D_Fig2BD.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import netneurotools as nnt\n",
    "from scipy.stats._stats_py import _chk2_asarray\n",
    "import shutil\n",
    "\n",
    "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",
    "\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": [
    "## functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "## copied from netneurotools: https://github.com/netneurolab/netneurotools/blob/master/netneurotools/stats.py\n",
    "def permtest_rel(a, b, axis=0, n_perm=1000, seed=0):\n",
    "    \"\"\"\n",
    "    Non-parametric equivalent of :py:func:`scipy.stats.ttest_rel`\n",
    "    Generates two-tailed p-value for hypothesis of whether related samples `a`\n",
    "    and `b` differ using permutation tests\n",
    "    Parameters\n",
    "    ----------\n",
    "    a, b : array_like\n",
    "        Sample observations. These arrays must have the same shape.\n",
    "    axis : int or None, optional\n",
    "        Axis along which to compute test. If None, compute over whole arrays\n",
    "        of `a` and `b`. Default: 0\n",
    "    n_perm : int, optional\n",
    "        Number of permutations to assess. Unless `a` and `b` are very small\n",
    "        along `axis` this will approximate a randomization test via Monte\n",
    "        Carlo simulations. Default: 1000\n",
    "    seed : {int, np.random.RandomState instance, None}, optional\n",
    "        Seed for random number generation. Set to None for \"randomness\".\n",
    "        Default: 0\n",
    "    Returns\n",
    "    -------\n",
    "    stat : float or numpy.ndarray\n",
    "        Average difference between `a` and `b`\n",
    "    pvalue : float or numpy.ndarray\n",
    "        Non-parametric p-value\n",
    "    Notes\n",
    "    -----\n",
    "    The lowest p-value that can be returned by this function is equal to 1 /\n",
    "    (`n_perm` + 1).\n",
    "    Examples\n",
    "    --------\n",
    "    >>> from netneurotools import stats\n",
    "    >>> np.random.seed(12345678)  # set random seed for reproducible results\n",
    "    >>> rvs1 = np.random.normal(loc=5, scale=10, size=500)\n",
    "    >>> rvs2 = (np.random.normal(loc=5, scale=10, size=500)\n",
    "    ...         + np.random.normal(scale=0.2, size=500))\n",
    "    >>> stats.permtest_rel(rvs1, rvs2)  # doctest: +SKIP\n",
    "    (-0.16506275161572695, 0.8021978021978022)\n",
    "    >>> rvs3 = (np.random.normal(loc=8, scale=10, size=500)\n",
    "    ...         + np.random.normal(scale=0.2, size=500))\n",
    "    >>> stats.permtest_rel(rvs1, rvs3)  # doctest: +SKIP\n",
    "    (2.40533726097883, 0.000999000999000999)\n",
    "    \"\"\"\n",
    "\n",
    "    a, b, axis = _chk2_asarray(a, b, axis)\n",
    "    rs = check_random_state(seed)\n",
    "\n",
    "    if a.shape[axis] != b.shape[axis]:\n",
    "        raise ValueError('Provided arrays do not have same length along axis')\n",
    "\n",
    "    if a.size == 0 or b.size == 0:\n",
    "        return np.nan, np.nan\n",
    "\n",
    "    # calculate original difference in means\n",
    "    ab = np.stack([a, b], axis=0)\n",
    "    if ab.ndim < 3:\n",
    "        ab = np.expand_dims(ab, axis=-1)\n",
    "    true_diff = np.squeeze(np.diff(ab, axis=0)).mean(axis=axis) / 1\n",
    "    abs_true = np.abs(true_diff)\n",
    "\n",
    "    # idx array\n",
    "    reidx = np.meshgrid(*[range(f) for f in ab.shape], indexing='ij')\n",
    "\n",
    "    permutations = np.ones(true_diff.shape)\n",
    "    for perm in range(n_perm):\n",
    "        # use this to re-index (i.e., swap along) the first axis of `ab`\n",
    "        swap = rs.random_sample(ab.shape[:-1]).argsort(axis=axis)\n",
    "        reidx[0] = np.repeat(swap[..., np.newaxis], ab.shape[-1], axis=-1)\n",
    "        # recompute difference between `a` and `b` (i.e., first axis of `ab`)\n",
    "        pdiff = np.squeeze(np.diff(ab[tuple(reidx)], axis=0)).mean(axis=axis)\n",
    "        permutations += np.abs(pdiff) >= abs_true\n",
    "\n",
    "    pvals = permutations / (n_perm + 1)  # + 1 in denom accounts for true_diff\n",
    "\n",
    "    return true_diff, pvals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_img(stat_img, cluster_extent, voxel_thresh=1.96, direction='both'):\n",
    "    \"\"\"\n",
    "    Parameters\n",
    "    ----------\n",
    "    stat_img : Niimg_like object\n",
    "        Thresholded statistical map image\n",
    "    cluster_extent : int\n",
    "        Minimum number of voxels required to consider a cluster\n",
    "    voxel_thresh : float, optional\n",
    "        Threshold to apply to `stat_img`. Use `direction` to specify the\n",
    "        directionality of the threshold. If a negative number is provided a\n",
    "        percentile threshold is used instead, where the percentile is\n",
    "        determined by the equation `100 - voxel_thresh`. Default: 1.96\n",
    "    direction : str, optional\n",
    "        Specifies the direction in which `voxel_thresh` should be applied.\n",
    "        Possible values are 'both', 'pos' or 'neg'. Default: 'both'\n",
    "    Returns\n",
    "    -------\n",
    "    cluster_img : Nifti1Image\n",
    "        4D image of brain regions, where each volume is a distinct cluster\n",
    "    \"\"\"\n",
    "    # get input data image\n",
    "    img_4d = check_niimg(stat_img, atleast_4d=True)\n",
    "    if img_4d.shape[-1] == 1:\n",
    "        stat_img = img_4d.slicer[..., 0]\n",
    "    else:\n",
    "        stat_img = image.index_img(img_4d, 0)\n",
    "\n",
    "    # threshold image\n",
    "    if voxel_thresh < 0:\n",
    "        voxel_thresh = '{}%'.format(100 + voxel_thresh)\n",
    "    else:\n",
    "        # ensure that threshold is not greater than most extreme value in image\n",
    "        if voxel_thresh > np.nan_to_num(np.abs(stat_img.get_fdata())).max():\n",
    "            empty = np.zeros(stat_img.shape + (1,))\n",
    "            return image.new_img_like(stat_img, empty)\n",
    "    thresh_img = image.threshold_img(stat_img, threshold=voxel_thresh)\n",
    "\n",
    "    # extract clusters\n",
    "    min_region_size = cluster_extent * np.prod(thresh_img.header.get_zooms())\n",
    "    clusters = []\n",
    "\n",
    "    if direction == 'both':\n",
    "        direction_list = ['pos', 'neg']\n",
    "    elif direction == 'pos':\n",
    "        direction_list = ['pos']\n",
    "    elif direction == 'neg':\n",
    "        direction_list = ['neg']\n",
    "\n",
    "    for sign in direction_list:\n",
    "        # keep only data of given sign\n",
    "        data = thresh_img.get_fdata().copy()\n",
    "        data[(data < 0) if sign == 'pos' else (data > 0)] = 0\n",
    "\n",
    "        # Do nothing if data array contains only zeros\n",
    "        if np.any(data):\n",
    "            try:\n",
    "                if min_region_size != 0.0:\n",
    "                    min_region_size -= 1e-8\n",
    "                clusters += [connected_regions(\n",
    "                    image.new_img_like(thresh_img, data),\n",
    "                    min_region_size=min_region_size,\n",
    "                    extract_type='connected_components')[0]]\n",
    "            except TypeError:  # for no clusters\n",
    "                pass\n",
    "\n",
    "    # Return empty image if no clusters were found\n",
    "    if len(clusters) == 0:\n",
    "        return image.new_img_like(thresh_img, np.zeros(data.shape + (1,)))\n",
    "\n",
    "    # Reorder clusters by their size\n",
    "    clust_img = image.concat_imgs(clusters)\n",
    "    cluster_size = (clust_img.get_fdata() != 0).sum(axis=(0, 1, 2))\n",
    "    new_order = np.argsort(cluster_size)[::-1]\n",
    "    clust_img_ordered = image.index_img(clust_img, new_order)\n",
    "\n",
    "    return "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## define variables (please check!!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Please which OEF / R2' / 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='rest'\n",
    "#baseline='control'\n",
    "task='calc'\n",
    "#task='mem'\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",
    "##########################################################\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",
    "group_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",
    "maskerGM = input_data.NiftiMasker(mask_img=YEO_SNR_mask) ## exclude areas with too less SNR (based on the localizer data)\n",
