F_Fig4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.simplefilter('ignore')\n",
    "import pandas as pd # for creation of data frames\n",
    "import numpy as np #\n",
    "import pyls # PLS: behavioral and mean-centered\n",
    "import seaborn as sns\n",
    "import nibabel as nib # for loading niftis etc. as arrays\n",
    "import matplotlib.pyplot as plt # for plotting data\n",
    "#the output of plotting commands is displayed inline, directly below the code cell that produced it\n",
    "%matplotlib inline \n",
    "import os,glob\n",
    "from nilearn import plotting, input_data, image #for plotting & working with niftis\n",
    "from scipy import stats \n",
    "\n",
    "import pathlib\n",
    "\n",
    "###################### update path!! #####################################\n",
    "base_path = '/root_dir/' ####\n",
    "##########################################################################\n",
    "\n",
    "#import qBOLD functions\n",
    "#import sys\n",
    "#sys.path.append(os.path.join(base_path + 'scripts/'))\n",
    "#import mqBOLD_functions as qB\n",
    "\n",
    "\n",
    "### define FSL directories ######################################################\n",
    "os.environ[\"FSLDIR\"]='/usr/share/fsl/5.0'\n",
    "os.environ[\"FSLOUTPUTTYPE\"]='NIFTI_GZ'\n",
    "os.environ[\"FSLTCLSH\"]='/usr/bin/tclsh'\n",
    "os.environ[\"FSLWISH\"]='/usr/bin/wish'\n",
    "os.environ[\"FSLMULTIFILEQUIT\"]=\"True\"\n",
    "os.environ[\"LD_LIBRARY_PATH\"]='/usr/share/fsl/5.0:/usr/lib/fsl/5.0'\n",
    "#################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## define variables (please check!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = base_path + 'data/'\n",
    "derivatives_dir = data_dir + 'derivatives'\n",
    "raw_dir = data_dir + 'rawdata'\n",
    "results_dir = base_path + 'results'\n",
    "MNI_2mm_brain = derivatives_dir + '/MNI152_T1_2mm_brain.nii.gz'\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "coords=(-15, 0, 15, 30, 45, 60, 75, 90)\n",
    "\n",
    "## run qBOLD toolbox\n",
    "%run qBOLD_fun.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Please which CMRO2 version to use ##\n",
    "###################################################\n",
    "\n",
    "#CMRO2_mode = 'orig' ## orig = CMRO2 maps NOT corrected for CBV changes, only CMRO2 maps of subj > 55 in CALC condition are corrected for CBV increases\n",
    "CMRO2_mode = 'corrected' ## CMRO2 CALC maps corrected for CBV changes (but MEM maps are not corrected)\n",
    "\n",
    "## Please specify task & baseline ##\n",
    "####################################\n",
    "\n",
    "baseline='control'\n",
    "task='calc'\n",
    "conds = [baseline, task]\n",
    "contrast=task+baseline\n",
    "\n",
    "## Please specify subject list##\n",
    "################################\n",
    "\n",
    "if baseline == 'control' and task == 'calc':\n",
    "    #N=40\n",
    "    sids = [19, 20, 21, 23, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 43, 44, 46, 47, 48, 49, 50, 51, 52, 54, 55, 58, 59, 60, 61, 63, 64, 65, 66, 67, 68]\n",
    "else: #N=30, without p053\n",
    "    sids = [19, 20, 21, 23, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 43, 44, 46, 47, 48, 49, 50, 51, 52, 54, 55]\n",
    "\n",
    "#N=30\n",
    "sids_30 = [19, 20, 21, 23, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 43, 44, 46, 47, 48, 49, 50, 51, 52, 54, 55]\n",
    "\n",
    "##########################################################\n",
    "## please select which group task mask to apply!! ##\n",
    "##########################################################\n",
    "\n",
    "#masks = ['Yeo', 'PLS BOLD', 'PLS CBF', 'GLM BOLD']\n",
    "#masks = ['PLS CBF', 'PLS CMRO2', 'PLS OEF']\n",
    "mask = [ 'PLS BOLD'] ## this is the PLS BOLD group mask\n",
    "#masks = [ 'PLS CBF']\n",
    "#masks = ['GLM_BOLD']\n",
    "\n",
    "\n",
    "### other variables ##############\n",
    "##################################\n",
    "\n",
    "N_subj = str(len(sids))\n",
    "GM_thresh = 0.5\n",
    "BSR_thr=2\n",
    "\n",
    "## Gray-matter mask, excluding areas with low signal-to-noise ratio according to fMRI data\n",
    "\n",
    "YEO_SNR_mask = os.path.join( derivatives_dir, 'task-all_space-MNI152_res-2_SNR_YEO_group_mask.nii.gz')\n",
    "mask_img = nib.load(YEO_SNR_mask)\n",
    "masker_GM = input_data.NiftiMasker(mask_img=YEO_SNR_mask) ## exclude areas with too less SNR (based on the localizer data)\n",
    "YEO_SNR_mask_arr = np.array(mask_img.dataobj)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Blue-red color palette"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fea80608760>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 300x50 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "import matplotlib as mpl\n",
    "\n",
    "colors = [\"darkblue\", \"blue\", \"dodgerblue\",  \"whitesmoke\",\"darkorange\", \"red\", \"darkred\"]\n",
    "BlueRed = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(3,0.5))\n",
    "fig.subplots_adjust(bottom=0.5)\n",
    "\n",
    "bounds = [-6, -4, -2, 0, 2, 4, 6]\n",
    "norm = mpl.colors.Normalize(vmin=-6, vmax=6)\n",
    "\n",
    "fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=BlueRed), \n",
    "             cax=ax, ticks=bounds, orientation='horizontal')\n",
    "\n",
    "## inverted colormap: BlueRed_r\n",
    "colors = [ \"darkred\",\"red\", \"darkorange\", \"whitesmoke\", \"dodgerblue\", \"blue\",\"darkblue\"]\n",
    "BlueRed_r = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 2))\n",
    "fig.subplots_adjust(bottom=0.5)\n",
    "\n",
    "bounds = [-6, -4, -2, 0, 2, 4, 6]\n",
    "norm = mpl.colors.Normalize(vmin=-6, vmax=6)\n",
    "\n",
    "fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=BlueRed_r), \n",
    "             cax=ax, ticks=bounds, orientation='horizontal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fig. 4A, baseline hemodynamics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "## difference in medians\n",
    "def statistic(x_true, x_false):\n",
    "        return np.median(x_true, axis=0) - np.median(x_false, axis=0)\n",
    "def statistic_GM(x_GM, x_false):\n",
    "        return np.median(x_GM, axis=0) - np.median(x_false, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "sub-p058\n",
      "sub-p059\n",
      "sub-p060\n",
      "sub-p061\n",
      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1540x230 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parameters = [ 'oef','cmro2', 'cbf', 'cbv']\n",
    "#parameters = [ 'rOEF', 'CMRO2']\n",
    "\n",
    "##choose if negative (DMN) or positive BOLD areas\n",
    "colormap = ([\"plum\", \"darkviolet\"])\n",
    "\n",
    "##only in baseline cond\n",
    "baseline_cond = 'control'\n",
    "BOLD_ROI = 'all'\n",
    "\n",
    "## create array of masks with all concordant and all discordant voxels ##\n",
    "#################################################################\n",
    "masks_concord=[]\n",
    "masks_discord=[]\n",
    "contrasts = ['calccontrol', 'memcontrol']\n",
    "## these masks are output from script E, Fig. 3\n",
    "for contrast in contrasts:\n",
    "\n",
    "    ## divide into: real deactivation and dissociation areas!\n",
    "    PLS_true_deact = os.path.join(results_dir, 'negBOLD_negCMRO2_' + contrast + '.nii.gz') #negBOLD_negCMRO2_memcontrol_CMRO2deact\n",
    "    #plotting.plot_img(PLS_true_deact, cmap=colormap,  bg_img=MNI_brain, threshold=0, display_mode='z')\n",
    "\n",
    "    PLS_dissociation = os.path.join(results_dir, 'negBOLD_posCMRO2_' + contrast + '.nii.gz')\n",
    "    #plotting.plot_img(PLS_dissociation, cmap=colormap,  bg_img=MNI_brain, threshold=0, display_mode='z')\n",
    "    \n",
    "    masks_concord.append(PLS_true_deact)\n",
    "    masks_discord.append(PLS_dissociation)\n",
    "    \n",
    "    ## divide into: real deactivation and dissociation areas!\n",
    "    PLS_true_act =  os.path.join(results_dir, 'posBOLD_posCMRO2_' + contrast + '.nii.gz')\n",
    "\n",
    "    #plotting.plot_img(PLS_true_act, cmap=colormap,  bg_img=MNI_brain, threshold=0, display_mode='z')\n",
    "\n",
    "    PLS_dissociation = os.path.join(results_dir,  'posBOLD_negCMRO2_' + contrast + '.nii.gz')\n",
    "    #plotting.plot_img(PLS_dissociation, cmap=colormap,  bg_img=MNI_brain, threshold=0, display_mode='z')\n",
    "\n",
    "    masks_concord.append(PLS_true_act)\n",
    "    masks_discord.append(PLS_dissociation)\n",
    "    \n",
    "    \n",
    "## create mask that only has GM voxels, but not all other PLS voxels\n",
    "GM_YEO = YEO_SNR_mask\n",
    "PLS_calccontrol = results_dir + '/N40_BSR_meanPLS_func_clusterBSRcalccontrol.nii.gz'\n",
    "PLS_calccontrol_bin = results_dir + '/N40_BSR_meanPLS_func_clusterBSRcalccontrol_bin.nii.gz'\n",
    "! fslmaths {PLS_calccontrol} -abs -bin {PLS_calccontrol_bin}\n",
    "PLS_memcontrol = results_dir + '/N30_BSR_meanPLS_func_clusterBSRmemcontrol.nii.gz'\n",
    "PLS_memcontrol_bin = results_dir + '/N30_BSR_meanPLS_func_clusterBSRmemcontrol_bin.nii.gz'\n",
    "GM_mask_without_PLS = results_dir + '/GM_vox_without_calccontrol_memcontrol_vox.nii.gz'\n",
    "! fslmaths {PLS_memcontrol} -abs -bin {PLS_memcontrol_bin}\n",
    "! fslmaths {GM_YEO} -add {PLS_calccontrol_bin} -add {PLS_memcontrol_bin} -uthr 1 {GM_mask_without_PLS}\n",
    "plotting.plot_img(GM_mask_without_PLS, cmap='jet',  bg_img=MNI_2mm_brain, threshold=0, display_mode='z')\n",
    "\n",
    "masker_GM = input_data.NiftiMasker(mask_img = YEO_SNR_mask)\n",
    "masker_GM_woPLS = input_data.NiftiMasker(mask_img = GM_mask_without_PLS)\n",
    "GM_mask_without_PLS_arr = np.array(nib.load(GM_mask_without_PLS).dataobj)\n",
    "\n",
    "for par in parameters: \n",
    "    vox_true= []\n",
    "    vox_diss= []\n",
    "    \n",
    "    ##################################\n",
    "    ## all concordant voxels ##\n",
    "    #################################\n",
    "    \n",
    "    for m, mask in enumerate(masks_concord): ## masks: 4 masks: all condordant, two contrasts, either both pos or both neg\n",
    "        masker = input_data.NiftiMasker(mask_img = mask) \n",
    "        mask_arr = np.array(nib.load(mask).dataobj)\n",
    "        mask_arr_GM = np.array(nib.load(YEO_SNR_mask).dataobj)\n",
    "        mask_arr_GM_woPLS = np.array(nib.load(GM_mask_without_PLS).dataobj)\n",
    "        par_median_vox = np.zeros((len(np.argwhere(mask_arr)), len(sids)))\n",
    "        par_median_vox_GM  = np.zeros((len(np.argwhere(mask_arr_GM)), len(sids)))\n",
    "        par_median_vox_GM_woPLS  = np.zeros((len(np.argwhere(mask_arr_GM_woPLS)), len(sids)))\n",
    "        for i, ID in enumerate(sids): #loop over subjects\n",
    "            sub = \"sub-p{:03d}\".format(ID) # subject id, eg 'p021'\n",
    "            print(sub)\n",
    "\n",
    "            sub_dir = os.path.join(data_dir, sub)\n",
    "\n",
    "            dir_anat = os.path.join(sub_dir, 'anat')\n",
    "            dir_func = os.path.join(sub_dir, 'func')\n",
    "            dir_perf = os.path.join(sub_dir, 'perf')\n",
    "\n",
    "            dir_anat_deriv = os.path.join(derivatives_dir, sub, 'anat')\n",
    "            dir_func_deriv = os.path.join(derivatives_dir, sub, 'func')\n",
    "            dir_perf_deriv = os.path.join(derivatives_dir, sub, 'perf')\n",
    "            dir_qmri_deriv = os.path.join(derivatives_dir, sub, 'qmri')\n",
    "\n",
    "            ## \n",
    "            if par !='cmro2':\n",
    "                par_map = os.path.join(dir_qmri_deriv, sub + '_task-' + baseline_cond + '_space-MNI152_'+par+'.nii.gz')\n",
    "            if par =='cmro2':\n",
    "                par_map = os.path.join(dir_qmri_deriv, sub + '_task-' + baseline_cond + '_space-MNI152_desc-orig_'+par+'.nii.gz')           \n",
    "            \n",
    "                                  \n",
    "            # mask with voxels that are within mask\n",
    "            par_arr = np.squeeze(masker.fit_transform(par_map)) \n",
    "            if par == 'cbf': ##25% too low\n",
    "                par_arr = par_arr/0.75\n",
    "            \n",
    "            if m == 0: ## get GM values (do not mask with mask, but with whole-brain GM mask)\n",
    "                if par != 'cbf':\n",
    "                    par_arr_GM = np.squeeze(masker_GM.fit_transform(par_map))   \n",
    "                    par_arr_GM_woPLS = np.squeeze(masker_GM_woPLS.fit_transform(par_map))   \n",
    "                if par == 'cbf':\n",
    "                    par_arr_GM = np.squeeze(masker_GM.fit_transform(par_map))/0.75   \n",
    "                    par_arr_GM_woPLS = np.squeeze(masker_GM_woPLS.fit_transform(par_map))/0.75   \n",
    "                \n",
    "            ## all voxels within mask\n",
    "            par_median_vox[:, i] = par_arr\n",
    "                \n",
    "            ## all voxels within GM\n",
    "            if m == 0:\n",
    "                par_median_vox_GM[:, i] = par_arr_GM\n",
    "                par_median_vox_GM_woPLS[:, i] = par_arr_GM_woPLS\n",
    "\n",
    "           \n",
    "        ##median across subjects per voxel \n",
    "        par_median_voxel = np.nanmedian(par_median_vox, axis = 1)\n",
    "        if m == 0:\n",
    "            par_median_GM =  np.nanmedian(par_median_vox_GM, axis = 1)\n",
    "            par_median_GM_woPLS =  np.nanmedian(par_median_vox_GM_woPLS, axis = 1)\n",
    "            \n",
    "        ## append all voxels \n",
    "        vox_true.append(par_median_voxel)\n",
    "        vox_GM = par_median_GM\n",
    "        vox_GM_woPLS = par_median_GM_woPLS\n",
    "        \n",
    "        if par == 'cmro2':\n",
    "            vox_conc_CMRO2 = np.array([item for sublist in vox_true for item in sublist])\n",
    "            vox_GM_CMRO2 = vox_GM\n",
    "            vox_GM_woPLS_CMRO2 =  vox_GM_woPLS\n",
    "        if par == 'oef':\n",
    "            vox_conc_OEF = np.array([item for sublist in vox_true for item in sublist])\n",
    "            vox_GM_OEF =  vox_GM\n",
    "            vox_GM_woPLS_OEF= vox_GM_woPLS\n",
    "        if par == 'cbf':\n",
    "            vox_conc_CBF = np.array([item for sublist in vox_true for item in sublist])\n",
    "            vox_GM_CBF =  vox_GM\n",
    "            vox_GM_woPLS_CBF = vox_GM_woPLS\n",
    "        if par == 'cbv':\n",
    "            vox_conc_CBV = np.array([item for sublist in vox_true for item in sublist])\n",
    "            vox_GM_CBV =  vox_GM\n",
    "            vox_GM_woPLS_CBV = vox_GM_woPLS\n",
    "            \n",
    "    ##################################\n",
    "    ## all discordant voxels ##\n",
    "    #################################\n",
    "    \n",
    "    for m, mask in enumerate(masks_discord):\n",
    "        masker = input_data.NiftiMasker(mask_img = mask) \n",
    "        mask_arr = np.array(nib.load(mask).dataobj)\n",
    "        par_median_vox = np.zeros((len(np.argwhere(mask_arr)), len(sids)))\n",
    "        for i, ID in enumerate(sids): #loop over subjects\n",
    "            sub = \"sub-p{:03d}\".format(ID) # subject id, eg 'p021'\n",
    "            print(sub)\n",
    "\n",
    "            sub_dir = os.path.join(data_dir, sub)\n",
    "\n",
    "            dir_anat = os.path.join(sub_dir, 'anat')\n",
    "            dir_func = os.path.join(sub_dir, 'func')\n",
    "            dir_perf = os.path.join(sub_dir, 'perf')\n",
    "\n",
    "            dir_anat_deriv = os.path.join(derivatives_dir, sub, 'anat')\n",
    "            dir_func_deriv = os.path.join(derivatives_dir, sub, 'func')\n",
    "            dir_perf_deriv = os.path.join(derivatives_dir, sub, 'perf')\n",
    "            dir_qmri_deriv = os.path.join(derivatives_dir, sub, 'qmri')\n",
    "\n",
    "            ## \n",
    "            if par !='cmro2':\n",
    "                par_map = os.path.join(dir_qmri_deriv, sub + '_task-' + baseline_cond + '_space-MNI152_'+par+'.nii.gz')\n",
    "            if par =='cmro2':\n",
    "                par_map = os.path.join(dir_qmri_deriv, sub + '_task-' + baseline_cond + '_space-MNI152_desc-orig_'+par+'.nii.gz')           \n",
    "            \n",
    "            \n",
    "            # mask with voxels that are within mask\n",
    "            par_arr = np.squeeze(masker.fit_transform(par_map))   \n",
    "            if par == 'cbf':\n",
    "                par_arr = par_arr/0.75\n",
    "                \n",
    "            ## all voxels within mask\n",
    "            par_median_vox[:, i] = par_arr\n",
    "\n",
    "           \n",
    "        ##median across subjects per voxel \n",
    "        par_median_voxel = np.nanmedian(par_median_vox, axis = 1)\n",
    "            \n",
    "        ## append all voxels \n",
    "        vox_diss.append(par_median_voxel)\n",
    "        \n",
    "        if par == 'cmro2':\n",
    "            vox_disc_CMRO2 = np.array([item for sublist in vox_diss for item in sublist])\n",
    "        if par == 'oef':\n",
    "            vox_disc_OEF = np.array([item for sublist in vox_diss for item in sublist])\n",
    "        if par == 'cbf':\n",
    "            vox_disc_CBF = np.array([item for sublist in vox_diss for item in sublist])      \n",
    "        if par == 'cbv':\n",
    "            vox_disc_CBV = np.array([item for sublist in vox_diss for item in sublist])    \n",
    "            \n",
    "##create dataframes\n",
