuuQ1lZGYLB4EnjBYPBk/7OjAGmUH+kFZCv16TvGbVD8pOUhQtkt5F+SO7vYEXcja3919rGxPz444+xbt26rx1cP3LkSMTjcXz00Ufe7CCREK1bzsbE/PDDD/HKK6+gc+fOX/tvKioqYJomCgoKPNhDIjltmpy1tbXYtWtX4uc9e/agoqIC+fn56N69Oy699FK8+eabeO6552BZFiorKwEA+fn5CAQCKC8vx9atWzFu3DhkZ2ejvLwc8+fPx49+9COusk0pr02Tc9u2bRg3blzi58b6cfr06Vi8eDGeeeYZAMCZZ56Z9O9eeeUVjB07FsFgEKtXr8bixYsRiUTQu3dvzJ8/P6kObQ4jZsA0hfo5AyJhk/iEa04rW77mdHktoCZMl+tCJ+Xy+W/T5Bw7diyU+vI/+Ff9DgDOOussvP76627vFpEWtL8gRNRRMTmJNMXkJNKU1l0pXjPjcoMQbA/OdDxT9oKNrzb1P8ur+8vdzW03uBs79c82UTvF5CTSFJOTSFOsOR0MW26CKcuDQQhWfkw0fuBf8gcRzZcdhmCG5dojt987bDmJNMXkJNIUk5NIU6w5HSRrTi8mlTb9sjMyG5b8DePwy/bVSh6D27HZchJpislJpCkmJ5GmWHM62RBbrEf55G9UtmOyha0XCwBDuKyVvCHdcDk2W04iTTE5iTTF5CTSFGtOB0OdeEhQXnwMCvdDGtKzbwHix5BKCxmx5STSFJOTSFNMTiJNseZ0UpCrSTz4GPRVCf855btqxbchWfu7HZstJ5GmmJxEmmJyEmmKyUmkKV4QchAdhCATNonUvjcyPRiEYERl2wsl+I53OzZbTiJNMTmJNMXkJNIUa852JJ4XF40fPJomGh84sZiUJFtwAjG3Y7PlJNIUk5NIU0xOIk2x5nSSHPjuASOW+p+1Rkz2Zms7Xe4PzIHvRB0Ek5NIU0xOIk2x5vSKB2sA+Y7LftZKj90FACW8kJEKyQ0QVsrd2Gw5iTTF5CTSFJOTSFOsOT1ip3mwkFG27A2Xqkp+bK1hCxfnkuFdjs2Wk0hTTE4iTTE5iTTFmtPJgFhNYmfIrzybdlT2z2l78G7xNcjWnPF8wdrf5P2cRB0Ck5NIU0xOIk0xOYk0xQtCDrYfMITOiC87JhPYwQoLD3yXPwT4hS8IpRK2nESaYnISaYrJSaQp1pwOVgBAQCZ2ekZEJrBDpCEkGt8WOjdOZo30BlInNltOIk0xOYk0xeQk0hRrTgc7qICQzMDodL/wCj0AavNkb7Y2LPk+SN9+n+wGJBfndTk2W04iTTE5iTTF5CTSFGtOBysEKKGuQtOLSaVzZAe/WvXybxflk6050z6Vi2+H3Y3NlpNIU0xOIk0xOYk0xZrTwQ7ZQLrMRFxxS/5zULwm9GAlo1imbPzgUbni34q4G5stJ5GmmJxEmmJyEmmKNaeDCtpQQZmaMxoXHjMKIHBY9s8Z7So/PthKl61rJecoMlx+67DlJNIUk5NIU0xOIk0xOYk0xQtCDkbQghGUuWE5FpU/1eJjBDwYvB/PlD2I0Ce8IERErcTkJNIUk5NIU6w5HXw+G6ZfZhCCFZf/HJT+YxrCCyUBgEqTrTmV4CG4HZstJ5GmmJxEmmJyEmmKNaeDL82CL02mnzPcEBSJ6yVfnfxneTxHdmLsWJZcbCvN3XhsOYk0xeQk0hSTk0hTrDkdfD4Fn0+mnxNxDwamCo+t9YfljyHeSbif0y8XX8Xdjc2Wk0hTTE4iTbVpcm7evBkXXHABioqKYBgG1qxZk/T7GTNmwDCMpMfkyZOTXnPs2DFMmzYNOTk5yMvLw6xZs1BbW+vhURDJaNOas66uDkOGDMHMmTMxZcqUk75m8uTJWLlyZeLnYDC5v3DatGk4ePAg1q1bh1gshmuuuQbXXnstVq1a1ez98Zk2fKZMzWl4MLZWmiE/vxcQEKr5P2MF5GLbLu96myZnSUkJSkpKvvI1wWAQhYWFJ/3d+++/j7Vr1+KNN97A2WefDQC47777cN555+FXv/oVioqKTvrvIpEIIpFI4ueampoWHgGRHO0/zjdu3IiCggL0798fN9xwA44ePZr4XXl5OfLy8hKJCQATJkyAaZrYunXrl8YsKytDbm5u4lFcXCx6DEQtoXVyTp48GY8//jjWr1+PO+64A5s2bUJJSQks68QQr8rKShQUFCT9G7/fj/z8fFRWVn5p3NLSUlRXVyce+/btEz0OopbQup/ziiuuSPz/oEGDMHjwYPTt2xcbN27E+PHjWxw3GAw2qV0BwDQUTKGJeMx6+c9BW3j4ri8sGx8AYMv2pdoBuX5O2+7A/Zx9+vRBly5dsGvXLgBAYWEhDh8+nPSaeDyOY8eOfWmdSpQqUio59+/fj6NHj6J79+4AgFGjRqGqqgrbt29PvGbDhg2wbRsjR45sq90kckWbfq2tra1NtIIAsGfPHlRUVCA/Px/5+flYsmQJpk6disLCQuzevRs333wz+vXrh0mTJgEATjvtNEyePBmzZ8/GQw89hFgshrlz5+KKK6740iu1RKmiTVvObdu2YejQoRg6dCgAYMGCBRg6dChuu+02+Hw+7NixAxdeeCFOPfVUzJo1C8OGDcOrr76aVC8+8cQTGDBgAMaPH4/zzjsPo0ePxsMPP9xWh0TkmjZtOceOHQulvryIfvHFF782Rn5+fosGHJyMrQwYSuaCRKBGftB4NFe2A1/q3CSJybYXogPffR34ghBRR8LkJNIUk5NIU1oPQvBaLG7CFlqBOu24SNgkdppsTehFyWl4cVN6imDLSaQpJieRppicRJpizekQi/ph+WVOSXqD9Mq2QJ3wwrNplnw9aMSE62bBge9w+XIFW04iTTE5iTTF5CTSFGtOByvihzJlTokXk2NJ90NK3qjcyIzKHkQ8Q26hJOXywstsOYk0xeQk0hSTk0hTrDmdIiZgCn1eeTBkNO247GdtPFP2flEA8EWET5RkeJdjs+Uk0hSTk0hTTE4iTTE5iTTFC0IOZsSEach8XtkenGkrKDtIoD0MQoDk4H2XY7PlJNIUk5NIU0xOIk2x5nQwIwZMQ6YmsQNeTFwlWxNKTsic2IbM/GoJ/uNyG7DD7sZmy0mkKSYnkaaYnESaYs3pYMZPPCRYAZm4SYQ/aj1ZyEiYGRUM7nJstpxEmmJyEmmKyUmkKdacDkbcgCm0kI6dJhI2iZUhvHiu9I3QgPhN6abgpNXK5dhsOYk0xeQk0hSTk0hTrDkdDEtu8mcvak5lyo599cXkP8ulx+8qU7DmdPn0sOUk0hSTk0hTTE4iTbHmdDDsEw8JXsy/A+F6zZBbAyhBCb8jJY/B7fcOW04iTTE5iTTF5CTSFJOTSFO8IOSkIDZHlvTEVQAQOiA70sH2eTDBl/TY+hS6X5wtJ5GmmJxEmmJyEmmKNaeDoU48JLg9KLot+KQXGQIQz5Sta82YXGzl8k0T7eAtQ9Q+MTmJNMXkJNIUa06PeDIfs3A3pNSN6EnbED4Gyb+D27HZchJpislJpCkmJ5GmWHM6CY6t9WJMZ6Sz7N3Qmfs9GCAszBZcUMrmzdZEHQOTk0hTTE4iTbHmbE/aweK2sGWPIZ4lt9iT7Xc3NltOIk0xOYk0xeQk0hRrTq94UQ4Kz/Fje9DNaQpPXB0Pyp0jO+5ubLacRJpichJpislJpCkmJ5GmeEHIQXKCr/ZActB4I57+z7HlJNIUk5NIU0xOIk2x5vSKB8VU8IjsKAFbeOVsQHbSZwCwBQfWGy7feMCWk0hTTE4iTTE5iTTFmrMdiXSVHTWeVi3/WS59v7gy5epm5XInOVtOIk0xOYk0xeQk0hRrzvZE+oZuD/pqlfA70lcv1x4ZYXdjs+Uk0hSTk0hTTE4iTbHmdJJcyMiTek1uwuQTG5D/LDeED8EKCU7wpdjPSdQhMDmJNMXkJNIUa852xIjKftZ6Mb+SGZbtrLXSBQ/C5V1ny0mkKSYnkabaNDk3b96MCy64AEVFRTAMA2vWrEn6vWEYJ33cddddidf06tWrye+XLVvm8ZEQua9Nk7Ourg5DhgzBAw88cNLfHzx4MOnx6KOPwjAMTJ06Nel1S5cuTXrdvHnzvNh9IlFtekGopKQEJSUlX/r7wsLCpJ//+te/Yty4cejTp0/S89nZ2U1e2yKSgxA8+Bj018luxPbg3RKol41vCN6P7nbslKk5Dx06hOeffx6zZs1q8rtly5ahc+fOGDp0KO666y7E4/GvjBWJRFBTU5P0INJNynSl/P73v0d2djamTJmS9PyNN96Is846C/n5+diyZQtKS0tx8OBB3HPPPV8aq6ysDEuWLJHeZaJWSZnkfPTRRzFt2jSEQqGk5xcsWJD4/8GDByMQCOC6665DWVkZgsHgSWOVlpYm/buamhoUFxfL7DhRC6VEcr766qvYuXMn/vjHP37ta0eOHIl4PI6PPvoI/fv3P+lrgsHglyauFEtwReVGwWOyVUosS/4Y0mpl49f1kBtZb8Pd2ClRcz7yyCMYNmwYhgwZ8rWvraiogGmaKCgo8GDPiOS0actZW1uLXbt2JX7es2cPKioqkJ+fjx49egA48ZXzT3/6E+6+++4m/768vBxbt27FuHHjkJ2djfLycsyfPx8/+tGP0KlTJ8+Og0hCmybntm3bMG7cuMTPjXXg9OnT8dhjjwEAVq9eDaUUrrzyyib/PhgMYvXq1Vi8eDEikQh69+6N+fPnJ9WTRKnKUMrlO0RTUE1NDXJzc3Hq/NvhC4a+/h+0QN2pUZG4TqG9sqvbRjsJ3wkNIHOvbKVVOzgiFttuCGPf9UtQXV2NnJycVsdLiZqTqCNichJpislJpKmU6Of0iu0HDKEzkpYpX3MCsjWn8mDx3HCB7DaMT9PkYofdHVzLlpNIU0xOIk0xOYk0xZrTwQ4oGEJjYNNDMZG4TpZcFx4AQAXl+zmtuOwEX0ZMLr7h8r6z5STSFJOTSFNMTiJNseZ0sEMAZIbWwu8TnLzmM1HhW1QDOcJFLYCoKdtXKzn+2HJ5Qmy2nESaYnISaYrJSaQpJieRpnhByMEK2VDpMh3tSsl2rgNwfZWrL1K2/DGYNbJvyXh/uVmr7fqwq/HYchJpislJpCkmJ5GmWHM6qKAtNrg7ZvlE4jqFu8sOrjdi8segMmQHa9h1cjdb2w282ZqoQ2ByEmmKyUmkKdacDkbQghGUqXmiUQ9OtU92ciwVl/8sN4Rv6Fax1GmPUmdPiToYJieRppicRJpizeng89sw02RqHisu30eY1Vlu3CgA1H6aIRrfC0ZUrj1yOzZbTiJNMTmJNMXkJNIUa04H02/B55fp54zUyU5cBQBmZuqvg6wisu2FIdmN6nJstpxEmmJyEmmKyUmkKdacDn6/gs8vU5REPBiXWnMkS3YDRurXtKLNkcux2XISaYrJSaQpJieRppicRJriBSEHn2nDZwr1Uguv2AwACAmvZCY4aDxBeOJq33HBge9hDnwn6hCYnESaYnISaYo1p4NkzWkKD+gGANsvPEjAg8WYDEt4GxyEQEStxeQk0hSTk0hTrDk94q+Vr9eimcIb8GDcuyHcH6wEJ952OzZbTiJNMTmJNMXkJNIUa06HuG1C2TKfV2nHPag5C4Q34EnNKRtfCb7jlcvzhrPlJNIUk5NIU0xOIk2x5nSIxXywYzKnJFQrEjaZ8L2QnpA+BE4qTUStxeQk0hSTk0hTrDkdYhE/LJ/MKcmMyncSGunCnYS1abLxAdjS70hTcGxtnGNriToEJieRppicRJpichJpiheEHOyIDzBdHr3cyIsFuoQn4DJi8oMclOAFmxMbSJ3YbDmJNMXkJNIUk5NIU6w5HYyID4ZQzen2jbgnY/gkR3XL3wgNAIaSbS/sdMFzxEmliToGJieRppicRJpizelgRA0YpkxfnhWQ7yO0hQemG/Lj3pH1sex5qu0rGNzlXWfLSaQpJieRppicRJpizelgRg2YQjWnHRAJm+SUdbL12r++Jz9AOFAtu43gEbkOZyvsbmy2nESaYnISaYrJSaQp1pwOpnXiIcHyoOb01wnt/GcMoQm3nax02bo5Z7dcTWu5PIkbW04iTTE5iTTF5CTSFGtOJwswhMo224NxqYEXt4nG948eJRofAIRv50Rag9z9nEbM3dhsOYk0xeQk0hSTk0hTTE4iTfGCkINhGzCEVoe20+QHjZsZGaLx/fXyN4zHMmXj2z65Y7Bdfu+w5STSVJsmZ1lZGYYPH47s7GwUFBTg4osvxs6dO5NeEw6HMWfOHHTu3BlZWVmYOnUqDh06lPSavXv34vzzz0dGRgYKCgrw05/+FPG4B/M4Eglq0+TctGkT5syZg9dffx3r1q1DLBbDxIkTUVdXl3jN/Pnz8eyzz+JPf/oTNm3ahAMHDmDKlCmJ31uWhfPPPx/RaBRbtmzB73//ezz22GO47bbb2uKQiFxjKKW8WGLnGzly5AgKCgqwadMmjBkzBtXV1ejatStWrVqFSy+9FADwwQcf4LTTTkN5eTnOOeccvPDCC/jBD36AAwcOoFu3bgCAhx56CAsXLsSRI0cQCHz9iPOamhrk5uai76Lb4QuGRI7NSpc/zZ3ek41fNUA2PiA/+XaXt+T+DvFYGNv/fAuqq6uRk5PT6nha1ZzV1dUAgPz8fADA9u3bEYvFMGHChMRrBgwYgB49eqC8vBwAUF5ejkGDBiUSEwAmTZqEmpoavPvuuyfdTiQSQU1NTdKDSDfaJKdt27jpppvwne98B2eccQYAoLKyEoFAAHl5eUmv7datGyorKxOvcSZm4+8bf3cyZWVlyM3NTTyKi4tdPhqi1tMmOefMmYN33nkHq1evFt9WaWkpqqurE499+/aJb5OoubTo55w7dy6ee+45bN68Gaecckri+cLCQkSjUVRVVSW1nocOHUJhYWHiNX//+9+T4jVezW18zRcFg0EEg0GXj+KrSQ/oBoBPB8rG92Iho1iu7A3jh0fI/SHsMIA/uxevTVtOpRTmzp2Lp59+Ghs2bEDv3r2Tfj9s2DCkpaVh/fr1ied27tyJvXv3YtSoE3dIjBo1Cm+//TYOHz6ceM26deuQk5OD008/3ZsDIRLQpi3nnDlzsGrVKvz1r39FdnZ2okbMzc1Feno6cnNzMWvWLCxYsAD5+fnIycnBvHnzMGrUKJxzzjkAgIkTJ+L000/HVVddhTvvvBOVlZW45ZZbMGfOHM9bRyI3tWlyrlixAgAwduzYpOdXrlyJGTNmAADuvfdemKaJqVOnIhKJYNKkSXjwwQcTr/X5fHjuuedwww