    "#mask_arr = np.array(mask_img.dataobj)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BlueRed color palette"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f323bb1dcc0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TOM2L4Jo/JinHjVnOph9R/3/n6elp8j7ay8sLaWlp0Gg0yMvLg0KhMCm406FDB/Tq1Qt5eXkAtF8NYF5sZuDAgdi+fTs0Gk29BVWmTp2KJUuW4MSJExg6dCjGjh2Lvn37Nhqvm5tbrbasrCxs2LABN2/eRGVlJTQaDf7991/8888/hlogCoXCZN8+ffqgXbt2yMvLg4eHh+T5NBoNNm7ciIyMDJSWlkKtVkOtVuOll15qNHZjz3rcWMP4moWdUD2VAciozmV9fRoTGhqKI0eOIDg4GLm5uZg8eTJ27NjR6H7mJ2VxcTFmzJiBN998EykpKdi/fz8WLVoEAKiurjbpW9fFxcYuOJp/z8eWLVuwdetWxMTEYNu2bThw4ACGDx9u8cXPZz1urGGcLGwoOzu71naPHj0gl8vxxhtvoLq62qRPeXk5bt++baiX2KdPH1y4cMFkjAsXLqBnz56G344ODg6G4sjGunTpgoiICKSmpiI6OtpQ+t3BwQGA9rd4Y65cuQKNRoOEhAR4eXmhV69eKC0trdWvurra5Nut8vPzUVFRgd69ezc6h7Hz589j1KhRCA4ORt++fdG9e/daF3zre7zGpBw3ZjlOFjZ09+5dqFQq5Ofn49ChQ9i5c6fhrxE9e/bEqFGjkJiYiHPnzuH69ev49NNP0blzZ8NL6GnTpuH06dNYu3Ytbt26hR9++AG7du0y/KUCAJydnXH27FmUlJQYyvAtW7YMv/76KwoLC5GTk4OsrCxDAnJ2doYgCDh27BjKyspM/rJh7vXXX0d1dTV27NiBwsJCHDhwAHv27KnVz8HBAcnJycjOzkZOTg4WLFgALy+vBt+C1DffqVOncOHCBeTl5WHRokW1igY7OzsjOzsbRUVFKCsrqzNxSDluzHKcLGwoJCQET58+RWhoKJKSkhAZGYmwsDDD/SqVCq6uroiLi0NYWBiICBs3bjT89nd1dcXXX3+NjIwMBAUFYc2aNZg9e7ZJ+bjZs2ejuLgYo0ePho+PDwBAFEUkJSUhMDAQMTEx6NWrFxYvXgwA6Ny5Mz788EN89dVXGDp0aIPVw/v164f58+dj06ZNmDBhAg4ePGgoJ2+sZcuWiI2Nxdy5cxEWFoYWLVpg1apVFh+vmTNnon///pg+fTqioqLg5OSE0aNHm/SZNm0a5HI5xo8fDx8fH/z555+1xpFy3JjluKweey779+/H559/3iy+NJnZFr+yYIxJwsmCMSYJvw1hjEnCrywYY5JwsmCMScLJgjEmCScLxpgknCwYY5JwsmCMScLJgjEmCScLxpgknCwYY5L8DyNzAztBYNrcAAAAAElFTkSuQmCC\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', label='bootstrap ratio')\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', label='bootstrap ratio')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fig. 2B: Percent-signal change values within PLS mask (pos & neg separately)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fontsize = 25\n",
    "plt.rcParams['legend.title_fontsize'] = 'x-large'\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "##########################################################\n",
    "## please select which parameters you want to look at! ##\n",
    "##########################################################\n",
    "parameters = ['func',  'cbf','cmro2']\n",
    "#parameters = ['func']\n",
    "\n",
    "df_percchange_median = pd.DataFrame(columns=parameters)\n",
    "df_percchange_subj = pd.DataFrame(columns=parameters)\n",
    "df_percchange_vox = pd.DataFrame(columns=parameters)\n",
    "\n",
    "for mask in group_mask:\n",
    "    \n",
    "\n",
    "    if mask == 'PLS BOLD':\n",
    "        BSR_pos_nii_mask = results_dir + '/N' + N_subj +'_BSR_meanPLS_func_clusterBSR'+contrast+ '_pos_bin.nii.gz'\n",
    "        plotting.plot_img(BSR_pos_nii_mask, bg_img = MNI_2mm_brain, cmap=BlueRed, cut_coords=(10, 20, 30, 40), display_mode='z',colorbar = True, threshold=0, vmin = 0, vmax = 1, title= 'new mask')\n",
    "        masker_pos = input_data.NiftiMasker(mask_img = BSR_pos_nii_mask)        \n",
    "        pos_img=masker_pos.fit_transform(BSR_pos_nii_mask)\n",
    "        BSR_neg_nii_mask = results_dir + '/N' + N_subj +'_BSR_meanPLS_func_clusterBSR'+contrast+'_neg_bin.nii.gz'\n",
    "        plotting.plot_img(BSR_neg_nii_mask,bg_img = MNI_2mm_brain,  cmap=BlueRed, cut_coords=(10, 20, 30, 40), display_mode='z',colorbar = True, threshold=0,vmin = 0, vmax = 1, title= 'new mask')\n",
    "        masker_neg = input_data.NiftiMasker(mask_img = BSR_neg_nii_mask) \n",
    "        neg_img=masker_neg.fit_transform(BSR_neg_nii_mask)\n",
    "    if mask == 'PLS CBF':\n",
    "        BSR_pos_nii = results_dir + '/N' + N_subj +'_BSR_meanPLS_cbf_clusterBSR'+contrast+'.nii.gz' + '_pos.nii.gz'\n",
    "        masker_pos = input_data.NiftiMasker(mask_img = BSR_pos_nii_mask)        \n",
    "        pos_img=masker_pos.fit_transform(BSR_pos_nii_mask)\n",
    "        BSR_neg_nii = results_dir + '/N' + N_subj +'_BSR_meanPLS_cbf_clusterBSR'+contrast+'.nii.gz' + '_neg.nii.gz'\n",
    "        masker_neg = input_data.NiftiMasker(mask_img = BSR_neg_nii_mask) \n",
    "        neg_img=masker_neg.fit_transform(BSR_neg_nii_mask)\n",
    "    if mask == 'PLS CMRO2':\n",
    "        BSR_pos_nii = results_dir + '/N' + N_subj +'_BSR_meanPLS_cmro2_clusterBSR'+contrast+'.nii.gz' + '_pos.nii.gz'\n",
    "        masker_pos = input_data.NiftiMasker(mask_img = BSR_pos_nii_mask)        \n",
    "        pos_img=masker_pos.fit_transform(BSR_pos_nii_mask)\n",
    "        BSR_neg_nii = results_dir + '/N' + N_subj +'_BSR_meanPLS_cmro2_clusterBSR'+contrast+'.nii.gz' + '_neg.nii.gz'\n",
    "        masker_neg = input_data.NiftiMasker(mask_img = BSR_neg_nii_mask) \n",
    "        neg_img=masker_neg.fit_transform(BSR_neg_nii_mask)\n",
    "\n",
    "    for par in parameters: \n",
    "        print(par)\n",
    "        percchange_pos = np.zeros((len(sids), len(np.argwhere(pos_img))))\n",
    "        percchange_neg = np.zeros((len(sids), len(np.argwhere(neg_img))))\n",
    "    \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",
    "            #load percent signal change image\n",
    "            if par == 'func':\n",
    "                #percchange_img = nib.load(dir_func_deriv + '/' + sub + '_task-' + task + baseline + '_space-MNI152_BOLD_percchange.nii.gz')\n",
    "                percchange_img = dir_func_deriv + '/' + sub + '_task-'+task + baseline+'_space-MNI152_desc-fmriprep_BOLD_percchange.nii.gz'\n",
    "                #percchange_img = nib.load(dir_func_deriv + '/' + sub + '_task-'+task + baseline+'_space-MNI152_BOLD_percchange_desc-preproc.nii.gz')\n",
    "                percchange_arr_pos = masker_pos.fit_transform(percchange_img)\n",
    "                percchange_arr_neg = masker_neg.fit_transform(percchange_img)\n",
    "            if par !='func' and par !='cmro2':\n",
    "                par_map_task = dir_qmri_deriv +  '/' + sub + '_task-' + task + '_space-MNI152_' + par + '.nii.gz'\n",
    "                par_map_baseline = dir_qmri_deriv +  '/' + sub + '_task-' + baseline + '_space-MNI152_' + par + '.nii.gz'\n",
    "            if par =='cmro2':\n",
    "                if task == 'calc':\n",
    "                    par_map_task =  dir_qmri_deriv +  '/' + sub + '_task-' + task + '_space-MNI152_desc-CBV_' + par + '.nii.gz'\n",
    "                if task !='calc':\n",
    "                    par_map_task =  dir_qmri_deriv +  '/' + sub + '_task-' + task + '_space-MNI152_desc-orig_' + par + '.nii.gz'   \n",
    "                par_map_baseline =  dir_qmri_deriv +  '/' + sub + '_task-' + baseline + '_space-MNI152_desc-orig_' + par + '.nii.gz'                \n",
    "\n",
    "                \n",
    "            task_arr_pos = masker_pos.fit_transform(par_map_task)\n",
    "            task_arr_neg = masker_neg.fit_transform(par_map_task)\n",
    "            baseline_arr_pos = masker_pos.fit_transform(par_map_baseline)\n",
    "            baseline_arr_neg = masker_neg.fit_transform(par_map_baseline)\n",
    "            if par != 'func':\n",
    "                percchange_arr_pos = (task_arr_pos - baseline_arr_pos) / baseline_arr_pos *100\n",
    "                percchange_arr_neg = (task_arr_neg - baseline_arr_neg) / baseline_arr_neg *100\n",
    "            \n",
    "            \n",
    "            percchange_pos[i,:] = percchange_arr_pos\n",
    "            percchange_neg[i,:] = percchange_arr_neg\n",
    "                    \n",
    "        ## calculate median values across subjects\n",
    "        percchange_median_pos = np.median(percchange_pos, axis=0)\n",
    "        percchange_median_neg = np.median(percchange_neg, axis=0)\n",
    "        df_percchange_vox[par] = [percchange_median_pos, percchange_median_neg]\n",
    "\n",
    "        ## store median across voxels, per subject\n",
    "        df_percchange_subj[par] =  [np.nanmedian(percchange_pos, axis=1), np.nanmedian(percchange_neg, axis=1)]\n",
    "            \n",
    "        ## median across all voxels\n",
    "        pos_median = np.nanmedian(percchange_median_pos)\n",
    "        neg_median = np.nanmedian(percchange_median_neg)\n",
    "        df_percchange_median[par]=[pos_median, neg_median]\n",
    "        \n",
    "        print(par + ' positive median: ' + str(pos_median))\n",
    "        \n",
    "        ## print interquartile range\n",
    "        # First quartile (Q1) \n",
    "        Q1 = np.percentile(percchange_median_pos, 25, interpolation = 'midpoint') \n",
    "        # Third quartile (Q3) \n",
    "        Q3 = np.percentile(percchange_median_pos, 75, interpolation = 'midpoint') \n",
    "        # Interquaritle range (IQR) \n",
    "        IQR = Q3 - Q1 \n",
    "\n",
    "        print('IQR positive:  ' + str(IQR))\n",
    "        \n",
    "        print(par + ' negative median: ' + str(neg_median))\n",
    "        ## print interquartile range\n",
    "        # First quartile (Q1) \n",
    "        Q1 = np.percentile(percchange_median_neg, 25, interpolation = 'midpoint') \n",
    "        # Third quartile (Q3) \n",
    "        Q3 = np.percentile(percchange_median_neg, 75, interpolation = 'midpoint') \n",
    "        # Interquaritle range (IQR) \n",
    "        IQR = Q3 - Q1 \n",
    "\n",
    "        print('IQR negative:  ' + str(IQR))\n",
    "            \n",
    "        ## plot only BOLD masked values        \n",
    "        fig,ax = plt.subplots(1)      \n",
    "        \n",
    "\n",
    "        if par == 'func': \n",
    "            ax.set_xlim(-1.5, 1.5)\n",
    "            width = 0.01\n",
    "            brange = (-1.3, 1.5)\n",
    "            #brange_neg = (-2.5, 0.5)\n",
    "        else:\n",
    "            ax.set_xlim(-20, 30)\n",
    "            width = 0.1\n",
    "            brange= (-20, 30)\n",
    "            \n",
    "        sns.histplot(percchange_median_pos, ax=ax, color='darkorange', alpha=0.9, bins=500, binrange = brange, binwidth= width)\n",
    "        sns.histplot(percchange_median_neg, ax=ax, color='dodgerblue', alpha=0.7,  bins=500, binrange=brange, binwidth=width)\n",
    "        \n",
    "        #[t, p_pos] = stats.ttest_1samp(a=percchange_median_pos, popmean=0)\n",
    "        #[t, p_neg] = stats.ttest_1samp(a=percchange_median_neg, popmean=0)\n",
    "        #print('p-value positive change: ' + str(round(p_pos, 3)))           \n",
    "        #print('p-value negative change: ' + str(round(p_neg, 3)))         \n",
    "        \n",
    "        ymin,ymax = ax.get_ylim()\n",
    "        max_val = ymax - (0.05*ymax)\n",
    "        plt.vlines(0, 0, max_val, color='black', alpha=0.7, linestyles='dashed')\n",
    "        #ax.set_ylim(0, max_val)\n",
    "        ax.set_xlabel('%' + par, fontsize=fontsize)\n",
    "        ax.set_ylabel('voxel count', fontsize =fontsize)\n",
    "        #ax.set_title(N_subj + ' subjects:'  + ' ' +  par + ' ' + CMRO2_mode + ' % signal change within ' + mask + ' mask')\n",
    "        ax.plot([pos_median, pos_median], [0, max_val], color='darkred')\n",
    "        ax.plot([neg_median, neg_median], [0, max_val], color='darkblue')\n",
    "        if par == 'func' and task == 'calc':\n",
    "            ax.text(brange[1]-0.7*brange[1], 350, str(round(np.nanmedian(percchange_median_pos), 2))+' %', color='darkorange', fontsize=fontsize)\n",
    "            ax.text(brange[1]-1.7*brange[1], 350, str(round(np.nanmedian(percchange_median_neg), 2))+' %', color='dodgerblue', fontsize=fontsize)\n",
    "        if par == 'func' and task == 'mem':\n",
    "            ax.text(brange[1]-0.7*brange[1], 200, str(round(np.nanmedian(percchange_median_pos), 2))+' %', color='darkorange', fontsize=fontsize)\n",
    "            ax.text(brange[1]-1.7*brange[1], 200, str(round(np.nanmedian(percchange_median_neg), 2))+' %', color='dodgerblue', fontsize=fontsize)\n",
    "        if par != 'func':\n",
    "            ax.text(brange[1]-0.7*brange[1], 150, str(round(np.nanmedian(percchange_median_pos), 2))+' %', color='darkorange', fontsize=fontsize)\n",
    "            ax.text(brange[1]-1.7*brange[1], 150, str(round(np.nanmedian(percchange_median_neg), 2))+' %', color='dodgerblue', fontsize=fontsize)\n",
    "            \n",
    "        ## plot median %change values as legend\n",
    "        #ax.legend(fontsize=16, loc='upper right', bbox_to_anchor=(0.4, 1))\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",
    "        for pos in ['right', 'top']:\n",
    "            ax.spines[pos].set_visible(False)\n",
    "\n",
    "        fig.show()\n",
    "        fig.savefig(results_dir + '/N' + N_subj +'_' + mask + '_'+ contrast + '_' + par+'_percchange_histogram.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### delta values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy \n",
    "from scipy import stats\n",
    "from sklearn.utils import check_random_state\n",
    "\n",
    "fontsize = 25\n",
    "plt.rcParams['legend.title_fontsize'] = 'x-large'\n",
    "\n",
    "\n",
    "##########################################################\n",
    "## please select which parameters you want to look at! ##\n",
    "##########################################################\n",
    "parameters = ['cbf','cmro2']\n",
    "parameters = ['cbf','cmro2']\n",
    "\n",
    "if mask == 'PLS BOLD':\n",
    "        BSR_pos_nii_mask = results_dir + '/N' + N_subj +'_BSR_meanPLS_func_clusterBSR'+contrast+ '_pos_bin.nii.gz'\n",
    "        plotting.plot_img(BSR_pos_nii_mask, bg_img = MNI_2mm_brain, cmap=BlueRed, cut_coords=(10, 20, 30, 40), display_mode='z',colorbar = True, threshold=0, vmin = 0, vmax = 1, title= 'new mask')\n",
    "        masker_pos = input_data.NiftiMasker(mask_img = BSR_pos_nii_mask)        \n",
    "        pos_img=masker_pos.fit_transform(BSR_pos_nii_mask)\n",
    "        BSR_neg_nii_mask = results_dir + '/N' + N_subj +'_BSR_meanPLS_func_clusterBSR'+contrast+'_neg_bin.nii.gz'\n",
    "        plotting.plot_img(BSR_neg_nii_mask,bg_img = MNI_2mm_brain,  cmap=BlueRed, cut_coords=(10, 20, 30, 40), display_mode='z',colorbar = True, threshold=0,vmin = 0, vmax = 1, title= 'new mask')\n",
    "        masker_neg = input_data.NiftiMasker(mask_img = BSR_neg_nii_mask) \n",
    "        neg_img=masker_neg.fit_transform(BSR_neg_nii_mask)\n",
    "if mask == 'PLS CBF':\n",
    "        BSR_pos_nii = results_dir + '/N' + N_subj +'_BSR_meanPLS_cbf_clusterBSR'+contrast+'.nii.gz' + '_pos.nii.gz'\n",
    "        masker_pos = input_data.NiftiMasker(mask_img = BSR_pos_nii_mask)        \n",
    "        pos_img=masker_pos.fit_transform(BSR_pos_nii_mask)\n",
    "        BSR_neg_nii = results_dir + '/N' + N_subj +'_BSR_meanPLS_cbf_clusterBSR'+contrast+'.nii.gz' + '_neg.nii.gz'\n",
    "        masker_neg = input_data.NiftiMasker(mask_img = BSR_neg_nii_mask) \n",
    "        neg_img=masker_neg.fit_transform(BSR_neg_nii_mask)\n",
    "if mask == 'PLS CMRO2':\n",
    "        BSR_pos_nii = results_dir + '/N' + N_subj +'_BSR_meanPLS_cmro2_clusterBSR'+contrast+'.nii.gz' + '_pos.nii.gz'\n",
    "        masker_pos = input_data.NiftiMasker(mask_img = BSR_pos_nii_mask)        \n",
    "        pos_img=masker_pos.fit_transform(BSR_pos_nii_mask)\n",
    "        BSR_neg_nii = results_dir + '/N' + N_subj +'_BSR_meanPLS_cmro2_clusterBSR'+contrast+'.nii.gz' + '_neg.nii.gz'\n",
    "        masker_neg = input_data.NiftiMasker(mask_img = BSR_neg_nii_mask) \n",
    "        neg_img=masker_neg.fit_transform(BSR_neg_nii_mask)\n",
    "\n",
    "\n",
    "for par in parameters:\n",
    "        delta_pos = np.zeros((len(sids), len(np.argwhere(pos_img))))\n",
    "        delta_neg = np.zeros((len(sids), len(np.argwhere(neg_img))))\n",
    "        task_pos = np.zeros((len(sids), len(np.argwhere(pos_img))))\n",
    "        task_neg = np.zeros((len(sids), len(np.argwhere(neg_img))))\n",
    "        baseline_pos = np.zeros((len(sids), len(np.argwhere(pos_img))))\n",
    "        baseline_neg = np.zeros((len(sids), len(np.argwhere(neg_img))))\n",
    "        \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",
    "            #load percent signal change image\n",
    "            if par !='func' and par !='cmro2':\n",
    "                par_map_task =  dir_qmri_deriv +  '/' + sub + '_task-' + task + '_space-MNI152_' + par + '.nii.gz'               \n",
    "                par_map_baseline = dir_qmri_deriv +  '/' + sub + '_task-' + baseline + '_space-MNI152_' + par + '.nii.gz'\n",
    "\n",
    "            if par =='cmro2':\n",
    "                if task == 'calc':\n",
    "                    par_map_task = dir_qmri_deriv +  '/' + sub + '_task-' + task + '_space-MNI152_desc-CBV_' + par + '.nii.gz'\n",
    "                if task !='calc':\n",
    "                    par_map_task = dir_qmri_deriv +  '/' + sub + '_task-' + task + '_space-MNI152_desc-orig_' + par + '.nii.gz'\n",
    "                par_map_baseline =  dir_qmri_deriv +  '/' + sub + '_task-' + baseline + '_space-MNI152_desc-orig_' + par + '.nii.gz'\n",
    "\n",
    "                    \n",
    "            task_arr_pos = masker_pos.fit_transform(par_map_task)\n",
    "            task_arr_neg = masker_neg.fit_transform(par_map_task)\n",
    "            baseline_arr_pos = masker_pos.fit_transform(par_map_baseline)\n",
    "            baseline_arr_neg = masker_neg.fit_transform(par_map_baseline)\n",
    "            delta_arr_pos = task_arr_pos - baseline_arr_pos\n",
    "            delta_arr_neg = task_arr_neg - baseline_arr_neg\n",
    "\n",
    "            delta_pos[i,:] = delta_arr_pos\n",
    "            delta_neg[i,:] = delta_arr_neg\n",
    "            task_pos[i,:] = task_arr_pos\n",
    "            task_neg[i,:] = task_arr_neg\n",
    "            baseline_pos[i,:] = baseline_arr_pos\n",
    "            baseline_neg[i,:] = baseline_arr_neg\n",
    "            \n",
    "        ## calculate median values across subjects\n",
    "        delta_median_pos = np.median(delta_pos, axis=0)\n",
    "        delta_median_neg = np.median(delta_neg, axis=0)\n",
    "        task_median_pos = np.median(task_pos, axis=0)\n",
    "        task_median_neg = np.median(task_neg, axis=0)\n",
    "        baseline_median_pos = np.median(baseline_pos, axis=0)\n",
    "        baseline_median_neg = np.median(baseline_neg, axis=0)\n",