    "\n",
    "## all GM voxels\n",
    "df_GM_woPLS = pd.DataFrame()\n",
    "df_GM_woPLS['cmro2'] = vox_GM_woPLS_CMRO2\n",
    "df_GM_woPLS['oef'] = vox_GM_woPLS_OEF\n",
    "df_GM_woPLS['cbf'] = vox_GM_woPLS_CBF\n",
    "df_GM_woPLS['cbv'] = vox_GM_woPLS_CBV\n",
    "\n",
    "## all concordant\n",
    "df_conc_vox = pd.DataFrame()\n",
    "df_conc_vox['cmro2'] = vox_conc_CMRO2\n",
    "df_conc_vox['oef'] = vox_conc_OEF\n",
    "df_conc_vox['cbf'] = vox_conc_CBF\n",
    "df_conc_vox['cbv'] = vox_conc_CBV\n",
    "\n",
    "## all discordant\n",
    "df_disc_vox = pd.DataFrame()\n",
    "df_disc_vox['cmro2'] = vox_disc_CMRO2\n",
    "df_disc_vox['oef'] = vox_disc_OEF\n",
    "df_disc_vox['cbf'] = vox_disc_CBF\n",
    "df_disc_vox['cbv'] = vox_disc_CBV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "permutation statistics true vs. false regions: 0.0004\n",
      "permutation statistics false vs GM value: 0.0004\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "regplot() takes from 0 to 1 positional arguments but 2 positional arguments (and 3 keyword-only arguments) were given",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[42], line 65\u001b[0m\n\u001b[1;32m     62\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(\u001b[38;5;241m1\u001b[39m, figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m6\u001b[39m, \u001b[38;5;241m5\u001b[39m))\n\u001b[1;32m     64\u001b[0m \u001b[38;5;66;03m##all other GM voxels\u001b[39;00m\n\u001b[0;32m---> 65\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mregplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_GM_woPLS\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcmro2\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdropna\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_GM_woPLS\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43moef\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdropna\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mgray\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscatter_kws\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43ms\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     66\u001b[0m (r, p) \u001b[38;5;241m=\u001b[39m stats\u001b[38;5;241m.\u001b[39mpearsonr(df_GM_woPLS[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcmro2\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mdropna(), df_GM_woPLS[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moef\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mdropna())\n\u001b[1;32m     67\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m p \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0.001\u001b[39m:\n",
      "\u001b[0;31mTypeError\u001b[0m: regplot() takes from 0 to 1 positional arguments but 2 positional arguments (and 3 keyword-only arguments) were given"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 250x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAg8AAAGyCAYAAABjmSeUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAcqklEQVR4nO3df2zV9b348ReU1e1uJBA6ZJd5r6YJmkIFys2dE5QJu9fJXVwsciVpZoIZBO4ud+QaUW+vmjmk6OjlbnfjZo2D3O126FbuuNORy2bM9U7IjUCYKZSYC3EBL3UU7LK73pnaw+f+Ycr321GVV21P+fF4JP7Rdz+fft7nlep59vzwjCmKoggAgPM0drQ3AABcXMQDAJAiHgCAFPEAAKSIBwAgRTwAACniAQBIEQ8AQIp4AABSxAMAkJKOh71798bKlStj3rx5ce2118Zzzz33nue89NJLUV9fH7W1tbFw4cLYtm3bkDYLAIy+dDz87//+b1x77bXx8MMPn9fxx48fjxUrVsScOXNix44dsXLlynjsscdi165d6c0CAKNvXPaE+fPnx/z588/7+Keeeio+9rGPRWNjY0REVFdXR3t7e2zZsiVuvfXW7OUBgFGWjoesn//85zF37twBazfddFNs37493nrrrfjABz7wnj/jzJkz0dfXF2PHjo0xY8aM1FYB4JJTFEWcOXMmxo0bF2PHDs9LHUc8Hk6dOhVVVVUD1iZNmhR9fX3R3d0dkydPfs+f0dfXF+3t7SO1RQC45NXW1kZlZeWw/KwRj4eIOOfRgqIoBl1/J/2ldO211w7bDefdlUql6OjoiJqamqioqBjt7VwWzLz8zLz8zLz8ent745VXXhm2Rx0iyhAPVVVV0dXVNWDtjTfeiHHjxsWECRPO62f0R0ZlZaV4KJNSqRQRb8/cv+DlYeblZ+blZ+ajZzif9h/x/8/DrFmzYs+ePQPWXnzxxZgxY8Z5vd4BALiwpOOhp6cnDh8+HIcPH46IiNdeey0OHz4cJ06ciIiI5ubmWLt27dnjly5dGidOnIimpqY4evRotLW1xfbt2+Oee+4ZppsAAJRT+mmLgwcPxt13333266ampoiIuOOOO2LDhg3R1dUVnZ2dZ79/1VVXRUtLSzQ1NUVra2tMnjw5GhsbvU0TAC5S6Xj4xCc+Ea+88so7fn/Dhg3nrP3xH/9x/PCHP8xeCgC4APlsCwAgRTwAACniAQBIEQ8AQIp4AABSxAMAkCIeAIAU8QAApIgHACBFPAAAKeIBAEgRDwBAingAAFLEAwCQIh4AgBTxAACkiAcAIEU8AAAp4gEASBEPAECKeAAAUsQDAJAiHgCAFPEAAKSIBwAgRTwAACniAQBIEQ8AQIp4AABSxAMAkCIeAIAU8QAApIgHACBFPAAAKeIBAEgRDwBAingAAFLEAwCQIh4AgBTxAACkiAcAIEU8AAAp4gEASBEPAECKeAAAUsQDAJAiHgCAFPEAAKSIBwAgRTwAACniAQBIEQ8AQIp4AABSxAMAkCIeAIAU8QAApIgHACBFPAAAKeIBAEgRDwBAingAAFLEAwCQIh4AgBTxAACkiAcAIEU8AAApQ4qH1tbWWLBgQdTW1kZ9fX3s27fvXY//0Y9+FLfffnvMnDkz5s2bFw8++GB0d3cPacMAwOhKx8POnTujqakpVq1aFTt27Ig5c+bE8uXL48SJE4Mev2/fvrj//vvjzjvvjGeffTb+/u//Ptrb2+Nv//Zv3/fmAYDyS8fD1q1bY/HixbFkyZKorq6OxsbGmDJlSmzbtm3Q419++eWYOnVq3H333XHVVVfFH/3RH8Vdd90VBw8efN+bBwDKb1zm4N7e3jh06FCsWLFiwPrcuXPjwIEDg54ze/bs2LRpU7zwwgtx8803x+nTp2PXrl0xf/789GZLpVKUSqX0eeT1z9m8y8fMy8/My8/My28kZp2Kh+7u7iiVSjFp0qQB61VVVdHV1TXoOXV1dbFx48ZYs2ZN9Pb2Rl9fXyxYsCAeeuih9GY7OjrS5/D+tLe3j/YWLjtmXn5mXn5mfnFLxUO/MWPGDPi6KIpz1vodOXIk1q1bF1/84hdj3rx50dXVFU888UQ88sgjsX79+tR1a2pqorKycihbJqlUKkV7e3vU1tZGRUXFaG/nsmDm5Wfm5Wfm5dfb2zvsf3yn4mHixIlRUVERp06dGrB++vTpqKqqGvScb33rW1FXVxdf+MIXIiLiuuuuiw996EPR0NAQa9asicmTJ5/39SsqKvyylZmZl5+Zl5+Zl5+Zl89IzDn1gsnKysqYPn167N69e8D6nj17Yvbs2YOe8+abb8bYsQMv039DiqLIXB4AuACk322xbNmyaGtri7a2tjh69GisX78+Ojs7Y+nSpRER0dzcHGvXrj17/C233BI//elP43vf+14cP3489u/fH+vWrYvrr78+rrzyyuG7JQBAWaRf87Bo0aLo7u6OzZs3x8mTJ2PatGnR0tISU6dOjYiIrq6u6OzsPHt8fX199PT0RGtrazz++OMxfvz4uOGGG+K+++4bvlsBAJTNkF4w2dDQEA0NDYN+b8OGDeesff7zn4/Pf/7zQ7kUAHCB8dkWAECKeAAAUsQDAJAiHgCAFPEAAKSIBwAgRTwAACniAQBIEQ8AQIp4AABSxAMAkCIeAIAU8QAApIgHACBFPAAAKeIBAEgRDwBAingAAFLEAwCQIh4AgBTxAACkiAcAIEU8AAAp4gEASBEPAECKeAAAUsQDAJAiHgCAFPEAAKSIBwAgRTwAACniAQBIEQ8AQIp4AABSxAMAkCIeAIAU8QAApIgHACBFPAAAKeIBAEgRDwBAingAAFLEAwCQIh4AgBTxAACkiAcAIEU8AAAp4gEASBEPAECKeAAAUsQDAJAiHgCAFPEAAKSIBwAgRTwAACniAQBIEQ8AQIp4AABSxAMAkCIeAIAU8QAApIgHACBFPAAAKeIBAEgRDwBAingAAFKGFA+tra2xYMGCqK2tjfr6+ti3b9+7Ht/b2xubNm2KW265JWbMmBGf/vSno62tbUgbBgBG17jsCTt37oympqZ45JFHoq6uLp566qlYvnx5/PjHP47f//3fH/ScL33pS3H69Ol47LHH4g/+4A/ijTfeiL6+vve9eQCg/NLxsHXr1li8eHEsWbIkIiIaGxvjxRdfjG3btsW99957zvH/8R//EXv37o3nnnsuJkyYEBERH//4x9/frgGAUZOKh97e3jh06FCsWLFiwPrcuXPjwIEDg57z/PPPx4wZM+LJJ5+Mf/3Xf43f+73fiwULFsSXvvSl+OAHP5jabKlUilKplDqHoemfs3mXj5mXn5mXn5mX30jMOhUP3d3dUSqVYtKkSQPWq6qqoqura9Bzjh8/Hvv3748rrrgivvnNb0Z3d3d8+ctfjl/96lfR1NSU2mxHR0fqeN6/9vb20d7CZcfMy8/My8/ML27ppy0iIsaMGTPg66Iozln73e9t3Lgxxo8fHxERDzzwQPzVX/1VPPLII6lHH2pqaqKysnIoWyapVCpFe3t71NbWRkVFxWhv57Jg5uVn5uVn5uXX29s77H98p+Jh4sSJUVFREadOnRqwfvr06aiqqhr0nI9+9KNx5ZVXng2HiIjq6uooiiJef/31uPrqq8/7+hUVFX7ZyszMy8/My8/My8/My2ck5px6q2ZlZWVMnz49du/ePWB9z549MXv27EHPqauri5MnT0ZPT8/ZtVdffTXGjh0bU6ZMGcKWAYDRlP7/PCxbtiza2tqira0tjh49GuvXr4/Ozs5YunRpREQ0NzfH2rVrzx7/2c9+NiZMmBAPPvhgHDlyJPbu3Rtf/epXY/HixekXTAIAoy/9modFixZFd3d3bN68OU6ePBnTpk2LlpaWmDp1akREdHV1RWdn59njP/zhD8eWLVti3bp1sXjx4pgwYULcdtttsWbNmmG7EQBA+QzpBZMNDQ3R0NAw6Pc2bNhwzlp1dXVs3bp1KJcCAC4wPtsCAEgRDwBAingAAFLEAwCQIh4AgBTxAACkiAcAIEU8AAAp4gEASBEPAECKeAAAUsQDAJAiHgCAFPEAAKSIBwAgRTwAACniAQBIEQ8AQIp4AABSxAMAkCIeAIAU8QAApIgHACBFPAAAKeIBAEgRDwBAingAAFLEAwCQIh4AgBTxAACkiAcAIEU8AAAp4gEASBEPAECKeAAAUsQDAJAiHgCAFPEAAKSIBwAgRTwAACniAQBIEQ8AQIp4AABSxAMAkCIeAIAU8QAApIgHACBFPAAAKeIBAEgRDwBAingAAFLEAwCQIh4AgBTxAACkiAcAIEU8AAAp4gEASBEPAECKeAAAUsQDAJAiHgCAFPEAAKSIBwAgRTwAACniAQBIEQ8AQMqQ4qG1tTUWLFgQtbW1UV9fH/v27Tuv8/bv3x81NTXxuc99biiXBQAuAOl42LlzZzQ1NcWqVatix44dMWfOnFi+fHmcOHHiXc/7n//5n7j//vvjk5/85JA3CwCMvnQ8bN26NRYvXhxLliyJ6urqaGxsjClTpsS2bdve9byHH344PvvZz8asWbOGulcA4AIwLnNwb29vHDp0KFasWDFgfe7cuXHgwIF3PG/79u1x7Nix+OpXvxr/+I//OLSdRkSpVIpSqTTk8zl//XM27/Ix8/Iz8/Iz8/IbiVmn4qG7uztKpVJMmjRpwHpVVVV0dXUNes4vfvGLaG5ujtbW1hg3LnW5c3R0dLyv88lrb28f7S1cdsy8/My8/Mz84jake/MxY8YM+LooinPWIt6unXvvvTdWr14d11xzzdB2+P+pqamJysrK9/1zeG+lUina29ujtrY2KioqRns7lwUzLz8zLz8zL7/e3t5h/+M7FQ8TJ06MioqKOHXq1ID106dPR1VV1TnH9/T0xMGDB+Pw4cPxla98JSIizpw5E0VRRE1NTXz7299OvYCyoqLCL1uZmXn5mXn5mXn5mXn5jMScU/FQWVkZ06dPj927d8ef/MmfnF3fs2dPLFy48JzjP/KRj8QzzzwzYO173/te/Od//md8/etfj49//OND3DYAMFrST1ssW7Ys1q5dGzNmzIjZs2fH008/HZ2dnbF06dKIiGhubo5f/vKX8cQTT8TYsWNj2rRpA86fNGlSXHHFFeesAwAXh3Q8LFq0KLq7u2Pz5s1x8uTJmDZtWrS0tMTUqVMjIqKrqys6OzuHfaMAwIVhSC+YbGhoiIaGhkG/t2HDhnc9d/Xq1bF69eqhXBYAuAD4bAsAIEU8AAAp4gEASBEPAECKeAAAUsQDAJAiHgCAFPEAAKSIBwAgRTwAACniAQBIEQ8AQIp4AABSxAMAkCIeAIAU8QAApIgHACBFPAAAKeIBAEgRDwBAingAAFLEAwCQIh4AgBTxAACkiAcAIEU8AAAp4gEASBEPAECKeAAAUsQDAJAiHgCAFPEAAKSIBwAgRTwAACniAQBIEQ8AQIp4AABSxAMAkCIeAIAU8QAApIgHACBFPAAAKeIBAEgRDwBAingAAFLEAwCQIh4AgBTxAACkiAcAIEU8AAAp4gEASBEPAECKeAAAUsQDAJAiHgCAFPEAAKSIBwAgRTwAACniAQBIEQ8AQIp4AABSxAMAkCIeAIAU8QAApIgHACBFPAAAKUOKh9bW1liwYEHU1tZGfX197Nu37x2P/clPfhLLli2LG264Ierq6uKuu+6Kn/3sZ0PeMAAwutLxsHPnzmhqaopVq1bFjh07Ys6cObF8+fI4ceLEoMfv3bs3brzxxmhpaYl/+Zd/iU984hOxatWq6OjoeN+bBwDKLx0PW7dujcWLF8eSJUuiuro6GhsbY8qUKbFt27ZBj29sbIzly5fH9ddfH1dffXX89V//dfzhH/5hPP/88+978wBA+Y3LHNzb2xuHDh2KFStWDFifO3duHDhw4Lx+xpkzZ6KnpycmTJiQuXRERJRKpSiVSunzyOufs3mXj5mXn5mXn5mX30jMOhUP3d3dUSqVYtKkSQPWq6qqoqur67x+xpYtW+K3v/1t3HbbbZlLR0R4qmMUtLe3j/YWLjtmXn5mXn5mfnFLxUO/MWPGDPi6KIpz1gbz7LPPxje+8Y3YvHnzOQFyPmpqaqKysjJ9HnmlUina29ujtrY2KioqRns7lwUzLz8zLz8zL7/e3t5h/+M7FQ8TJ06MioqKOHXq1ID106dPR1VV1bueu3PnzmhsbIyvfe1rceONN+Z3GhEVFRV+2crMzMvPzMvPzMvPzMtnJOacesFkZWVlTJ8+PXbv3j1gfc+ePTF79ux3PO/ZZ5+NBx54IJqbm+NTn/rUkDYKAFwY0k9bLFu2LNauXRszZsyI2bNnx9NPPx2dnZ2xdOnSiIhobm6OX/7yl/HEE09ExNvhcP/998ff/M3fxMyZM8++NuKDH/xgjB8/fhhvCgBQDul4WLRoUXR3d8fmzZvj5MmTMW3atGhpaYmpU6dGRERXV1d0dnaePf7pp5+Ovr6+ePTRR+PRRx89u37HHXfEhg0bhuEmAADlNKQXTDY0NERDQ8Og3/vdIPjud787lEsAABcon20BAKSIBwAgRTwAACniAQBIEQ8AQIp4AABSxAMAkCIeAIAU8QAApIgHACBFPAAAKeIBAEgRDwBAingAAFLEAwCQIh4AgBTxAACkiAcAIEU8AAAp4gEASBEPAECKeAAAUsQDAJAiHgCAFPEAAKSIBwAgRTwAACniAQBIEQ8AQIp4AABSxAMAkCIeAIAU8QAApIgHACBFPAAAKeIBAEgRDwBAingAAFLEAwCQIh4AgBTxAACkiAcAIEU8AAAp4gEASBEPAECKeAAAUsQDAJAiHgCAFPEAAKSIBwAgRTwAACniAQBIEQ8AQIp4AABSxAMAkCIeAIAU8QAApIgHACBFPAAAKeIBAEgRDwBAingAAFLEAwCQIh4AgBTxAACkiAcAIGVI8dDa2hoLFiyI2traqK+vj3379r3r8S+99FLU19dHbW1tLFy4MLZt2zakzQIAoy8dDzt37oympqZYtWpV7NixI+bMmRPLly+PEydODHr88ePHY8WKFTFnzpzYsWNHrFy5Mh577LHYtWvX+948AFB+6XjYunVrLF68OJYsWRLV1dXR2NgYU6ZMecdHE5566qn42Mc+Fo2NjVFdXR1LliyJ+vr62LJly/vePABQfuMyB/f29sahQ4dixYoVA9bnzp0bBw4cGPScn//85zF37twBazfddFNs37493nrrrfjABz7wntctiuLs9SmPUqkUEW/PvKKiYpR3c3kw8/Iz8/Iz8/Lrv+/svy8dDql46O7ujlKpFJMmTRqwXlVVFV1dXYOec+rUqaiqqhqwNmnSpOjr64vu7u6YPHnye173zJkzERHxyiuvZLbLMOjo6BjtLVx2zLz8zLz8zLz8+u9Lh0MqHvqNGTNmwNdFUZyz9l7HD7b+TsaNGxe1tbUxduzY8z4HAHj7PvfMmTMxbtyQ7vIHlfpJEydOjIqKijh16tSA9dOnT5/z6EK/wR6VeOONN2LcuHExYcKE87ru2LFjo7KyMrNVAGCEpF4wWVlZGdOnT4/du3cPWN+zZ0/Mnj170HNmzZoVe/bsGbD24osvxowZM87r9Q4AwIUl/W6LZcuWRVtbW7S1tcXRo0dj/fr10dnZGUuXLo2IiObm5li7du3Z45cuXRonTpyIpqamOHr0aLS1tcX27dvjnnvuGb5bAQCUTfoJkEWLFkV3d3ds3rw5Tp48GdOmTYuWlpaYOnVqRER0dXVFZ2fn2eOvuuqqaGlpiaampmhtbY3JkydHY2Nj3HrrrcN3KwCAshlTDOd7NwCAS57PtgAAUsQDAJAiHgCAFPEAAKRcMPHgY77LLzPzn/zkJ7Fs2bK44YYboq6uLu6666742c9+VsbdXhqyv+f99u/fHzU1NfG5z31uhHd46cnOvLe3NzZt2hS33HJLzJgxIz796U9HW1tbmXZ7acjO/Ec/+lHcfvvtMXPmzJg3b148+OCD0d3dXabdXtz27t0bK1eujHnz5sW1114bzz333HueMyz3n8UF4Mc//nExffr04vvf/35x5MiRYt26dcWsWbOK//7v/x70+GPHjhUzZ84s1q1bVxw5cqT4/ve/X0yfPr34t3/7tzLv/OKVnfm6deuKlpaW4uWXXy5effXVorm5uZg+fXpx6NChMu/84pWdeb9f//rXxcKFC4t77rmnuP3228u020vDUGa+cuXKYsmSJcXu3buL48ePFy+//HKxf//+Mu764pad+d69e4vrrruu+Kd/+qfi2LFjxd69e4s/+7M/K/7iL/6izDu/OP37v/978Xd/93fFrl27imnTphU//elP3/X44br/vCDi4c477ywefvjhAWuf+cxnio0bNw56/BNPPFF85jOfGbD20EMPFX/+538+Ynu81GRnPphFixYV//AP/zDcW7tkDXXma9asKTZt2lR8/etfFw9J2Zm/8MILxZw5c4ru7u4y7O7SlJ35k08+WSxcuHDA2ne+853i5ptvHrE9XqrOJx6G6/5z1J+26P+Y73nz5g1YH8rHfB88eDDeeuutEdvrpWIoM/9dZ86ciZ6envP+fJLL3VBnvn379jh27Fj85V/+5Uhv8ZIzlJk///zzMWPGjHjyySfjpptuiltvvTUef/zxePPNN8ux5YveUGY+e/bseP311+OFF16Ioiji1KlTsWvXrpg/f345tnzZGa77z+H7iK0hGq2P+b6cDWXmv2vLli3x29/+Nm677baR2OIlZygz/8UvfhHNzc3R2to6rJ+Gd7kYysyPHz8e+/fvjyuuuCK++c1vRnd3d3z5y1+OX/3qV9HU1FSObV/UhjLzurq62LhxY6xZsyZ6e3ujr68vFixYEA899FA5tnzZGa77z1F/5KFfuT/mm/zM+z377LPxjW98IzZt2nTOfyR4d+c781KpFPfee2+sXr06rrnmmnJt75KU+T3v/97GjRvj+uuvj/nz58cDDzwQP/zhDz36kJCZ+ZEjR2LdunXxxS9+MbZv3x5PPvlkvPbaa/HII4+UY6uXpeG4/xz1P2dG62O+L2dDmXm/nTt3RmNjY3zta1+LG2+8cSS3eUnJzrynpycOHjwYhw8fjq985SsR8fZTRUVRRE1NTXz729+OT37yk2XZ+8VqKL/nH/3oR+PKK6+M8ePHn12rrq6Ooiji9ddfj6uvvnokt3zRG8rMv/Wtb0VdXV184QtfiIiI6667Lj70oQ9FQ0NDrFmzxiPJw2y47j9H/ZEHH/NdfkOZecTbjzg88MAD0dzcHJ/61KdGeJeXluzMP/KRj8QzzzwTO3bsOPvP0qVL45prrokdO3bEzJkzy7X1i9ZQfs/r6uri5MmT0dPTc3bt1VdfjbFjx8aUKVNGdL+XgqHM/M0334yxYwfeFVVUVETE//uLmOEzbPefqZdXjpD+t/b84Ac/KI4cOVI89thjxaxZs4rXXnutKIqi2LhxY3HfffedPb7/rSbr168vjhw5UvzgBz/wVs2k7MyfeeaZoqampvjnf/7n4uTJk2f/+fWvfz1aN+Gik5357/Jui7zszH/zm98UN998c7F69eriv/7rv4qXXnqp+NM//dOisbFxtG7CRSc78+3btxc1NTVFa2trcezYsWLfvn1FfX19ceedd47WTbio/OY3vyk6OjqKjo6OYtq0acXWrVuLjo6Os2+NHan7z1F/2iLCx3yPhuzMn3766ejr64tHH300Hn300bPrd9xxR2zYsKHs+78YZWfO+5ed+Yc//OHYsmVLrFu3LhYvXhwTJkyI2267LdasWTNKt+Dik515fX199PT0RGtrazz++OMxfvz4uOGGG+K+++4brZtwUTl48GDcfffdZ7/uf2Fv/3+bR+r+00dyAwApo/6aBwDg4iIeAIAU8QAApIgHACBFPAAAKeIBAEgRDwBAingAAFLEAwCQIh4AgBTxAACkiAcAIOX/AGF+ukdK6nr2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats \n",
    "\n",
    "fontsize=25\n",
    "\n",
    "### kick out values that are too high\n",
    "df_GM_woPLS[df_GM_woPLS['cmro2'] > 350] = np.nan\n",
    "df_GM_woPLS[df_GM_woPLS['oef'] > 1] = np.nan\n",
    "df_disc_vox[df_disc_vox['cmro2'] > 350] = np.nan\n",
    "df_disc_vox[df_disc_vox['oef'] > 1] = np.nan\n",
    "df_conc_vox[df_conc_vox['cmro2'] > 350] = np.nan\n",
    "df_conc_vox[df_conc_vox['oef'] > 1] = np.nan\n",
    "\n",
    "\n",
    "    ############################################\n",
    "    ## plot ####################################\n",
    "    ############################################\n",
    "\n",
    "colormap = ([\"plum\", \"darkviolet\", \"gray\"])\n",
    "\n",
    "############################################\n",
    "## OEF baseline\n",
    "############################################\n",
    "    \n",
    "\n",
    "##permutation test, because data are smoothed/non-independent\n",
    "res = stats.permutation_test((df_conc_vox['oef'] , df_disc_vox['oef']), statistic, permutation_type='independent', n_resamples=5000, alternative='two-sided')\n",
    "p_true_false = res.pvalue\n",
    "print('permutation statistics true vs. false regions: ' + str(round(p_true_false, 6)))\n",
    "res_GM = stats.permutation_test((df_GM_woPLS['oef'], df_disc_vox['oef']), statistic_GM, permutation_type='independent', n_resamples=5000, alternative='two-sided')\n",
    "p_false_GM = res_GM.pvalue\n",
    "print('permutation statistics false vs GM value: ' + str(round(p_false_GM, 6))) \n",
    "\n",
    "\n",
    "fig, (ax1) = plt.subplots(1, figsize=(2.5, 4.5))\n",
    "sns.violinplot(data=[df_conc_vox['oef'], df_disc_vox['oef']],inner='box',\n",
    "                    ax=ax1, palette=colormap)\n",
    "ax1.set_xticklabels(['conc.', 'disc.'])\n",
    "ax1.hlines(np.nanmedian(df_GM_woPLS['oef'] ),-0.5, 1.5, color='gray', linestyles='dashed')\n",
    "ax1.hlines(np.nanmedian(df_conc_vox['oef'] ),-0.5, 1.5, color=colormap[0], linestyles='dashed')        \n",
    "ax1.hlines(np.nanmedian(df_disc_vox['oef']),-0.5, 1.5, color=colormap[1], linestyles='dashed')\n",
    "if p_true_false < 0.001:\n",
    "            ax1.text(0.3, 1, '**', color='grey', fontsize=fontsize)\n",
    "ax1.set_ylabel('OEF [ratio]', fontsize=fontsize)\n",
    "ax1.set_ylim(0, 1.1)\n",
    "ax1.text(1.5, 0.45, round(np.nanmedian(df_GM_woPLS['oef']), 2), color=colormap[2], fontsize=fontsize)\n",
    "ax1.text(1.5, 0.38, round(np.nanmedian(df_conc_vox['oef']), 2), color=colormap[0], fontsize=fontsize)\n",
    "ax1.text(1.5, 0.3, round(np.nanmedian(df_disc_vox['oef']), 2), color=colormap[1], fontsize=fontsize)\n",
    "#ax1.set_title('CTRL', fontsize=fontsize)\n",
    "for label in (ax1.get_xticklabels() + ax1.get_yticklabels()):\n",
    "            label.set_fontsize(fontsize)\n",
    "        #ax.set_title('coupling CBF/BOLD, CMRO2/BOLD')\n",
    "\n",
    "for pos in ['right', 'top']:\n",
    "            ax1.spines[pos].set_visible(False)\n",
    "\n",
    "fig.savefig(results_dir + '/N' + N_subj + '_BOLD' + BOLD_ROI + '_' + par + '_disc_concord_allCTRLcontrasts.png', dpi=300, bbox_inches='tight')\n",
    "fig.show()\n",
    "        \n",
    "        \n",
    "### CMRO2 base vs OEFbase\n",
    "############################################\n",
    "fig, ax = plt.subplots(1, figsize=(6, 5))\n",
    "\n",
    "##all other GM voxels\n",
    "sns.regplot(df_GM_woPLS['cmro2'].dropna(), df_GM_woPLS['oef'].dropna(), ax=ax, color='gray', scatter_kws={'s':2})\n",
    "(r, p) = stats.pearsonr(df_GM_woPLS['cmro2'].dropna(), df_GM_woPLS['oef'].dropna())\n",
    "if p < 0.001:\n",
    "    ax.text(200,0.3, '**r= ' + str(round(r, 2)), fontsize=fontsize, color='gray')    \n",
    "\n",
    "##discordant\n",
    "sns.regplot(df_disc_vox['cmro2'].dropna(), df_disc_vox['oef'].dropna(), ax=ax, color='darkviolet', scatter_kws={'s':3})\n",
    "(r, p) = stats.pearsonr(df_disc_vox['cmro2'].dropna(), df_disc_vox['oef'].dropna())  \n",
    "if p < 0.001:\n",
    "    ax.text(200,0.2, '**r= ' + str(round(r, 2)), fontsize=fontsize, color='darkviolet')  \n",
    "    \n",
    "##concordant\n",
    "sns.regplot(df_conc_vox['cmro2'].dropna(), df_conc_vox['oef'].dropna(), ax=ax, color='plum', scatter_kws={'s':3})\n",
    "(r, p) = stats.pearsonr(df_conc_vox['cmro2'].dropna(), df_conc_vox['oef'].dropna())\n",
    "if p < 0.001:\n",
    "    ax.text(200,0.1, '**r= ' + str(round(r, 2)), fontsize=fontsize, color='plum') \n",
    "\n",
    "\n",
    "ax.set_xlim(0, 350)\n",
    "ax.set_ylim(0, 1)\n",
    "\n",
    "ax.set_xlabel('CMRO2 [μmol/100g/min]', fontsize=fontsize)\n",