03YNSoUcjMzMT06dOxdOlSrw6DSIRW/ZxtxYt+zliO7MK2AGDGZce++o/Lj62NDqwXjW+acn8Huz6MPdfc3j77OYnoc0xOIk0xOYk0pUU/p1aEyiov+jnjwuN3A1XyNadVL/uWNLOiovHdxJaTSFNMTiJNMTmJNMWa0yvy5RpUULYvVRnyn+VmSHZsbaxGbtSY3cCFjIg6BCYnkaaYnESaYnISaYoXhJwMyF248eCCUKBTWHYDe7Nk4wOA9G0YfsENuBybLSeRppicRJpichJpijWngzJOPFJVp2zZG5VrfPI1pxJaSKqRYcrVnG7HZstJpCkmJ5GmmJxEmmLN6WQqKKGaxItatiGaJhrfCsnPBafqhG+2zomJxTZ87t54wJaTSFNMTiJNMTmJNMWa00lybK0Hjteki8Y3PFgA2AzLthe2T+4tbze4G5stJ5GmmJxEmmJyEmmKNadTitec6e/LLMLUqOH/yU6+BQBGTPgPIDl21+XYbDmJNMXkJNIUk5NIU6w5HSTv5zQ8WKLYEu6HVGnyCwAbcZ/wBlInNltOIk0xOYk0xeQk0hSTk0hTvCDklOKDEAzhybFEJ2T+jPgghBzBi1oWb7Ym6hCYnESaYnISaYo1p1OK15zSiwD5hFedBoDgsYBo/LBf8C0f5s3WRB0Ck5NIU0xOIk2x5nRQECzbPBj4Lr0N0ydfc/obZONnHJRrj6yIu7HZchJpislJpCkmJ5GmWHM6pXg/pyF8L7QlfSM0AFt2LSaEjsgV5laUi+cSdQhMTiJNMTmJNMWa04k151eya4ULQgCxbNnO2swDgvFZcxJ1DExOIk0xOYk0xZrTQXJS6fYwttaIyhfk0VzZg7CCcsdgGVzIiKhDYHISaYrJSaQpJieRpnhByMlQ3iwHJiSeKbvv/jr5z/K45KTPAKr6y10QssPuxmPLSaQpJieRppicRJpizekRL0pZJXwvtC8sPwgh1kn2RNkBufi2zYHvRB0Ck5NIU0xOIk2x5nRK8Zut4xmy9ZovksInp5HkKXI5NltOIk0xOYk0xeQk0hRrzvYkLyoaXlWFROMDAHyydbNkf7PbsdlyEmmKyUmkKSYnkaZYc3rFi9tEq2UnffbkVlfhbUiOP3Y7NltOIk0xOYk0xeQk0hRrTgfJSaU9uZ/TL9xHaImGP7GNqGx7IXmOlMt9tGw5iTTF5CTSFJOTSFNMTiJN8YKQV8SWL/ucKX0xRXgCMQAIfiK7kXBhXCy22xeb2HISaYrJSaQpJieRplhztiOm8KTPdpr8SIrQJ7LHEM0TbI8i7sZmy0mkKSYnkab4tRaAUie+rtlhlxdYdDAs+a4U8bGvHoyttYTnxrXDcut/Nr5/Gt9PrWUotyKlsP3796O4uLitd4PaiX379uGUU05pdRwmJwDbtnHgwAFkZ2fDMNrBrObUJpRSOH78OIqKimCara8YmZxEmuIFISJNMTmJNMXkJNIUk5NIU0xOIk0xOYk0xeQk0tT/DyoEJOgRV/KtAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 238x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.11/site-packages/nilearn/glm/_utils.py:344: RuntimeWarning: divide by zero encountered in divide\n",
      "  return np.where(X <= 0, 0, 1.0 / X)\n",
      "/usr/local/lib/python3.11/site-packages/nilearn/plotting/displays/_slicers.py:382: UserWarning: empty mask\n",
      "  get_mask_bounds(new_img_like(img, not_mask, affine))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 730x260 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropping columns with high VIF: [ 2  7 10 11]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 307x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.11/site-packages/nilearn/glm/_utils.py:344: RuntimeWarning: divide by zero encountered in divide\n",
      "  return np.where(X <= 0, 0, 1.0 / X)\n",