    "        \n",
    "        print(par)\n",
    "\n",
    "        ##print baseline IQR ## \n",
    "        #######################\n",
    "        # First quartile (Q1) \n",
    "        Q1 = np.percentile(baseline_median_pos, 25, interpolation = 'midpoint') \n",
    "        # Third quartile (Q3) \n",
    "        Q3 = np.percentile(baseline_median_pos, 75, interpolation = 'midpoint') \n",
    "        # Interquaritle range (IQR) \n",
    "        IQR = Q3 - Q1 \n",
    "        print('baseline positive ROI: ' + str(np.nanmedian(baseline_median_pos)) + ', IQR: ' + str(IQR))\n",
    "        # First quartile (Q1) \n",
    "        Q1 = np.percentile(baseline_median_neg, 25, interpolation = 'midpoint') \n",
    "        # Third quartile (Q3) \n",
    "        Q3 = np.percentile(baseline_median_neg, 75, interpolation = 'midpoint') \n",
    "        # Interquaritle range (IQR) \n",
    "        IQR = Q3 - Q1         \n",
    "        print('baseline negative ROI: ' + str(np.nanmedian(baseline_median_neg))+ ', IQR: ' + str(IQR))\n",
    "        \n",
    "        ##print delta IQR ## \n",
    "        #######################\n",
    "        # First quartile (Q1) \n",
    "        Q1 = np.percentile(delta_median_pos, 25, interpolation = 'midpoint') \n",
    "        # Third quartile (Q3) \n",
    "        Q3 = np.percentile(delta_median_pos, 75, interpolation = 'midpoint') \n",
    "        # Interquaritle range (IQR) \n",
    "        IQR = Q3 - Q1 \n",
    "        print('baseline positive ROI: ' + str(np.nanmedian(delta_median_pos)) + ', IQR: ' + str(IQR))\n",
    "        # First quartile (Q1) \n",
    "        Q1 = np.percentile(delta_median_neg, 25, interpolation = 'midpoint') \n",
    "        # Third quartile (Q3) \n",
    "        Q3 = np.percentile(delta_median_neg, 75, interpolation = 'midpoint') \n",
    "        # Interquaritle range (IQR) \n",
    "        IQR = Q3 - Q1         \n",
    "        print('baseline negative ROI: ' + str(np.nanmedian(delta_median_neg))+ ', IQR: ' + str(IQR))\n",
    "        \n",
    "        \n",
    "        ## test for normality: if significant,no normal distribution!\n",
    "        stat_pos_norm, p_value_pos_norm= stats.normaltest(delta_median_pos, axis=0, nan_policy='propagate')\n",
    "        stat_neg_norm, p_value_neg_norm= stats.normaltest(delta_median_neg, axis=0, nan_policy='propagate')\n",
    "        print('normal distribution within positive BOLD mask, p-value: ' + str(p_value_pos_norm))\n",
    "        print('normal distribution within negative BOLD mask, p-value: ' + str(p_value_neg_norm))\n",
    "\n",
    "        ##permutation test, because data are smoothed/non-independent\n",
    "        [true_diff_pos, p_pos] = permtest_rel(task_median_pos, baseline_median_pos, n_perm=2000)\n",
    "        print('permutation statistics within BOLD pos regions: ' + str(round(p_pos, 6)))\n",
    "\n",
    "        [true_diff_neg, p_neg] = permtest_rel(task_median_neg, baseline_median_neg, n_perm=2000)\n",
    "        print('permutation statistics within BOLD neg regions: ' + str(round(p_neg, 6)))\n",
    "        \n",
    "\n",
    "        ## plot only BOLD masked values        \n",
    "        fig,ax = plt.subplots(1)            \n",
    "\n",
    "        if par == 'cbf':\n",
    "            x_val = 2.1\n",
    "            max_val = 90\n",
    "            #max_val = 70\n",
    "            ax.set_xlim(-8, 8)\n",
    "            brange = (-8,8)\n",
    "            width = brange[1]/500\n",
    "            axis_title = 'Δ CBF [ml/100g/min]'\n",
    "        if par == 'cmro2':\n",
    "            #max_val = 400\n",
    "            max_val = 90\n",
    "            x_val = 0.6\n",
    "            ax.set_xlim(-30,30)\n",
    "            brange = (-30,30)\n",
    "            width = brange[1]/500\n",
    "            axis_title = 'Δ CMRO2 [μmol/100g/min]'\n",
    "\n",
    "        \n",
    "        sns.histplot(delta_median_pos, ax=ax, color='darkorange', alpha=0.9, bins=500, binrange = brange, binwidth= width)\n",
    "        sns.histplot(delta_median_neg, ax=ax, color='dodgerblue', alpha=0.7,  bins=500, binrange=brange, binwidth=width)\n",
    "        \n",
    "        #max_val = ymax - (0.1*ymax)\n",
    "\n",
    "        plt.vlines(0, 0, max_val, color='black', alpha=0.7, linestyles='dashed')\n",
    "        #ax.set_xlim(-x_val, x_val)\n",
    "        ax.set_ylim(0, max_val)\n",
    "        ax.set_yticks((0, 20, 40, 60, 80))\n",
    "        ax.set_xlabel(axis_title, fontsize=fontsize)\n",
    "        ax.set_ylabel('voxel count', fontsize=fontsize)\n",
    "        #ax.set_title(N_subj + ' subjects:'  + ' ' +  par + ' ' + CMRO2_mode + ' raw delta within ' + mask + ' mask')\n",
    "        ax.plot([np.median(delta_median_pos), np.median(delta_median_pos)], [0, max_val], color='darkred')\n",
    "        ax.plot([np.median(delta_median_neg), np.median(delta_median_neg)], [0, max_val], color='darkblue')\n",
    "        # absolute median values as text in plot\n",
    "        if par == 'cbf':\n",
    "            if p_neg < 0.001:\n",
    "                ax.text(brange[0]-0.05*brange[0], 65,''+ str(round(np.median(delta_median_neg), 1))+' ΔCBF\\n (='+ str(round(df_percchange_median[par][1], 2))+' %)', color='dodgerblue', fontsize=fontsize)            \n",
    "            else:\n",
    "                ax.text(brange[0]-0.05*brange[0], 65, str(round(np.median(delta_median_neg), 1)), color='darkblue', fontsize=fontsize)            \n",
    "            if p_pos < 0.001:\n",
    "                ax.text(brange[1]-0.7*brange[1], 65, ''+str(round(np.median(delta_median_pos), 1))+' ΔCBF\\n (='+ str(round(df_percchange_median[par][0], 2))+' %)', color='darkorange', fontsize=fontsize)\n",
    "            else:    \n",
    "                ax.text(brange[1]-0.7*brange[1], 65, str(round(np.median(delta_median_pos), 1)), color='darkred', fontsize=fontsize)\n",
    "        if par == 'cmro2':\n",
    "            if p_neg < 0.001:\n",
    "                ax.text(brange[0]-1.3*brange[0], 55,''+ str(round(np.median(delta_median_neg), 1))+' ΔCMRO2\\n (='+ str(round(df_percchange_median[par][1], 2))+' %)', color='dodgerblue', fontsize=fontsize)            \n",
    "            else:\n",
    "                ax.text(brange[0]-1.3*brange[0], 55, str(round(np.median(delta_median_neg), 1))+' ΔCMRO2', color='dodgerblue', fontsize=fontsize)            \n",
    "            if p_pos < 0.001:\n",
    "                ax.text(brange[1]-0.7*brange[1], 75,''+ str(round(np.median(delta_median_pos), 1))+' ΔCMRO2\\n (='+ str(round(df_percchange_median[par][0],2))+' %)', color='darkorange', fontsize=fontsize)\n",
    "            else:    \n",
    "                ax.text(brange[1]-0.7*brange[1], 75, str(round(np.median(delta_median_pos), 1))+' ΔCMRO2', color='darkorange', fontsize=fontsize)\n",
    "                \n",
    "        print('median pos. delta value: ' + str(round(np.median(delta_median_pos), 4)))\n",
    "        print('median neg. delta value: ' + str(round(np.median(delta_median_neg), 4)))\n",
    "        \n",
    "        for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "            label.set_fontsize(fontsize)\n",
    "            \n",
    "        ## plot median %change values as legend\n",
    "        #ax.legend(fontsize=18, loc='upper right', bbox_to_anchor=(0.35, 1.05))\n",
    "       \n",
    "        for pos in ['right', 'top']:\n",
    "            ax.spines[pos].set_visible(False)\n",
    "\n",
    "        fig.show()\n",
    "        fig.savefig(results_dir + '/N' + N_subj +'_mask_'  + mask + contrast + '_' + par+'_delta_histogram.png', dpi=300, bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fig. S1E supplements: Which Yeo networks do PLS clusters belong to?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### YEO networks\n",
    "VIS = os.path.join(derivatives_dir,'Yeo2011_7Networks_MNI152_FreeSurferConformed2mm_MNI_VIS.nii.gz')\n",
    "\n",
    "SMN = os.path.join(derivatives_dir,'Yeo2011_7Networks_MNI152_FreeSurferConformed2mm_MNI_SMN.nii.gz')\n",
    "\n",
    "DAN = os.path.join(derivatives_dir,'Yeo2011_7Networks_MNI152_FreeSurferConformed2mm_MNI_DAN.nii.gz')\n",
    "\n",
    "VAN = os.path.join(derivatives_dir,'Yeo2011_7Networks_MNI152_FreeSurferConformed2mm_MNI_VAN.nii.gz')\n",
    "\n",
    "LIM = os.path.join(derivatives_dir,'Yeo2011_7Networks_MNI152_FreeSurferConformed2mm_MNI_LIM.nii.gz')\n",
    "\n",
    "FPN = os.path.join(derivatives_dir,'Yeo2011_7Networks_MNI152_FreeSurferConformed2mm_MNI_FPN.nii.gz')\n",
    "\n",
    "DMN = os.path.join(derivatives_dir,'Yeo2011_7Networks_MNI152_FreeSurferConformed2mm_MNI_DMN.nii.gz')\n",
    "\n",
    "\n",
    "yeo_nw = [VIS, SMN, DAN, VAN, LIM, FPN, DMN]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## which YEO networks are the voxels parts of\n",
    "\n",
    "if group_mask[0] == 'PLS BOLD':\n",