    "ax.set_ylabel('OEF [ratio]', fontsize=fontsize)\n",
    "\n",
    "for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "            label.set_fontsize(fontsize)\n",
    "        #ax.set_title('coupling CBF/BOLD, CMRO2/BOLD')\n",
    "\n",
    "for pos in ['right', 'top']:\n",
    "            ax.spines[pos].set_visible(False)\n",
    "\n",
    "fig.savefig(results_dir + '/N' + N_subj + '_BOLD' + BOLD_ROI + '_disc_concord_base_OEF_CMRO2_allCTRLcontrasts.png', dpi=300, bbox_inches='tight')\n",
    "fig.show()\n",
    "\n",
    "### CMRO2 base vs CBFbase\n",
    "############################################\n",
    "fig, ax = plt.subplots(1, figsize=(6, 5))\n",
    "\n",
    "##all other GM voxels\n",
    "sns.regplot(df_GM_woPLS['cmro2'].dropna(), df_GM_woPLS['cbf'].dropna(), ax=ax, color='gray', scatter_kws={'s':2})\n",
    "(r, p) = stats.pearsonr(df_GM_woPLS['cmro2'].dropna(), df_GM_woPLS['cbf'].dropna())\n",
    "if p < 0.001:\n",
    "    ax.text(200,20, '**r= ' + str(round(r, 2)), fontsize=fontsize, color='gray')    \n",
    "\n",
    "##discordant\n",
    "sns.regplot(df_disc_vox['cmro2'].dropna(), df_disc_vox['cbf'].dropna(), ax=ax, color='darkviolet', scatter_kws={'s':3})\n",
    "(r, p) = stats.pearsonr(df_disc_vox['cmro2'].dropna(), df_disc_vox['cbf'].dropna())\n",
    "if p < 0.001:\n",
    "    ax.text(200,13, '**r= ' + str(round(r, 2)), fontsize=fontsize, color='darkviolet')    \n",
    "\n",
    "##concordant\n",
    "sns.regplot(df_conc_vox['cmro2'].dropna(), df_conc_vox['cbf'].dropna(), ax=ax, color='plum', scatter_kws={'s':3})\n",
    "(r, p) = stats.pearsonr(df_conc_vox['cmro2'].dropna(), df_conc_vox['cbf'].dropna())\n",
    "if p < 0.001:\n",
    "    ax.text(200,6, '**r= ' + str(round(r, 2)), fontsize=fontsize, color='plum') \n",
    "\n",
    "ax.set_xlim(0, 350)\n",
    "ax.set_ylim(0, 70)\n",
    "\n",
    "ax.set_xlabel('CMRO2 [μmol/100g/min]', fontsize=fontsize)\n",
    "ax.set_ylabel('CBF [ml/100g/min]', fontsize=fontsize)\n",
    "\n",
    "for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "            label.set_fontsize(fontsize)\n",
    "        #ax.set_title('coupling CBF/BOLD, CMRO2/BOLD')\n",
    "\n",
    "for pos in ['right', 'top']:\n",
    "            ax.spines[pos].set_visible(False)\n",
    "\n",
    "fig.savefig(results_dir + '/N' + N_subj + '_BOLD' + BOLD_ROI + '_disc_concord_base_CBF_CMRO2_allCTRLcontrasts.png', dpi=300, bbox_inches='tight')\n",
    "fig.show()\n",
    "\n",
    "\n",
    "### CMRO2 base vs CBVbase\n",
    "############################################\n",
    "fig, ax = plt.subplots(1, figsize=(6, 5))\n",
    "\n",
    "##all other GM voxels\n",
    "sns.regplot(df_GM_woPLS['cmro2'].dropna(), df_GM_woPLS['cbv'].dropna(), ax=ax, color='gray', scatter_kws={'s':2})\n",
    "(r, p) = stats.pearsonr(df_GM_woPLS['cmro2'].dropna(), df_GM_woPLS['cbv'].dropna())\n",
    "if p < 0.001:\n",
    "    ax.text(200,1.5, '**r= ' + str(round(r, 2)), fontsize=fontsize, color='gray')    \n",
    "\n",
    "##discordant\n",
    "sns.regplot(df_disc_vox['cmro2'].dropna(), df_disc_vox['cbv'].dropna(), ax=ax, color='darkviolet', scatter_kws={'s':3})\n",
    "(r, p) = stats.pearsonr(df_disc_vox['cmro2'].dropna(), df_disc_vox['cbv'].dropna())\n",
    "if p < 0.001:\n",
    "    ax.text(200,0.8, '**r= ' + str(round(r, 2)), fontsize=fontsize, color='darkviolet')    \n",
    "\n",
    "##concordant\n",
    "sns.regplot(df_conc_vox['cmro2'].dropna(), df_conc_vox['cbv'].dropna(), ax=ax, color='plum', scatter_kws={'s':3})\n",
    "(r, p) = stats.pearsonr(df_conc_vox['cmro2'].dropna(), df_conc_vox['cbv'].dropna())\n",
    "if p < 0.001:\n",
    "    ax.text(200,0.1, '**r= ' + str(round(r, 2)), fontsize=fontsize, color='plum') \n",
    "\n",
    "ax.set_xlim(0, 350)\n",
    "ax.set_ylim(0, 9)\n",
    "\n",
    "ax.set_xlabel('CMRO2 [μmol/100g/min]', fontsize=fontsize)\n",
    "ax.set_ylabel('CBV [%]', fontsize=fontsize)\n",
    "\n",
    "for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "            label.set_fontsize(fontsize)\n",
    "        #ax.set_title('coupling CBF/BOLD, CMRO2/BOLD')\n",
    "\n",
    "for pos in ['right', 'top']:\n",
    "            ax.spines[pos].set_visible(False)\n",
    "\n",
    "fig.savefig(results_dir + '/N' + N_subj + '_BOLD' + BOLD_ROI + '_disc_concord_base_CBV_CMRO2_allCTRLcontrasts.png', dpi=300, bbox_inches='tight')\n",
    "fig.show()\n",
    "\n",
    "        \n",
    "## pie-plot discordant/concordant voxels\n",
    "fig, ax = plt.subplots(1, figsize=(7,7))\n",
    "   \n",
    "num_vox = [len(df_GM_woPLS), len(df_conc_vox), len(df_disc_vox)]\n",
    "ax.pie(num_vox, labels=['','',''],autopct = '%0.0f%%', startangle = 79.5, explode = [0.03,0.03, 0.03],\n",
    "        textprops = {'fontsize':25, 'color':'white'}, colors=['gray', 'plum', 'darkviolet'], wedgeprops = {'linewidth': 6, 'alpha':0.8}, center = (0.1,0.1))\n",
    "\n",
    "\n",
    "ax.legend( bbox_to_anchor=(0.8, 0), labels = ['GM', 'concordant vox', 'discordant vox'], fontsize=14, shadow=True)\n",
    "\n",
    "for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "            label.set_fontsize(fontsize)\n",
    "        #ax.set_title('coupling CBF/BOLD, CMRO2/BOLD')\n",
    "\n",
    "for pos in ['right', 'top']:\n",
    "            ax.spines[pos].set_visible(False)\n",
    "\n",
    "fig.savefig(results_dir + '/N' + N_subj + '_BOLD' + BOLD_ROI + '_pieplot_allCTRLcontrasts.png', dpi=300, bbox_inches='tight')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple linear regression predicting CMRO2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### concordant voxels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 = 0.9083707161849212\n",
      "beta values intercept, OEF, CBF, CBV = [139.92001577 321.08705777   3.33774887   0.37534278]\n",
      "p-values OEF, CBF, CBV = [0.00000000e+00 0.00000000e+00 0.00000000e+00 4.58657183e-06]\n",
      "df = 21809\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>names</th>\n",
       "      <th>relimp</th>\n",
       "      <th>relimp_perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>oef</td>\n",
       "      <td>0.640104</td>\n",
       "      <td>70.467269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cbf</td>\n",
       "      <td>0.255241</td>\n",
       "      <td>28.098820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cbv</td>\n",
       "      <td>0.013025</td>\n",
       "      <td>1.433911</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       names    relimp  relimp_perc\n",
       "0  Intercept       NaN          NaN\n",
       "1        oef  0.640104    70.467269\n",
       "2        cbf  0.255241    28.098820\n",
       "3        cbv  0.013025     1.433911"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pingouin as pg\n",
    "\n",
    "## concordant voxels\n",
    "\n",
    "## center predictors beforehands: \n",
    "df_conc_vox_centered = pd.DataFrame()\n",
    "df_conc_vox_centered['cmro2'] = df_conc_vox['cmro2'].dropna()\n",
    "df_conc_vox_centered['oef'] = df_conc_vox['oef'].dropna() - np.nanmean(df_conc_vox['oef'].dropna())\n",
    "df_conc_vox_centered['cbf'] = df_conc_vox['cbf'].dropna() - np.nanmean(df_conc_vox['cbf'].dropna())\n",
    "df_conc_vox_centered['cbv'] = df_conc_vox['cbv'].dropna() - np.nanmean(df_conc_vox['cbv'].dropna())\n",
    "\n",
    "x = df_conc_vox_centered[['oef', 'cbf', 'cbv']].dropna()\n",
    "y = df_conc_vox_centered['cmro2'].dropna()\n",
    "\n",
    "lm = pg.linear_regression(x,y, as_dataframe=False)\n",
    "\n",
    "print('R2 = ' + str(lm['r2']))\n",
    "print('beta values intercept, OEF, CBF, CBV = ' + str(lm['coef']))\n",
    "print('p-values OEF, CBF, CBV = ' + str(lm['pval']))\n",
    "print('df = ' + str(lm['df_resid']))\n",
    "\n",
    "##Get the relative importance of predictors\n",
    "lm = pg.linear_regression(x, y, remove_na=True, relimp=True)\n",
    "lm[['names', 'relimp', 'relimp_perc']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  cmro2   R-squared:                       0.908\n",
      "Model:                            OLS   Adj. R-squared:                  0.908\n",
      "Method:                 Least Squares   F-statistic:                 7.207e+04\n",
      "Date:                Wed, 31 Jan 2024   Prob (F-statistic):               0.00\n",
      "Time:                        18:10:08   Log-Likelihood:                -85774.\n",
      "No. Observations:               21813   AIC:                         1.716e+05\n",
      "Df Residuals:                   21809   BIC:                         1.716e+05\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    139.9200      0.084   1673.717      0.000     139.756     140.084\n",
      "oef          321.0871      0.754    425.782      0.000     319.609     322.565\n",
      "cbf            3.3377      0.011    304.523      0.000       3.316       3.359\n",
      "cbv            0.3753      0.082      4.584      0.000       0.215       0.536\n",
      "==============================================================================\n",
      "Omnibus:                     3102.452   Durbin-Watson:                   1.204\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            19405.709\n",
      "Skew:                          -0.529   Prob(JB):                         0.00\n",
      "Kurtosis:                       7.498   Cond. No.                         75.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.formula.api import ols\n",
    "\n",
    "## concordant voxels\n",
    "\n",
    "model = ols('cmro2 ~ oef + cbf + cbv', data=df_conc_vox_centered).fit()\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### discordant voxels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 = 0.9089429434134586\n",
      "beta values intercept, OEF, CBF, CBV = [128.09995832 309.2379128    3.21388852   1.33398861]\n",
      "p-values OEF, CBF, CBV = [0.00000000e+00 0.00000000e+00 0.00000000e+00 3.70873362e-44]\n",
      "df = 14544\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>names</th>\n",
       "      <th>relimp</th>\n",
       "      <th>relimp_perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>oef</td>\n",
       "      <td>0.615690</td>\n",
       "      <td>67.736911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cbf</td>\n",
       "      <td>0.269788</td>\n",
       "      <td>29.681547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cbv</td>\n",
       "      <td>0.023465</td>\n",
       "      <td>2.581542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       names    relimp  relimp_perc\n",
       "0  Intercept       NaN          NaN\n",
       "1        oef  0.615690    67.736911\n",
       "2        cbf  0.269788    29.681547\n",
       "3        cbv  0.023465     2.581542"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## discordant voxels\n",
    "\n",
    "import pingouin as pg\n",
    "\n",
    "## center predictors beforehands: \n",
    "df_disc_vox_centered = pd.DataFrame()\n",
    "df_disc_vox_centered['cmro2'] = df_disc_vox['cmro2'].dropna()\n",
    "df_disc_vox_centered['oef'] = df_disc_vox['oef'].dropna() - np.nanmean(df_disc_vox['oef'].dropna())\n",
    "df_disc_vox_centered['cbf'] = df_disc_vox['cbf'].dropna() - np.nanmean(df_disc_vox['cbf'].dropna())\n",
    "df_disc_vox_centered['cbv'] = df_disc_vox['cbv'].dropna() - np.nanmean(df_disc_vox['cbv'].dropna())\n",
    "\n",
    "x = df_disc_vox_centered[['oef', 'cbf', 'cbv']].dropna()\n",
    "y = df_disc_vox_centered['cmro2'].dropna()\n",
    "\n",
    "lm = pg.linear_regression(x,y, as_dataframe=False)\n",
    "\n",
    "print('R2 = ' + str(lm['r2']))\n",
    "print('beta values intercept, OEF, CBF, CBV = ' + str(lm['coef']))\n",
    "print('p-values OEF, CBF, CBV = ' + str(lm['pval']))\n",
    "print('df = ' + str(lm['df_resid']))\n",
    "\n",
    "##Get the relative importance of predictors\n",
    "lm = pg.linear_regression(x, y, remove_na=True, relimp=True)\n",
    "lm[['names', 'relimp', 'relimp_perc']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  cmro2   R-squared:                       0.909\n",
      "Model:                            OLS   Adj. R-squared:                  0.909\n",
      "Method:                 Least Squares   F-statistic:                 4.839e+04\n",
      "Date:                Wed, 31 Jan 2024   Prob (F-statistic):               0.00\n",
      "Time:                        18:14:25   Log-Likelihood:                -57105.\n",
      "No. Observations:               14548   AIC:                         1.142e+05\n",
      "Df Residuals:                   14544   BIC:                         1.142e+05\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    128.1000      0.102   1260.128      0.000     127.901     128.299\n",
      "oef          309.2379      0.910    339.897      0.000     307.455     311.021\n",
      "cbf            3.2139      0.013    253.774      0.000       3.189       3.239\n",
      "cbv            1.3340      0.095     13.985      0.000       1.147       1.521\n",
      "==============================================================================\n",
      "Omnibus:                     2147.639   Durbin-Watson:                   1.179\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            14102.916\n",
      "Skew:                          -0.540   Prob(JB):                         0.00\n",
      "Kurtosis:                       7.701   Cond. No.                         78.6\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "## discordant voxels\n",
    "\n",
    "model = ols('cmro2 ~ oef + cbf + cbv', data=df_disc_vox_centered).fit()\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### all GM voxels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>names</th>\n",
       "      <th>relimp</th>\n",
       "      <th>relimp_perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>oef</td>\n",
       "      <td>0.615690</td>\n",
       "      <td>67.736911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cbf</td>\n",
       "      <td>0.269788</td>\n",
       "      <td>29.681547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cbv</td>\n",
       "      <td>0.023465</td>\n",
       "      <td>2.581542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       names    relimp  relimp_perc\n",
       "0  Intercept       NaN          NaN\n",
       "1        oef  0.615690    67.736911\n",
       "2        cbf  0.269788    29.681547\n",
       "3        cbv  0.023465     2.581542"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## GM voxels\n",
    "\n",
    "x = df_disc_vox[['oef', 'cbf', 'cbv']].dropna()\n",
    "y = df_disc_vox['cmro2'].dropna()\n",
    "\n",
    "lm = pg.linear_regression(x,y)\n",
    "\n",
    "lm.round(2)\n",
    "\n",
    "##Get the relative importance of predictors\n",
    "lm = pg.linear_regression(x, y, remove_na=True, relimp=True)\n",
    "lm[['names', 'relimp', 'relimp_perc']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fig. 4B: Differences in baseline hemodynamics across subjects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = [ 'oef', 'cbf', 'cbv']\n",
    "\n",
    "##choose if negative (DMN) or positive BOLD areas\n",
    "colormap = ([\"plum\", \"darkviolet\"])\n",
    "\n",
    "baseline_cond = 'control'\n",
    "BOLD_ROI = 'all'\n",
    "\n",
    "mask_GM_woPLS = results_dir + '/GM_vox_without_calccontrol_memcontrol_vox.nii.gz'\n",
    "mask_arr_GM = np.array(nib.load(results_dir + '/GM_vox_without_calccontrol_memcontrol_vox.nii.gz').dataobj)\n",
    "masker_GM_woPLS = input_data.NiftiMasker(mask_img = mask_GM_woPLS) \n",
    "\n",
    "masks_concord=[]\n",
    "masks_discord=[]\n",
    "contrasts = ['calccontrol', 'memcontrol']\n",
    "len_masks_conc = []\n",
    "len_masks_disc = []\n",
    "for contrast in contrasts:\n",
    "\n",
    "    ## divide into: real deactivation and dissociation areas!\n",
    "    PLS_true_deact = os.path.join(results_dir, 'negBOLD_negCMRO2_' + contrast + '.nii.gz') #negBOLD_negCMRO2_memcontrol_CMRO2deact\n",
    "    #plotting.plot_img(PLS_true_deact, cmap=colormap,  bg_img=MNI_brain, threshold=0, display_mode='z')\n",
    "\n",
    "    PLS_dissociation = os.path.join(results_dir, 'negBOLD_posCMRO2_' + contrast + '.nii.gz')\n",
    "    #plotting.plot_img(PLS_dissociation, cmap=colormap,  bg_img=MNI_brain, threshold=0, display_mode='z')\n",
    "    \n",
    "    masks_concord.append(PLS_true_deact)\n",
    "    masks_discord.append(PLS_dissociation)\n",
    "    len_mask = len(np.argwhere(np.array(nib.load(PLS_dissociation).dataobj))) ## how many voxels\n",
    "    len_masks_disc.append(len_mask)    \n",
    "    len_mask = len(np.argwhere(np.array(nib.load(PLS_true_deact).dataobj))) ## how many voxels\n",
    "    len_masks_conc.append(len_mask)\n",
    "    \n",
    "    ## divide into: real deactivation and dissociation areas!\n",
    "    PLS_true_act =  os.path.join(results_dir, 'posBOLD_posCMRO2_' + contrast + '.nii.gz')\n",
    "\n",
    "    #plotting.plot_img(PLS_true_act, cmap=colormap,  bg_img=MNI_brain, threshold=0, display_mode='z')\n",
    "\n",
    "    PLS_dissociation = os.path.join(results_dir, 'posBOLD_negCMRO2_' + contrast + '.nii.gz')\n",
    "    #plotting.plot_img(PLS_dissociation, cmap=colormap,  bg_img=MNI_brain, threshold=0, display_mode='z')\n",
    "\n",
    "    masks_concord.append(PLS_true_act)\n",
    "    masks_discord.append(PLS_dissociation)\n",
    "\n",
    "    ## how many voxels in conc & disc masks\n",