      "/usr/local/lib/python3.11/site-packages/nilearn/plotting/displays/_slicers.py:382: UserWarning: empty mask\n",
      "  get_mask_bounds(new_img_like(img, not_mask, affine))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 730x260 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropping columns with high VIF: [2 6 7 8 9]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 284x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.11/site-packages/nilearn/plotting/displays/_slicers.py:382: UserWarning: empty mask\n",
      "  get_mask_bounds(new_img_like(img, not_mask, affine))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 730x260 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# looping through the functional data and fitting the GLM model\n",
    "for i in range(len(files_trial)):\n",
    "    # save the first 5 charaters of the file name as a subject_id string\n",
    "    subject_id = os.path.basename(files_trial[i])[:11] \n",
    "    glm_model = FirstLevelModel(subject_label=subject_id, t_r=0.8, hrf_model='spm+derivative',\n",
    "                                drift_model='polynomial', noise_model='ols', standardize=False, signal_scaling=False)\n",
    "\n",
    "    # Identifying multicollinearity using VIF (Variance Inflation Factor) and dropping columns with VIF > 5\n",
    "    vif = pd.DataFrame()\n",
    "    vif[\"conf_columns\"] = des_trials[i].columns # independent variables\n",
    "    vif[\"VIF\"] = [variance_inflation_factor(des_trials[i].values, j) for j in range(des_trials[i].shape[1])]\n",
    "\n",
    "    # Drop columns with VIF > 5 (this is just a commonly used value, adjust according to your needs)\n",
    "    high_vif_columns = vif[vif[\"VIF\"] > 5][\"conf_columns\"]\n",
    "\n",
    "    if len(high_vif_columns) > 0:\n",
    "        des_trials[i] = des_trials[i].drop(columns=high_vif_columns)\n",
    "        # print the statement to drop the columns\n",
    "        print(\"Dropping columns with high VIF: \" + str(high_vif_columns.values))\n",
    "\n",
    "\n",
    "    # plot the design matrix using plot_design_matrix\n",
    "    plot_design_matrix(des_trials[i])\n",
    "    plt.show()\n",
    "\n",
    "    # glm_model = glm_model.fit(files, design_matrices=design_fsl_hvc)\n",
    "    glm_model = glm_model.fit(files_trial[i], design_matrices=des_trials[i])\n",
    "\n",
    "    # making a dictionary of conditions\n",
    "    ncols = des_trials[i].shape[1]\n",