    "    BSR_pos_nii_mask = results_dir + '/N' + N_subj +'_BSR_meanPLS_func_clusterBSR'+contrast+'_pos_bin.nii.gz'\n",
    "    masker_pos = input_data.NiftiMasker(mask_img = BSR_pos_nii_mask)        \n",
    "    pos_img=masker_pos.fit_transform(BSR_pos_nii_mask)\n",
    "    len_pos_img = len(np.argwhere(pos_img))\n",
    "\n",
    "    BSR_neg_nii_mask = results_dir + '/N' + N_subj +'_BSR_meanPLS_func_clusterBSR'+contrast+'_neg_bin.nii.gz'\n",
    "    masker_neg = input_data.NiftiMasker(mask_img = BSR_neg_nii_mask) \n",
    "    neg_img=masker_neg.fit_transform(BSR_neg_nii_mask)\n",
    "    len_neg_img = len(np.argwhere(neg_img))\n",
    "\n",
    "yeo_nw = [VIS, SMN, DAN, VAN, LIM, FPN, DMN]\n",
    "nw_names = ['VIS', 'SMN', 'DAN', 'VAN', 'LIM', 'FPN', 'DMN']\n",
    "cmap = (mpl.colors.ListedColormap(['darkviolet',\n",
    "                                   'dodgerblue',\n",
    "                                   'forestgreen',\n",
    "                                   'violet', \n",
    "                                   'palegoldenrod',\n",
    "                                   'darkorange',\n",
    "                                   'indianred']), np.linspace(0, 7.5,8))\n",
    "colors=['indigo','dodgerblue',\n",
    "                                   'forestgreen',\n",
    "                                   'orchid', \n",
    "                                   'palegoldenrod',\n",
    "                                   'darkorange',\n",
    "                                   'brown']\n",
    "nw_len_pos = pd.DataFrame()\n",
    "nw_len_neg = pd.DataFrame()\n",
    "\n",
    "for n, nw in enumerate(yeo_nw):\n",
    "    nw_arr_pos = masker_pos.fit_transform(nw)\n",
    "    len_vox_pos = len(np.argwhere(nw_arr_pos==1))\n",
    "    nw_len_pos[nw_names[n]] = [(len_vox_pos)/nw_arr_pos.shape[1] * 100]\n",
    "    \n",
    "    nw_arr_neg = masker_neg.fit_transform(nw)\n",
    "    len_vox_neg = len(np.argwhere(nw_arr_neg==1))\n",
    "    nw_len_neg[nw_names[n]] = [(len_vox_neg)/nw_arr_neg.shape[1] * 100]\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, (ax1, ax2) = plt.subplots(1,2, figsize=(10,10))\n",
    "    \n",
    "patches, texts, autotexts = ax1.pie(np.squeeze(nw_len_neg.values), labels=['','','','','','',''],autopct = my_autopct, startangle = 79.5, explode = [0.03,0.03,0.03,0.03,0.03,0.03,0.03],\n",
    "        textprops = {'fontsize':25}, colors=colors, wedgeprops = {'linewidth': 6, 'alpha':0.9}, center = (0.1,0.1))\n",
    "autotexts[0].set_color('lightgray')\n",
    "#autotexts[1].set_color('lightgray')\n",
    "\n",
    "ax1.set_title('negative BOLD voxels')\n",
    "\n",
    "patches, texts, autotexts = ax2.pie(np.squeeze(nw_len_pos.values), labels=['','','','','','',''],autopct = my_autopct, startangle = 79.5, explode = [0.03,0.03,0.03,0.03,0.03,0.03,0.03],\n",
    "        textprops = {'fontsize':25}, colors=colors, wedgeprops = {'linewidth': 6, 'alpha':0.9}, center = (0.1,0.1))\n",
    "autotexts[0].set_color('lightgray')\n",
    "#autotexts[1].set_color('lightgray')\n",
    "\n",
    "ax2.set_title('positive BOLD voxels')\n",
    "\n",
    "fig.savefig(results_dir + '/Pieplot_N' + N_subj + '_'+ contrast + '_PLS_BOLD_YEO_networks.png', dpi=300, bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fig. 2D: Which YEO networks do clusters belong to? \n",
    "[This part only works for the CALC-CTRL and CALC-REST contrasts! We manually/visually summarized smaller clusters into bigger ones depending on their location]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1830x230 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1830x230 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nilearn import glm\n",
    "from IPython.display import Image\n",
    "from nilearn import datasets, image, regions\n",
    "\n",
    "BSR_thr = 3\n",
    "cluster_size =10\n",
    "coords = (-20, -10, 0,10, 20, 30, 40,  50)\n",
    "\n",
    "if group_mask[0] == 'PLS BOLD':\n",
    "    BSR_masked_BOLD_YEO = results_dir + '/N' + N_subj +'_BSR_meanPLS_func_'+contrast+ '.nii.gz'\n",
    "    plotting.plot_img(BSR_masked_BOLD_YEO, bg_img = MNI_2mm_brain, cmap=BlueRed, cut_coords=coords, display_mode='z',colorbar = True, threshold=0, vmin = -3, vmax = 3, title= '')\n",
    "\n",
    "BSR_cluster_thr = glm.threshold_stats_img(BSR_masked_BOLD_YEO, mask_img=None, alpha=0.05, threshold=BSR_thr, height_control=None, cluster_threshold=cluster_size, two_sided=True)\n",
    "nib.save(BSR_cluster_thr[0],results_dir + '/N' + N_subj +'_BSR_meanPLS_BOLD_clusterBSR'+contrast+'.nii.gz')\n",
    "BSR_cluster_thr_nii = results_dir + '/N' + N_subj +'_BSR_meanPLS_BOLD_clusterBSR'+contrast+'.nii.gz'\n",
    "plotting.plot_img(BSR_cluster_thr_nii, bg_img = MNI_2mm_brain, cmap=BlueRed, cut_coords=coords, display_mode='z',colorbar = True, threshold=0, vmin = -3, vmax = 3, title= 'BSR>3, cluster-thresholded')\n",
    "\n",
    "BSR_cluster_thr_nii_nib = nib.load(BSR_cluster_thr_nii)\n",
    "inverse_MNI_affine = np.linalg.inv(BSR_cluster_thr_nii_nib.affine)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##cluster-threshold PLS BOLD mask and mask with BSR>3, do this once!\n",
    "\n",
    "##########################################\n",
    "## task-negative clusters#################\n",
    "##########################################\n",
    "\n",
    "BSR_cluster_thr_arr = np.array(BSR_cluster_thr_nii_nib.dataobj)\n",
    "\n",
    "BSR_cluster_thr_arr[BSR_cluster_thr_arr>0] =0\n",
    "\n",
    "PLS_nii_neg = nib.Nifti1Image(BSR_cluster_thr_arr, BSR_cluster_thr_nii_nib.affine)\n",
    "plotting.plot_img(PLS_nii_neg, cmap=BlueRed, vmin=-7, vmax=7, bg_img=MNI_2mm_brain, threshold=0, display_mode='z', cut_coords=coords, title=' PLS results')\n",
    "\n",
    "if baseline == 'control':\n",
    "    [cluster_4D_img, cluster_index] = regions.connected_regions(PLS_nii_neg, min_region_size=1000, extract_type='connected_components', smoothing_fwhm=6,  mask_img=None)\n",
    "if baseline == 'rest':\n",
    "    [cluster_4D_img, cluster_index] = regions.connected_regions(PLS_nii_neg, min_region_size=500, extract_type='connected_components', smoothing_fwhm=6,  mask_img=None)\n",
    "\n",
    "print(image.load_img(cluster_4D_img).shape)\n",
    "\n",
    "## save each cluster as separate nifti and plot\n",
    "for cluster in range(0, (image.load_img(cluster_4D_img).shape[3])):\n",
    "    cluster_image = image.index_img(cluster_4D_img, cluster)\n",
    "    plotting.plot_img(cluster_image, cmap=BlueRed, vmin=-7, vmax=7, bg_img=MNI_2mm_brain, threshold=0, display_mode='z', cut_coords=coords, title='ROI' + str(cluster+1))\n",
    "    ## save\n",
    "    nib.save(cluster_image,  results_dir + '/cluster' + str(cluster+1)+'.nii.gz')\n",
    "    \n",
    "#################################    \n",
    "## binarize and order networks ##\n",
    "#################################\n",
    "\n",
    "##binarize regions\n",
    "for ROI in range(0, (image.load_img(cluster_4D_img).shape[3])):\n",
    "    cluster_img =   results_dir + '/cluster' + str(ROI+1)+'.nii.gz'\n",
    "    cluster_img_bin =  results_dir + '/cluster' + str(ROI+1)+'_bin.nii.gz'\n",
    "    ! fslmaths {cluster_img} -abs -bin {cluster_img_bin}   \n",
    "\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "\n",
    "colors = [\"dodgerblue\", \"dodgerblue\"]\n",
    "Blue = LinearSegmentedColormap.from_list(\"mycmap\", colors)\n",
    "\n",
    "if baseline == 'control':## add the somatosensory regions together (upon visual inspection)\n",
    "    ## add ROI 4 and ROI5\n",
    "    ROI4 =  results_dir + '/cluster4_bin.nii.gz'\n",
    "    ROI5 =  results_dir + '/cluster5_bin.nii.gz'\n",
    "    ROI4_5 =  results_dir + '/cluster4_5_bin.nii.gz'\n",
    "    ! fslmaths {ROI4} -add {ROI5} {ROI4_5}\n",
    "\n",
    "    ## add ROI 7 and ROI 8 \n",
    "    ROI7 =  results_dir + '/cluster7_bin.nii.gz'\n",
    "    ROI8 =  results_dir + '/cluster8_bin.nii.gz'\n",
    "    ROI7_8 =  results_dir + '/cluster7_8_bin.nii.gz'\n",
    "    ! fslmaths {ROI7} -add {ROI8} {ROI7_8}\n",
    "\n",
    "    ## create one image with all clusters\n",
    "    all_clusters_nii = results_dir + '/all_negative_clusters_bin.nii.gz'\n",
    "    ! fslmaths {results_dir + '/cluster1_bin.nii.gz'} -add {results_dir + '/cluster2_bin.nii.gz'} -add {results_dir + '/cluster3_bin.nii.gz'} -add {ROI4_5} -add  {results_dir + '/cluster6_bin.nii.gz'} -add {ROI7_8} -add {results_dir + '/cluster9_bin.nii.gz'} -add {results_dir + '/cluster10_bin.nii.gz'} -add {results_dir + '/cluster11_bin.nii.gz'} {all_clusters_nii}\n",
    "    plotting.plot_img(all_clusters_nii, cmap=Blue, vmin=-7, vmax=7, bg_img=MNI_2mm_brain, threshold=0, display_mode='z', cut_coords=coords, title='all clusters')\n",
    "    plotting.plot_img(all_clusters_nii, cmap=Blue, vmin=-7, vmax=7, bg_img=MNI_2mm_brain, threshold=0, display_mode='z', cut_coords=coords, title='all clusters',\n",