    "    len_mask = len(np.argwhere(np.array(nib.load(PLS_true_act).dataobj))) ## how many voxels\n",
    "    len_masks_conc.append(len_mask)\n",
    "    len_mask = len(np.argwhere(np.array(nib.load(PLS_dissociation).dataobj))) ## how many voxels\n",
    "    len_masks_disc.append(len_mask)\n",
    "    \n",
    "    \n",
    "## how many voxels all in all in masks\n",
    "len_conc = np.sum(len_masks_conc)\n",
    "len_disc = np.sum(len_masks_disc)\n",
    "\n",
    "    \n",
    "for par in parameters:      \n",
    "    par_median_vox_GM  = np.zeros( (len(sids),len(np.argwhere(mask_arr_GM))))\n",
    "    par_median_vox_conc = np.zeros((len(sids), len_conc ))\n",
    "    par_median_vox_disc = np.zeros((len(sids), len_disc))\n",
    "    for i, ID in enumerate(sids): #loop over subjects\n",
    "        sub = \"sub-p{:03d}\".format(ID) # subject id, eg 'p021'\n",
    "        print(sub)\n",
    "\n",
    "        sub_dir = os.path.join(data_dir, sub)\n",
    "        dir_qmri_deriv = os.path.join(derivatives_dir, sub, 'qmri')\n",
    "\n",
    "        ## \n",
    "        if par !='cmro2':\n",
    "            par_map = os.path.join(dir_qmri_deriv, sub + '_task-' + baseline_cond + '_space-MNI152_'+par+'.nii.gz')\n",
    "        if par =='cmro2':\n",
    "            par_map = os.path.join(dir_qmri_deriv, sub + '_task-' + baseline_cond + '_space-MNI152_desc-orig_'+par+'.nii.gz')         \n",
    "        \n",
    "        \n",
    "        mask_append_conc= []\n",
    "        mask_append_disc= []\n",
    "\n",
    "        for m, mask in enumerate(masks_concord):\n",
    "            masker = input_data.NiftiMasker(mask_img = mask) \n",
    "            mask_arr = np.array(nib.load(mask).dataobj)\n",
    "                                 \n",
    "            # mask with voxels that are within mask\n",
    "            par_arr = np.squeeze(masker.fit_transform(par_map)) \n",
    "            if par == 'cbf':\n",
    "                par_arr = par_arr/0.75\n",
    "            \n",
    "            if m == 0:\n",
    "                if par != 'cbf':\n",
    "                    par_arr_GM = np.squeeze(masker_GM_woPLS.fit_transform(par_map))   \n",
    "                if par == 'cbf':\n",
    "                    par_arr_GM = np.squeeze(masker_GM_woPLS.fit_transform(par_map))/0.75   \n",
    "        \n",
    "            ## all voxels within GM\n",
    "            if m == 0:\n",
    "                par_median_vox_GM[i, :] = par_arr_GM\n",
    "                                \n",
    "            ## append all voxels\n",
    "            #print('length of array: ' + str(len(par_arr)))\n",
    "            mask_append_conc.append(par_arr)\n",
    "                \n",
    "        ## all voxels within mask\n",
    "        par_median_vox_conc[i,:] =  np.array([item for sublist in mask_append_conc for item in sublist])\n",
    "    \n",
    "\n",
    "        for m, mask in enumerate(masks_discord):\n",
    "            masker = input_data.NiftiMasker(mask_img = mask) \n",
    "            mask_arr = np.array(nib.load(mask).dataobj)\n",
    "                                 \n",
    "            # mask with voxels that are within mask\n",
    "            par_arr = np.squeeze(masker.fit_transform(par_map))   \n",
    "            if par == 'cbf':\n",
    "                par_arr = par_arr/0.75\n",
    "                \n",
    "            ## append all voxels\n",
    "            mask_append_disc.append(list(par_arr))\n",
    "\n",
    "           \n",
    "        ## all voxels within mask\n",
    "        par_median_vox_disc[i,:] =  np.array([item for sublist in mask_append_disc for item in sublist])\n",
    "                \n",
    "           \n",
    "    ##median across voxels per subject \n",
    "    par_median_voxel = np.nanmedian(par_median_vox_conc, axis = 1)\n",
    "    par_median_GM =  np.nanmedian(par_median_vox_GM, axis = 1)\n",
    "            \n",
    "    ## rename\n",
    "    vox_true = par_median_voxel\n",
    "    vox_GM = par_median_GM\n",
    "        \n",
    "    ##median across voxels per subject \n",
    "    par_median_voxel = np.nanmedian(par_median_vox_disc, axis = 1)\n",
    "\n",
    "            \n",
    "    ## append all voxels \n",
    "    vox_diss = par_median_voxel\n",
    "    \n",
    "    \n",
    "    ## significance: paired t-tests\n",
    "    [tvalue, pvalue] = stats.ttest_rel(vox_true,vox_diss)\n",
    "    print('paired t-test true vs. false regions: ' + str(round(pvalue, 6)))\n",
    "    [tvalue, pvalue_GM] = stats.ttest_rel(vox_GM,vox_diss)\n",
    "    print('paired t-test GM vs. false regions: ' + str(round(pvalue_GM, 6)))\n",
    "\n",
    "    percchange = np.nanmedian((vox_diss-vox_true)/vox_true*100)\n",
    "\n",
    "\n",
    "    ############################################\n",
    "    ## plot ####################################\n",
    "    ############################################\n",
    "    ID_colour_code = []\n",
    "    for s in range(0, len(sids)):\n",
    "        if (vox_true[s]-vox_diss[s])<=0:\n",
    "            ID_colour_code.append('red')\n",
    "        if (vox_true[s]-vox_diss[s])>0:\n",
    "            ID_colour_code.append('grey') \n",
    "    if par == 'cbv':\n",
    "        ID_colour_code = []\n",
    "        for s in range(0, len(sids)):\n",
    "            if (vox_true[s]-vox_diss[s])>=0:\n",
    "                ID_colour_code.append('red')\n",
    "            if (vox_true[s]-vox_diss[s])<0:\n",
    "                ID_colour_code.append('grey')             \n",
    "    \n",
    "    fig, (ax) = plt.subplots(1, figsize=(2.5, 6))\n",
    "    sns.boxplot(data = [vox_true, vox_diss],  orient='v', ax=ax,palette=colormap, saturation=0.8,\n",
    "                   boxprops={\"edgecolor\": (.6, .6, .6, .5)}, flierprops={\"marker\": \"x\"},\n",
    "                   medianprops={\"color\": \"black\"})\n",
    "    sns.stripplot(data = [vox_true, vox_diss], orient='v', ax=ax, color='grey')\n",
    "    for line, ID in enumerate(sids): # for each subject separately\n",
    "        ax.plot((vox_true[line], vox_diss[line]),   \n",
    "                  linewidth=1, color=ID_colour_code[line]) #plot lines = subjects         \n",
    "    ax.hlines(np.nanmedian(vox_GM),-0.5, 1.5, color='gray',  linewidth=2, linestyles='dashed')\n",
    "\n",
    "    if par == 'oef':\n",
    "        if pvalue < 0.001:\n",
    "            ax.text(0.4, 0.73, '**', color='grey', fontsize=fontsize)\n",
    "        ax.text(0.2, 0.7, str(round(percchange, 2)) + '%', color='grey', fontsize=fontsize)\n",
    "        ax.set_ylabel('OEF [ratio]', fontsize=fontsize)\n",
    "        ax.set_ylim(0.2, 0.75)\n",
    "        ax.set_xticklabels(['conc.', 'disc.'])\n",
    "        #ax.text(1.5, 0.4, round(np.nanmedian(vox_true), 2), color=colormap[0], fontsize=fontsize)\n",
    "        #ax.text(1.5, 0.3, round(np.nanmedian(vox_diss), 2), color=colormap[1], fontsize=fontsize)\n",
    "        #ax1.set_title('CTRL', fontsize=fontsize)\n",
    "        for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "            label.set_fontsize(fontsize)\n",
    "        #ax.set_title('coupling CBF/BOLD, CMRO2/BOLD')\n",
    "\n",
    "        for pos in ['right', 'top']:\n",
    "            ax.spines[pos].set_visible(False)\n",
    "\n",
    "        fig.savefig(results_dir + '/N' + N_subj + '_BOLD' + BOLD_ROI + '_' + par + '_disc_concord_subj_allCTRLcontrasts.png', dpi=300, bbox_inches='tight')\n",
    "        fig.show()\n",
    "        \n",
    "    if par == 'cmro2':\n",
    "        if pvalue < 0.001:\n",
    "            ax.text(0.4, 250, '**', color='grey', fontsize=fontsize)\n",
    "        ax.text(0.2, 230, str(round(percchange, 2)) + '%', color='grey', fontsize=fontsize)\n",
    "        ax.set_ylabel(par, fontsize=fontsize)\n",
    "        ax.set_ylim(50, 250)\n",
    "        ax.set_xticklabels(['conc.', 'disc.'])\n",
    "        ax.set_ylabel('CMRO2 [umol/100g/min]', fontsize=fontsize)\n",
    "        for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "            label.set_fontsize(fontsize)\n",
    "\n",
    "        for pos in ['right', 'top']:\n",
    "            ax.spines[pos].set_visible(False)\n",
    "\n",
    "        fig.savefig(results_dir + '/N' + N_subj + '_BOLD' + BOLD_ROI + '_' + par + '_disc_concord_subj_allCTRLcontrasts.png', dpi=300, bbox_inches='tight')\n",
    "        fig.show()\n",
    "        \n",
    "    if par == 'cbf':\n",
    "        if pvalue < 0.001:\n",
    "            ax.text(0.4, 70, '**', color='grey', fontsize=fontsize)\n",
    "        ax.text(0.2, 68, str(round(percchange, 2)) + '%', color='grey', fontsize=fontsize)\n",
    "        ax.set_ylim(20, 70)\n",
    "        ax.set_xticklabels(['conc.', 'disc.'])\n",
    "        ax.set_ylabel('CBF [ml/100g/min]', fontsize=fontsize)\n",
    "        for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "            label.set_fontsize(fontsize)\n",
    "\n",
    "        for pos in ['right', 'top']:\n",
    "            ax.spines[pos].set_visible(False)\n",
    "\n",
    "        fig.savefig(results_dir + '/N' + N_subj + '_BOLD' + BOLD_ROI + '_' + par + '_disc_concord_subj_allCTRLcontrasts.png', dpi=300, bbox_inches='tight')\n",
    "        fig.show()\n",
    "\n",
    "        \n",
    "    if par == 'cbv':\n",
    "        if pvalue < 0.001:\n",
    "            ax.text(0.4, 6, '**', color='grey', fontsize=fontsize)\n",
    "        ax.text(0.2, 5.8, str(round(percchange, 2)) + '%', color='grey', fontsize=fontsize)\n",
    "        ax.set_ylabel(par, fontsize=fontsize)\n",
    "        ax.set_ylim(2.5, 6)\n",
    "        ax.set_xticklabels(['conc.', 'disc.'])\n",
    "\n",
    "        for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "            label.set_fontsize(fontsize)\n",
    "        ax.set_ylabel('CBV [%]', fontsize=fontsize)\n",
    "\n",
    "\n",
    "        for pos in ['right', 'top']:\n",
    "            ax.spines[pos].set_visible(False)\n",
    "\n",
    "        fig.savefig(results_dir + '/N' + N_subj + '_BOLD' + BOLD_ROI + '_' + par + '_disc_concord_subj_allCTRLcontrasts.png', dpi=300, bbox_inches='tight')\n",
    "        fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get intersect regions: MEMneg-CALCpos - CALCpos-MEMneg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## overlap between MEM neg and CALC pos ##\n",
    "##########################################\n",
    "\n",
    "PLS_mask_CALC_pos_bin =  results_dir + '/N40_BSR_meanPLS_func_clusterBSRcalccontrol_pos_bin.nii.gz'\n",
    "PLS_mask_MEM_neg_bin = results_dir + '/N30_BSR_meanPLS_func_clusterBSRmemcontrol_neg_bin.nii.gz'\n",
    "\n",
    "MEMneg_CALCpos_overlap = results_dir + '/BSR_meanPLS_BOLD_clusterBSR_intersect_MEMneg_CALCpos_bin.nii.gz'\n",
    "\n",
    "! fslmaths {PLS_mask_CALC_pos_bin} -mas {PLS_mask_MEM_neg_bin}  {MEMneg_CALCpos_overlap}\n",
    "\n",
    "plotting.plot_img(MEMneg_CALCpos_overlap, bg_img = MNI_2mm_brain, threshold=0, cmap='jet')\n",
    "\n",
    "\n",
    "## overlap between MEM pos and CALC neg ##\n",
    "##########################################\n",
    "\n",
    "PLS_mask_CALC_neg_bin = results_dir + '/N40_BSR_meanPLS_func_clusterBSRcalccontrol_neg_bin.nii.gz'\n",
    "PLS_mask_MEM_pos_bin = results_dir + '/N30_BSR_meanPLS_func_clusterBSRmemcontrol_pos_bin.nii.gz'\n",
    "\n",
    "\n",
    "MEMpos_CALCneg_overlap = results_dir + '/BSR_meanPLS_BOLD_clusterBSR_intersect_MEMpos_CALCneg_bin.nii.gz'\n",
    "\n",
    "! fslmaths {PLS_mask_CALC_neg_bin} -mas {PLS_mask_MEM_pos_bin}  {MEMpos_CALCneg_overlap}\n",
    "\n",
    "plotting.plot_img(MEMpos_CALCneg_overlap, bg_img = MNI_2mm_brain, threshold=0, cmap='jet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fig. 4C: external atlas validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VENAT atlas: venous density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Final result: \n",
      "1.000000 0.000000 -0.000019 0.236901 \n",
      "-0.000000 1.000000 -0.000368 -1.209569 \n",
      "0.000019 0.000368 1.000000 0.203290 \n",
      "0.000000 0.000000 0.000000 1.000000 \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<nilearn.plotting.displays._slicers.ZSlicer at 0x7fea7ada0ca0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1610x230 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1610x230 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## first, coregister VENAT atlas to MNI space\n",
    "VENAT_partial_volume = os.path.join(derivatives_dir, 'VENAT_PartialVolume.nii.gz')\n",
    "VENAT_partial_volume_MNI2mm = os.path.join(derivatives_dir, 'VENAT_PartialVolume_MNI2mm.nii.gz')\n",
    "! flirt -in {VENAT_partial_volume} -ref {VENAT_partial_volume} -applyisoxfm 2 -out {VENAT_partial_volume_MNI2mm}\n",
    "\n",
    "VENAT_density = os.path.join(derivatives_dir,'VENAT_DensityMap.nii.gz') \n",
    "VENAT_density_MNI2mm = os.path.join(derivatives_dir, 'VENAT_DensityMap_MNI2mm.nii.gz')\n",
    "! flirt -in {VENAT_density} -ref {VENAT_density} -applyisoxfm 2 -out {VENAT_density_MNI2mm}\n",
    "! flirt -in {VENAT_density} -ref {MNI_2mm_brain} -out {VENAT_density_MNI2mm} -dof 6\n",
    "\n",
    "\n",
    "plotting.plot_img(VENAT_partial_volume_MNI2mm, bg_img=MNI_2mm_brain, threshold=0.01, display_mode='z',colorbar=True, black_bg=True, \n",
    "                              title = ('VENAT partial volume'), cut_coords = (-30, -15, 0, 15, 30, 45, 60))\n",
    "\n",
    "plotting.plot_roi(VENAT_density_MNI2mm, bg_img=MNI_2mm_brain, threshold=0.01, display_mode='z',  colorbar=True, black_bg=True, \n",
    "                              title = ('VENAT density'), cut_coords = (-30, -15, 0, 15, 30, 45, 60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "permutation statistics true vs. false, memcontrol all concordant/discordant voxels :0.001\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 300x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BOLD_ROI = 'all'\n",
    "\n",
    "## create array of masks with all concordant and all discordant voxels ##\n",
    "#################################################################\n",
    "masks_concord=[]\n",
    "masks_discord=[]\n",
    "contrasts = ['calccontrol', 'memcontrol']\n",
    "for contrast in contrasts:\n",
    "\n",
    "    ## divide into: real deactivation and dissociation areas!\n",
    "    PLS_true_deact = os.path.join(results_dir, 'negBOLD_negCMRO2_' + contrast + '.nii.gz') #negBOLD_negCMRO2_memcontrol_CMRO2deact\n",
    "    #plotting.plot_img(PLS_true_deact, cmap=colormap,  bg_img=MNI_brain, threshold=0, display_mode='z')\n",
    "\n",
    "    PLS_dissociation = os.path.join(results_dir, 'negBOLD_posCMRO2_' + contrast + '.nii.gz')\n",
    "    #plotting.plot_img(PLS_dissociation, cmap=colormap,  bg_img=MNI_brain, threshold=0, display_mode='z')\n",
    "    \n",
    "    masks_concord.append(PLS_true_deact)\n",
    "    masks_discord.append(PLS_dissociation)\n",
    "    \n",
    "    ## divide into: real deactivation and dissociation areas!\n",
    "    PLS_true_act =  os.path.join(results_dir, 'posBOLD_posCMRO2_' + contrast + '.nii.gz')\n",
    "\n",
    "    #plotting.plot_img(PLS_true_act, cmap=colormap,  bg_img=MNI_brain, threshold=0, display_mode='z')\n",
    "\n",
    "    PLS_dissociation = os.path.join(results_dir, 'posBOLD_negCMRO2_' + contrast + '.nii.gz')\n",
    "    #plotting.plot_img(PLS_dissociation, cmap=colormap,  bg_img=MNI_brain, threshold=0, display_mode='z')\n",
    "\n",
    "    masks_concord.append(PLS_true_act)\n",
    "    masks_discord.append(PLS_dissociation)\n",
    "\n",
    "\n",
    "palette=['plum', 'darkviolet']\n",
    "\n",
    "VENAT_density_MNI2mm = os.path.join(derivatives_dir, 'VENAT_DensityMap_MNI2mm.nii.gz')\n",
    "\n",
    "mask_GM = results_dir + '/GM_vox_without_calccontrol_memcontrol_vox.nii.gz'\n",
    "masker_GM = input_data.NiftiMasker(mask_img=mask_GM)\n",
    "VENAT_values_GM = (np.squeeze(masker_GM.fit_transform(VENAT_density_MNI2mm)))\n",
    "\n",
    "\n",
    "## fill with values\n",
    "VENAT_values_true = []\n",
    "for m,mask in enumerate(masks_concord):\n",
    "    masker = input_data.NiftiMasker(mask_img=mask)\n",
    "    VENAT_values_true.append(np.squeeze(masker.fit_transform(VENAT_density_MNI2mm)))\n",
    "VENAT_values_true = np.array([item for sublist in VENAT_values_true for item in sublist])\n",
    "\n",
    "VENAT_values_false = []\n",
    "for m,mask in enumerate(masks_discord):\n",
    "    masker = input_data.NiftiMasker(mask_img=mask)\n",
    "    VENAT_values_false.append(np.squeeze(masker.fit_transform(VENAT_density_MNI2mm)))\n",
    "VENAT_values_false = np.array([item for sublist in VENAT_values_false for item in sublist])\n",
    "\n",
    "##kick out zeros and negative values\n",
    "VENAT_values_true[VENAT_values_true<0]=np.nan\n",
    "VENAT_values_false[VENAT_values_false<0]=np.nan\n",
    "\n",
    "##permutation test, because data are smoothed/non-independent\n",
    "## difference in medians\n",
    "def statistic(VENAT_values_true, VENAT_values_false):\n",
    "        return np.median(VENAT_values_true, axis=0) - np.median(VENAT_values_false, axis=0)\n",
    "def statistic_gm(VENAT_values_GM, VENAT_values_false):\n",
    "        return np.median(VENAT_values_GM, axis=0) - np.median(VENAT_values_false, axis=0)\n",
    "    \n",