    "\n",
    "    conditions = {\"Pain\": np.zeros(ncols), \"rest\": np.zeros(ncols)}\n",
    "    conditions[\"Pain\"][0] = 1\n",
    "    conditions[\"rest\"][1] = 1\n",
    "\n",
    "    pain_minus_rest = conditions[\"Pain\"] - conditions[\"rest\"]\n",
    "\n",
    "    # saving the stat maps\n",
    "    z_map = glm_model.compute_contrast(pain_minus_rest, output_type=\"z_score\")\n",
    "    # z_map = glm_model.compute_contrast(conditions[\"Pain\"], output_type=\"z_score\")\n",
    "    # p_map = glm_model.compute_contrast(conditions[\"Pain\"], output_type=\"p_value\")\n",
    "    # t_map = glm_model.compute_contrast(conditions[\"Pain\"], output_type=\"stat\")\n",
    "\n",
    "    # save the z_map as a nifti file by replacing the suffix with _z_map.nii.gz in the folder statspath\n",
    "    # z_map.to_filename(statspath + os.path.basename(files_trial[i])[:-7] + '_z_map.nii.gz')\n",
    "    z_map.to_filename(statspath + subject_id + '_z_map_pain.nii.gz')\n",
    "\n",
    "    # plotting the stat maps\n",
    "    plot_stat_map(\n",
    "    z_map,\n",
    "    bg_img=mean_img_func,\n",
    "    # bg_img='/serverpath/NLA36/Anat/NLA36_T1_anat_brain.nii.gz',\n",
    "    # bg_img=bkg,\n",
    "    threshold=2.0,\n",
    "    display_mode=\"ortho\",\n",
    "    cut_coords=[3, 32, 40],\n",
    "    black_bg=True,\n",
    "    draw_cross=False,\n",
    "    title=\"Pain (Z>2)\",\n",
    "    )\n",
    "    plt.show()\n",
    "\n",
    "    # plot_stat_map(\n",
    "    # z_map,\n",
    "    # display_mode=\"mosaic\",\n",
    "    # title=\"Pain(Z>2)\",\n",
    "    # threshold=2.0,\n",
    "    # # cut_coords=[3, 32, 40],\n",
    "    # black_bg=True,\n",
    "    # draw_cross=False,\n",
    "    # # bg_img='/serverpath/NLA02/Anat/NLA02_T1_anat_brain.nii.gz')\n",
    "    # bg_img=mean_img_func)\n",
    "    # plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The statistical maps are saved as and when the GLM is fit for each run. The lines of code for plotting them can be separated into a different function for ease and modular scripting.\n",
    "\n",
    "Since the resulting z_maps have unreliably less voxels of activation and error-prone due to apparent multicollinearity, the new GLM design files are provided as new confounds .txt files to the FSL fsf templates using the batch processing shell script."
   ]
  }
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