    "                     output_file = results_dir + '/all_negative_clusters.pdf')\n",
    "\n",
    "    ## rename regions\n",
    "    shutil.copy(results_dir + '/cluster1_bin.nii.gz', results_dir + '/AU1_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster10_bin.nii.gz', results_dir + '/AU2_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster4_5_bin.nii.gz', results_dir + '/SM1_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster7_8_bin.nii.gz', results_dir + '/SM2_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster2_bin.nii.gz', results_dir + '/DMN1_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster11_bin.nii.gz', results_dir + '/DMN2_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster3_bin.nii.gz', results_dir + '/DMN3_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster6_bin.nii.gz', results_dir + '/DMN4_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster9_bin.nii.gz', results_dir + '/DMN5_bin.nii.gz')\n",
    "\n",
    "if baseline == 'rest':## add the somatosensory regions together (upon visual inspection)\n",
    "    ROI1 =  results_dir + '/cluster1_bin.nii.gz'\n",
    "    ROI4 =  results_dir + '/cluster4_bin.nii.gz'\n",
    "    ROI5 =  results_dir + '/cluster5_bin.nii.gz'\n",
    "    ROI1_4_5 =  results_dir + '/cluster1_4_5_bin.nii.gz'\n",
    "    ! fslmaths {ROI1} -add {ROI4} -add {ROI5} {ROI1_4_5}\n",
    "    \n",
    "    ROI3 =  results_dir + '/cluster3_bin.nii.gz'\n",
    "    ROI13 =  results_dir + '/cluster13_bin.nii.gz'\n",
    "    ROI3_13=  results_dir + '/cluster3_13_bin.nii.gz'\n",
    "    ! fslmaths {ROI3} -add {ROI13} {ROI3_13}\n",
    "\n",
    "    ROI8 =  results_dir + '/cluster8_bin.nii.gz'\n",
    "    ROI10 =  results_dir + '/cluster10_bin.nii.gz'\n",
    "    ROI8_10=  results_dir + '/cluster8_10_bin.nii.gz'\n",
    "    ! fslmaths {ROI8} -add {ROI10}  {ROI8_10}\n",
    "    \n",
    "    ## create one image with all clusters\n",
    "    all_clusters_nii = results_dir + '/all_negative_clusters_bin_rest.nii.gz'\n",
    "    ! fslmaths {ROI8_10} -add {ROI1_4_5} -add {ROI3_13}  -add {results_dir + '/cluster2_bin.nii.gz'} -add {results_dir + '/cluster6_bin.nii.gz'} -add {results_dir + '/cluster7_bin.nii.gz'} -add {results_dir + '/cluster9_bin.nii.gz'} -add {results_dir + '/cluster11_bin.nii.gz'} -add {results_dir + '/cluster12_bin.nii.gz'} {all_clusters_nii}\n",
    "    plotting.plot_img(all_clusters_nii, cmap=Blue, vmin=-7, vmax=7, bg_img=MNI_2mm_brain, threshold=0, display_mode='z', cut_coords=coords, title='all clusters')\n",
    "    plotting.plot_img(all_clusters_nii, cmap=Blue, vmin=-7, vmax=7, bg_img=MNI_2mm_brain, threshold=0, display_mode='z', cut_coords=coords, title='all clusters',\n",
    "                     output_file = results_dir + '/all_negative_clusters_rest.pdf')\n",
    "\n",
    "    ## rename regions\n",
    "    shutil.copy(results_dir + '/cluster1_4_5_bin.nii.gz', results_dir + '/NEG1_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster8_10_bin.nii.gz', results_dir + '/NEG2_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster3_13_bin.nii.gz', results_dir + '/NEG3_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster2_bin.nii.gz', results_dir + '/NEG6_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster6_bin.nii.gz', results_dir + '/NEG4_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster7_bin.nii.gz', results_dir + '/NEG5_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster9_bin.nii.gz', results_dir + '/NEG7_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster11_bin.nii.gz', results_dir + '/NEG8_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster12_bin.nii.gz', results_dir + '/NEG9_bin.nii.gz')\n",
    "    \n",
    "    \n",
    "    \n",
    "############################\n",
    "## task-positive clusters ##\n",
    "#############################\n",
    "\n",
    "## all positives\n",
    "BSR_cluster_thr_arr = np.array(BSR_cluster_thr_nii_nib.dataobj)\n",
    "BSR_cluster_thr_arr[BSR_cluster_thr_arr<0] =0\n",
    "\n",
    "PLS_nii_pos = nib.Nifti1Image(BSR_cluster_thr_arr, BSR_cluster_thr_nii_nib.affine)\n",
    "plotting.plot_img(PLS_nii_pos, cmap=BlueRed, vmin=-7, vmax=7, bg_img=MNI_2mm_brain, threshold=0, display_mode='z', cut_coords=coords)\n",
    "\n",
    "[cluster_4D_img, cluster_index] = regions.connected_regions(PLS_nii_pos, min_region_size=1200, extract_type='connected_components', smoothing_fwhm=6,  mask_img=None)\n",
    "\n",
    "print(image.load_img(cluster_4D_img).shape)\n",
    "\n",
    "## save each cluster as separate nifti and plot\n",
    "for cluster in range(0, (image.load_img(cluster_4D_img).shape[3])):\n",
    "    cluster_image = image.index_img(cluster_4D_img, cluster)\n",
    "    plotting.plot_img(cluster_image, cmap=BlueRed, vmin=-7, vmax=7, bg_img=MNI_2mm_brain, threshold=0, display_mode='z', cut_coords=coords, title='ROI' + str(cluster+1))\n",
    "    ## binarize\n",
    "    nib.save(cluster_image,  results_dir + '/cluster' + str(cluster+1)+'.nii.gz')\n",
    "    \n",
    "#################################    \n",
    "## binarize and order networks ##\n",
    "#################################\n",
    "\n",
    "for ROI in range(0, (image.load_img(cluster_4D_img).shape[3])):\n",
    "    cluster_img =   results_dir + '/cluster' + str(ROI+1)+'.nii.gz'\n",
    "    cluster_img_bin =  results_dir + '/cluster' + str(ROI+1)+'_bin.nii.gz'\n",
    "    ! fslmaths {cluster_img} -abs -bin {cluster_img_bin}   \n",
    "\n",
    "\n",
    "## add the somatosensory regions together (upon visual inspection)\n",
    "## add ROI 4 and ROI5\n",
    "if baseline == 'control':\n",
    "    ROI2 =  results_dir + '/cluster2_bin.nii.gz'\n",
    "    ROI3 =  results_dir + '/cluster3_bin.nii.gz'\n",
    "    ROI5 =  results_dir + '/cluster5_bin.nii.gz'\n",
    "    ROI2_3_5 =  results_dir + '/cluster2_3_5_bin.nii.gz'\n",
    "    ! fslmaths {ROI2} -add {ROI3} -add {ROI5} {ROI2_3_5}\n",
    "\n",
    "    ## add ROI 7 and ROI 8 \n",
    "    ROI7 =  results_dir + '/cluster7_bin.nii.gz'\n",
    "    ROI8 =  results_dir + '/cluster8_bin.nii.gz'\n",
    "    ROI10 =  results_dir + '/cluster10_bin.nii.gz'\n",
    "    ROI7_8_10 =  results_dir + '/cluster7_8_10_bin.nii.gz'\n",
    "    ! fslmaths {ROI7} -add {ROI8} -add {ROI10} {ROI7_8_10}\n",
    "\n",
    "    ## create one image with all clusters\n",
    "    all_clusters_nii = results_dir + '/all_positive_clusters_bin.nii.gz'\n",
    "    ! fslmaths {results_dir + '/cluster1_bin.nii.gz'} -add {ROI2_3_5} -add {results_dir + '/cluster4_bin.nii.gz'}  -add {results_dir + '/cluster6_bin.nii.gz'} -add {ROI7_8_10} -add {results_dir + '/cluster9_bin.nii.gz'} -add {results_dir + '/cluster11_bin.nii.gz'} -add {results_dir + '/cluster12_bin.nii.gz'} {all_clusters_nii}\n",
    "    plotting.plot_img(all_clusters_nii, cmap='hot', vmin=-7, vmax=7, bg_img=MNI_2mm_brain, threshold=0, display_mode='z', cut_coords=coords, title='all clusters')\n",
    "    plotting.plot_img(all_clusters_nii, cmap='hot', vmin=-7, vmax=7, bg_img=MNI_2mm_brain, threshold=0, display_mode='z', cut_coords=coords, title='all clusters',\n",
    "                     output_file = results_dir + '/all_positive_clusters.pdf')\n",
    "\n",
    "    ## rename regions\n",
    "    shutil.copy(results_dir + '/cluster1_bin.nii.gz', results_dir + '/ROI1_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster7_8_10_bin.nii.gz', results_dir + '/ROI2_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster2_3_5_bin.nii.gz', results_dir + '/ROI3_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster11_bin.nii.gz', results_dir + '/ROI4_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster4_bin.nii.gz', results_dir + '/ROI5_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster12_bin.nii.gz', results_dir + '/ROI6_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster6_bin.nii.gz', results_dir + '/ROI7_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster9_bin.nii.gz', results_dir + '/ROI8_bin.nii.gz')\n",
    "\n",
    "    ## create list with  cluster masks to be applied\n",
    "    masks = [results_dir + '/ROI1_bin.nii.gz',\n",
    "            results_dir + '/ROI2_bin.nii.gz',\n",
    "            results_dir + '/ROI3_bin.nii.gz',\n",
    "            results_dir + '/ROI4_bin.nii.gz',\n",
    "            results_dir + '/ROI5_bin.nii.gz',\n",
    "            results_dir + '/ROI6_bin.nii.gz',\n",
    "            results_dir + '/ROI7_bin.nii.gz',\n",
    "            results_dir + '/ROI8_bin.nii.gz']\n",
    "\n",
    "    for mask in masks:\n",
    "        plotting.plot_img(mask, cmap='hot', vmin=-7, vmax=7, bg_img=MNI_2mm_brain, threshold=0, display_mode='z', cut_coords=coords, title= os.path.basename(mask)[:-11])\n",
    "\n",
    "if baseline == 'rest':\n",
    "    ROI2 =  results_dir + '/cluster2_bin.nii.gz'\n",
    "    ROI3 =  results_dir + '/cluster3_bin.nii.gz'\n",
    "    ROI2_3 =  results_dir + '/cluster2_3_bin.nii.gz'\n",