    "result = stats.permutation_test((VENAT_values_true, VENAT_values_false), statistic, permutation_type='independent', n_resamples=2000, alternative='two-sided')\n",
    "p_value = result.pvalue\n",
    "print('permutation statistics true vs. false, ' + contrast + ' all concordant/discordant voxels'  + ' :' + str(round(p_value, 6)))\n",
    "\n",
    "## put into dataframe\n",
    "df_VENAT = pd.DataFrame()\n",
    "df_VENAT['values'] = np.concatenate((VENAT_values_true, VENAT_values_false, VENAT_values_GM), axis=0)\n",
    "df_VENAT['ROI'] = np.concatenate((len(VENAT_values_true)*['concordant'], len(VENAT_values_false)*['discordant'], len(VENAT_values_GM)*['GM']), axis=0)\n",
    "    \n",
    "df_VENAT_melted = pd.melt(df_VENAT, id_vars='ROI')\n",
    "\n",
    "\n",
    "## plot barplots\n",
    "fontsize=20\n",
    "fig, ax = plt.subplots(1, figsize= (3,5))\n",
    "sns.boxplot(x='ROI', y='value',  data=df_VENAT_melted, ax=ax, palette=palette, saturation=0.9,\n",
    "                   boxprops={\"edgecolor\": (.6, .6, .6, .5)}, flierprops={\"marker\": \"x\"},\n",
    "                   medianprops={\"color\": \"black\"})\n",
    "#bar = sns.barplot(x='ROI', y='value',  data=df_VENAT_melted, ax=ax, palette=palette,  \n",
    "#            estimator=np.median, ci=95, capsize=.05, n_boot=2000, errwidth=1, saturation=0.9, alpha=0.8) \n",
    "ax.hlines(np.nanmedian(VENAT_values_GM),-0.5, 1.5, color='grey', linestyle='dashed')\n",
    "\n",
    "#ax.set_ylim(-0.6, 0.8)\n",
    "#ax.set_yticks([-0.5, -0.25, 0, 0.25, 0.5])\n",
    "#ax.set_ylim(-0.2, 1.25)\n",
    "#ax.set_yticks([0, 0.25, 0.5, 0.75, 1, 1.25])\n",
    "#ax.tick_params(axis='y', labelcolor=color)\n",
    "ax.set_ylabel('venous density', fontsize = fontsize) \n",
    "ax.set_xlim([-0.5, 1.5]) \n",
    "ax.set_xticks([0, 1]) \n",
    "ax.set_xticklabels(['conc.', 'disc.']) \n",
    "ax.set_ylim(-0.1, 1)\n",
    "\n",
    "if p_value < 0.001:\n",
    "    ax.text(0.4, 1, '**', color='grey', fontsize=fontsize)\n",
    "#ax.legend(fontsize=fontsize-5,loc=\"upper left\")\n",
    "#set x-tick and y-ticke label size\n",
    "for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    label.set_fontsize(fontsize)\n",
    "\n",
    "fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
    "\n",
    "fig.savefig(results_dir + '/VENAT_values_' + contrast + '_all_conc_discord_voxels.png', dpi=300, bbox_inches='tight')\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QSM:human phantom data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "permutation statistics conc vs. disc. : 0.001\n",
      "permutation statistics disc vs. GM: 0.001\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 730x260 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 450x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BOLD_ROI = 'all'\n",
    "QSM_MNI2mm = os.path.join( derivatives_dir, 'sub-yv98_ses-4088+4137_QSM_L2000-vsmv3_2mm.nii.gz')\n",
    "plotting.plot_img(QSM_MNI2mm, bg_img=MNI_2mm_brain, threshold=0, colorbar=True, black_bg=True, \n",
    "                    cmap='jet', vmin=0, vmax=0.13, title = ('QSM'))\n",
    "\n",
    "palette=['plum', 'darkviolet']\n",
    "\n",
    "## fill with values\n",
    "QSM_values_true = []\n",
    "for m,mask in enumerate(masks_concord):\n",
    "    masker = input_data.NiftiMasker(mask_img=mask)\n",
    "    QSM_values_true.append(np.squeeze(masker.fit_transform(QSM_2mm)))\n",
    "QSM_values_true = np.array([item for sublist in QSM_values_true for item in sublist])\n",
    "\n",
    "QSM_values_false = []\n",
    "for m,mask in enumerate(masks_discord):\n",
    "    masker = input_data.NiftiMasker(mask_img=mask)\n",
    "    QSM_values_false.append(np.squeeze(masker.fit_transform(QSM_2mm)))\n",
    "QSM_values_false = np.array([item for sublist in QSM_values_false for item in sublist])\n",
    "\n",
    "QSM_GM= []\n",
    "mask_GM_woPLS = results_dir + '/GM_vox_without_calccontrol_memcontrol_vox.nii.gz'\n",
    "masker_GM = input_data.NiftiMasker(mask_img=mask_GM_woPLS)\n",
    "QSM_GM = np.squeeze(masker_GM.fit_transform(QSM_2mm))\n",
    "\n",
    "##kick out zeros\n",
    "QSM_GM[QSM_GM==0]=np.nan\n",
    "QSM_values_true[QSM_values_true==0]=np.nan\n",
    "QSM_values_false[QSM_values_false==0]=np.nan\n",
    "\n",
    "##permutation test, because data are smoothed/non-independent\n",
    "## difference in medians\n",
    "def statistic(QSM_values_true, QSM_values_false):\n",
    "        return np.median(QSM_values_true, axis=0) - np.median(QSM_values_false, axis=0)\n",
    "def statistic_gm(QSM_GM, QSM_values_false):\n",
    "        return np.median(QSM_GM, axis=0) - np.median(QSM_values_false, axis=0)\n",
    "    \n",
    "res_CALCneg = stats.permutation_test((QSM_values_true, QSM_values_false), statistic, permutation_type='independent', n_resamples=2000, alternative='two-sided')\n",
    "p_true_false = res_CALCneg.pvalue\n",
    "print('permutation statistics conc vs. disc. : ' + str(round(p_true_false, 6)))\n",
    "res_GM = stats.permutation_test((QSM_GM, QSM_values_false), statistic_gm, permutation_type='independent', n_resamples=2000, alternative='two-sided')\n",
    "p_GM_false = res_GM.pvalue\n",
    "print('permutation statistics disc vs. GM: ' + str(round(p_GM_false, 6)))\n",
    "\n",
    "## put into dataframe\n",
    "df_QSM = pd.DataFrame()\n",
    "df_QSM['values'] = np.concatenate((QSM_values_true, QSM_values_false, QSM_GM), axis=0)\n",
    "df_QSM['ROI'] = np.concatenate((len(QSM_values_true)*['concordant'], len(QSM_values_false)*['discordant'], len(QSM_GM)*['GM']), axis=0)\n",
    "    \n",
    "#df_QSM_melted = pd.melt(df_QSM, id_vars='ROI')\n",
    "GM_median = np.nanmedian(df_QSM.query(f\"ROI=='GM'\")['values'].values)\n",
    "\n",
    "\n",
    "\n",
    "## plot barplots\n",
    "fontsize=25\n",
    "fig, ax = plt.subplots(1, figsize= (4,5))\n",
    "#bar = sns.barplot(x='ROI', y='values',  data=df_QSM, ax=ax, palette=['plum', 'darkviolet'],  \n",
    "#            estimator=np.median, ci=95, capsize=.05, n_boot=3000, errwidth=1, saturation=0.9, alpha=0.8) \n",
    "\n",
    "sns.boxplot(x='ROI', y='values',  data=df_QSM, ax=ax, palette=['plum', 'darkviolet'], \n",
    "                   boxprops={\"edgecolor\": (.6, .6, .6, .5)}, flierprops={\"marker\": \"x\"},\n",
    "                   medianprops={\"color\": \"black\"})\n",
    "#sns.violinplot(x='ROI', y='values',  data=df_QSM, ax=ax, palette=['plum', 'darkviolet'], \n",
    "#                   boxprops={\"edgecolor\": (.6, .6, .6, .5)}, flierprops={\"marker\": \"x\"},\n",
    "#                   medianprops={\"color\": \"black\"})\n",
    "ax.set_ylabel('susceptibility', fontsize = fontsize) \n",
    "ax.set_xlim([-0.5, 1.5]) \n",
    "ax.set_xticks([0, 1]) \n",
    "ax.set_xticklabels(['conc.', 'disc.']) \n",
    "ax.set_ylim([-0.025, 0.025]) \n",
    "ax.hlines(GM_median, -0.5, 1.5, color='gray', linestyle='--')\n",
    "\n",
    "if p_true_false <= 0.001:\n",
    "    ax.text(0.4, 0.025, '**', color='grey', fontsize=fontsize)\n",
    "#ax.legend(fontsize=fontsize-5,loc=\"upper left\")\n",
    "#set x-tick and y-ticke label size\n",
    "for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    label.set_fontsize(fontsize)\n",
    "\n",
    "fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
    "\n",
    "fig.savefig(results_dir + '/QSM_values_all_conc_disc_voxels.png', dpi=300, bbox_inches='tight')\n",
    "\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fig. 4D: Task-effects; delta task responses delta CMRO2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "concordant\n",
      "calc\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "mem\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "discordant\n",
      "calc\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
      "mem\n",
      "sub-p019\n",
      "sub-p020\n",
      "sub-p021\n",
      "sub-p023\n",
      "sub-p026\n",
      "sub-p027\n",
      "sub-p028\n",
      "sub-p029\n",
      "sub-p030\n",
      "sub-p031\n",
      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
      "sub-p035\n",
      "sub-p036\n",
      "sub-p037\n",
      "sub-p038\n",
      "sub-p039\n",
      "sub-p040\n",
      "sub-p043\n",
      "sub-p044\n",
      "sub-p046\n",
      "sub-p047\n",
      "sub-p048\n",
      "sub-p049\n",
      "sub-p050\n",
      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n"
     ]
    }
   ],
   "source": [
    "## create discordant and concordant masks\n",
    "pos_neg_disc_calc = os.path.join(results_dir, 'BOLD_CMRO2_discordant_calc'+baseline+'.nii.gz')\n",
    "negBOLD_disc_calc = os.path.join(results_dir, 'negBOLD_posCMRO2_calc' +baseline+ '.nii.gz')\n",
    "posBOLD_disc_calc = os.path.join(results_dir, 'posBOLD_negCMRO2_calc' +baseline+ '.nii.gz')\n",
    "! fslmaths {negBOLD_disc_calc} -add {posBOLD_disc_calc} -bin {pos_neg_disc_calc}\n",
    "\n",
    "pos_neg_disc_mem = os.path.join(results_dir, 'BOLD_CMRO2_discordant_mem'+baseline+'.nii.gz')\n",
    "negBOLD_disc_mem = os.path.join(results_dir, 'negBOLD_posCMRO2_mem' +baseline+ '.nii.gz')\n",
    "posBOLD_disc_mem = os.path.join(results_dir, 'posBOLD_negCMRO2_mem' +baseline+ '.nii.gz')\n",
    "! fslmaths {negBOLD_disc_mem} -add {posBOLD_disc_mem} -bin {pos_neg_disc_mem}\n",
    "\n",
    "pos_neg_conc_calc = os.path.join(results_dir, 'BOLD_CMRO2_concordant_calc'+baseline +'.nii.gz')\n",
    "negBOLD_conc_calc = os.path.join(results_dir, 'negBOLD_negCMRO2_calc' +baseline+ '.nii.gz')\n",
    "posBOLD_conc_calc = os.path.join(results_dir, 'posBOLD_posCMRO2_calc' +baseline+ '.nii.gz')\n",
    "! fslmaths {negBOLD_conc_calc} -add {posBOLD_conc_calc} -bin {pos_neg_conc_calc}\n",
    "\n",
    "pos_neg_conc_mem = os.path.join(results_dir,  'BOLD_CMRO2_concordant_mem'+baseline +'.nii.gz')    \n",
    "negBOLD_conc_mem = os.path.join(results_dir, 'negBOLD_negCMRO2_mem' +baseline+ '.nii.gz')\n",
    "posBOLD_conc_mem = os.path.join(results_dir, 'posBOLD_posCMRO2_mem' +baseline+ '.nii.gz')\n",
    "! fslmaths {negBOLD_conc_mem} -add {posBOLD_conc_mem} -bin {pos_neg_conc_mem}\n",
    "\n",
    "## only mem or calc condition / contrast\n",
    "#disc_masks = [pos_neg_disc_mem]\n",
    "\n",
    "#conc_masks = [pos_neg_conc_mem]\n",
    "\n",
    "#task = ['mem']\n",
    "#baseline = ['control']\n",
    "\n",
    "## both calc and mem contrasts / regions\n",
    "disc_masks = [pos_neg_disc_calc, pos_neg_disc_mem]\n",
    "conc_masks = [pos_neg_conc_calc, pos_neg_conc_mem]\n",
    "\n",
    "task = ['calc', 'mem']\n",
    "baseline = 'control'\n",
    "\n",
    "mask_types = ['concordant', 'discordant'] ## in both: first calc, then mem\n",
    "conc_df_calc = pd.DataFrame()       \n",
    "disc_df_mem = pd.DataFrame()\n",
    "conc_df_mem = pd.DataFrame()       \n",
    "disc_df_calc= pd.DataFrame()\n",
    "\n",
    "for mask_type in mask_types: \n",
    "    print(mask_type)\n",
    "    \n",
    "    if mask_type == 'concordant':\n",
    "        masks = conc_masks\n",
    "    \n",
    "    if mask_type == 'discordant':\n",
    "        masks = disc_masks\n",
    "        \n",
    "    for m, mask in enumerate(masks):\n",
    "        print(task[m])\n",
    "        contrast = task[m] + baseline[0] ## first calc then mem \n",
    "\n",
    "        PLS_masker = input_data.NiftiMasker(mask_img = mask)\n",
    "        len_mask =  len(np.argwhere(np.array(nib.load(mask).dataobj)))\n",
    "\n",
    "        BOLD_percchange = np.zeros((len(sids_30), len_mask))\n",
    "        CBV_baseline = np.zeros((len(sids_30),len_mask))\n",
    "        CMRO2_base = np.zeros((len(sids_30), len_mask))\n",
    "        OEF_base = np.zeros((len(sids_30), len_mask))\n",
    "        CBF_base = np.zeros((len(sids_30), len_mask))\n",
    "        CMRO2_task = np.zeros((len(sids_30), len_mask))\n",
    "        OEF_task = np.zeros((len(sids_30), len_mask))\n",
    "        CBF_task = np.zeros((len(sids_30), len_mask))\n",
    "        CMRO2_delta = np.zeros((len(sids_30), len_mask))\n",
    "        OEF_delta = np.zeros((len(sids_30), len_mask))\n",
    "        CBF_delta= np.zeros((len(sids_30), len_mask))\n",
    "        CMRO2_percchange = np.zeros((len(sids_30), len_mask))\n",
    "        OEF_percchange = np.zeros((len(sids_30), len_mask))\n",
    "        CBF_percchange= np.zeros((len(sids_30), len_mask))\n",
    "\n",
    "\n",
    "        for i, ID in enumerate(sids_30): #loop over subjects\n",
    "            sub = \"sub-p{:03d}\".format(ID) # subject id, eg 'p021'\n",
    "            print(sub)\n",
    "\n",
    "            sub_dir = os.path.join(data_dir, sub)\n",
    "\n",
    "            dir_anat = os.path.join(sub_dir, 'anat')\n",
    "            dir_func = os.path.join(sub_dir, 'func')\n",
    "            dir_perf = os.path.join(sub_dir, 'perf')\n",
    "\n",
    "            dir_anat_deriv = os.path.join(derivatives_dir, sub, 'anat')\n",
    "            dir_func_deriv = os.path.join(derivatives_dir, sub, 'func')\n",
    "            dir_perf_deriv = os.path.join(derivatives_dir, sub, 'perf')\n",
    "            dir_qmri_deriv = os.path.join(derivatives_dir, sub, 'qmri')\n",
    "\n",
    "\n",
    "            #load baseline & task images\n",
    "            base_CMRO2 = np.squeeze(PLS_masker.fit_transform( os.path.join(dir_qmri_deriv, sub + '_task-' + baseline + '_space-MNI152_desc-orig_cmro2.nii.gz')))\n",
    "            base_OEF = np.squeeze(PLS_masker.fit_transform( os.path.join(dir_qmri_deriv, sub + '_task-' + baseline + '_space-MNI152_oef.nii.gz')))\n",
    "            base_CBF = np.squeeze(PLS_masker.fit_transform(os.path.join(dir_qmri_deriv, sub + '_task-' + baseline + '_space-MNI152_cbf.nii.gz')))\n",
    "            task_CBF = np.squeeze(PLS_masker.fit_transform(os.path.join(dir_qmri_deriv, sub + '_task-' + task[m] + '_space-MNI152_cbf.nii.gz')))\n",
    "            if CMRO2_mode == 'orig' or task[m]!='calc': ## without CBV correction\n",
    "                task_CMRO2 = np.squeeze(PLS_masker.fit_transform( os.path.join(dir_qmri_deriv, sub + '_task-' + task[m] + '_base-' + baseline + '_space-MNI152_desc-semi-quant_cmro2.nii.gz')))\n",
    "                task_OEF = np.squeeze(PLS_masker.fit_transform( os.path.join(dir_qmri_deriv, sub + '_task-' + task[m] + '_base-' + baseline + '_space-MNI152_desc-semi-quant_oef.nii.gz')))                \n",
    "            if CMRO2_mode == 'corrected' and task[m]=='calc': ## with CBV correction\n",
    "                task_CMRO2 = np.squeeze(PLS_masker.fit_transform( os.path.join(dir_qmri_deriv, sub + '_task-' + task[m] + '_base-' + baseline +'_space-MNI152_desc-semi-quant-corrected_cmro2.nii.gz')))\n",
    "                task_OEF = np.squeeze(PLS_masker.fit_transform( os.path.join(dir_qmri_deriv, sub + '_task-' + task[m] + '_base-' + baseline +'_space-MNI152_desc-semi-quant-corrected_oef.nii.gz')))\n",
    "\n",
    "            percchange_CMRO2 = (task_CMRO2- base_CMRO2) / base_CMRO2 *100 \n",
    "            percchange_CBF = (task_CBF- base_CBF) / base_CBF *100               \n",
    "            percchange_OEF = (task_OEF- base_OEF) / base_OEF *100               \n",
    "            delta_CMRO2 = task_CMRO2 - base_CMRO2\n",
    "            delta_CBF = task_CBF - base_CBF\n",
    "            delta_OEF = task_OEF - base_OEF\n",
    "            CMRO2_base[i, :] = base_CMRO2\n",
    "            OEF_base[i, :] = base_OEF\n",
    "            CBF_base[i, :] = base_CBF/0.75\n",
    "            CMRO2_task[i, :] = task_CMRO2\n",
    "            OEF_task[i, :] = task_OEF\n",
    "            CBF_task[i, :] = task_CBF/0.75\n",
    "            CMRO2_delta[i, :] = delta_CMRO2\n",
    "            OEF_delta[i, :] = delta_OEF\n",
    "            CBF_delta[i, :] = delta_CBF/0.75\n",
    "            CMRO2_percchange[i,:] = percchange_CMRO2\n",
    "            OEF_percchange[i, :] = percchange_OEF\n",
    "            CBF_percchange[i, :] = percchange_CBF\n",
    "\n",
    "\n",
    "        ## create df\n",
    "        if task[m] == 'calc':\n",
    "            if mask_type == 'concordant':\n",
    "                conc_df_calc['CMRO2_base'] = np.nanmedian(CMRO2_base, axis=0)\n",
    "                conc_df_calc['OEF_base'] = np.nanmedian(OEF_base, axis=0)\n",
    "                conc_df_calc['CBF_base' ] = np.nanmedian(CBF_base, axis=0)\n",
    "                conc_df_calc['CMRO2_task' ] = np.nanmedian(CMRO2_task, axis=0)\n",
    "                conc_df_calc['OEF_task' ] = np.nanmedian(OEF_task, axis=0)\n",
    "                conc_df_calc['CBF_task' ] = np.nanmedian(CBF_task, axis=0)\n",
    "                conc_df_calc['CMRO2_delta'] = np.nanmedian(CMRO2_delta, axis=0)\n",
    "                conc_df_calc['OEF_delta' ] = np.nanmedian(OEF_delta, axis=0)\n",
    "                conc_df_calc['CBF_delta'] = np.nanmedian(CBF_delta, axis=0)\n",
    "                conc_df_calc['CMRO2_percchange' ] = np.nanmedian(CMRO2_percchange, axis=0)\n",
    "                conc_df_calc['OEF_percchange' ] = np.nanmedian(OEF_percchange, axis=0)\n",
    "                conc_df_calc['CBF_percchange' ] = np.nanmedian(CBF_percchange, axis=0)   \n",
    "            if mask_type == 'discordant':\n",
    "                disc_df_calc['CMRO2_base' ] = np.nanmedian(CMRO2_base, axis=0)\n",
    "                disc_df_calc['OEF_base' ] = np.nanmedian(OEF_base, axis=0)\n",
    "                disc_df_calc['CBF_base' ] = np.nanmedian(CBF_base, axis=0)\n",
    "                disc_df_calc['CMRO2_task' ] = np.nanmedian(CMRO2_task, axis=0)\n",
