{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computation of motion artifaction correction metrics:\n",
    "\n",
    "1. QC-FC correlation\n",
    "2. QC-FC distance dependence\n",
    "3. Modularity quality\n",
    "4. Modularity quality-motion correlation\n",
    "5. Recovery of canonical resting state networks (RSNs) via seed-based connectivity \n",
    "\n",
    "For computation of RSN recovery via Group ICA, we refer users to the [GIFT toolbox](https://trendscenter.org/software/gift/).\n",
    "\n",
    "Copyright (c) 2021 The University of Texas Southwestern Medical Center."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/project/bioinformatics/DLLab/shared/CondaEnvironments/KevinMoCorr/lib/python3.9/site-packages/nilearn/datasets/__init__.py:87: FutureWarning: Fetchers from the nilearn.datasets module will be updated in version 0.9 to return python strings instead of bytes and Pandas dataframes instead of Numpy arrays.\n",
      "  warn(\"Fetchers from the nilearn.datasets module will be \"\n",
      "Failed to import duecredit due to No module named 'duecredit'\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import nilearn.plotting\n",
    "\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "from qctool import QCTool"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "User-specific paths to functional brain atlas and DMN template (binary mask). \n",
    "\n",
    "The functional brain atlas is used to compute functional connectivity for the QC-FC, QC-FC distance dependence, modularity quality, and modularity quality-mFD correlation metrics.\n",
    "\n",
    "This particular DMN template was downloaded from https://brainnexus.com/resting-state-fmri-templates/. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ATLAS = '/project/bioinformatics/DLLab/Kevin/MotionCorrection/MotionCorrectionCode/atlases/Parcels/Parcels_MNI_333.nii'\n",
    "DMN = '/project/bioinformatics/DLLab/shared/Atlases/brainnexus templates/rsfmrinetwork_default.nii.gz'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create lists of fMRI files and corresponding head motion parameter files. We used [FSL MCFLIRT](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/MCFLIRT) to compute these head motion parameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Example only:\n",
    "\n",
    "# PROCESSING_DIR = '/project/bioinformatics/DLLab/Vyom/Pipelines/ABIDE1/NYU_041620_ExpandedPipelines'\n",
    "\n",
    "# lsImages = glob.glob(os.path.join(PROCESSING_DIR, 'sub*', 'motion', '*hmpSaromaSphysioSfreq_bold.nii.gz'))\n",
    "# lsImages.sort()\n",
    "\n",
    "# lsHmp = glob.glob(os.path.join(PROCESSING_DIR, 'sub*', 'motion', 'hmp_exp.csv'))\n",
    "# lsHmp.sort()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize QCTool with the lists of fMRI and head motion parameter files. Processing can be parallelized using the `n_procs` argument. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "qc = QCTool(lsImages, lsHmp, n_procs=4)\n",
    "qc.set_atlas(ATLAS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute QC-FC and QC-FC distance dependence metrics. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing mean framewise displacement\n",
      "Computing functional connectivity with the following atlas: /project/bioinformatics/DLLab/Kevin/MotionCorrection/MotionCorrectionCode/atlases/Parcels/Parcels_MNI_333.nii\n",
      "100%|██████████| 120/120 [11:48<00:00,  5.90s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='mFD', ylabel='Count'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfQcFc, dictQcFcDD = qc.compute_qcfc()\n",
    "sns.histplot(qc.dfMfd) # mean framewise displacement\n",
    "sns.histplot(dfQcFc['QC-FC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QC-FC distance dependence:\n",
      "{'Spearman_r': 0.004287983237572309, 'Spearman_p': 0.31338662364885606, 'Pearson_r': 0.0028478641274723845, 'Pearson_p': 0.5031412843765252}\n"
     ]
    }
   ],
   "source": [
    "print('QC-FC distance dependence:')\n",
    "print(dictQcFcDD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute network modularity quality (using the Louvain algorithm) and modularity quality-mFD correlation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='modularity', ylabel='Count'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfModularity, dictModularityFD = qc.compute_modularity()\n",
    "sns.histplot(dfModularity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modularity-mFD correlation\n",
      "{'Spearman_r': 0.10758385999027707, 'Spearman_p': 0.24216813248845886, 'Pearson_r': 0.05515819946999107, 'Pearson_p': 0.5496002935333373}\n"
     ]
    }
   ],
   "source": [
    "print('Modularity-mFD correlation')\n",
    "print(dictModularityFD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute seed-based connectivity maps using a posterior cingulate seed (4, -54, 26), then binarize at a Z-threshold of 0.4 and compute Dice similarity with the DMN template."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 120/120 [16:26<00:00,  8.22s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sbc_dice', ylabel='Count'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "dfSBCDice = qc.compute_sbc_template_dice(DMN, seed=(4, -54, 26), threshold=0.4)\n",
    "sns.histplot(dfSBCDice)"
   ]
  }
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