    "    ! fslmaths {ROI2} -add {ROI3}  {ROI2_3}\n",
    "\n",
    "    ## add ROI 7 and ROI 8 \n",
    "    ROI7 =  results_dir + '/cluster7_bin.nii.gz'\n",
    "    ROI8 =  results_dir + '/cluster8_bin.nii.gz'\n",
    "    ROI7_8=  results_dir + '/cluster7_8_bin.nii.gz'\n",
    "    ! fslmaths {ROI7} -add {ROI8} {ROI7_8}\n",
    "\n",
    "    ## create one image with all clusters\n",
    "    all_clusters_nii = results_dir + '/all_positive_clusters_bin.nii.gz'\n",
    "    ! fslmaths {results_dir + '/cluster1_bin.nii.gz'} -add {ROI2_3} -add {results_dir + '/cluster4_bin.nii.gz'}  -add {results_dir + '/cluster5_bin.nii.gz'} -add {results_dir + '/cluster6_bin.nii.gz'} -add {ROI7_8} {all_clusters_nii}\n",
    "    plotting.plot_img(all_clusters_nii, cmap='hot', vmin=-7, vmax=7, bg_img=MNI_brain, threshold=0, display_mode='z', cut_coords=coords, title='all clusters')\n",
    "    plotting.plot_img(all_clusters_nii, cmap='hot', vmin=-7, vmax=7, bg_img=MNI_brain, threshold=0, display_mode='z', cut_coords=coords, title='all clusters',\n",
    "                     output_file = results_dir + '/all_positive_clusters_rest.pdf')\n",
    "\n",
    "    ## rename regions\n",
    "    shutil.copy(results_dir + '/cluster1_bin.nii.gz', results_dir + '/ROI1_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster2_3_bin.nii.gz', results_dir + '/ROI2_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster4_bin.nii.gz', results_dir + '/ROI3_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster5_bin.nii.gz', results_dir + '/ROI4_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster6_bin.nii.gz', results_dir + '/ROI5_bin.nii.gz')\n",
    "    shutil.copy(results_dir + '/cluster7_8_bin.nii.gz', results_dir + '/ROI6_bin.nii.gz')\n",
    "\n",
    "    ## create list with  cluster masks to be applied\n",
    "    masks = [results_dir + '/ROI1_bin.nii.gz',\n",
    "            results_dir + '/ROI2_bin.nii.gz',\n",
    "            results_dir + '/ROI3_bin.nii.gz',\n",
    "            results_dir + '/ROI4_bin.nii.gz',\n",
    "            results_dir + '/ROI5_bin.nii.gz',\n",
    "            results_dir + '/ROI6_bin.nii.gz']\n",
    "\n",
    "    for mask in masks:\n",
    "        plotting.plot_img(mask, cmap='hot', vmin=-7, vmax=7, bg_img=MNI_brain, threshold=0, display_mode='z', cut_coords=coords, title= os.path.basename(mask)[:-11])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 2500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parameters = [ 'func', 'cmro2']\n",
    "\n",
    "###################################\n",
    "## plot negative clusters ##\n",
    "#################################\n",
    "\n",
    "if baseline == 'control':## create list with  cluster masks to be applied\n",
    "    masks = [results_dir + '/AU1_bin.nii.gz',\n",
    "            results_dir + '/AU2_bin.nii.gz',\n",
    "            results_dir + '/SM1_bin.nii.gz',\n",
    "            results_dir + '/SM2_bin.nii.gz',\n",
    "            results_dir + '/DMN1_bin.nii.gz',\n",
    "            results_dir + '/DMN2_bin.nii.gz',\n",
    "            results_dir + '/DMN3_bin.nii.gz',\n",
    "            results_dir + '/DMN4_bin.nii.gz',\n",
    "            results_dir + '/DMN5_bin.nii.gz']\n",
    "\n",
    "if baseline == 'rest':## create list with  cluster masks to be applied\n",
    "    masks = [results_dir + '/NEG1_bin.nii.gz',\n",
    "            results_dir + '/NEG2_bin.nii.gz',\n",
    "            results_dir + '/NEG3_bin.nii.gz',\n",
    "            results_dir + '/NEG4_bin.nii.gz',\n",
    "            results_dir + '/NEG5_bin.nii.gz',\n",
    "            results_dir + '/NEG6_bin.nii.gz',\n",
    "            results_dir + '/NEG7_bin.nii.gz',\n",
    "            results_dir + '/NEG8_bin.nii.gz',\n",
    "            results_dir + '/NEG9_bin.nii.gz']\n",
    "\n",
    "df_percchange_median = pd.DataFrame(columns=[\"ID\",\"parameter\", \"ROI\", \"percchange\"])\n",
    "\n",
    "\n",
    "for par in parameters: \n",
    "    print(par)\n",
    "\n",
    "    for m, mask in enumerate(masks):\n",
    "        masker = input_data.NiftiMasker(mask_img = mask) \n",
    "        mask_img=masker.fit_transform(mask)\n",
    "\n",
    "        percchange = np.zeros((len(sids), len(np.argwhere(mask_img))))\n",
    "    \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",
    "            #load percent signal change image\n",
    "            if par == 'func':\n",
    "                #percchange_img = nib.load(dir_func_deriv + '/' + sub + '_task-' + task + baseline + '_space-MNI152_BOLD_percchange.nii.gz')\n",
    "                percchange_img = nib.load(dir_func_deriv + '/' + sub + '_task-'+task + baseline+'_space-MNI152_desc-fmriprep_BOLD_percchange.nii.gz')\n",
    "                #percchange_img = nib.load(dir_func_deriv + '/' + sub + '_task-'+task + baseline+'_space-MNI152_BOLD_percchange_desc-preproc.nii.gz')\n",
    "            if par !='func' and par !='cmro2':\n",
    "                task_arr = np.array(nib.load( dir_qmri_deriv +  '/' + sub + '_task-' + task + '_space-MNI152_' + par + '.nii.gz').dataobj)\n",
    "                base_arr = np.array(nib.load( dir_qmri_deriv +  '/' + sub + '_task-' + baseline + '_space-MNI152_' + par + '.nii.gz').dataobj)\n",
    "                percchange = (task_arr - base_arr) / base_arr * 100\n",
    "                percchange_img = nib.Nifti1Image(percchange, nib.load(BSR_pos_nii_mask).affine)\n",
    "            if par =='cmro2':\n",
    "                if task == 'calc':\n",
    "                    task_arr = np.array(nib.load( dir_qmri_deriv +  '/' + sub + '_task-' + task + '_space-MNI152_desc-CBV_' + par + '.nii.gz').dataobj)\n",
    "                if task !='calc':\n",
    "                    task_arr = np.array(nib.load( dir_qmri_deriv +  '/' + sub + '_task-' + task + '_space-MNI152_desc-orig_' + par + '.nii.gz').dataobj)    \n",
    "                base_arr = np.array(nib.load( dir_qmri_deriv +  '/' + sub + '_task-' + baseline + '_space-MNI152_desc-orig_' + par + '.nii.gz').dataobj)\n",
    "                percchange = (task_arr - base_arr) / base_arr * 100               \n",
    "                percchange_img = nib.Nifti1Image(percchange,  nib.load(BSR_pos_nii_mask).affine)\n",
    "                \n",
    "            ## mask with ROI    \n",
    "            percchange = masker.fit_transform(percchange_img)\n",
    "            percchange = percchange[percchange!=0]\n",
    "            \n",
    "            ## different axis for CMRO2 / CBV\n",
    "            if par == 'cmro2' or par == 'cbf':\n",
    "                percchange = percchange/10\n",
    "            \n",
    "            par_name=par\n",
    "            \n",
    "            mask_name=os.path.basename(mask)[:-11]\n",
    "            new_row = {'ID':ID, 'parameter':par_name, 'ROI': mask_name, 'percchange':np.nanmedian(percchange)}\n",
    "            df_percchange_median = df_percchange_median.append(new_row, ignore_index = True)\n",
    "            \n",
    "##plot as barplot ##\n",
    "fontsize=50\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "Blue_palette=[\"dodgerblue\", \"mediumblue\"]\n",
    "\n",
    "fig, ax1= plt.subplots(1, figsize=(25,10))\n",
    "\n",
    "ax1 = sns.barplot(data=df_percchange_median, x=\"ROI\", y=\"percchange\", hue=\"parameter\",  estimator=np.median, ci=95, capsize=.07, n_boot=2000, \n",
    "                  errwidth=5, saturation=0.9, alpha=0.7, palette=Blue_palette)\n",
    "#ax1 = sns.boxplot(x=\"ROI\", y=\"percchange\", hue=\"parameter\", data=df_percchange_median, saturation=0.9,ax=ax1,\n",
    "#                   palette=Blue_palette, boxprops={\"edgecolor\": (.6, .6, .6, .5)}, flierprops={\"marker\": \"x\"},\n",
    "#                   medianprops={\"color\": \"black\"})\n",
    "ax1 = sns.stripplot(data = df_percchange_median, x=\"ROI\", y=\"percchange\", hue=\"parameter\",  palette=Blue_palette, dodge=True,marker='o', \n",
    "                    size=8, alpha=0.6)\n",
    "ax1.set_ylim(-2, 3)\n",
    "ax1.set_xlim(-0.5, 8.5)\n",
    "ax1.set_xticklabels(['1', '2', '3', '4', '5', '6', '7', '8', '9'], fontsize=fontsize+10)\n",
    "ax1.set_yticks([-2,  -1,0,  1, 2, 3])\n",
    "ax1.hlines(0,-0.5, 9.5, color='red', linestyle='solid', linewidth = 3)\n",
    "ax1.set_ylabel('ΔBOLD[%]', fontsize=fontsize+10, color='dodgerblue')  # we already handled the x-label with ax1\n",
    "ax1.tick_params(axis='y', labelcolor='dodgerblue')\n",
    "#ax1.legend(fontsize=fontsize-5,loc=\"upper right\")\n",
    "ax1.legend([], [], frameon=False)\n",
    "## add asterix for significance based on visual inspection (error-bars do not cross zero), not possible to access CIs in seaborn\n",
    "ax1.text(0, 3, '*', color='grey', fontsize=fontsize)\n",
    "ax1.text(1, 3, '*', color='grey', fontsize=fontsize)\n",
    "            \n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "\n",