    "                disc_df_calc['OEF_task' ] = np.nanmedian(OEF_task, axis=0)\n",
    "                disc_df_calc['CBF_task' ] = np.nanmedian(CBF_task, axis=0)\n",
    "                disc_df_calc['CMRO2_delta'] = np.nanmedian(CMRO2_delta, axis=0)\n",
    "                disc_df_calc['OEF_delta' ] = np.nanmedian(OEF_delta, axis=0)\n",
    "                disc_df_calc['CBF_delta' ] = np.nanmedian(CBF_delta, axis=0)\n",
    "                disc_df_calc['CMRO2_percchange' ] = np.nanmedian(CMRO2_percchange, axis=0)\n",
    "                disc_df_calc['OEF_percchange' ] = np.nanmedian(OEF_percchange, axis=0)\n",
    "                disc_df_calc['CBF_percchange' ] = np.nanmedian(CBF_percchange, axis=0)\n",
    "        if task[m] == 'mem':\n",
    "            if mask_type == 'concordant':\n",
    "                conc_df_mem['CMRO2_base'] = np.nanmedian(CMRO2_base, axis=0)\n",
    "                conc_df_mem['OEF_base'] = np.nanmedian(OEF_base, axis=0)\n",
    "                conc_df_mem['CBF_base' ] = np.nanmedian(CBF_base, axis=0)\n",
    "                conc_df_mem['CMRO2_task' ] = np.nanmedian(CMRO2_task, axis=0)\n",
    "                conc_df_mem['OEF_task' ] = np.nanmedian(OEF_task, axis=0)\n",
    "                conc_df_mem['CBF_task' ] = np.nanmedian(CBF_task, axis=0)\n",
    "                conc_df_mem['CMRO2_delta'] = np.nanmedian(CMRO2_delta, axis=0)\n",
    "                conc_df_mem['OEF_delta' ] = np.nanmedian(OEF_delta, axis=0)\n",
    "                conc_df_mem['CBF_delta'] = np.nanmedian(CBF_delta, axis=0)\n",
    "                conc_df_mem['CMRO2_percchange'] = np.nanmedian(CMRO2_percchange, axis=0)\n",
    "                conc_df_mem['OEF_percchange'] = np.nanmedian(OEF_percchange, axis=0)\n",
    "                conc_df_mem['CBF_percchange'] = np.nanmedian(CBF_percchange, axis=0)   \n",
    "            if mask_type == 'discordant':\n",
    "                disc_df_mem['CMRO2_base' ] = np.nanmedian(CMRO2_base, axis=0)\n",
    "                disc_df_mem['OEF_base' ] = np.nanmedian(OEF_base, axis=0)\n",
    "                disc_df_mem['CBF_base' ] = np.nanmedian(CBF_base, axis=0)\n",
    "                disc_df_mem['CMRO2_task' ] = np.nanmedian(CMRO2_task, axis=0)\n",
    "                disc_df_mem['OEF_task' ] = np.nanmedian(OEF_task, axis=0)\n",
    "                disc_df_mem['CBF_task'] = np.nanmedian(CBF_task, axis=0)\n",
    "                disc_df_mem['CMRO2_delta'] = np.nanmedian(CMRO2_delta, axis=0)\n",
    "                disc_df_mem['OEF_delta' ] = np.nanmedian(OEF_delta, axis=0)\n",
    "                disc_df_mem['CBF_delta' ] = np.nanmedian(CBF_delta, axis=0)\n",
    "                disc_df_mem['CMRO2_percchange' ] = np.nanmedian(CMRO2_percchange, axis=0)\n",
    "                disc_df_mem['OEF_percchange' ] = np.nanmedian(OEF_percchange, axis=0)\n",
    "                disc_df_mem['CBF_percchange' ] = np.nanmedian(CBF_percchange, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "regplot() takes from 0 to 1 positional arguments but 2 positional arguments (and 3 keyword-only arguments) were given",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[53], line 76\u001b[0m\n\u001b[1;32m     73\u001b[0m     scatter \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39mscatter(dataframe_disc[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCBF_percchange\u001b[39m\u001b[38;5;124m'\u001b[39m], dataframe_disc[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOEF_percchange\u001b[39m\u001b[38;5;124m'\u001b[39m], c\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdarkviolet\u001b[39m\u001b[38;5;124m'\u001b[39m, s\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, vmin \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m20\u001b[39m, vmax\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdiscordant\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     75\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(task)\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m2\u001b[39m:\n\u001b[0;32m---> 76\u001b[0m     \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mregplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mappend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconc_df_calc\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mCBF_percchange\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconc_df_mem\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mCBF_percchange\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mappend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconc_df_calc\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mOEF_percchange\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconc_df_mem\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mOEF_percchange\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mplum\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscatter_kws\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43ms\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     77\u001b[0m     (r, p) \u001b[38;5;241m=\u001b[39m stats\u001b[38;5;241m.\u001b[39mpearsonr(np\u001b[38;5;241m.\u001b[39mappend(conc_df_calc[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCBF_percchange\u001b[39m\u001b[38;5;124m'\u001b[39m ], conc_df_mem[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCBF_percchange\u001b[39m\u001b[38;5;124m'\u001b[39m ]), np\u001b[38;5;241m.\u001b[39mappend(conc_df_calc[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOEF_percchange\u001b[39m\u001b[38;5;124m'\u001b[39m], conc_df_mem[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOEF_percchange\u001b[39m\u001b[38;5;124m'\u001b[39m]))\n\u001b[1;32m     78\u001b[0m     ax\u001b[38;5;241m.\u001b[39mtext(\u001b[38;5;241m5\u001b[39m, \u001b[38;5;241m10\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr= \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mround\u001b[39m(r, \u001b[38;5;241m2\u001b[39m)), fontsize\u001b[38;5;241m=\u001b[39mfontsize, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mplum\u001b[39m\u001b[38;5;124m'\u001b[39m)    \n",
      "\u001b[0;31mTypeError\u001b[0m: regplot() takes from 0 to 1 positional arguments but 2 positional arguments (and 3 keyword-only arguments) were given"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## mask voxels with CMRO2 300 and OEF > 1  ##\n",
    "################################################\n",
    "if len(task)==1 and task[0]=='calc':\n",
    "    conc_df_calc = conc_df_calc.drop(conc_df_calc[conc_df_calc['CMRO2_base' ]> 300].index)\n",
    "    conc_df_calc = conc_df_calc.drop(conc_df_calc[conc_df_calc['OEF_base' ] > 1].index)\n",
    "    conc_df_calc = conc_df_calc.drop(conc_df_calc[conc_df_calc['OEF_base'] == 0 ].index)\n",
    "    conc_df_calc = conc_df_calc.drop(conc_df_calc[conc_df_calc['CMRO2_base' ] == 0 ].index)\n",
    "\n",
    "    disc_df_calc = disc_df_calc.drop(disc_df_calc[disc_df_calc['CMRO2_base' ]> 300].index)\n",
    "    disc_df_calc = disc_df_calc.drop(disc_df_calc[disc_df_calc['OEF_base' ] > 1].index)\n",
    "    disc_df_calc = disc_df_calc.drop(disc_df_calc[disc_df_calc['OEF_base'] == 0 ].index)\n",
    "    disc_df_calc = disc_df_calc.drop(disc_df_calc[disc_df_calc['CMRO2_base' ] == 0 ].index)\n",
    "    \n",
    "##apply same to discordant voxels\n",
    "if len(task)==1 and task[0]=='mem':\n",
    "    conc_df_mem = conc_df_mem.drop(conc_df_mem[conc_df_mem['CMRO2_base' ] > 300].index)\n",
    "    conc_df_mem = conc_df_mem.drop(conc_df_mem[conc_df_mem['OEF_base' ] > 1].index)\n",
    "    conc_df_mem = conc_df_mem.drop(conc_df_mem[conc_df_mem['OEF_base' ] == 0 ].index)\n",
    "    conc_df_mem = conc_df_mem.drop(conc_df_mem[conc_df_mem['CMRO2_base' ] == 0 ].index)\n",
    "    \n",
    "    disc_df_mem = disc_df_mem.drop(disc_df_mem[disc_df_mem['CMRO2_base' ] > 300].index)\n",
    "    disc_df_mem = disc_df_mem.drop(disc_df_mem[disc_df_mem['OEF_base' ] > 1].index)\n",
    "    disc_df_mem = disc_df_mem.drop(disc_df_mem[disc_df_mem['OEF_base' ] == 0 ].index)\n",
    "    disc_df_mem = disc_df_mem.drop(disc_df_mem[disc_df_mem['CMRO2_base' ] == 0 ].index)\n",
    "\n",
    "if len(task)==2:\n",
    "    conc_df_mem = conc_df_mem.drop(conc_df_mem[conc_df_mem['CMRO2_base' ] > 300].index)\n",
    "    conc_df_mem = conc_df_mem.drop(conc_df_mem[conc_df_mem['OEF_base' ] > 1].index)\n",
    "    conc_df_mem = conc_df_mem.drop(conc_df_mem[conc_df_mem['OEF_base' ] == 0 ].index)\n",
    "    conc_df_mem = conc_df_mem.drop(conc_df_mem[conc_df_mem['CMRO2_base' ] == 0 ].index)\n",
    "    \n",
    "    conc_df_calc = conc_df_calc.drop(conc_df_mem[conc_df_mem['CMRO2_base' ] > 300].index)\n",
    "    conc_df_calc = conc_df_calc.drop(conc_df_mem[conc_df_mem['OEF_base' ] > 1].index)\n",
    "    conc_df_calc = conc_df_calc.drop(conc_df_mem[conc_df_mem['OEF_base' ] == 0 ].index)\n",
    "    conc_df_calc = conc_df_calc.drop(conc_df_mem[conc_df_mem['CMRO2_base' ] == 0 ].index)\n",
    "    \n",
    "    disc_df_mem = disc_df_mem.drop(disc_df_mem[disc_df_mem['CMRO2_base' ] > 300].index)\n",
    "    disc_df_mem = disc_df_mem.drop(disc_df_mem[disc_df_mem['OEF_base' ] > 1].index)\n",
    "    disc_df_mem = disc_df_mem.drop(disc_df_mem[disc_df_mem['OEF_base' ] == 0 ].index)\n",
    "    disc_df_mem = disc_df_mem.drop(disc_df_mem[disc_df_mem['CMRO2_base' ] == 0 ].index)\n",
    "\n",
    "    disc_df_calc= disc_df_calc.drop(disc_df_mem[disc_df_mem['CMRO2_base' ] > 300].index)\n",
    "    disc_df_calc = disc_df_calc.drop(disc_df_mem[disc_df_mem['OEF_base' ] > 1].index)\n",
    "    disc_df_calc = disc_df_calc.drop(disc_df_mem[disc_df_mem['OEF_base' ] == 0 ].index)\n",
    "    disc_df_calc = disc_df_calc.drop(disc_df_mem[disc_df_mem['CMRO2_base' ] == 0 ].index)\n",
    "    \n",
    "## condordant vs. discordant voxels percchange ##\n",
    "################################################\n",
    "\n",
    "colors = [\"darkviolet\", \"darkviolet\"]\n",
    "Violet = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n",
    "\n",
    "\n",
    "### percchange CBF vs. percchange OEF\n",
    "    \n",
    "fig, ax = plt.subplots(1, figsize=(7, 5))\n",
    "\n",
    "if len(task)==1 and task[0] == 'mem':\n",
    "    dataframe_con = conc_df_mem\n",
    "    dataframe_disc = disc_df_mem\n",
    "if len(task)==1 and task[0] == 'calc':\n",
    "    dataframe_con = conc_df_calc\n",
    "    dataframe_disc = disc_df_calc\n",
    "if len(task)==1:\n",
    "    sns.regplot(dataframe_con['CBF_percchange' ], dataframe_con['OEF_percchange'], ax=ax, color='plum', scatter_kws={'s':1})\n",
    "    (r, p) = stats.pearsonr(dataframe_con['CBF_percchange'], dataframe_con['OEF_percchange'])\n",
    "    ax.text(5, 10, 'r= ' + str(round(r, 2)), fontsize=fontsize, color='plum')    \n",
    "    scatter = ax.scatter(dataframe_con['CBF_percchange'], dataframe_con['OEF_percchange'], c='plum', s=2,  vmin = -20, vmax=20, label='condordant')\n",
    "\n",
    "    sns.regplot(dataframe_disc['CBF_percchange'], dataframe_disc['OEF_percchange'], ax=ax, color='darkviolet', scatter_kws={'s':1})\n",
    "    (r, p) = stats.pearsonr(dataframe_disc['CBF_percchange'], dataframe_disc['OEF_percchange'])\n",
    "    ax.text(15, 10, 'r= ' + str(round(r, 2)), fontsize=fontsize, color='darkviolet')    \n",
    "    scatter = ax.scatter(dataframe_disc['CBF_percchange'], dataframe_disc['OEF_percchange'], c='darkviolet', s=2, vmin = -20, vmax=20, label='discordant')\n",
    "\n",
    "if len(task)==2:\n",
    "    sns.regplot(np.append(conc_df_calc['CBF_percchange' ], conc_df_mem['CBF_percchange']), np.append(conc_df_calc['OEF_percchange'], conc_df_mem['OEF_percchange']), ax=ax, color='plum', scatter_kws={'s':1})\n",
    "    (r, p) = stats.pearsonr(np.append(conc_df_calc['CBF_percchange' ], conc_df_mem['CBF_percchange' ]), np.append(conc_df_calc['OEF_percchange'], conc_df_mem['OEF_percchange']))\n",
    "    ax.text(5, 10, 'r= ' + str(round(r, 2)), fontsize=fontsize, color='plum')    \n",
    "    scatter = ax.scatter(np.append(conc_df_calc['CBF_percchange' ], conc_df_mem['CBF_percchange' ]), np.append(conc_df_calc['OEF_percchange'], conc_df_mem['OEF_percchange']), c='plum', s=2,  vmin = -20, vmax=20, label='condordant')\n",
    "\n",
    "    sns.regplot(np.append(disc_df_calc['CBF_percchange'], disc_df_mem['CBF_percchange']), np.append(disc_df_calc['OEF_percchange'], disc_df_mem['OEF_percchange']), ax=ax, color='darkviolet', scatter_kws={'s':1})\n",
    "    (r, p) = stats.pearsonr(np.append(disc_df_calc['CBF_percchange'], disc_df_mem['CBF_percchange']), np.append(disc_df_calc['OEF_percchange'], disc_df_mem['OEF_percchange']))\n",
    "    ax.text(15, 10, 'r= ' + str(round(r, 2)), fontsize=fontsize, color='darkviolet')    \n",
    "    scatter = ax.scatter(np.append(disc_df_calc['CBF_percchange'], disc_df_mem['CBF_percchange']), np.append(disc_df_calc['OEF_percchange'], disc_df_mem['OEF_percchange']), c='darkviolet', s=2, vmin = -20, vmax=20, label='discordant')\n",
    "    \n",
    "ax.legend(loc='upper left', fontsize='x-large', markerscale=10)\n",
    "\n",
    "ax.set_xlabel('ΔCBF [%]', fontsize=fontsize)\n",
    "ax.set_ylabel('ΔOEF [%]', fontsize=fontsize)\n",
    "ax.set_ylim(-15, 15)\n",
    "ax.set_xlim(-15, 25)\n",
    "\n",
    "for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "            label.set_fontsize(fontsize)\n",
    "        #ax.set_title('coupling CBF/BOLD, CMRO2/BOLD')\n",
    "\n",
    "for pos in ['right', 'top']:\n",
    "            ax.spines[pos].set_visible(False)\n",
    "        \n",
    "fig.savefig(results_dir + '/N' + N_subj + '_BOLDall_disc_concord_percchange_CBF_OEF.png', dpi=300, bbox_inches='tight')\n",
    "fig.show() \n",
    "    \n",
    "### percchange CMRO2 vs. percchange OEF\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize=(7, 5))\n",
    "\n",
    "if len(task)==1 and task[0] == 'mem':\n",
    "    dataframe_con = conc_df_mem\n",
    "    dataframe_disc = disc_df_mem\n",
    "if len(task)==1 and task[0] == 'calc':\n",
    "    dataframe_con = conc_df_calc\n",
    "    dataframe_disc = disc_df_calc\n",
    "if len(task)==1:\n",
    "    sns.regplot(dataframe_con['CMRO2_percchange'], dataframe_con['OEF_percchange'], ax=ax, color='plum', scatter_kws={'s':1}, label='condordant')\n",
    "    (r, p) = stats.pearsonr(dataframe_con['CMRO2_percchange'], dataframe_con['OEF_percchange'])\n",
    "    if p < 0.001:\n",
    "        ax.text(15, -10, '**r= ' + str(round(r, 2)), fontsize=fontsize, color='plum')    \n",
    "    scatter = ax.scatter(dataframe_con['CMRO2_percchange'], dataframe_con['OEF_percchange'], c='plum', s=2,  vmin = -5, vmax=5)\n",
    "\n",
    "    sns.regplot(dataframe_disc['CMRO2_percchange'], dataframe_disc['OEF_percchange'], ax=ax, color='darkviolet', scatter_kws={'s':1}, label='discordant')\n",
    "    (r, p) = stats.pearsonr(dataframe_disc['CMRO2_percchange'], dataframe_disc['OEF_percchange'])\n",
    "    if p < 0.001:\n",
    "        ax.text(15, 13, '**r= ' + str(round(r, 2)), fontsize=fontsize, color='darkviolet')    \n",
    "    scatter = ax.scatter(dataframe_disc['CMRO2_percchange'], dataframe_disc['OEF_percchange'], c='darkviolet', s=2,  vmin = -5, vmax=5)\n",
    "\n",
    "if len(task)==2:\n",
    "    sns.regplot(np.append(conc_df_calc['CMRO2_percchange'], conc_df_mem['CMRO2_percchange']), np.append(conc_df_calc['OEF_percchange'],conc_df_mem['OEF_percchange']), ax=ax, color='plum', scatter_kws={'s':1}, label='condordant')\n",
    "    (r, p) = stats.pearsonr(np.append(conc_df_calc['CMRO2_percchange'], conc_df_mem['CMRO2_percchange']), np.append(conc_df_calc['OEF_percchange'],conc_df_mem['OEF_percchange']))\n",
    "    if p < 0.001:\n",
    "        ax.text(15, -10, '**r= ' + str(round(r, 2)), fontsize=fontsize, color='plum')    \n",
    "    scatter = ax.scatter(np.append(conc_df_calc['CMRO2_percchange'], conc_df_mem['CMRO2_percchange']), np.append(conc_df_calc['OEF_percchange'],conc_df_mem['OEF_percchange']), c='plum', s=2,  vmin = -5, vmax=5)\n",
    "\n",
    "    sns.regplot(np.append(disc_df_calc['CMRO2_percchange'], disc_df_mem['CMRO2_percchange']), np.append(disc_df_calc['OEF_percchange'], disc_df_mem['OEF_percchange']), ax=ax, color='darkviolet', scatter_kws={'s':1}, label='discordant')\n",
    "    (r, p) = stats.pearsonr(np.append(disc_df_calc['CMRO2_percchange'], disc_df_mem['CMRO2_percchange']), np.append(disc_df_calc['OEF_percchange'], disc_df_mem['OEF_percchange']))\n",
    "    if p < 0.001:\n",
    "        ax.text(15, 13, '**r= ' + str(round(r, 2)), fontsize=fontsize, color='darkviolet')    \n",
    "    scatter = ax.scatter(np.append(disc_df_calc['CMRO2_percchange'], disc_df_mem['CMRO2_percchange']), np.append(disc_df_calc['OEF_percchange'], disc_df_mem['OEF_percchange']), c='darkviolet', s=2,  vmin = -5, vmax=5)\n",
    "    \n",
    "ax.legend(loc='upper left', fontsize='x-large', markerscale=10)\n",
    "ax.set_xlabel('ΔCMRO2 [%]', fontsize=fontsize)\n",
    "ax.set_ylabel('ΔOEF [%]', fontsize=fontsize)\n",
    "ax.set_ylim(-15, 15)\n",
    "ax.set_xlim(-15, 25)\n",
    "\n",
    "for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "            label.set_fontsize(fontsize)\n",
    "        #ax.set_title('coupling CBF/BOLD, CMRO2/BOLD')\n",
    "\n",
    "for pos in ['right', 'top']:\n",
    "            ax.spines[pos].set_visible(False)\n",
    "        \n",
    "fig.savefig(results_dir + '/N' + N_subj + '_BOLDall_disc_concord_percchange_OEF_CMRO2.png', dpi=300, bbox_inches='tight')\n",
    "fig.show()\n",
    "\n",
    "### percchange CMRO2 vs. percchange CBF\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize=(7, 5))\n",
    "if len(task)==1 and task[0] == 'mem':\n",
    "    dataframe_con = conc_df_mem\n",
    "    dataframe_disc = disc_df_mem\n",
    "if len(task)==1 and task[0] == 'calc':\n",
    "    dataframe_con = conc_df_calc\n",
    "    dataframe_disc = disc_df_calc\n",
    "if len(task)==1:\n",
    "    sns.regplot(dataframe_con['CMRO2_percchange'], dataframe_con['CBF_percchange'], ax=ax, color='plum', scatter_kws={'s':1})\n",
    "    (r, p) = stats.pearsonr(dataframe_con['CMRO2_percchange'], dataframe_con['CBF_percchange'])\n",
    "    if p < 0.001:\n",
    "        ax.text(18, 18, '**r= ' + str(round(r, 2)), fontsize=fontsize, color='plum')    \n",
    "    scatter = ax.scatter(dataframe_con['CMRO2_percchange'], dataframe_con['CBF_percchange'], c='plum', s=2,  vmin = -0.04, vmax=0.04, label='concordant')\n",
    "\n",
    "    sns.regplot(dataframe_disc['CMRO2_percchange'], dataframe_disc['CBF_percchange'], ax=ax, color='darkviolet', scatter_kws={'s':1})\n",
    "    (r, p) = stats.pearsonr(dataframe_disc['CMRO2_percchange'], dataframe_disc['CBF_percchange'])\n",
    "    if p < 0.001:\n",
    "        ax.text(18, 3, '**r= ' + str(round(r, 2)), fontsize=fontsize, color='darkviolet')    \n",
    "    scatter = ax.scatter(dataframe_disc['CMRO2_percchange'], dataframe_disc['CBF_percchange'], c='darkviolet', s=2, vmin = -0.04, vmax=0.04,label='discordant')\n",
    "\n",
    "if len(task)==2:\n",
    "\n",
    "    sns.regplot(np.append(conc_df_calc['CMRO2_percchange'], conc_df_mem['CMRO2_percchange']), np.append(conc_df_calc['CBF_percchange'], conc_df_mem['CBF_percchange']), ax=ax, color='plum', scatter_kws={'s':1})\n",
    "    (r, p) = stats.pearsonr(np.append(conc_df_calc['CMRO2_percchange'], conc_df_mem['CMRO2_percchange']), np.append(conc_df_calc['CBF_percchange'], conc_df_mem['CBF_percchange']))\n",
    "    if p < 0.001:\n",
    "        ax.text(18, 18, '**r= ' + str(round(r, 2)), fontsize=fontsize, color='plum')    \n",
    "    scatter = ax.scatter(np.append(conc_df_calc['CMRO2_percchange'], conc_df_mem['CMRO2_percchange']), np.append(conc_df_calc['CBF_percchange'], conc_df_mem['CBF_percchange']), c='plum', s=2,  vmin = -0.04, vmax=0.04, label='concordant')\n",
    "\n",
    "    sns.regplot(np.append(disc_df_calc['CMRO2_percchange'],disc_df_mem['CMRO2_percchange']), np.append(disc_df_calc['CBF_percchange'],disc_df_mem['CBF_percchange']), ax=ax, color='darkviolet', scatter_kws={'s':1})\n",
    "    (r, p) = stats.pearsonr(np.append(disc_df_calc['CMRO2_percchange'],disc_df_mem['CMRO2_percchange']), np.append(disc_df_calc['CBF_percchange'],disc_df_mem['CBF_percchange']))\n",
    "    if p < 0.001:\n",
    "        ax.text(18, 3, '**r= ' + str(round(r, 2)), fontsize=fontsize, color='darkviolet')    \n",
    "    scatter = ax.scatter(np.append(disc_df_calc['CMRO2_percchange'],disc_df_mem['CMRO2_percchange']), np.append(disc_df_calc['CBF_percchange'],disc_df_mem['CBF_percchange']), c='darkviolet', s=2, vmin = -0.04, vmax=0.04,label='discordant')\n",
    "\n",
    "ax.legend(loc='upper left', fontsize='x-large', markerscale=10)\n",
    "ax.set_xlabel('ΔCMRO2 [%]', fontsize=fontsize)\n",
    "ax.set_ylabel('ΔCBF [%]', fontsize=fontsize)\n",
    "ax.set_ylim(-15, 25)\n",
    "ax.set_xlim(-15, 25)\n",
    "\n",
    "for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "            label.set_fontsize(fontsize)\n",
    "        #ax.set_title('coupling CBF/BOLD, CMRO2/BOLD')\n",
    "\n",
    "for pos in ['right', 'top']:\n",
    "            ax.spines[pos].set_visible(False)\n",
    "        \n",
    "fig.savefig(results_dir + '/N' + N_subj + '_BOLDall_disc_concord_percchange_CBF_CMRO2.png', dpi=300, bbox_inches='tight')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### delta CMRO2 vs. delta CBV - for N=10 subjects and calc only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## create discordant and concordant masks\n",
    "pos_neg_disc_calc = os.path.join(results_dir, 'BOLD_CMRO2_discordant_calc'+baseline+'.nii.gz')\n",
    "\n",
    "pos_neg_conc_calc = os.path.join(results_dir, 'BOLD_CMRO2_concordant_calc'+baseline +'.nii.gz')\n",
    "\n",
    "sids_CBV = [58, 59, 60, 61, 63, 64, 65, 66, 67, 68]\n",
    "\n",
    "\n",
    "## both calc and mem contrasts / regions\n",
    "disc_masks = [pos_neg_disc_calc]\n",
    "conc_masks = [pos_neg_conc_calc]\n",
    "\n",
    "task = ['calc']\n",
    "baseline = 'control'\n",
    "\n",
    "mask_types = ['concordant', 'discordant'] ## in both: first calc, then mem\n",
    "conc_df_calc_N10 = pd.DataFrame()          \n",
    "disc_df_calc_N10 = pd.DataFrame()\n",
    "\n",
    "for mask_type in mask_types: \n",
    "    print(mask_type)\n",
    "    \n",
    "    if mask_type == 'concordant':\n",
    "        masks = conc_masks\n",
    "    \n",
    "    if mask_type == 'discordant':\n",
    "        masks = disc_masks\n",
    "        \n",
    "    for m, mask in enumerate(masks):\n",
    "        print(task[m])\n",
    "        contrast = task[m] + baseline[0] ## first calc then mem \n",
    "\n",
    "        PLS_masker = input_data.NiftiMasker(mask_img = mask)\n",
    "        len_mask =  len(np.argwhere(np.array(nib.load(mask).dataobj)))\n",
    "\n",
    "        CBV_base = np.zeros((len(sids_CBV),len_mask))\n",
    "        CBV_task = np.zeros((len(sids_CBV),len_mask))\n",
    "        CBV_percchange = np.zeros((len(sids_CBV),len_mask))\n",
    "        CMRO2_base = np.zeros((len(sids_CBV), len_mask))\n",
    "        CMRO2_task = np.zeros((len(sids_CBV), len_mask))\n",
    "        CMRO2_percchange = np.zeros((len(sids_CBV), len_mask))\n",
    "\n",
    "\n",
    "        for i, ID in enumerate(sids_CBV): #loop over subjects\n",
    "            sub = \"sub-p{:03d}\".format(ID) # subject id, eg 'p021'\n",
    "            print(sub)\n",
    "\n",
    "            sub_dir = os.path.join(data_dir, sub)\n",
    "\n",
    "            dir_anat = os.path.join(sub_dir, 'anat')\n",
    "            dir_func = os.path.join(sub_dir, 'func')\n",
    "            dir_perf = os.path.join(sub_dir, 'perf')\n",
    "\n",
    "            dir_qmri_deriv = os.path.join(derivatives_dir, sub, 'qmri')\n",
    "\n",
    "\n",
    "            #load baseline & task images\n",
    "            base_CMRO2 = np.squeeze(PLS_masker.fit_transform( os.path.join(dir_qmri_deriv, sub + '_task-' + baseline + '_space-MNI152_desc-orig_cmro2.nii.gz')))\n",
    "            base_CBV = np.squeeze(PLS_masker.fit_transform(os.path.join(dir_qmri_deriv, sub + '_task-' + baseline + '_space-MNI152_cbv.nii.gz')))\n",
    "            task_CBV = np.squeeze(PLS_masker.fit_transform(os.path.join(dir_qmri_deriv, sub + '_task-' + task[m] + '_space-MNI152_cbv.nii.gz')))\n",
    "            if CMRO2_mode == 'orig' or task[m]!='calc': ## without CBV correction\n",
    "                task_CMRO2 = np.squeeze(PLS_masker.fit_transform( os.path.join(dir_qmri_deriv, sub + '_task-' + task[m] + '_base-' + baseline + '_space-MNI152_desc-semi-quant_cmro2.nii.gz')))\n",
    "            if CMRO2_mode == 'corrected' and task[m]=='calc': ## with CBV correction\n",
    "                task_CMRO2 = np.squeeze(PLS_masker.fit_transform( os.path.join(dir_qmri_deriv, sub + '_task-' + task[m] + '_base-' + baseline +'_space-MNI152_desc-semi-quant-corrected_cmro2.nii.gz')))\n",
    "\n",
    "            percchange_CMRO2 = (task_CMRO2- base_CMRO2) / base_CMRO2 *100 \n",
    "            percchange_CBV = (task_CBV- base_CBV) / base_CBV *100               \n",
    "            CBV_percchange[i, :] = percchange_CBV\n",
    "            CMRO2_percchange[i, :] = percchange_CMRO2\n",
    "            CMRO2_base[i, :] = base_CMRO2\n",
    "            CBV_base[i, :] = base_CBV\n",
    "            CMRO2_task[i, :] = task_CMRO2\n",
    "            CBV_task[i, :] = task_CBV\n",
    "\n",
    "        ## create df\n",
    "        if task[m] == 'calc':\n",
    "            if mask_type == 'concordant':\n",
    "                conc_df_calc_N10['CMRO2_base'] = np.nanmedian(CMRO2_base, axis=0)\n",
    "                conc_df_calc_N10['CBV_base'] = np.nanmedian(CBV_base, axis=0)\n",
    "                conc_df_calc_N10['CMRO2_task'] = np.nanmedian(CMRO2_task, axis=0)\n",
    "                conc_df_calc_N10['CBV_task'] = np.nanmedian(CBV_task, axis=0)                \n",
    "                conc_df_calc_N10['CMRO2_percchange' ] = np.nanmedian(CMRO2_percchange, axis=0)\n",
    "                conc_df_calc_N10['CBV_percchange' ] = np.nanmedian(CBV_percchange, axis=0)\n",
    "            if mask_type == 'discordant':\n",
    "                disc_df_calc_N10['CMRO2_base'] = np.nanmedian(CMRO2_base, axis=0)\n",
    "                disc_df_calc_N10['CBV_base'] = np.nanmedian(CBV_base, axis=0)\n",
    "                disc_df_calc_N10['CMRO2_task'] = np.nanmedian(CMRO2_task, axis=0)\n",
    "                disc_df_calc_N10['CBV_task'] = np.nanmedian(CBV_task, axis=0)                \n",
    "                disc_df_calc_N10['CMRO2_percchange' ] = np.nanmedian(CMRO2_percchange, axis=0)\n",
    "                disc_df_calc_N10['CBV_percchange' ] = np.nanmedian(CBV_percchange, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## mask voxels with CMRO2 300 ##\n",
    "################################################\n",
    "conc_df_calc_N10 = conc_df_calc_N10.drop(conc_df_calc_N10[conc_df_calc_N10['CMRO2_base' ]> 300].index)\n",
    "conc_df_calc_N10 = conc_df_calc_N10.drop(conc_df_calc_N10[conc_df_calc_N10['CMRO2_base' ] == 0 ].index)\n",
    "\n",
    "disc_df_calc_N10 = disc_df_calc_N10.drop(disc_df_calc_N10[disc_df_calc_N10['CMRO2_base' ]> 300].index)\n",
    "disc_df_calc_N10 = disc_df_calc_N10.drop(disc_df_calc_N10[disc_df_calc_N10['CMRO2_base' ] == 0 ].index)\n",
    "    \n",
    "\n",
    "    \n",
    "## condordant vs. discordant voxels percchange ##\n",
    "################################################\n",
    "\n",
    "colors = [\"darkviolet\", \"darkviolet\"]\n",
    "Violet = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n",
    "\n",
    "\n",
    "### percchange CBF vs. percchange OEF\n",
    "    \n",
    "fig, ax = plt.subplots(1, figsize=(7, 5))\n",
    "\n",
    "dataframe_con = conc_df_calc_N10\n",
    "dataframe_disc = disc_df_calc_N10\n",
    "\n",
    "sns.regplot(dataframe_con['CMRO2_percchange' ], dataframe_con['CBV_percchange'], ax=ax, color='plum', scatter_kws={'s':1})\n",
    "(r, p) = stats.pearsonr(dataframe_con['CMRO2_percchange'], dataframe_con['CBV_percchange'])\n",
    "if p < 0.001:\n",
    "    ax.text(11, 13, '**r= ' + str(round(r, 2)), fontsize=fontsize, color='plum')    \n",
    "scatter = ax.scatter(dataframe_con['CMRO2_percchange'], dataframe_con['CBV_percchange'], c='plum', s=2,  vmin = -20, vmax=20, label='condordant')\n",
    "\n",
    "sns.regplot(dataframe_disc['CMRO2_percchange'], dataframe_disc['CBV_percchange'], ax=ax, color='darkviolet', scatter_kws={'s':1})\n",
    "(r, p) = stats.pearsonr(dataframe_disc['CMRO2_percchange'], dataframe_disc['CBV_percchange'])\n",
    "if p < 0.001:\n",
    "    ax.text(11, 8, '**r= ' + str(round(r, 2)), fontsize=fontsize, color='darkviolet')    \n",
    "scatter = ax.scatter(dataframe_disc['CMRO2_percchange'], dataframe_disc['CBV_percchange'], c='darkviolet', s=2, vmin = -20, vmax=20, label='discordant')\n",
    "\n",
    "    \n",
    "ax.legend(loc='upper left', fontsize='x-large', markerscale=10)\n",
    "\n",
    "ax.set_xlabel('ΔCMRO2 [%]', fontsize=fontsize)\n",
    "ax.set_ylabel('ΔCBV [%]', fontsize=fontsize)\n",
    "ax.set_ylim(-15, 15)\n",
    "ax.set_xlim(-15, 25)\n",
    "\n",
    "for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "            label.set_fontsize(fontsize)\n",
    "        #ax.set_title('coupling CBF/BOLD, CMRO2/BOLD')\n",
    "\n",
    "#for pos in ['right', 'top']:\n",
    "#            ax.spines[pos].set_visible(False)\n",
    "        \n",
    "fig.savefig(results_dir + '/N10_BOLDall_disc_concord_percchange_CMRO2_CBV.png', dpi=300, bbox_inches='tight')\n",
    "fig.show() \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### concordant voxels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 = 0.8608819727011594\n",
      "beta values intercept, OEF, CBF = [2.55501815 0.83437781 0.86398286]\n",
      "p-values OEF, CBF = [0. 0. 0.]\n",
      "df = 21971\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>names</th>\n",
       "      <th>relimp</th>\n",
       "      <th>relimp_perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rOEF</td>\n",
       "      <td>0.136728</td>\n",
       "      <td>15.882348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CBF</td>\n",
       "      <td>0.724154</td>\n",
       "      <td>84.117652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       names    relimp  relimp_perc\n",
       "0  Intercept       NaN          NaN\n",
       "1       rOEF  0.136728    15.882348\n",
       "2        CBF  0.724154    84.117652"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pingouin as pg\n",
    "\n",
    "## concordant voxels\n",
    "\n",
    "## center predictors beforehands: \n",
    "df_conc_delta_vox_centered = pd.DataFrame()\n",
    "df_conc_delta_vox_centered['CMRO2'] = np.append(conc_df_calc['CMRO2_percchange'], conc_df_mem['CMRO2_percchange'])\n",
    "df_conc_delta_vox_centered['rOEF'] =  np.append(conc_df_calc['OEF_percchange'], conc_df_mem['OEF_percchange']) - np.nanmean(np.append(conc_df_calc['OEF_percchange'], conc_df_mem['OEF_percchange']))\n",
    "df_conc_delta_vox_centered['CBF'] = np.append(conc_df_calc['CBF_percchange'], conc_df_mem['CBF_percchange']) - np.nanmean(np.append(conc_df_calc['CBF_percchange'], conc_df_mem['CBF_percchange']))\n",
    "\n",
    "x = df_conc_delta_vox_centered[['rOEF', 'CBF']].dropna()\n",
    "y = df_conc_delta_vox_centered['CMRO2'].dropna()\n",
    "\n",
    "lm = pg.linear_regression(x,y, as_dataframe=False)\n",
    "\n",
    "print('R2 = ' + str(lm['r2']))\n",
    "print('beta values intercept, OEF, CBF = ' + str(lm['coef']))\n",
    "print('p-values OEF, CBF = ' + str(lm['pval']))\n",
    "print('df = ' + str(lm['df_resid']))\n",
    "\n",
    "##Get the relative importance of predictors\n",
    "lm = pg.linear_regression(x, y, remove_na=True, relimp=True)\n",
    "lm[['names', 'relimp', 'relimp_perc']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  CMRO2   R-squared:                       0.861\n",
      "Model:                            OLS   Adj. R-squared:                  0.861\n",
      "Method:                 Least Squares   F-statistic:                 6.798e+04\n",
      "Date:                Wed, 31 Jan 2024   Prob (F-statistic):               0.00\n",
      "Time:                        18:29:53   Log-Likelihood:                -41836.\n",
      "No. Observations:               21974   AIC:                         8.368e+04\n",
      "Df Residuals:                   21971   BIC:                         8.370e+04\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      2.5550      0.011    233.189      0.000       2.534       2.576\n",
      "rOEF           0.8344      0.006    129.521      0.000       0.822       0.847\n",
      "CBF            0.8640      0.003    330.981      0.000       0.859       0.869\n",
      "==============================================================================\n",
      "Omnibus:                     1669.028   Durbin-Watson:                   1.482\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             7769.819\n",
      "Skew:                          -0.223   Prob(JB):                         0.00\n",
      "Kurtosis:                       5.879   Cond. No.                         6.40\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.formula.api import ols\n",
    "\n",
    "## concordant voxels\n",
    "\n",
    "model = ols('CMRO2 ~ rOEF + CBF', data=df_conc_delta_vox_centered).fit()\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### discordant voxels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 = 0.8425617424473401\n",
      "beta values intercept, OEF, CBF = [1.1884269  1.07086873 0.88473903]\n",
      "p-values OEF, CBF = [0. 0. 0.]\n",
      "df = 14694\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>names</th>\n",
       "      <th>relimp</th>\n",
       "      <th>relimp_perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rOEF</td>\n",
       "      <td>0.488253</td>\n",
       "      <td>57.948649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CBF</td>\n",
       "      <td>0.354309</td>\n",
       "      <td>42.051351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       names    relimp  relimp_perc\n",
       "0  Intercept       NaN          NaN\n",
       "1       rOEF  0.488253    57.948649\n",
       "2        CBF  0.354309    42.051351"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## discordant voxels\n",
    "\n",
    "## center predictors beforehands: \n",
    "df_disc_delta_vox_centered = pd.DataFrame()\n",
    "df_disc_delta_vox_centered['CMRO2'] = np.append(disc_df_calc['CMRO2_percchange'], disc_df_mem['CMRO2_percchange'])\n",
    "df_disc_delta_vox_centered['rOEF'] =  np.append(disc_df_calc['OEF_percchange'], disc_df_mem['OEF_percchange']) - np.nanmean(np.append(disc_df_calc['OEF_percchange'], disc_df_mem['OEF_percchange']))\n",