    "color = 'mediumblue'\n",
    "ax2.set_ylabel('CMRO2', color=color)  # we already handled the x-label with ax1\n",
    "#ax2.plotsns.barplot(x=\"ROI\", y=\"percchange\", hue=\"parameter\", data=df_percchange_median, estimator=np.median, ci=95, capsize=.05, n_boot=2000, errwidth=1,\n",
    "#                 saturation=0.9, alpha=0.8)\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "ax2.set_xlim(-0.5, 8.5)\n",
    "ax2.set_ylim(-20, 30)\n",
    "ax2.set_ylabel('ΔCMRO2[%]', fontsize=fontsize+10)\n",
    "ax2.set_yticks([-20, -10, 0, 10,  20, 30])\n",
    "\n",
    "#set x-tick and y-ticke label size\n",
    "for label in (ax1.get_xticklabels() + ax1.get_yticklabels()):\n",
    "    label.set_fontsize(fontsize)\n",
    "for label in (ax2.get_xticklabels() + ax2.get_yticklabels()):\n",
    "    label.set_fontsize(fontsize)\n",
    "\n",
    "fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
    "fig.show()\n",
    "\n",
    "fig.savefig(results_dir + '/all_negative_ROIs_CBF_CMRO2_BOLD_baseline'+baseline+'.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "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",
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      "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",
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      "sub-p023\n",
      "sub-p026\n",
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      "sub-p032\n",
      "sub-p033\n",
      "sub-p034\n",
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      "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",
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      "sub-p051\n",
      "sub-p052\n",
      "sub-p054\n",
      "sub-p055\n",
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      "sub-p063\n",
      "sub-p064\n",
      "sub-p065\n",
      "sub-p066\n",
      "sub-p067\n",
      "sub-p068\n"
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\n",
      "text/plain": [
       "<Figure size 2500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parameters = [ 'func', 'cmro2']\n",
    "\n",
    "###################################\n",
    "## plot positive clusters ##\n",
    "#################################\n",
    "\n",
    "## create list with  cluster masks to be applied\n",
    "if baseline == 'control':\n",
    "    masks = [results_dir + '/ROI1_bin.nii.gz',\n",
    "            results_dir + '/ROI2_bin.nii.gz',\n",
    "            results_dir + '/ROI3_bin.nii.gz',\n",
    "            results_dir + '/ROI4_bin.nii.gz',\n",
    "            results_dir + '/ROI5_bin.nii.gz',\n",
    "            results_dir + '/ROI6_bin.nii.gz',\n",
    "            results_dir + '/ROI7_bin.nii.gz',\n",
    "            results_dir + '/ROI8_bin.nii.gz']\n",
    "\n",
    "## create list with  cluster masks to be applied\n",
    "if baseline == 'rest':\n",
    "    masks = [results_dir + '/ROI1_bin.nii.gz',\n",
    "            results_dir + '/ROI2_bin.nii.gz',\n",
    "            results_dir + '/ROI3_bin.nii.gz',\n",
    "            results_dir + '/ROI4_bin.nii.gz',\n",
    "            results_dir + '/ROI5_bin.nii.gz',\n",
    "            results_dir + '/ROI6_bin.nii.gz']\n",
    "\n",
    "\n",
    "df_percchange_median = pd.DataFrame(columns=[\"ID\",\"parameter\", \"ROI\", \"percchange\"])\n",
    "\n",
    "\n",
    "for par in parameters: \n",
    "    print(par)\n",
    "    for m, mask in enumerate(masks):\n",
    "        masker = input_data.NiftiMasker(mask_img = mask) \n",
    "        mask_img=masker.fit_transform(mask)\n",
    "\n",
    "        percchange = np.zeros((len(sids), len(np.argwhere(mask_img))))\n",
    "    \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",
    "            #load percent signal change image\n",
    "            if par == 'func':\n",
    "                percchange_img = nib.load(dir_func_deriv + '/' + sub + '_task-'+task + baseline+'_space-MNI152_desc-fmriprep_BOLD_percchange.nii.gz')\n",
    "            if par !='func' and par !='cmro2':\n",
    "                task_arr = np.array(nib.load( dir_qmri_deriv +  '/' + sub + '_task-' + task + '_space-MNI152_' + par + '.nii.gz').dataobj)\n",
    "                base_arr = np.array(nib.load( dir_qmri_deriv +  '/' + sub + '_task-' + baseline + '_space-MNI152_' + par + '.nii.gz').dataobj)\n",
    "                percchange = (task_arr - base_arr) / base_arr * 100\n",
    "                percchange_img = nib.Nifti1Image(percchange, nib.load(BSR_pos_nii_mask).affine)\n",
    "            if par =='cmro2':\n",
    "                if task == 'calc':\n",
    "                    task_arr = np.array(nib.load( dir_qmri_deriv +  '/' + sub + '_task-' + task + '_space-MNI152_desc-CBV_' + par + '.nii.gz').dataobj)\n",
    "                if task !='calc':\n",
    "                    task_arr = np.array(nib.load( dir_qmri_deriv +  '/' + sub + '_task-' + task + '_space-MNI152_desc-orig_' + par + '.nii.gz').dataobj)    \n",
    "                base_arr = np.array(nib.load( dir_qmri_deriv +  '/' + sub + '_task-' + baseline + '_space-MNI152_desc-orig_' + par + '.nii.gz').dataobj)\n",
    "                percchange = (task_arr - base_arr) / base_arr * 100               \n",
    "                percchange_img = nib.Nifti1Image(percchange,  nib.load(BSR_pos_nii_mask).affine)\n",
    "\n",
    "                \n",
    "            ## mask with ROI    \n",
    "            percchange = masker.fit_transform(percchange_img)\n",
    "            percchange = percchange[percchange!=0]\n",
    "            \n",
    "            ## different axis for CMRO2 / CBV\n",
    "            if par == 'cmro2' or par == 'cbf':\n",
    "                percchange = percchange/10\n",
    "            par_name=par\n",
    "            \n",
    "            mask_name=os.path.basename(mask)[:-11]\n",
    "            new_row = {'ID':ID, 'parameter':par_name, 'ROI': mask_name, 'percchange':np.nanmedian(percchange)}\n",
    "            df_percchange_median = df_percchange_median.append(new_row, ignore_index = True)\n",
    "            \n",
    "##plot as barplot ##\n",
    "\n",
    "fontsize=50\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "#Red_palette=[\"navajowhite\", \"darkorange\", \"firebrick\"]\n",
    "Red_palette=[ \"darkorange\", \"firebrick\"]\n",
    "\n",
    "fig, ax1= plt.subplots(1, figsize=(25,10))\n",
    "\n",
    "ax1 = sns.barplot(data=df_percchange_median, x=\"ROI\", y=\"percchange\", hue=\"parameter\",  estimator=np.median, ci=95, capsize=.07, n_boot=2000, \n",
    "                  errwidth=5, saturation=0.9, alpha=0.7, palette=Red_palette)\n",
    "ax1 = sns.stripplot(data = df_percchange_median, x=\"ROI\", y=\"percchange\", hue=\"parameter\",  palette=Red_palette, dodge=True,marker='o', \n",
    "                    size=8, alpha=0.6)\n",
    "ax1.hlines(0,-0.5, 9.5, color='red', linestyle='solid', linewidth = 3)\n",
    "\n",
    "ax1.set_ylim(-2, 3)\n",
    "ax1.set_xlim(-0.5, 7.5)\n",
    "ax1.set_yticks([-2, -1,  0, 1, 2, 3])\n",
    "if baseline == 'control':\n",
    "    ax1.set_xticklabels(['1', '2', '3', '4', '5', '6', '7', '8'], fontsize=fontsize+10)\n",
    "if baseline == 'rest':\n",
    "    ax1.set_xticklabels(['1', '2', '3', '4', '5', '6'], fontsize=fontsize+10)\n",
    "ax1.set_ylabel('ΔBOLD[%]', fontsize=fontsize+10, color='darkorange')  # we already handled the x-label with ax1\n",
    "ax1.tick_params(axis='y', labelcolor='darkorange')\n",
    "#ax1.legend(fontsize=fontsize-5,loc=\"upper right\")\n",
    "ax1.legend([], [], frameon=False)\n",
    "## add asterix for significance based on visual inspection (error-bars do not cross zero), not possible to access CIs in seaborn\n",
    "ax1.text(0, 3, '*', color='grey', fontsize=fontsize)\n",
    "ax1.text(4, 3, '*', color='grey', fontsize=fontsize)\n",
    "ax1.text(5, 3, '*', color='grey', fontsize=fontsize)\n",
    "\n",
    "            \n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "\n",
    "color = 'firebrick'\n",
    "#ax2.plotsns.barplot(x=\"ROI\", y=\"percchange\", hue=\"parameter\", data=df_percchange_median, estimator=np.median, ci=95, capsize=.05, n_boot=2000, errwidth=1,\n",
    "#                 saturation=0.9, alpha=0.8)\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "ax2.set_ylim(-20, 30)\n",
    "ax2.set_ylabel('ΔCMRO2[%]', fontsize=fontsize+10, color=color)\n",
    "ax2.set_yticks([-20, -10, 0, 10, 20, 30])\n",
    "ax1.set_xlim(-0.5, 7.5)\n",
    "\n",
    "#set x-tick and y-ticke label size\n",
    "for label in (ax1.get_xticklabels() + ax1.get_yticklabels()):\n",
    "    label.set_fontsize(fontsize)\n",
    "for label in (ax2.get_xticklabels() + ax2.get_yticklabels()):\n",
    "    label.set_fontsize(fontsize)\n",
    "\n",
    "fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
    "fig.show()\n",
    "\n",
    "fig.savefig(results_dir + '/all_positive_ROIs_CBF_CMRO2_BOLD.png', dpi=300)"
   ]
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