    "df_disc_delta_vox_centered['CBF'] = np.append(disc_df_calc['CBF_percchange'], disc_df_mem['CBF_percchange']) - np.nanmean(np.append(disc_df_calc['CBF_percchange'], disc_df_mem['CBF_percchange']))\n",
    "\n",
    "x = df_disc_delta_vox_centered[['rOEF', 'CBF']].dropna()\n",
    "y = df_disc_delta_vox_centered['CMRO2'].dropna()\n",
    "\n",
    "lm = pg.linear_regression(x,y, as_dataframe=False)\n",
    "\n",
    "print('R2 = ' + str(lm['r2']))\n",
    "print('beta values intercept, OEF, CBF = ' + str(lm['coef']))\n",
    "print('p-values OEF, CBF = ' + str(lm['pval']))\n",
    "print('df = ' + str(lm['df_resid']))\n",
    "\n",
    "##Get the relative importance of predictors\n",
    "lm = pg.linear_regression(x, y, remove_na=True, relimp=True)\n",
    "lm[['names', 'relimp', 'relimp_perc']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  CMRO2   R-squared:                       0.843\n",
      "Model:                            OLS   Adj. R-squared:                  0.843\n",
      "Method:                 Least Squares   F-statistic:                 3.932e+04\n",
      "Date:                Wed, 31 Jan 2024   Prob (F-statistic):               0.00\n",
      "Time:                        18:29:55   Log-Likelihood:                -29382.\n",
      "No. Observations:               14697   AIC:                         5.877e+04\n",
      "Df Residuals:                   14694   BIC:                         5.879e+04\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      1.1884      0.015     80.637      0.000       1.160       1.217\n",
      "rOEF           1.0709      0.004    239.415      0.000       1.062       1.080\n",
      "CBF            0.8847      0.004    211.703      0.000       0.877       0.893\n",
      "==============================================================================\n",
      "Omnibus:                     5692.499   Durbin-Watson:                   1.489\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           228646.178\n",
      "Skew:                           1.163   Prob(JB):                         0.00\n",
      "Kurtosis:                      22.182   Cond. No.                         4.10\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "## discordant voxels\n",
    "\n",
    "model = ols('CMRO2 ~ rOEF + CBF', data=df_disc_delta_vox_centered).fit()\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fig 4E: MEM-CTRL and CALC-CTRL conjunction voxels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "MEMneg_CALCpos_overlap = results_dir + '/BSR_meanPLS_BOLD_clusterBSR_intersect_MEMneg_CALCpos_bin.nii.gz'\n",
    "MEMpos_CALCneg_overlap = results_dir + '/BSR_meanPLS_BOLD_clusterBSR_intersect_MEMpos_CALCneg_bin.nii.gz'\n",
    "\n",
    "masks = [MEMneg_CALCpos_overlap, MEMpos_CALCneg_overlap]\n",
    "contrasts = ['calccontrol', 'memcontrol']\n",
    "baseline = 'control'\n",
    "task = ['calc', 'mem']\n",
    "    \n",
    "\n",
    "for m, mask in enumerate(masks):\n",
    "    PLS_masker = input_data.NiftiMasker(mask_img = mask)\n",
    "    len_mask = len(np.squeeze(PLS_masker.fit_transform(mask)))\n",
    "    vox_contrast_data = pd.DataFrame()\n",
    "    \n",
    "    for c, contrast in enumerate(contrasts):\n",
    "        vox_data_BOLD = np.zeros((len(sids_30), len_mask))\n",
    "        vox_data_CMRO2 = np.zeros((len(sids_30), len_mask))\n",
    "        vox_data_OEF = np.zeros((len(sids_30), len_mask))\n",
    "        vox_data_CBF = np.zeros((len(sids_30), len_mask))\n",
    "        \n",
    "        task_cond = task[c]\n",
    "        \n",
    "        for i, ID in enumerate(sids_30): #loop over subjects\n",
    "                sub = \"sub-p{:03d}\".format(ID) # subject id, eg 'p021'\n",
    "                print(sub)\n",
    "\n",
    "                sub_dir = os.path.join(data_dir, sub)\n",
    "\n",
    "                dir_anat = os.path.join(sub_dir, 'anat')\n",
    "                dir_func = os.path.join(sub_dir, 'func')\n",
    "                dir_perf = os.path.join(sub_dir, 'perf')\n",
    "\n",
    "                dir_anat_deriv = os.path.join(derivatives_dir, sub, 'anat')\n",
    "                dir_func_deriv = os.path.join(derivatives_dir, sub, 'func')\n",
    "                dir_perf_deriv = os.path.join(derivatives_dir, sub, 'perf')\n",
    "                dir_qmri_deriv = os.path.join(derivatives_dir, sub, 'qmri')\n",
    "\n",
    "\n",
    "                # load percchange BOLD\n",
    "                BOLD_percchange = dir_func_deriv + '/' + sub + '_task-'+contrast+'_space-MNI152_desc-fmriprep_BOLD_percchange.nii.gz'\n",
    "                vox_data_BOLD[i, :] = PLS_masker.fit_transform(BOLD_percchange)\n",
    "                \n",
    "                # create percchange CBF\n",
    "                cbf_base = PLS_masker.fit_transform(dir_qmri_deriv+ '/' + sub + '_task-'+baseline +'_space-MNI152_cbf.nii.gz')             \n",
    "                cbf_task =  PLS_masker.fit_transform(dir_qmri_deriv+ '/' + sub + '_task-'+task_cond +'_space-MNI152_cbf.nii.gz')                           \n",
    "                CBF_percchange = (cbf_task - cbf_base) / cbf_base *100\n",
    "                \n",
    "                # baseline CMRO2\n",
    "                base_CMRO2 = np.squeeze(PLS_masker.fit_transform(dir_qmri_deriv + '/' + sub + '_task-' + baseline + '_space-MNI152_desc-orig_cmro2.nii.gz'))\n",
    "                base_OEF = np.squeeze(PLS_masker.fit_transform(dir_qmri_deriv + '/' + sub + '_task-' + baseline + '_space-MNI152_oef.nii.gz'))\n",
    "                \n",
    "                if CMRO2_mode == 'corrected' and task_cond == 'calc':\n",
    "                    task_CMRO2 = PLS_masker.fit_transform(dir_qmri_deriv  + '/' +  sub + '_task-'+task_cond+'_base-'+baseline+'_space-MNI152_desc-semi-quant-corrected_cmro2.nii.gz')\n",
    "                if CMRO2_mode == 'corrected' and task_cond == 'mem':\n",
    "                     task_CMRO2 = PLS_masker.fit_transform(dir_qmri_deriv  + '/' +sub + '_task-'+task_cond+'_base-'+baseline+'_space-MNI152_desc-semi-quant_cmro2.nii.gz')\n",
    "                if CMRO2_mode == 'orig' :\n",
    "                    task_CMRO2 = PLS_masker.fit_transform(dir_qmri_deriv  + '/' + sub + '_task-' + task_cond + '_space-MNI152_desc-semi-quant_cmro2.nii.gz')\n",
    "\n",
    "                percchange_CMRO2 = (task_CMRO2- base_CMRO2) / base_CMRO2 *100     \n",
    "                vox_data_CMRO2[i, :] = percchange_CMRO2\n",
    "                vox_data_OEF[i, :] = base_OEF\n",
    "                vox_data_CBF[i, :] = CBF_percchange\n",
    "            \n",
    "        vox_contrast_data['CMRO2 ' + contrast] = np.nanmedian(vox_data_CMRO2, axis=0) ##median across subjects\n",
    "        vox_contrast_data['BOLD ' + contrast] = np.nanmedian(vox_data_BOLD, axis=0)\n",
    "        vox_contrast_data['CBF ' + contrast] = np.nanmedian(vox_data_CBF, axis=0)\n",
    "        vox_OEF = np.nanmedian(vox_data_OEF, axis=0)\n",
    "        \n",
    "    ## compare contrasts: consistent or inconsistent?\n",
    "    conc_disc_calc = np.sign(vox_contrast_data['CMRO2 calccontrol']) * np.sign(vox_contrast_data['BOLD calccontrol']) ## 1 = both negative or both positive (concordant); -1: one negative, the other positive (discordant)\n",
    "    conc_disc_mem = np.sign(vox_contrast_data['CMRO2 memcontrol']) * np.sign(vox_contrast_data['BOLD memcontrol']) ## 1 = both negative or both positive (concordant); -1: one negative, the other positive (discordant)\n",
    "    conc_disc_mem_calc = conc_disc_calc + conc_disc_mem ## 1+1 = 2: concordant in both, 1-1 = 0: inconsistent: -1 -1 = -2: discordant in both\n",
    "    \n",
    "    if m == 0:\n",
    "        consistent_inconsistent_df_DAN = conc_disc_mem_calc\n",
    "        vox_OEF_data_DAN = vox_OEF\n",
    "        n_factor_DAN_calc = vox_contrast_data['CBF calccontrol' ]/vox_contrast_data['CMRO2 calccontrol']\n",
    "        n_factor_DAN_mem = vox_contrast_data['CBF memcontrol' ]/vox_contrast_data['CMRO2 memcontrol']\n",
    "    if m == 1:\n",
    "        consistent_inconsistent_df_DMN = conc_disc_mem_calc\n",
    "        vox_OEF_data_DMN = vox_OEF\n",
    "        n_factor_DMN_calc = vox_contrast_data['CBF calccontrol' ]/vox_contrast_data['CMRO2 calccontrol']\n",
    "        n_factor_DMN_mem = vox_contrast_data['CBF memcontrol' ]/vox_contrast_data['CMRO2 memcontrol']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## plot as pieplot ##\n",
    "#############################################\n",
    "\n",
    "## len of overlap masks\n",
    "DANoverlap = np.array(nib.load(MEMneg_CALCpos_overlap).dataobj).flatten()\n",
    "DMNoverlap = np.array(nib.load(MEMpos_CALCneg_overlap).dataobj).flatten()\n",
    "len_mask_DANoverlap = len(np.argwhere(DANoverlap==1))\n",
    "len_mask_DMNoverlap = len(np.argwhere(DMNoverlap==1))\n",
    "\n",
    "## 1+1 = 2: concordant in both, 1-1 = 0: inconsistent: -1 -1 = -2: discordant in both\n",
    "df_conc_disc = pd.DataFrame()\n",
    "df_conc_disc['concordant'] = [sum(consistent_inconsistent_df_DAN ==2) + sum(consistent_inconsistent_df_DMN ==2)]\n",
    "df_conc_disc['discordant'] = [sum(consistent_inconsistent_df_DAN ==-2) + sum(consistent_inconsistent_df_DMN ==-2)]\n",
    "df_conc_disc['inconsistent'] = [sum(consistent_inconsistent_df_DAN ==0) + sum(consistent_inconsistent_df_DMN ==0)]\n",
    "\n",
    "## define which wedges should be labeled\n",
    "def my_autopct(pct):\n",
    "    return ('%0.0f%%' % pct) if pct > 6 else ''\n",
    "\n",
    "fig, ax= plt.subplots(1, figsize=(5,5))\n",
    "    \n",
    "patches, texts, autotexts = ax.pie(np.squeeze(df_conc_disc.values), labels=['','',''],autopct = my_autopct, startangle = 79.5, explode = [0.03,0.03,0.03],\n",
    "        textprops = {'fontsize':25}, colors=['plum', 'darkviolet', 'tomato'], wedgeprops = {'linewidth': 6, 'alpha':0.9}, center = (0.1,0.1))\n",
    "autotexts[1].set_color('lightgray')\n",
    "#autotexts[1].set_color('lightgray')\n",
    "#ax.legend()\n",
    "ax.set_title('negative BOLD voxels')\n",
    "\n",
    "\n",
    "fig.savefig(results_dir + '/Pieplot_MEM_CALC_overlap_conc_disc_inconsistent.png', dpi=300, bbox_inches='tight')\n",
    "\n",
    "\n",
    "## get baseline OEF differences ##\n",
    "################################################\n",
    "\n",
    "## difference in medians\n",
    "def statistic(x_true, x_false):\n",
    "        return np.median(x_true, axis=0) - np.median(x_false, axis=0)\n",
    "    \n",
    "OEF_concordant=np.append(vox_OEF_data_DAN[consistent_inconsistent_df_DAN ==2], vox_OEF_data_DMN[consistent_inconsistent_df_DMN ==2])\n",
    "OEF_discordant=np.append(vox_OEF_data_DAN[consistent_inconsistent_df_DAN ==-2], vox_OEF_data_DMN[consistent_inconsistent_df_DMN ==-2])\n",
    "OEF_inconsistent=np.append(vox_OEF_data_DAN[consistent_inconsistent_df_DAN ==0], vox_OEF_data_DMN[consistent_inconsistent_df_DMN ==0])\n",
    "\n",
    "##permutation test, because data are smoothed/non-independent\n",
    "res = stats.permutation_test((OEF_concordant,OEF_inconsistent), statistic, permutation_type='independent', n_resamples=2000, alternative='two-sided')\n",
    "p_conc_incon = res.pvalue\n",
    "res = stats.permutation_test((OEF_inconsistent, OEF_discordant), statistic, permutation_type='independent', n_resamples=2000, alternative='two-sided')\n",
    "p_inc_disc = res.pvalue\n",
    "\n",
    "fig, (ax1) = plt.subplots(1, figsize=(3,6))\n",
    "sns.violinplot(data=[OEF_concordant, OEF_inconsistent, OEF_discordant],inner='box',\n",
    "                ax=ax1, palette=['plum', 'tomato', 'darkviolet'])\n",
    "ax1.set_xticklabels(['concordant', 'discordant', 'inconsistent'])\n",
    "ax1.hlines(np.nanmedian(OEF_concordant ),-0.5, 2.5, color='plum', linestyles='dashed')\n",
    "ax1.text(2.5, 0.4,np.round(np.nanmedian(OEF_concordant),2) , color='violet', fontsize=fontsize )\n",
    "ax1.hlines(np.nanmedian(OEF_inconsistent),-0.5, 2.5, color='tomato', linestyles='dashed')\n",
    "ax1.text(2.5, 0.35,np.round(np.nanmedian(OEF_inconsistent),2) , color='tomato', fontsize=fontsize )\n",
    "ax1.hlines(np.nanmedian(OEF_discordant),-0.5, 2.5, color='darkviolet', linestyles='dashed')\n",
    "ax1.text(2.5, 0.3,np.round(np.nanmedian(OEF_discordant),2) , color='darkviolet', fontsize=fontsize )\n",
    "if p_conc_incon < 0.001:\n",
    "        ax1.text(0.3, 0.9, '**', color='grey', fontsize=fontsize)\n",
    "if p_inc_disc < 0.001:\n",
    "        ax1.text(1.3, 0.9, '**', color='grey', fontsize=fontsize)\n",
    "ax1.set_ylabel('rOEF [ratio]', fontsize=fontsize)\n",
    "ax1.set_ylim(0, 1)\n",
    "ax1.set_xticklabels(['conc.', 'incons.', 'disc.'])\n",
    "for label in (ax1.get_xticklabels() + ax1.get_yticklabels()):\n",
    "        label.set_fontsize(fontsize)\n",
    "\n",
    "for pos in ['right', 'top']:\n",
    "        ax1.spines[pos].set_visible(False)\n",
    "\n",
    "fig.savefig(results_dir + '/OEF_MEM_CALC_overlap_conc_disc_inconsistent.png', dpi=300, bbox_inches='tight')\n",
    "fig.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot pooled intersection voxels on brain surface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "from matplotlib.colors import ListedColormap\n",
    "from nilearn import surface \n",
    "from nilearn import datasets\n",
    "\n",
    "fsaverage = datasets.fetch_surf_fsaverage()\n",
    "\n",
    "## MEMact - CALC deact overlap\n",
    "maxval = 1\n",
    "\n",
    "cmap = ListedColormap([ \"lightgrey\", \"lightgrey\",\"springgreen\"])\n",
    "colormap_pos = ListedColormap([\"dodgerblue\", \"lightgrey\",  \"lightgrey\", \"darkorange\"])\n",
    "colormap_neg = ListedColormap([\"darkorange\", \"lightgrey\",  \"lightgrey\", \"dodgerblue\"])\n",
    "newcmp_surface_DMN = ListedColormap([ \"springgreen\",  \"springgreen\"])\n",
    "\n",
    "\n",
    "## intersect mask\n",
    "BSR_masked_BOLD_YEO_intersect_calc =  results_dir + '/BSR_meanPLS_BOLD_clusterBSR_intersect_MEMpos_CALCneg_bin.nii.gz'\n",
    "BSR_masked_BOLD_YEO_intersect_mem =  results_dir + '/BSR_meanPLS_BOLD_clusterBSR_intersect_MEMneg_CALCpos_bin.nii.gz'\n",
    "BSR_masked_BOLD_YEO_intersect =  results_dir + '/N30_BSR_meanPLS_BOLD_clusterBSR_intersect_CALCpos_CALCneg_MEMneg_MEMpos_bin.nii.gz'\n",
    "\n",
    "! fslmaths {BSR_masked_BOLD_YEO_intersect_calc} -add {BSR_masked_BOLD_YEO_intersect_mem} -bin {BSR_masked_BOLD_YEO_intersect}\n",
    "#plotting.plot_img(BSR_masked_BOLD_YEO_intersect, bg_img=MNI, threshold=0)\n",
    "\n",
    "\n",
    "## get number of intersection voxels\n",
    "MEMpos_CALCneg_overlap_arr = np.array(nib.load(BSR_masked_BOLD_YEO_intersect_calc).dataobj)\n",
    "len_mask = len(np.argwhere(MEMpos_CALCneg_overlap_arr[MEMpos_CALCneg_overlap_arr>0].flatten()))\n",
    "print(str(len_mask) + ' MEMpos CALCneg voxels')\n",
    "\n",
    "MEMneg_CALCpos_overlap_arr = np.array(nib.load(BSR_masked_BOLD_YEO_intersect_mem).dataobj)\n",
    "len_mask = len(np.argwhere(MEMneg_CALCpos_overlap_arr[MEMneg_CALCpos_overlap_arr>0].flatten()))\n",
    "print(str(len_mask) + ' MEMneg CALCpos overlap voxels')\n",
    "\n",
    "pooled_overlap_arr = np.array(nib.load(BSR_masked_BOLD_YEO_intersect).dataobj)\n",
    "len_mask = len(np.argwhere(pooled_overlap_arr[pooled_overlap_arr>0].flatten()))\n",
    "print(str(len_mask) + ' pooled overlap voxels')\n",
    "\n",
    "## CALC pos only\n",
    "texture_left = surface.vol_to_surf(BSR_masked_BOLD_YEO_intersect, fsaverage['pial_left'], interpolation=\"nearest\") \n",
    "texture_right = surface.vol_to_surf(BSR_masked_BOLD_YEO_intersect, fsaverage['pial_right'], interpolation=\"nearest\")\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 10), subplot_kw={\"projection\": \"3d\"})\n",
    "plotting.plot_surf_stat_map(fsaverage.infl_left, texture_left,\n",
    "                           hemi='left', view='lateral', colorbar=False,\n",
    "                           bg_map=fsaverage['sulc_left'], bg_on_data=True,\n",
    "                           darkness=.4,  vmax=1, threshold=0,\n",
    "                            cmap=cmap, axes=ax1)\n",
    "plotting.plot_surf_stat_map(fsaverage.infl_right, texture_right,\n",
    "                           hemi='right', view='lateral', colorbar=False,\n",
    "                           bg_map=fsaverage['sulc_right'], bg_on_data=True,\n",
    "                           darkness=.4,  vmax=1, threshold=0,\n",
    "                            cmap=cmap, axes=ax2)\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 10), subplot_kw={\"projection\": \"3d\"})\n",
    "plotting.plot_surf_stat_map(fsaverage.infl_left, texture_left,\n",
    "                           hemi='left', view='medial', colorbar=False,\n",
    "                           bg_map=fsaverage['sulc_left'], bg_on_data=True,\n",
    "                           darkness=.4,  vmax=1, threshold=0,\n",
    "                            cmap=cmap, axes=ax1)\n",
    "plotting.plot_surf_stat_map(fsaverage.infl_right, texture_right,\n",
    "                           hemi='right', view='medial', colorbar=False,\n",
    "                           bg_map=fsaverage['sulc_right'], bg_on_data=True,\n",
    "                           darkness=.4,  vmax=1, threshold=0,\n",
    "                            cmap=cmap, axes=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PR postPLS",
   "language": "python",
   "name": "pr_postpls_hist"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}